iiasa globiom overview
TRANSCRIPT
Overview of the GLOBIOM
model
REDD-PAC project kick-off meeting, São José dos
Campos, Brazil
Aline Mosnier, Michael Obersteiner, Petr Havlik, Hugo
Valin, Geraldine Bocqueho et al.
Ecosystems Services and Management Program
GLOBIOM
� Global : 30 regions (among them Brazil and Congo Basin)
� Geographically explicit land use activities
� Partial equilibrium model
� Agriculture: major agricultural crops and livestock products
� Forestry: managed forests for sawnwood, and pulp and
paper production
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paper production
� Bioenergy: conventional crops and dedicated forest
plantations
� Optimization model : maximization of the producer and
consumer surplus (endogenous prices balance supply and
demand)
� Recursive dynamic (10 year periods from 2000)
Inputs/Outputs from GLOBIOM
REGIONAL
INPUTS OUTPUTS
Population and GDP
Bioenergy use
Diet patterns (FAO, 2006)
Processing costs and
coefficients
International trade costs
Prices
Demand quantity
Processed quantity
Bilateral trade flows
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REGIONAL
SPATIAL
International trade costs
Land productivity for crops,
grass, timber
Input requirements
Carbon stock
(Internal transportation
costs)
Land use (ha)
Production (ton/m3)
Input use
GHG emissions (CO2,
CH4, N2O)
GLOBIOM
SUPPLY
Process
DEMAND
Wood products Food Bioenergy
Exogenous driversPopulation, GDP
Primary wood
products
Crops
PROCESS30 regions
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G4M
PX5
Biophysical
models
Between 10*10
km and 50*50
km
Aggregation
in larger
geographical
units
EPIC RUMINANT
SPATIALLY EXPLICIT INPUT DATA
Climate Soil and topography Management Land cover
Spatially explicit input data
Approach for data harmonization
• Homogeneous response units (HRU)
HRU = Altitude & Slope & Soil
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;
Source: Skalský et al. (2008)
Country HRU*PX30
LC&LUstat
> 200 000 SimU
� Simulation Units (SimU) = HRU & 50x50km grid & Country
Spatially explicit input data
PX5
SimU delineation relatedstatistics on LC classes and
Cropland management systems
reference for geo-coded data on crop management;
input statistical data for LC/LU economic optimizat ion;Source: Skalský et al. (2008)
Flexible aggregation in the model => trade-off between computational time and spatial variability
Spatially explicit information
� Initial land cover => GLC 2000
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CROPLAND FORESTS
GRASSLAND OTHER NATURAL LAND
GLOBIOM Products
AGRICULTURE FORESTRY BIOENERGY
Wheat Buffalo Biomass for log EthanolWheat
Rice
Maize
Soybean
Barley
Sorghum
Millet
Cotton
Dry beans
Rapeseed
Groundnut
Sugarcane
Potatoes
Cassava
Sunflower
Chickpeas
Oil Palm
Sweet potatoes
Buffalo
Cattle
Sheep
Goat
Pig
Poultry
Beef
Lamb
Pork
Poultry
Eggs
Milk
Biomass for log
production
Fuel wood
Other wood
Pulp wood
Logs
Ethanol
FAME
Methanol
Heat
Electricity
Biogas
Land Use Change
Natural ForestsNatural Forests Other natural Other natural
landland
Land use changes
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ManagedManaged
ForestsForests
TreeTree
PlantationsPlantationsCroplandCropland
GrasslandGrassland
Land use changes
are consistently
transferred from one
period to another
GLOBIOM: Typical applications
� Agricultural prospective� Schneider et al. (2011) Impacts of population growth, economic development, and
technical change on global food production and consumption. Agricultural Systems
� Smith et al. (2010) Competition for land, Philosophical transactions
� Applied scenarios such as Eastern Africa with CCAFS
� Deforestation� Mosnier et al. (2010) Modeling impacts of development trajectories on forest cover in the
Congo Basin� Living Forest Report – WWF (2011)� Living Forest Report – WWF (2011)
� Climate change� Valin et al. (2010) Climate change mitigation and food consumption patterns
� Biofuels� Frank et al. (2012) How effective are the sustainability criteria accompanying the EU 2020
biofuel targets? Global Change Bioenergy
� Mosnier et al. (2011) GLOBAL impact of US biofuels targets� Fuss et al. (2011) A stochastic analysis of biofuel policies� Havlik et al. (2010) Global land-use implications of first and second generation biofuel
targets. Energy Policy
� And several others…
The Congo Basin case study
CONGOBIOM
� 1550 simulation units
� Internal transportation
costs
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costs
� Spatial representation of
fuelwood demand
� Cocoa and coffee included
� Delineation of forest
concessions and protected
areas
The Congo Basin study
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Transport time with existing
infrastructures (Circa 2000)
Transport time with new
infrastructures
Source: National Ministries,
World Bank
The Congo Basin study
� 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
500
600
1.2
1.4
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0
100
200
300
400
500
0
0.2
0.4
0.6
0.8
1
1.2
BASE BIOFW MEAT INFRA TECHG
MtCO2/ye
ar
Mha/ye
ar
area deforested GHG emissions from deforestation
The Congo Basin study
Infrastructure scenario : + 0.6 Mha
deforested/year (x3)
Productivity scenario : +0.2 Mha
deforested/year
Transport cost difference Deforestation due to
croplandDeforestation due to
croplandMain cities
Different patterns of deforestation across scenarios
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=> Deforestation in DRC dense
forest
=> Deforestation close to the big
cities
cropland
The Congo Basin study
Indirect effects of RED
� Crop price index
500
1000
1500
2000
2500
3000
in 100
0 tons
Corn
OPAL
Rice
SugCGlobal reduction of GHG emissions from
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� Main imports (1000T)
0
500
BAU 50% 75% 90%
Global reduction of GHG emissions from deforestation
Whea
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
Conclusion
Regional study with GLOBIOM: national-international consistency
On the national/regional level refinement of the model with
� Validation of current input data
� Addition of higher quality data
� Better understanding of the LUC process
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� Better understanding of the LUC process
� Finer resolution level
� Implementation of national policies
+ interactions with other models
� Econometric models
� Downscaling models
� Biodiversity models
Conclusion
On the international level
� Bilateral trade flows with 29 other regions => leakages
between national/regional scale and international scale
� Implementation of global agreements/regulations
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⇒Optimal combination = national interest + information
available + political relevance
