A stepwise approach to reference levels

Louis Verchot, Arild Angelsen, Martin Herold, Arief Wijaya

A stepwise approach to FREL/FRLs

Criteria for comparing country circumstances and strategies

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 driver

Deforestation driver

AMERICA AFRICA ASIA

Deforestation

Degradation

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

5

2000 200920052004 2010

Estimated/Predicted deforestation Historical deforestation

Predictive model, based on structure from regression model

Regression model

Step 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

1,500 Vietnam

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)

Step 2: Brazil

Predict deforestation rates for legal Amazon2005- 2009

7

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

Step 2: Vietnam

Predict deforestation rates 2005- 2009

8

Category Regression coefficient

Deforestation rate (2000-2004) 1.464Trend 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

Preliminary 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

9

THINKING beyond the canopy

In-depth case study: Indonesia definitions matter

FAO forest definition – minimum 10% crown cover, minimum 0.5 ha and minimum height 5 m

Indonesia national forest definition – vegetation cover dominated by intertwined tree crowns with canopy cover of more than 60%

Indonesia – vegetation cover dominated by trees, with canopy cover between 25 and 60% is defined as bush

Natural forest definition – no plantations

THINKING beyond the canopy

Forest definitions affect estimates of deforestation

THINKING beyond the canopy

Assessment of national REL/RL for Indonesia

SourceCumulative Emission from

LUCF 2000 -2009(in Gg CO2e)*

Methods

FAOStat 3,140,033 FRA country report(EF = 138 ton C/ha)

MoE - Second National Communication to UNFCCC 7,443,064 IPCC Guidelines 2006

Winrock International (Harris, 2012) 3,468,150 Carbon Book keeping model

(RS + Field)

MOF (official) 1,760,000 Approach 1 + NFI(Tier 1 or 2)

MOF + Saatchi (CIFOR) 1,811,396 Approach 1 + Global EF(Tier 1 or 2)

* does not include peat emissions and peat fire

Comparison of national deforestation estimates

Validation of deforestation maps of Indonesia

Source: Wijaya, et.al, (In prep)

Validation of deforestation maps

1990-2000

2000-2001

2001-2002

2002-2003

2003-2004

2004-2005

2005-2006

2006-2007

2007-2008

2008-2009

2009-20100

100

200

300

400

500

600

700

800

900

1000

Indonesia MOFOR Indonesia Hansen

Indonesia JRC Indonesia Mean

Annu

al D

efor

esta

tion

(x 1

000

ha)

Previous deforestation rates are good predictors of future rates

National Bali Java Kalimantan Maluku

& Papua Sulawesi Sumatera

Log his def.

0.942 0.781 1.270 1.059 1.187 0.563 1.032

R2 0.574 0.517 0.187 0.869 0.848 0.589 0.524

Num. of obs

372 32 114 43 25 47 111

Using deforestation rates in 2003 to 2006 to predict deforestation in 2006 to 2009

Including socioeconomic factors improves the regressions

National Bali Java KalimantanMaluku &

Papua Sulawesi SumateraLog his def. 0.289 0.507 0.532 0.277 0.662 0.299 0.116Forest stock 10.121 -2.019 27.345 23.192 1.166 14.658 18.523

Forest stock sq -8.829 2.342 -43.279 -19.797 6.328 -8.653 -19.510

Log District GDP per capita

1.432 0.456 -0.255 -0.038 0.381 -1.136 1.688

Agric. GDP 0.033 0.015 -0.027 0.032 0.002 0.004 0.069

Log Pop. den. -0.357 0.291 0.145 0.089 -0.738 -0.404 -0.853

Road density -2.816 -4.355 0.000 0.494 5.134 6.912 1.089

R-square 0.777 0.665 0.549 0.980 0.965 0.707 0.858Num. of obs 371 32 114 43 25 47 110

Observations so far…

Forest definition matters

Selection of minimum mapping unit is important to determine the smallest units of deforested areas

Different satellite image classification methods may result in different estimate

There are several useful approaches to integrating drivers of deforestation and forest degradation into assessments of RELs

Thank you