Mitigation strategy and the REDD: Application of the GLOBIOM model to the Congo Basin region
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Transcript of Mitigation strategy and the REDD: Application of the GLOBIOM model to the Congo Basin region
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AGRODEP Workshop on Analytical Tools for Climate
Change Analysis
June 6-7, 2011 • Dakar, Senegal
ww
w.a
gro
dep
.org
Mitigation strategy
and the REDD:
Application of the
GLOBIOM model to
the Congo Basin region
Presented by:
Aline Mosnier, IIASA
Please check the latest version of this presentation on:Please check the latest version of this presentation on:
http://agrodep.cgxchange.org/first-annual-workshop
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AGRODEP members’ meeting and workshop- June 6-8 2011- Dakar, Sénégal
Mitigation strategy and the REDD:Application of the GLOBIOM
model to the Congo Basin region
A. Mosnier, M. Obersteiner , P. Havlík, H. Valin, S. Fuss, S. Frank, E. Schmid et al.
International Institute for Applied Systems Analysis (IIASA)
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INTRODUCTION
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Introduction
REDD+: Reducing Emissions from Deforestation and forest Degradation in developing countries
• Idea that reducing deforestation could be an efficient and cheap strategy to fight against climate change
• International community should transfer money to developing countries which make efforts to reduce deforestation or forest degradation
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Introduction
• First talks in 2005, launched in 2008, part of the post-2012 Kyoto protocol ?
• From RED to REDD+: avoiding deforestation + avoiding forest degradation + enhancing forest carbon sequestration
• Global REDD+ system has not been yet decided => gradually taking shape. One implementation could follow 3 phases:
1/ REDD+ strategy definition and capacity building
2/ implementation of policies and measures to reduce emissions
3/ full UNFCCC compliance
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IntroductionCore idea is performance-based-payments
• Main issues
– Reference level: historical vs. prospective approach
– Performance indicators and MRV (Monitoring, Reporting and Verification)
– Assessment of the cost of these efforts
– Source of the funding: carbon markets, fund-based finance, voluntary contributions
– Payments to carbon rights holders: direct, through government, through separate REDD+ fund
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Introduction
• Requirements
– Vertical integration across different scales: global-national-local system
– Horizontal integration across sectors: need for a broad set of policies (land tenure, institutions, forestry, agriculture, energy)
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Introduction
• The role of agriculture in REDD+
– agriculture is a driver of deforestation => ¾ of tropical deforestation is due to agriculture
– mitigation costs depend on the profitability of agriculture
– Status of agro forestry and plantations
– Alternatives to subsistence agriculture => off-farm jobs opportunities
– Role of agriculture/forests in national development strategy
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GLOBIOM
INTRODUCTION
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GLOBIOM
SUPPLY
Process
DEMAND
Wood products Food Bioenergy
G4M
Exogenous driversPopulation growth, economic growth
Primary woodproducts
Crops
PROCESS
PX5
Altitude class, Slope class,
Soil Class
PX5
Altitude class (m): 0 – 300, 300 – 600, 600 – 1200, 1200 – 2500 and > 2500;
Slope class (deg): 0 – 3, 3 – 6, 6 – 10, 10 – 15, 15 – 30, 30 – 50 and > 50;
Soil texture class: coarse, medium, fine, stony and peat;
HRU = Altitude & Slope & Soil
Biophysicalmodels
Between 10*10 km and 50*50
km
Aggregation in larger units (max 200*200
km)
28 regions
EPIC RUMINANT
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GLOBIOMBottom-up approach: detailed land
characteristics
• Global database (Skalsky et al., 2008)
– Sources of data: global observation data, digital maps, statistical and census data, results of complex modeling
– Thematic datasets: land cover, soil and topography, cropland management, climate
Support bio-physical models
Source of data on land cover for GLOBIOM
Tool for identification of the gaps in availability of necessary global data
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GLOBIOMApproach for data harmonization
• Homogeneous response units (HRU)
PX5
Altitude class, Slope class,
Soil Class
PX5
Altitude class (m): 0 – 300, 300 – 600, 600 – 1200, 1200 – 2500 and > 2500;
Slope class (deg): 0 – 3, 3 – 6, 6 – 10, 10 – 15, 15 – 30, 30 – 50 and > 50;
Soil texture class: coarse, medium, fine, stony and peat;
HRU = Altitude & Slope & Soil
Source: Skalský et al. (2008)
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Country HRU*PX30
PX5
SimU delineation related
statistics on LC classes and
Cropland management systems
reference for geo-coded data on crop management;
input statistical data for LC/LU economic optimization;
LC&LUstat
> 200 000 SimU
• Simulation Units (SimU) = HRU & 50x50km grid & Country
Source: Skalský et al. (2008)
GLOBIOM
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17 crops, 4 management systems (subsistence, low input, high input, high input irrigated)
=> Outputs: Crop yield, Water requirement, Fertilizers requirement, Environmental indicators
ProcessesWeather, Hydrology, Erosion,
Carbon sequestration, Crop growth, Crop rotations,
Fertilization, Irrigation,…
GLOBIOM
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Biophysical models: G4M for Forests
Step 1: Downscaling FAO country level information on above ground carbon in forests (FRA 2005) to 30 min grid (Kinderman et al., 2008)
Step 2: Forest growth functions estimated from yield tables
=> Outputs: Annual harvestable wood, Harvesting costs, Carbon stock
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GLOBIOMSupply representation
• Flexible Aggregation reflects the trade-off between computational time and land heterogeneity representation
• Implicit Leontieff supply functionstechnology 1 (rainfed) yield 1 + constant cost 1
technology 2 (irrigated) yield 2 + constant cost 2
• Endogenous productivity change possible through:– Reallocation of the production to more productive units
– Change in management systems
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GLOBIOMDemand representation
• Regional level (28 regions)
• Explicit demand functions (linearized non linear function), own-price elasticities taken from USDA
• Exogenous constraints– Minimum calorie intake (differentiated between
vegetable and meat) based on population increase and FAO food projections (Bruinsma)
– Minimum processed wood demand based on population and GDP p.c.
– Minimum bioenergy demand (POLES model, WEO,…)
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Supply chain
Natural Forests
Managed Forests
Short Rotation TreePlantations
Cropland
Grassland
Other natural land
BioenergyBioethanolBiodiesel MethanolHeatElectricityBiogas
Wood products
Sawn woodPulp
Livestock productsBeefLambPorkPoultryEggsMilk
CropsCornWheatCassavaPotatoesRapeseedetc…
LAN
D U
SE C
HA
NG
E
Saw and pulp mills
Biorefinery
Livestock
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GLOBIOMOptimization model
• Objective = Maximization
of global welfare i.e. producer
and consumer surplus
Spatial equilibrium model
• Homogenous product (price differences = trade costs)
• Endogenous bilateral trade flows (minimization of trading costs)
• Recursive dynamic
Q
P
Offre
Demande
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GLOBIOM
• Main outputs
– land use (cropland, natural and managed forests, short rotation tree plantations, grassland and other natural land),
– CO2 emissions related to land use change,
– spatially explicit agricultural production (19 crops, 6 livestock products),
– spatially explicit forest production,
– food consumption and food prices,
– bilateral trade flows
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GLOBIOMExamples of issues addressed with
GLOBIOM
• Bioenergy => Havlik et al. (2010), Mosnieret al. (2010), Fuss et al. (2011)
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GLOBIOM
• Deforestation: the Living Forest Report (WWF-2011)
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GLOBIOM
• GHG emissions
– “Climate change mitigation and food consumption patterns” , Valin et al. (2010)
– “Analysis of potential and costs of LULUCF use by EU member states”, Bottcher et al. (2009)
– “Production system based global livestock sector modeling: Good news for the future”, Havlík et al. (2011)
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REDD IN THE CONGO BASIN
INTRODUCTION
GLOBIOM
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REDD in the Congo Basin
6 countries: Cameroon, Republic of Congo,
Democratic Republic of Congo (DRC), Gabon,
Central African Republic (CAR), Equatorial Guinea
• Total dense forest area:
– 162 million ha
– 2d rainforest area after Amazon
– 80% of the Congo Basin countries
territory
=> Strong interest in REDD+
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REDD in the Congo Basin
• Low historical level of deforestation in the region: 0.17% per year over 1990-2000 compared to 0.5% in Brazil
• 70% of cropland for subsistence agriculture => food production per capita has decreased over the last decade
• Main drivers of deforestation and forest degradation:• shifting agriculture
• illegal logging
• fuel wood collection
• agricultural plantations in some part of the region
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REDD in the Congo Basin
CONGOBIOM
• Detailed representation of land use activities (1550 simu between 10x10 and 50x50 km)
• Internal transportation costs
• Spatial representation of wood demand
• Cocoa and coffee added
• Delineation of forest concessions and
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REDD in the Congo BasinScenario Description REDD
Inte
rnat
ion
al
dri
vers
Meat ↑15% global demand↓ 0%, 50%, 75%,
95%
Biofuel↑100% demand for 1st
generation biofuels
↓ 0%, 50%, 75%, 95%
Inte
rnal
dri
vers
InfrastructurePlanned infrastructures realized
↓ 0%, 50%, 75%, 95%
Productivity↑30% yields for cash crops, 100% for other crops
↓ 0%, 50%, 75%, 95%
RED
D
REDD-LNo participation of Congo Basin in REDD
↓ 0%, 50%, 75%, 95% (only for
ROW)
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REDD in the Congo Basin
Transport time with existinginfrastructures (Circa 2000)
Transport time with new infrastructures
Source: National Ministries, World Bank
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REDD in the Congo Basin
• Average deforested area (in million hectares) and average GHG emissions (in million tons CO2) from deforestation per year over the period 2020-2030 in the Congo Basin
0
100
200
300
400
500
600
0
0.2
0.4
0.6
0.8
1
1.2
1.4
BASE BIOFW MEAT INFRA TECHG
MtC
O2/
year
Mh
a/ye
ar
area deforested GHG emissions from deforestation
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External drivers
Channels of transmission
1/ Through trade of the product itself
2/ Through trade of the intermediate product
3/ Through trade of other crops
Without increase in agricultural competitiveness in Congo Basin, the substitution effect dominates (3)
Biofuel scenario = +0.14 million ha deforested per year (+50%)Meat scenario = +0.09 million ha deforested per year (+30%)
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Internal drivers
Calorie Consumption (kcal/cap/day) Crop price index
COST / TON= (Cost of production per hectare+ other costs per hectare)/ Yield
+ Internal transportation cost
Both scenarios (productivity and infrastructures) reduce the unit cost of agricultural and forestry products => stimulate local demand
BASE INFRA PRODTY BASE INFRA PRODTY
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Internal drivers
Infrastructure scenario : + 0.6 Mhadeforested/year (x3)
=> Deforestation in DRC dense forest
Productivity scenario : +0.2 Mhadeforested/year
=> Deforestation close to the big cities
Transport cost difference Deforestation due to cropland
Deforestation due to croplandMain cities
But very different effects in terms of deforestation patterns !
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REDD in the Congo BasinFood security
• Crop price index
• Main imports (1000T)
0
500
1000
1500
2000
2500
3000
BAU 50% 75% 90%
in 1
00
0 t
on
s
Global reduction of GHG emissions from deforestation
Corn
OPAL
Rice
SugC
Whea
Global reduction of GHG emissions from
deforestation
BAU -50% -75% -90%
Congo Basin
BASE 1.02 1.19 1.38 1.61
BIOFW 1.02 1.42 1.85 2.52
MEAT 1.02 1.28 1.49 1.71
INFRA 0.90 1.09 1.24 1.47
TECHG 0.59 0.68 0.81 0.96
REDL 1.02 1.04 1.06 1.07
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REDD in the Congo Basin
Leakage effect
0
500
1000
1500
2000
2500
BAU RED_50% RED_75% RED_90%
MtC
O2
/ye
ar
Congo Basin
Rest of the world
Total without Congo Basin in REDD
Total with Congo Basin in REDD
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CONCLUSION
INTRODUCTION
GLOBIOM
REDD IN THE CONGO BASIN
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Conclusion
REDD issues that can be addressed with GLOBIOM:
– what would be the deforestation levels without REDD (reference levels) ?
– impact of different deforestation and forest degradation drivers (external/internal, energy/wood/food production)
– cost and efficiency of different mechanisms to reduce deforestation and forest degradation
– impact of these mechanisms on agricultural sector and food markets
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Conclusion
From the Congo Basin experience, the modeling exercise:
– gives an illustration of the value of information
– highlights the necessity of including agriculture in discussions around REDD+ and the need for horizontal cooperation
– what will the future look like ? Prospective exercise which requires long term view => what is the development strategy of the country ?
– help building an argumentation for international negotiations
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Conclusion
Future challenges relevant for Africa
• Adaptation of GLOBIOM to local context is time consuming => requires human resources
• Data availability and quality
• Poverty analysis
• Governance issues