This presentation by Louis Verchot and others from CIFOR describes how reference levels can be determined step by step by e.g. comparing country circumstances and strategies, using regression models and other data. This also leads to some preliminary conclusions.

1. A stepwise approach to reference
levels
Louis Verchot, Arild Angelsen, Martin Herold, Arief Wijaya

2. A stepwise approach to FREL/FRLs

3. Criteria for comparing country circumstances
and strategies

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

5. RLs using regression models
– Simple, easy to understand and test new variables
– But, data demanding
– Predicting deforestation in a period: Pt – Pt+1, based on
deforestation in the previous period Pt-1 – Pt and a set of
other factors (observed at time t).
– Using structure (coefficients) from the estimated
regression equation to predict deforestation in period Pt+1 –
Pt+2, based on observed values at time t+1
2000
2004 2005
Historical deforestation
2009
2010
Estimated/Predicted deforestation
Regression model
Predictive model, based on
structure from regression model
5

6. Step 1 case for 4 countries using FAO FRA data
Indonesia
3,500
Forest C stock (Mt)
Forest C stock (Mt)
Cameroon
3,000
2,500
2,000
1,500
1,000
500
0
18,000
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
1985 1990 1995 2000 2005 2010 2015 2020 2025
1985 1990 1995 2000 2005 2010 2015 2020 2025
Year
Year
Forest C stock (Mt)
Forest C stock (Mt)
Brazil
Vietnam
1,500
1,200
900
600
300
70,000
60,000
50,000
40,000
30,000
20,000
10,000
0
0
1985
80,000
1990
1995
2000
2005
Year
2010
2015
2020
2025
1985 1990 1995 2000 2005 2010 2015 2020 2025
Year

7. Step 2:
Brazil
Predict
deforestation
rates for legal
Amazon
2005- 2009
Category
Deforestation rate (2000-2004)
Trend variable
Deforestation dummy
Forest stock
Forest stock squared
Log per capita GDP
Agric GDP (%GDP)
Population density
Road denisty
R2
N
Regression coefficient
0.395
-0.136
-0.373
2.18
-1.8
-0.034
0.28
0.081
0.039
-0.145
-0.773
4.756
-3.826
-0.13
0.28
-0.81
0.076
0.831
3595
0.789
3595

8. Step 2:
Vietnam
Predict
deforestation
rates
2005- 2009
Category
Deforestation rate (2000-2004)
Trend variable
Deforestation dummy
Forest stock
Forest stock squared
Population density
Road denisty
R2
N
Regression coefficient
1.464
-0.006
-0.011
0.067
-0.189
-1.177
0.004
0.003
-0.031
0.260
-0.463
1.036
-0.001
0.515
301
0.052
301

9. Preliminary conclusions

Historical def. is key to predict future deforestation
– Coefficients below one
misleading

simple extrapolation can be
Some evidence of forest transition (FT) hypothesis
– Robustness of FT depends on the measure of forest stock
FT supported when forest stock is measured relative to total
land area, otherwise mixed results emerge


Other national circumstances have contradictory
effects
Contradictory relationships may be linked to data
quality and interrelations of econ. & institutions
differ
THINKING beyond the canopy

10. In-depth case study: Indonesia
definitions matter

FAO forest definition – minimum 10% crown
cover, minimum 0.5 ha and minimum height 5 m

Indonesia national forest definition – vegetation
cover dominated by intertwined tree crowns with
canopy cover of more than 60%

Indonesia – vegetation cover dominated by
trees, with canopy cover between 25 and 60% is
defined as bush

Natural forest definition – no plantations
THINKING beyond the canopy

11. Forest definitions affect estimates of deforestation
THINKING beyond the canopy

12. THINKING beyond the canopy

13. Assessment of national REL/RL for Indonesia
Cumulative Emission from
LUCF 2000 -2009
(in Gg CO2e)*
Source
Methods
3,140,033
FRA country report
(EF = 138 ton C/ha)
7,443,064
IPCC Guidelines 2006
3,468,150
Carbon Book keeping model
(RS + Field)
MOF (official)
1,760,000
Approach 1 + NFI
(Tier 1 or 2)
MOF + Saatchi (CIFOR)
1,811,396
Approach 1 + Global EF
(Tier 1 or 2)
FAOStat
MoE - Second National
Communication to UNFCCC
Winrock International
(Harris, 2012)
* does not include
peat emissions and peat fire

14. Comparison of national deforestation estimates

15. Validation of deforestation maps of Indonesia
Source: Wijaya, et.al, (In prep)

16. Validation of deforestation maps
1000
Annual Deforestation (x 1000 ha)
900
800
700
600
500
400
300
200
100
0
Indonesia MOFOR
Indonesia Hansen
Indonesia JRC
Indonesia Mean

17. Previous deforestation rates are good
predictors of future rates
Using deforestation rates in 2003 to 2006 to predict deforestation in 2006 to 2009
National
Bali
Java
Kalimantan
Maluku
& Papua
Log his
def.
0.942
0.781
1.270
1.059
1.187
0.563
1.032
R2
0.574
0.517
0.187
0.869
0.848
0.589
0.524
372
32
114
43
25
47
111
Num. of
obs
Sulawesi Sumatera

18. Including socioeconomic factors
improves the regressions
National
0.289
10.121
Bali
0.507
-2.019
Java
0.532
27.345
Kalimantan
0.277
23.192
Maluku &
Papua
0.662
1.166
-8.829
2.342
-43.279
-19.797
6.328
-8.653
-19.510
1.432
0.456
-0.255
-0.038
0.381
-1.136
1.688
0.033
0.015
-0.027
0.032
0.002
0.004
0.069
Log Pop. den.
-0.357
0.291
0.145
0.089
-0.738
-0.404
-0.853
Road density
-2.816
-4.355
0.000
0.494
5.134
6.912
1.089
R-square
Num. of obs
0.777
371
0.665
32
0.549
114
0.980
43
0.965
25
0.707
47
0.858
110
Log his def.
Forest stock
Forest stock sq
Log District
GDP per
capita
Agric. GDP
Sulawesi
0.299
14.658
Sumatera
0.116
18.523

20. Observations so far…

Forest definition matters
 Selection of minimum mapping unit is important to
determine the smallest units of deforested areas

Different satellite image classification methods
may result in different estimate

There are several useful approaches to
integrating drivers of deforestation and forest
degradation into assessments of RELs

21. Thank you