The GLOBIOM model is a global partial equilibrium model that can be used to study land use and its impacts. It has 30 geographical regions modeled at varying spatial resolutions. It optimizes land allocation between major agricultural crops, livestock, forestry, and bioenergy to maximize economic surplus. Model outputs include production, trade, land use, and greenhouse gas emissions. The model was applied in a case study of the Congo Basin that examined scenarios of infrastructure development, productivity increases, and REDD policies on deforestation and its impacts.

1. 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
mosnier@iiasa.ac.at

2. 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
2
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)

3. 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
3
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)

4. GLOBIOM
SUPPLY
Process
DEMAND
Wood products Food Bioenergy
Exogenous drivers
Population, GDP
Primary wood
products
Crops
PROCESS 30 regions
4
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

5. 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)

6. Country HRU*PX30
LC&LUstat
> 200 000 SimU
Simulation Units (SimU) = HRU & 50x50km grid & Country
Spatially explicit input data
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;
Source: Skalský et al. (2008)
Flexible aggregation in the model => trade-off
between computational time and spatial variability

7. Spatially explicit information
Initial land cover => GLC 2000
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CROPLAND FORESTS
GRASSLAND OTHER NATURAL LAND

8. 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

9. Land Use Change
Natural ForestsNatural Forests Other naturalOther natural
landland
Land use changes
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ManagedManaged
ForestsForests
TreeTree
PlantationsPlantations
CroplandCropland
GrasslandGrassland
Land use changes
are consistently
transferred from one
period to another

10. 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…

11. 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

12. 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

13. 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
13
0
100
200
300
400
500
0
0.2
0.4
0.6
0.8
1
1.2
BASE BIOFW MEAT INFRA TECHG
MtCO2/year
Mha/year
area deforested GHG emissions from deforestation

14. 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
cropland
Deforestation 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

15. The Congo Basin study
Indirect effects of RED
Crop price index
500
1000
1500
2000
2500
3000
in1000tons
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

16. 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

17. 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

18. Thank you !
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For more information :
www.globiom.org
Contact: mosnier@iiasa.ac.at