WP2 Update June 2010

Meine van Noordwijk,

Sonya Dewi , Andree

Ekadinata, Atiek

Widayati, Valentina Robiglio

Co-funding/Building blocksGlobally: OpCost (FCPF)

Indonesia: Allreddi+ Realu+various site-level studies

Vietnam: Realu

Cameroon: Realu

Peru: Realu

Deforestation is often measured in ‘football fields per hour’; is football compatible with

avoided deforestation?For example, “Amazon destruction has accelerated to record le-vels, according to figures released by the Brazilian government. The annual rate has reached 26,130 square km, the second highest ever - an area equivalent to about six football fields a minute are destroyed.

http://www.greenpeace.org/international/news/amazon-destruction

Is the goal achievable?

Is the playing field level?

Are the lines clearly marked?

What is the ball?

Is one tree + 30% grass enough to qualify as forest?

The white-man referee in the shade?

Who is watching on the sideline?

Who are the defenders?

Made from cer-tified wood?

Who is at play?

Multiple-goal planning on uneven playing fields with trees and avoided

deforestation

CG Centers

0

10

20

30

40

50

60

70

80

90

100

1 6 11 16 21 26 31 36 41 46 51 56 61

Country rank

Cu

mu

lati

ve f

ore

st C

sto

ck (

%)

Lowest

Highest

Brasil + DR Congo + Indonesia contain 50% of

total forest C stock, 10 countries contain 2/3

Emissions from deforestationIndonesia +

Brasil + Malaysia cause 2/3 of REDD domain

emissions

WP 2. Quanti-fying and Monito-ring Landuse Change

ICRAF,IITA, CIAT, ISRI, RCFEE, IRAD, INIA

D.2.1. Publication and policy brief on quantification of aboveground C stock loss due to land use change in the bench-mark area, and analysis of the area change (‘deforestation’) and C stock density change (‘degrada-tion’) in the benchmark areas

Octo-ber 2010

WP Detail

Partici-pants

Deliverables Date-line

Status

D.2.2. Publication and policy briefs on the abatement cost curves relating past GHG emissions to the economic gains they allowed

April 2011

D.2.3. Recommendations on the design of national monitoring systems (at IPCC AFOLU Tier 2+ or Tier 3) that relate the costs of monitoring to the expected benefits of higher quality of data

April 2012

On track for IndonesiaVietnam (?)Cameroon (?)Peru (??)

On track for IndonesiaVietnam (?)Cameroon (?)Peru (??)

Aboveground C-stock Aboveground C-stock maps of Indonesia in 1990, maps of Indonesia in 1990,

2000 & 20052000 & 2005

Results of ALLREDDI Results of ALLREDDI analysisanalysis

Net Emissions: 0.6 Gt year-1

19902000

2005

v

Indonesia forest ~5.4% of global total emissions

National sovereignty, responsibility, pride and good name

Guidelines for sustainable & legal production

Reducing Emis-sions from Defor-estation and De-gradation below agrees refer-ence emission levels

Locally Appropriate Adaptation and Mitigation Actions

Self-regulation of business entities and voluntary action of global citizens

Agreements between all countries of the world, seeking consensus

Sustainable Forest Management

Climate variability, water flows & PFES

NAMA• Nationally Appropriate Mitigation

Actions (2007 Bali Roadmap UNFCCC)

-26%-15%

Indonesia cari NAMA yang baik di dunia

Our data…

Globally Appropriate Mitigation Actions (GAMA)

Nationally Appropriate Mitigation Actions (NAMA)

Locally Appropriate Adaptation & Mitigation Actions

(LAAMA)

Landscape approaches to adaptation +

mitigation

Ahead of COP15 negotiations, Indonesia's President Susilo Bambang Yudhoyono has committed cuts of up to 26 percent by 2020, or 41 percent with funding and technological support from developed countries.

2005

Jambi province – among last candidates for Norway-Indonesia

REDD+

Lamandau Ex-Mega Rice Project

BerauHeart of Borneo

Tripa, Batang

Toru

Deg

rada

tion

Def

ores

tatio

n

Re/A

gro-

fore

stati

on

C st

ocks

, Mg

ha-1

Forest & tree cover transition

Natural forest

Logged-over forest

Shrub (young secondary

forest)

Mixed-tree systems & (’agroforest’)

100%

10

1

0.1 10 100 1000 km-2

Tree crop monocultures

Cropland

Human population density

Fra

ctio

n o

f la

nd

u

se/c

ove

r

SPACE ≈ TIME

EFFICIENT

FAIR

FAIR, EFFICENTJambi

Co-benefitBiodiversity

Jambi

200520001990

Aboveground C-stock Aboveground C-stock maps of Indonesia in maps of Indonesia in

1990, 2000 & 20051990, 2000 & 2005

Results of ALLREDDI Results of ALLREDDI analysisanalysis

Net Emissions: 0.6 Gt year-1

FOREST COVER CHANGE VS FOREST ALLOCATION

Annual emissions per ha within each land use allocation across 3 periods

REDD+• Institutiuonal forest vs ‘Other Land Use’Area 2/3 1/3 of IndonesiaEmission 2/3 1/3C-stock 4/5 1/5

Average emission for Indonesia 3.38 t ha-1 yr-1

JAMBI LAND COVER MAP 1990s

JAMBI LAND COVER MAP 2000s

JAMBI LAND COVER MAP 2005

JAMBI LAND COVER CHANGE 1990-2005

Merangin, Bungo,

Sarolangun

Muarojambi. TanjungJabungBarat

Batahnghari

Cref_LU Equation:logCorg = 1.333 - 0.014 depth +

0.00994*** Clay% + 0.00699** Silt% - 0.156** pH + 0.000427*Altitide - 0.00264** Slope+0 (if soil is Oxi- or Ultisol)+ 0.011NS (if soil is Incepti+sol)+ 0.834** (if soil is Andisol)+0.363** (if soil is fluvic or Aquic suborder)+ 0 (if LU is Swamp forest) -0.335* (if LU is Sedge) -0.433* (if LU is Sawah)+

-0.077NS (if LU is Primary forest) -0.082NS (if LU is Secondary forest) )-0.288* (if LU is Shrub) -0.267* (if LU is Perennial crops) -0.169NS (if LU is Upland crop)-0.245* (if LU is Alang-alang)

y = 39.657ln(x) - 46.683R² = 0.5614

-200

-175

-150

-125

-100

-75

-50

-25

0

0 5 10

Depth,

cm

Corg, %

-200

-175

-150

-125

-100

-75

-50

-25

0

0.01 0.1 1 10 100

Depth,

cm

Corg, %

Mineral soilsWetland

Jambi soil data:We updated the depth factor in Cref and found no evidence that LU-impacts on soil C depend on depth

Remaining uncer-tainty:‘Wetland’ vs ‘normal’ and ‘peat’ vs’ ‘mine-ral’ soil’ classifica-tions have fuzzy boundaries

WHERE WERE THE EMISSION HAPPENED?

Lampung

Emissions counted (annual cummulative emissions in t CO2-eq/(ha y))

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

log

$/t

CO

2_

eq

1e-3

1e-2

1e-1

1e+0

1e+1

1e+2

1e+3

RED_ARED_BRED_CREDD_ABREDD_CREDD+_ABREALU$ 5

Jambi

Emissions counted (annual cummulative emissions in t CO2-eq/(ha y))

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0

log

$/t C

O2_

eq

1e-3

1e-2

1e-1

1e+0

1e+1

1e+2

1e+3

RED_ARED_BRED_CREDD_ABREDD_CREDD+_ABREDD+_CREALU$ 5

Kalimantan Timur

Emissions counted (annual cummulative emissions in t CO2-eq/(ha y))

2 4 6 8 10 12

log

$/t C

O2_

eq

1e-3

1e-2

1e-1

1e+0

1e+1

1e+2

1e+3

RED_A

RED_B

RED_C

RED_D

RED_E

REDD_AB

REDD_C

REDD_D

REDD_E

REDD+_AB

REDD+_C

REDD+_D

REALU

$ 5

Oil palm – one of the most productive tree crops of the world, at the basis of many food products, and also a potential biofuel, potentially replacing fossil fuel dieselbut…Converting forests or peatland to oil palm leaves a negative ‘footprint’. EU importers now require data on this footprint.

A. Carbon debt from land use change history: I.Quantify, II. Attribute

Natural forest logging cycles Imperata Oil palm

Drought year

1st logging

2nd logging Clear

felling, fire

Annual grass fire

Land clearing

End of 1st oil palm rotation

Car

bon

stoc

k, M

g C

/ha

Full responsibility oil palm producers

Responsibility of entity issuing permits and/or not monitoring implementation

Method:

1. Interpret remote sensing imagery for reconstruction of LU history, combined with field interviews of local history

2. C-stock estimated of land cover types

3. Attribution rules to be negotiated

WP 2. Quanti-fying and Monito-ring Landuse Change

ICRAF,IITA, CIAT, ISRI, RCFEE, IRAD, INIA

D.2.1. Publication and policy brief on quantification of aboveground C stock loss due to land use change in the bench-mark area, and analysis of the area change (‘deforestation’) and C stock density change (‘degrada-tion’) in the benchmark areas

Octo-ber 2010

WP Detail

Partici-pants

Deliverables Date-line

Status

D.2.2. Publication and policy briefs on the abatement cost curves relating past GHG emissions to the economic gains they allowed

April 2011

D.2.3. Recommendations on the design of national monitoring systems (at IPCC AFOLU Tier 2+ or Tier 3) that relate the costs of monitoring to the expected benefits of higher quality of data

April 2012

On track for IndonesiaVietnam (?)Cameroon (?)Peru (??)

On track for IndonesiaVietnam (?)Cameroon (?)Peru (??)

WP 6. Development of REDD negotiation support system (RTD)

ICRAF,MLURI, IVM, CIFOR, IITA, CIAT, ISRI, RCFEE, IRAD, INIA

D.6.1. Publication and policy briefs on the way local perspectives on REDD can be compared with those at the international negotiation table and used in quantitative scenario models

April 2011

D.6.2. Publication and policy briefs on the stakeholder perspectives on ‘fair and efficient’ benefit distribution along the CREDD value chain

April 2011

D.6.3. Publication(s) on participatory scenario analysis of plausible REDD mechanisms in benchmarks in Asia, Africa and Latin America

April 2012

Hutan Desa an alysis: manuscript under review + W.Paper

Manuscript in prep on results of similation game with villagers ~ CES/COS/CIS motivational analysis

Vietnam – manuscript in prep. On C. Highlands OpCost + local perspectives

FERVA: OIdonesia + Peru results, manuscript in prep.

WP 5. Integra-tion and model-ling (RTD)

MLURI,UCL, UGOE, ICRAF, CIFOR, IITA, CIAT, ISRI, RCFEE, IRAD, INIA

D.5.1. Simple robust model for IPCC AFOLU Tier 3 national accounting of at least two of the study countries

April 2010

D.5.2. CHE model tested for historical changes, and able to respond to climate change, REDD policy levels, and shifts in global food crop vs. bio-energy demand

October 2011

D.5.3. Emission reduction estimates for 2020 under various scenarios for at least two of the study countries

October 2011

D.5.4. Recommendations to policy, land-managers, and other stakeholders regarding deforestation: how to develop policies, management tools and livelihood strategies that take GHG emissions into account

April 2012

D.5.5. Popularized publication in stakeholder literature and in media directed at the general public

April 2012.

D.5.6. Scientific papers submitted to peer-reviewed journals

April 2012

Manuscript submitted by Grace c.s. on agent based modelling aproaches

Herri c.s. provin-cial scale agent-based model

Fallow vs Agent-based model comparison in progress Grace/Betha

1

10

100

1 100 10000

Loss of forest cover, ha/yr

Nu

mb

er

of

RE

DD

pilo

ts

1

10

100

100 1,000 10,000 100,000

National forest C stock

Nu

mb

er

of

RE

DD

pilo

ts

000,National Forest C stock Loss of forest cover, ha/year

Indonesia

DR Congo

Brasil

Ecuador

PNG

Indonesia

Brasil

EcuadorDR Congo

y = 0.09x0.42

R2 = 0.40y = 0.37x0.42

R2 = 0.48

Current REDD pilots relate to stock and

threat, but have strong ‘fairness’

aspect (scale 0.42)

Vietnam

Vietnam: increase in forest area

1. Changes in land cover C-stock classes C = iCi,t+1 - iCi,t = jfj,t+1Cj,t+1 - jfj,tCj,t

C-stock, Ci

Frequency, f

i

C-stock, Ci

Frequency, f

i

t + 1 minus t

C-stock, Ci

Frequency, f

i

gain

loss

C =jfj,t+1Cj,t+1 - jfj,tCj,t

= j (fj,t+1 - fj,t ) Cj,t+1 + jfj,t ( Cj,t+1 - Cj,t )

Area change for each class j of C stock densities

Change in mean C stock density for each class j

Rather than measuring C stock in each pixel, we may first class them by land cover type

This works if C stock can be correctly identified based on land cover

C =jfj,t+1Cj,t+1 - jfj,tCj,t

= j (fj,t+1 - fj,t ) Cj,t+1 + jfj,t ( Cj,t+1 - Cj,t )

Area change for each class j of C stock densities

Change in mean C stock density for each class j

Weaknesses of the approach derive from:

1.Errors in classification of the pixels into cover classes error in the fj

2.Uncertainty on the average C stock values per class error in Cj

3.Changes in Cj over time

Remedies:

\

Use classes with narrow C stock ranges

Approach 2. Using gain & loss data: C=i

(Ci,t+1 – Ci,t) = iGaini - iLossi

• In many land use types, small annual gains in C stocks are balanced by fewer but larger removals. If good data on both gains and removals exist, this can be used for deriving net change.

• The annual gain of C stocks in any parcel of land can be estimated from the growth rate of trees (in line with forestry research traditions) in combination with changes in the stocking rate and soil C models.

• Removals for timber, firewood, fodder, forest litter or other use may be available from existing statistics.

• Losses by fire may be estimated from remote sensing data in combination with ground truthing.

Weaknesses of the approach derive from:

1.Errors in assigning overall trade and use statistics to specific land cover classes

2.Uncertainty on interaction between changes in stocking and growth rates

Remedies:

Adjust the clas-sification to the scales and systems at which use data are available

C =jfj,t+1Cj,t+1 - jfj,tCj,t

= j (fj,t+1 - fj,t ) Cj,t+1 + jfj,t ( Cj,t+1 - Cj,t )

Area change for each class j of C stock densities

Change in mean C stock density for each class j

3. Land use classes: Changes in land use using typical C-stock classes + shifts in average age

C-stock, Ci

Fre

q.

C =kfk,t+1Ck,t+1 - kfk,tCk,t

= k (fk,t+1 - fk,t ) Ck,t+1 + kfk,t ( Ck,t+1 - Ck,t )

Area change for each LU class k of C stock densities

Change in mean C stock density for each LU class k: below or above average age

Open field agricultureAgroforestryTree crop plantationsPlantation forestryAgroforestLogged forestNatural forest Time

C-s

tock FN

FLLogged forest

Natural forest

FP

Plantation forestry

Time-aver-aged C-stocks

Ai

Open-field agriculture

Weaknesses of the approach derive from:

1.Errors in spatial classification by land use types, combining ‘land cover phases’ with on-the-ground charac-teristics and management styles

2.Uncertainty on shifts in time-averaged C stocks within the LU categories

Remedies:

Combine the approach with models that link to ‘drivers’: material flows, labour, rights

C =kfk,t+1Ck,t+1 - kfk,tCk,t

= k (fk,t+1 - fk,t ) Ck,t+1 + kfk,t ( Ck,t+1 - Ck,t )

Area change for each class j of C stock densities

Change in mean C stock density for each class j

C = iCi,t+1 - iCi,t

C-stock, Ci

Frequency, f

i

C-stock, Ci

Frequency, f

i

t + 1 minus t

C-stock, Ci

Frequency, f

igain

loss

Approaches:

1. Land cover classes

2. Using gain & loss data

3. Land use classes

Uncertainty, bias and its consequences in C accounting

Tree: size (diameter, height,…)

shape (allometrics)

wood density C-concentration

Species ID &lookup tables

Forest/Ag patch: frequency distribution . of trees of various types

Land area: mosaic of Forest/Ag patches

Time series: temporal change in mosaics

Mg C / year

Mg C

Mg C / ha

Mg C / tree

Trees / ha =

x

=

xha / LUtype

=

d /dt

Drivers State Consequences

DemographyLivelihood

Trade & in- deficit &Vestment Environmen-

tal deficitClimate change

Stakeholders G o v e r n a n c e d e f i c i t

Actual forest &

tree cover

Mosaic landscape with agroforestry, plantations, crop fields, woodlots

Core Logged Secondary&forest over Agro-forest forest

Annual Grasscrops land

Deg

rada

tion

Def

ores

tatio

n

Re/A

gro-

fore

stati

on

C st

ocks

, Mg

ha-1

Forest & tree cover transition

The efficiency versus fairness challenge

Emissions not covered by

mechanisms discussed, e.g.

peatland Sinks not covered by current mechanisms

C-stocks

t/ha

Time, national land-use-change trajectories

Fairness criterion: reward conservation ethic

Efficiency criterion: focus on verifiable emission reduction

Depends on definitions used

Conservation Production Conversion

forest forest forest

Forest without

trees

Non-forest without trees

Trees outside forest

Forest with trees

Forest definition based on insti-tutions & intent

Forest definition based on X% canopy cover

Including e.g. agroforests, oil palm plantation

Clearfelling/ re-plant is accep-

ted as forest; no time-limit on

‘replant’

Annex-I Emissions all sectors

Non-Annex-I CDM

REDD and SFM

PEAT SLM Agricult. intensi-fication

Alleviating rural poverty

Biofuel, agrocommoditiesExport of woodNon-accountable footprint

A/R

Day Year Plant life cycle Ecosystem succession GeomorphologicalTime scales

Background soilrespiration

Rhizosphere-induced soilrespiration

Episodic droughts fire & respiration

Respiration linked to shifts in regional hydrologyRoot + rhizosphere-

respiration of recent photosynthates

Surface litter respiration

Surface woody residue respiration

Incorporation of aboveground residue & structural root turnover, followed by respiration

CO2CO2

CH2OCH4

or CO2

CO2

CH4

or CO2

CH4

or CO2

CO2CO2

Root and stem aerenchyma based gas

emissions from root and rhizosphere respiration

(bypassing aerobic zones)

Organic acids and particulate leachates

Tradeoffs in lowland Sumatra/Kalimantan without ‘intermediate intensity rubber agroforestry

Opportunities for C offset incentives

Tradeoffs in lowland Sumatra/Kalimantan with new ‘intermediate intensity’ rubber agroforestry

An economic development success story

Keeping far-mers on-site

Destroying options for C-offsets

Reducing landscape C

stocks