This presentation was given by CIFOR scientist Louis Verchot on 28 November 2012 at a joint CIFOR and GOFC-GOLD (Global Observation of Forest Cover and Land Dynamics) UNFCCC COP18 side-event in Doha, Qatar.

1. A stepwise approach to reference
levels
Louis
Verchot

2. 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
• Data on existing national monitoring capacities
THINKING beyond the canopy

3. Table 9: Activity data on the national level can be estimated from the different approaches as suggested by the IPCC GPG:
Ac#vity
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:
Na:onal
level
remote
sensing
Example:
FAO
FRA
data
data
on
gross
forest
changes
through
a
change
matrix
(i.e.
deforesta:on
vs.
reforesta:on),
ideally
disaggregated
by
administra:ve
regions
THINKING beyond the canopy

4. 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 Guildelines.
• 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

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

6. 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

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

8. Criteria
for
comparing
country
circumstances
and
strategies

9. Criteria
for
comparing
country
circumstances
and
strategies

10. RLs
using
regression
models
– Simple,
easy
to
understand
and
test
new
variables
– But,
data
demanding
– Predic:ng
deforesta:on
in
a
period:
Pt
–
Pt+1,
based
on
deforesta:on
in
the
previous
period
Pt-­‐1
–
Pt
and
a
set
of
other
factors
(observed
at
:me
t).
– Using
structure
(coefficients)
from
the
es:mated
regression
equa:on
to
predict
deforesta:on
in
period
Pt+1
–
Pt+2,
based
on
observed
values
at
:me
t+1
2000
2004
2005
2009
2010
Historical
deforesta:on
Es:mated/Predicted
deforesta:on
Regression
model
Predic#ve
model,
based
on
structure
from
regression
model
10
THINKING beyond the canopy

11. 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

12. Category
Regression
coefficient
Deforesta#on
rate
(2000-­‐2004)
0.395
Trend
variable
-­‐0.136
-­‐0.145
Step
2:
Deforesta#on
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
deforesta#on
rates
Popula#on
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

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

14. Conclusions
• Historical
def.
is
key
to
predict
future
deforesta:on
– Coefficients
below
one
→
simple
extrapola:on
can
be
misleading
• Some
evidence
of
forest
transi:on
(FT)
hypothesis
– Robustness
of
FT
depends
on
the
measure
of
forest
stock
• FT
supported
when
forest
stock
is
measured
rela:ve
to
total
land
area,
otherwise
mixed
results
emerge
• Other
na:onal
circumstances
have
contradictory
effects
• Contradictory
rela:onships
may
be
linked
to
data
quality
and
interrela:ons
of
econ.
&
ins:tu:ons
differ
THINKING beyond the canopy
14

15. 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
rela:on
to
the
net
change
in
total
forest
area
between
2005
and
2010
(FAO
FRA)

16. We surveyed 17 REDD+ demonstration projects
§ 53% use site specific biomass equations
§ 24% had methods for belowgound C
§ 41% had methods for dead wood and litter
§ Most projects will use IPCC defaults for soil-C
THINKING beyond the canopy

17. Thank
you