Download - Stepwise approach to Reference Levels REDD+
A stepwise approach to reference levels
Louis Verchot
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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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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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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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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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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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)
Criteria for comparing country circumstances and strategies
Criteria for comparing country circumstances and strategies
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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
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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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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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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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
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)
Thank you