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

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:

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

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

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

(%)

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)

Criteria for comparing country circumstances and strategies

Criteria for comparing country circumstances and strategies

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

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)

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

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

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

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