A stepwise approach to reference
levels
Louis Verchot

Business as usual and national capacity
• Activity data – types of deforestation and forest degradation
• Emission factors – carbon loss per unit area for a specific
activity
• Drivers – to describe how much DD are caused by each
specific change activity
THINKING beyond the canopy

Table 9: Activity data on the national level can be estimated from the different approaches as suggested by the IPCC GPG:
Activity data
Approach 1 Approach 2 Approach 3
Data on forest TOTAL LAND-USE TOTAL LAND-USE AREA, SPATIALLY-EXPLICIT
change (or
emissions) AREA, NO DATA ON INCLUDING CHANGES LAND-USE
following IPCC CONVERSIONS BETWEEN CATEGORIES CONVERSION DATA
approaches
BETWEEN LAND USES
Example: data from
Example: National level remote sensing
Example: FAO FRA
data data on gross forest
changes through a
change matrix (i.e.
deforestation vs.
reforestation), ideally
disaggregated by
administrative regions
THINKING beyond the canopy

Three levels of emission factors
• Tier 1 methods are designed to be the simplest to use,
for which equations and default parameter values (e.g.,
emission and stock change factors) are provided by
IPCC Guidelines.
• Tier 2 can use the same methodological approach as Tier
1 but applies emission and stock change factors that are
based on country- or region-specific data
• Tier 3, higher order methods are used, including models
and inventory measurement systems tailored to address
national circumstances, repeated over time, and driven
by high-resolution activity data and disaggregated at sub-
national level.
THINKING beyond the canopy

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

Changes of Deforestation Drivers:
Important for assessing historical deforestation
Phase1 Phase2 Phase3 Phase4
Pre Early Late Post
Transition Transition Transition Transition
Forest Cover (%)
Time
Using national data from 46 countries: REDD-related data and
publications
THINKING beyond the canopy

Deforestation Drivers
Deforested-area ratio of Deforested area
deforestation drivers km2
100% 700 Urban expansion
600
80% Infrastructure
500
60% 400 Mining
40% 300 Agriculture
200 (subsistence)
(local-slash & burn)
20%
Agriculture
100
0% (commercial)
0
pre early late post pre early late post
Distribution of 46 countries - Pre: 7, early: 23, late: 12, post: 4
 Agriculture (commercial) is 45%, agriculture (local/subsistence) 38%, mining
7%, infrastructure 8%, urban expansion 3% and only agriculture make up 83% of total
 Ratio of mining is decreasing and urban expansion is relatively increasing over time
THINKING beyond the canopy

Criteria for comparing country circumstances and
strategies

Criteria for comparing country circumstances and
strategies

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 2009 2010
Historical deforestation Estimated/Predicted deforestation
Regression model Predictive model, based on structure
from regression model
10
THINKING beyond the canopy

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

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

Category Regression coefficient
Deforestation rate (2000-2004) 01.464
Trend variable -0.006 0.003
Step 2: Deforestation dummy -0.011 -0.031
Forest stock 0.067 0.260
Vietnam Forest stock squared -0.189 -0.463
Population density -1.177 1.036
Predict Road denisty 0.004 -0.001
deforestation rates
2005- 2009 R2 0.515 0.052
N 301 301
THINKING beyond the canopy

Conclusions
• Historical def. is key to predict future deforestation
– Coefficients below one simple extrapolation can be
misleading
• 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
14

MRV capacity gap analysis
3000
Net change in forest area since 1990
2000
1000
(1000ha)
0
-1000
-2000
-3000
Very large Large Medium Small Very small
Capacity gap
MRV capacity gap in relation to the net change in total forest area
between 2005 and 2010 (FAO FRA)

Thank you