Stepwise approach to Reference Levels REDD+
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Transcript of Stepwise approach to Reference Levels REDD+
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A stepwise approach to reference levels
Louis Verchot
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THINKING beyond the canopy
• 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
Business as usual and national capacity
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THINKING beyond the canopy
Activity data
Approach 1 Approach 2 Approach 3Data on forest change (or emissions) following IPCC approaches
TOTAL LAND-USE AREA, NO DATA ON CONVERSIONSBETWEEN LAND USES
Example: FAO FRA data
TOTAL LAND-USE AREA, INCLUDING CHANGESBETWEEN CATEGORIES
Example: National level data on gross forest changes through a change matrix (i.e. deforestation vs. reforestation), ideally disaggregated by administrative regions
SPATIALLY-EXPLICIT LAND-USE CONVERSION DATA
Example: data from remote sensing
Table 9: Activity data on the national level can be estimated from the different approaches as suggested by the IPCC GPG:
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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.
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THINKING beyond the canopy
Deforestation/degradation drivers for each continent
39%
35%
13%
11%
2%
57%36%
2%4%
1%
41%
36%
7%
10%
7%
26%
62%
4% 8%
70%
9%
17%
4%
67%
19%
6%7%
Forest degradation driverDeforestation driver
AMERICA AFRICA ASIA
Deforestation
Degradation
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THINKING beyond the canopy
Changes of Deforestation Drivers:Important for assessing historical deforestation
Using national data from 46 countries: REDD-related data and publications
Time
Phase1Pre
Transition
Phase4Post
Transition
Phase2Early
Transition
Phase3Late
Transition
Fo
rest
Co
ver
(%)
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THINKING beyond the canopy
Deforestation Drivers
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
Deforested-area ratio of deforestation drivers
Deforested area
pre early late post0
100
200
300
400
500
600
700Urban expansion
Infrastructure
Mining
Agriculture (local-slash & burn)
Agriculture (commercial)
km2
pre early late post0%
10%20%30%40%50%60%70%80%90%
100%
Distribution of 46 countries - Pre: 7, early: 23, late: 12, post: 4
(subsistence)
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Criteria for comparing country circumstances and strategies
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Criteria for comparing country circumstances and strategies
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THINKING beyond the canopy
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
10
2000 200920052004 2010
Estimated/Predicted deforestation Historical deforestation
Predictive model, based on structure from regression model
Regression model
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Tier 1 case for 4 countries using FAO FRA data
1985 1990 1995 2000 2005 2010 2015 2020 20250
500
1,000
1,500
2,000
2,500
3,000
3,500
Cameroon
Year
Fore
st
C s
tock (
Mt)
1985 1990 1995 2000 2005 2010 2015 2020 20250
2,0004,0006,0008,00010,00012,00014,00016,00018,000
Indonesia
Year
Fore
st
C s
tock (
Mt)
1985 1990 1995 2000 2005 2010 2015 2020 20250
300
600
900
1,200
1,500Vietnam
Year
Fore
st
C s
tock (
Mt)
1985 1990 1995 2000 2005 2010 2015 2020 20250
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
Brazil
Year
Fore
st
C s
tock (
Mt)
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THINKING beyond the canopy
Step 2: Brazil
Predict deforestation rates for legal Amazon2005- 2009
12
Category Regression coefficient
Deforestation rate (2000-2004) 0.395Trend variable -0.136 -0.145Deforestation dummy -0.373 -0.773Forest stock 2.18 4.756Forest stock squared -1.8 -3.826Log per capita GDP -0.034 -0.13Agric GDP (%GDP) 0.28 0.28Population density 0.081 -0.81Road denisty 0.039 0.076
R2 0.831 0.789N 3595 3595
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THINKING beyond the canopy
Step 2: Vietnam
Predict deforestation rates 2005- 2009
13
Category Regression coefficient
Deforestation rate (2000-2004) 01.464Trend variable -0.006 0.003Deforestation dummy -0.011 -0.031Forest stock 0.067 0.260Forest stock squared -0.189 -0.463Population density -1.177 1.036Road denisty 0.004 -0.001
R2 0.515 0.052N 301 301
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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
14
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MRV capacity gap analysis
MRV capacity gap in relation to the net change in total forest area between 2005 and 2010 (FAO FRA)
Very large Large Medium Small Very small-3000
-2000
-1000
0
1000
2000
3000
Capacity gap
Net
cha
nge
in fo
rest
are
a s
ince
199
0 (1
000h
a)
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Thank you