REDD+ Implementation andSFMTokyo, 6-7 February 2014

REDD+ in landscapes: drivers ofdeforestation, institutions and jurisdictions
L. Verchot,LVerchot . Angelsen,Angelsen M.
M Brockhaus,Brockhaus N.
N De Sy,Sy M.
M Herold,
Herold N.
N
Hosonuma, M. Kanninen, K. Korhonen-Kurki, A. Larson, A. Ravikumar,
A. Wijaya

REDD

Examples of transformational change

Changes in economic, regulatory and governance frameworks,
g the devolution of rights
gto local users;;
includingRemovals of perverse incentives, such as subsidies andconcessions that serve selective economic interests and stimulate
deforestation and forest degradation; and

Reforms of forest industry policies and regulations that effectively
reduce unsustainable extraction

REDD

Both institutional and agency factors affectthe direction of REDD+ policies

Institutions: the formal andinformal regulations,
regulations rules andnorms that are established overtime and that are not easilychanged or transformed

Policy arena: framed by institutions
and shaped by the actions of theactors. It is characterized byhierarchical or inclusive processes,
involving a range of powerfulactors, which can foster or prevent
certain policies and influence policyformulation

Analysis: Two-step QCA
Outcome variable: Establishment of comprehensive policies targetingtransformational change in the REDD+ policy domain (phase II)
Successes: Indonesia, Vietnam, Brazil

Six factors divided into two categories to explain outcome
 Institutional setting:• pressure from forest
forest-resource
resource shortage• effective forest legislation, policy and governance• previously initiated policy change The policy arena:• national ownership• transformational coalitions• inclusiveness of the policy process

QCA Step 1: Institutional setting
results
Pressure on forests
Effective forest legislation
Previouslyy initiated changegResults I: Institutional setting
The results reveal path dependencies and institutional
stickiness in all the study countries: Only countries already undertaking institutionalchange (CHA) have been able to establish REDD+
policies in a relatively short period
but only in the presence of either• highhi h pressure fromf
forest‐resourceftshortagesh t(PRES(PRES:
Brazil and Indonesia)• or key features of effective forest legislation, policy
and governance (EFF: Vietnam).

QCA StepQ
p 2: Policy
y arena results
National ownership of process
Inclusive process
Coalitions for changeIndonesia

Note: Indonesia has the alternative configuration for enabling environment(PRES
environment(PRES*eff*CHA)
eff CHA)
and the policy arena configuration is *OWN*COAL*incl
THINKING beyond the canopy

Results II: Policy arena
Where an enabling institutional setting is in place (EFF*CHA or
PRES*eff*CHA), two conditions of the policy arena proved tobe crucial for all three successful countries (Brazil, Vietnamand Indonesia): National ownership (OWN) Transformational coalitions (COAL)Countries that have these two conditions of the policy arena,
but not the enabling institutional setting (e.g.
(e g Peru andMozambique), were not successful in establishing REDD+ yet.
The country with enabling policy conditions, but neither
national ownership nor coalitions for transformation (Bolivia)
was unsuccessful

Factors affecting national REDD+ policies
CCaseI tit ti
Institutionall settingtti

P li arena
PolicyO t
OutcomePRESEFFCHAOWNCOALINCLREDD

B li i
Bolivia

1110000Brazil

1111111B ki Faso
Burkina
F1000000

Cameroon

1100000DRC

0010110Indonesia

1011101Mozambique

1001110

Nepal

0

100

1

10Peru

00

11

1

10

PNG

0000

100

Tanzania

0000

110

Vietnam

0

1

1

1

101

KeyFindings
gMeasuring progress: Some reflections

 Context matters: previously initiated institutional change
allowsll
ffor fasterf t REDD+ design,d ibbutt iis nott sufficient.
ffi i tThere must either be pressure on forests or effective
forest legislation, policy and governance in place.
 Actor-related factors of national ownership andtransformational coalitions are crucial: but can only
be effective in an enabling institutional setting

REDDImplementationp

How do we translate thisto the realities on the ground Understanding drivers – for effective implementation
 Land-use planning Multiple jurisdictions in landscapes

Apparent deforestation/degradation drivers for each continent
LATIN AMERICA
‐2%

‐4%

AFRICA
‐2%

‐1%

‐7%

11%
‐11%

Deforestation

ASIA

‐10%
‐39%

‐13%

‐41%

‐7%

‐36%
‐57%
‐37%

‐35%

4%
4%

8%

17%

Degradation

6%

26%

7%

20%

9%

67%

70%
62%

Deforestation driver

Forest degradation
gdriver(Subsistence)

THINKING beyond the canopy

Apparent drivers of deforestation

Def. due tto water reservoirs
D

Preliminary results – South America

100%

Clouds or
resolution too low‐>> need of higher
resolution data

90%
80%
70%

Other
Built‐up (roads,(roads urbanurban,...))

60%

Mining50%

Water (river, reservoir,...)
Other

40%

Agriculture
30%
20%
10%
0%

Venezuela Venezuela Paraguay
1990 –2000 –1990 –
2000
2005
2000

Paraguay
2000 –2005

Bolivia
1990 –
2000

Bolivia
2000 –2005

Argentina Argentina
1990 –
2000 –
2000
2005“Other” =deforestation
without clear
cause or follow‐up
land use
THINKING beyond the canopy

REDD
Implementationp

Most drivers of DD are not forest related

In seeking to address the drivers of deforestation and forest
ddegradation,
d ti
REDD+ necessarily
il challenges
h ll
multiplelti l establishedt bli h dinstitutions and policies, and hence is likely to encounter resistancefrom existing institutional logics and actors.
To do this, we must move beyond apparent drivers of DD
and address the political economy of the status quo

Projecting deforestation in landscapes
Carbon data fordifferent land uses

Scenarios of land
use change
PastFuture 1

PresentFuture 2

Carbon outcomes indifferent land use scenarios

Land-useLand
use change matrix
Land use in 2000
Land‐use
PastureOakforest

Pineforest

Scrubland

Pine‐oakforest

Lakes

Agriculture

Agriculture No
irrigatedvegetation

Fruit
crops

Dry forest

Spruce Planted
forest forest

Urban

Total

5,470

386

2,014

1,697

025915300

2,286

6

16

0

11,988

Pasture19,198

829

65

37

2

4,512

81

341

04,6186

0

1

19,691

Scrub land

137

241

2,951

148

45

3877

16

6

29

3,695

56

0

60

8,264

Pine forest

1,104

71

197

58,454

9,905

0

1,580

5,856

0

12

25,41963323

0102,982

Pine‐oakPine
oakforest

1,507

21

44

6,912

62,779

0

1,334

2,429

33

24

12,4806174

087,745

0

51

10

7

13 11,740

149

104

73

0

1

012,050

Agriculture

616

6,908

3,577

2,161

3,556

53

115,263

10,1332,148

41

47,6691,102

150

189

193,566

Fruit crops

115

80

5

2,261

630

23

402

22,069

28

23

2,176

336

1

0

28,149Agriculture
irrigated

0

995

15

1,018

577

3

1,917

507

12,646

0

3,884

288

4

12

21,866

No veg.

72

1

15

261

621

0

558

535

0

3,481

428

0

5

0

5,978

Dry forest

569

6,103

2,732

14,640

6,366

66

7,647

9,566

1,077

139

90,492219

5

99

139,722

Urban

0

74

11

43

20

117

48

19

0

111

7,405

0

8

7,840

18

0

2

173

296

0

12

17

0

0

500

0

6,707

0

7,725

0
1
9,609 23,748

11
10,484

8
88,1652086,527 11,890

37
134,663

051,4131
16,300

1
3,755

107
193,940

12
9,500

0
7,384

400
768

580
648,147

Land‐use in 19993

Oakforest

Lakes

Spruce for.
Planted for.
Total

x 1000 H
Ha

Simulation of deforestation 2000‐2025
2000
2005
2010
2015
2020
2025

ForestNon-forest
Deforestation
P
Protectedd areas
No dataPurépecha, Michoacán, Mexico

approx. 600 000 ha.

Source: Adapted from Google Maps Inc., 2013

Simple REL for 4 countries using FAO FRA data
Indonesia

3,500

Forrest C stock ((Mt)

Forrest C stock ((Mt)

Cameroon
3,000
2,500
2,0001,500
1 000
1,00050001985

1990

1995

2000

2005

2010

2015

2020

2025

18,00016,00014,00012,00010,0008,000
6,000,4,000
2,00001985 1990 1995 2000 2005 2010 2015 2020 2025
YearYearFores
st C stock (Mtt)Forest C stock (Mt)

BrazilVietnam

1,500
1,200
90060030001985

1990

1995

2000

2005
Year

2010

2015

2020

2025

80,0000 000
70,00060,00050,00040,00030 00030,00020,00010,00001985 1990 1995 2000 2005 2010 2015 2020 2025
YearStep 2:
BrazilPredict
deforestation
rates for legal
Amazon2005- 2009

Category

Regression coefficient

Deforestation rate (2000‐2004)
Trend variable
Deforestation dummyy
Forest stock
Forest stock squared
Log per capita GDP
Agric GDP (%GDP)
Population density
Road denisty

0.395‐0.136‐0.3732.18‐1.8‐0 034‐0.034
0.28
0.081
0 039
0.039

‐0.145‐0.7734.756‐3.826
‐0 13
‐0.13
0.28
‐0.81
0 076
0.076

R2N

0.831
3595

0.789
3595

21

Step 2:
Vietnam
PredictP
dideforestation
rates
2005 20092005-

Category

Regression coefficient

Deforestation rate (2000‐2004)
Trend variable
Deforestation dummyy
Forest stock
Forest stock squared
Population density
Road denisty

1.464‐0.006‐0.011
0.067‐0.189‐1 177‐1.1770.004

0.003‐0.0310.260‐0.463
1 036
1.036‐0.001

R2N

0.515
0515
301

0.052
0052
301

22

We have tools: CO2FIX model structure
Carbon in the atmosphere
Increment(yield tables)
Competition(between or within cohorts)
Biofuels forCohortC
h t3
Tree Cohort
biomass 2
Cohort 1
Tree biomass• stemwood

energyTimber harvesting• stemwood
Tree biomass• foliage• stemwoodtd• foliage• branches• foliage• branches• branches• roots• roots• roots

D
Decomposition
iti

Harvest residues
and mortality due
to management

Litt ffallll
LitterLitterHumification
Intermediate humus

Humification

Raw material

Primary
Processing

Fossil Fuels
for energy

Burning ofby products
by-products

Burning ofdisposed-off
products top
generateenergy.
Production line:
recycling
Products in use• sawnwooduse• boards
disposal• paper• firewood

Products
in landfill

Stable humus

23

decay

CO2FIX REDD+ case:
Baseline data from Costa Rica

24

Disturbance (fire) and clearing
modeled with the CO2FIX modelWe have the tools for land use planningand comparative analysis

Conservation ‐ Baseline

26

AGRICULTURE
SECTOR‐ Regional (Titling)‐ National(Regulation)
FORESTRY SECTOR‐ National((Regulation)g)‐ Regional(Permitting)
AGRICULTURE
SECTOR‐ Regionalg(Titling)‐ Regional(Projects)
ENVIRONMENTSECTOR‐ National(Regulation)

STATE LANDSOIL PALM PLANTATIONNATIVECOMMUNITIESSMALLHOLDERCOMMUNITIESREDD+CONSERVATION
PROJECT

TIMBER
CONCESSION

CULTURESECTOR‐ Regional(Regulation)
AGRICULTURE
SECTOR‐ Regional((Titling)g)
FORESTRYSECTOR‐ National(Regulation)‐ Regional(Permitting)Further reading:Masera, O.R., et al. 2003. Modeling carbon sequestration in afforestation, agroforestry andforest management projects. Ecological Modelling 164, 177‐199.
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deforestation projects. MITI 12, 1001‐1026.
Hosonuma, N., et al. 2012. An assessment of deforestation and forest degradation drivers
in developing countries. Environ. Res. Lett. 7 p.12
Herold, M.,
HeroldM et al.
al 2012.
2012 A step
step‐wise
wise framework for setting REDD+ forest referenceemission levels and forest reference levels. CIFOR Info Brief No. 52. CIFOR.
Romijn, E., et al. 2013. Different forest definitions and their impact on developing REDD+
reference emission levels: a case study for Indonesia.
Indonesia Environ.Sci.
Environ Sci &Policy 33:246
33:246‐250.
250Angelsen, A., et al. 2013. Testing methodologies for REDD+: Deforestation drivers, costsand reference levels. Technical Report, UK Department of Energy and Climate Change,
L dLondon,
UK pp. 138
UK.
138.
Korhonen‐Kurki, K, et al. 2014. Enabling factors for establishing REDD+ in a context of weak
governance. Climate Policy. In press, available online.