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Introducing land use and agriculture in TIAM to asses land demand of future bioenergy deployment scenarios
1. Introducing land use and agriculture in
TIAM to assess land demand of future
bioenergy deployment scenarios
Alexandre Kรถberle
Grantham Institute, Imperial College London
ETSAP Workshop
Gรถteborg, 18 June 2018
2. Bioenergy: linking energy markets and agriculture
โMassive production of energy, mainly liquid fuels, from agricultural resources will
link agricultural and energy markets tightly. The new market integration is perhaps
the most fundamentally important change to occur in agriculture in decades. The
link between energy and agricultural markets requires an integrated environment
to study these markets and design policy alternatives to guide them toward
designated goals.โ
Tyner and Taheripour (2008)
โMassive production of energy, mainly liquid fuels, from agricultural resources will
link agricultural and energy markets tightly. The new market integration is perhaps
the most fundamentally important change to occur in agriculture in decades. The
link between energy and agricultural markets requires an integrated environment
to study these markets and design policy alternatives to guide them toward
designated goals.โ
Tyner and Taheripour (2008)
โข Currently, TIAM lacks adequate representation of both land use and agriculture
โข This undermines the credibility of resulting high bioenergy scenarios
3. TIAM: linking bioenergy, agriculture and land use
โข Ideally, gridded, spatially explicit land use and crop models should be used
โข This is resource-intensive (funds, person-hours, computing power)
โข Mostly done via soft-link approach:
โข Models exchange key variables in iterated runs
โข This method may result in sub-optimal solutions
โข LU and agriculture can also be represented as commodity flows and processes
โข Introduce a methodology to include it in TIAM
6. Ethanol in BLUES (Kรถberle, 2018)
2nd Gen EtOH
prod
Biomass
processing
Grassy
prod
Woody
prod
BECCS elec
Bioelectricity
generation
Sequestered
CO2
Crushing
Sugar prod
1st Gen EtOH
prod loEff
Surplus
export
1st Gen EtOH
prod hiEff
Sugcn
prod
EtOH CCS
EtOH aux
Existing
Existing must
change
New
7. Proposed Land Use transitions matrix
Forest
Low Cap
Pasture
High Cap
Pasture
Cropland
Planted
Forest
Savanna
Managed
Forests
Deforestation
8. Example of a land use conversion process in BLUES,
showing deforestation to create low-capacity pastures.
3/9/2018 Alexandre Koberle DSc defense 8
Land use classes can be converted from one to another
via conversion processes
Methods
๐,๐
๐ฟ๐ด๐๐ท๐,๐ = ๐๐๐_๐ฟ๐ด๐๐ท๐,๐
11. But what about costs?
Highly influenced by:
โข Local conditions (grid cell level)
โข Management systems (challenging to model)
โข Dominated by short-term decisions (agriculture)
โข Carbon stocks in natural land uncertain (LU transitions)
Lack of reliable global cost data => model it
12. Cost calculation model: proxy inputs
Travel time to nearest city
โข Source: ESA 2008
โข 14 classes (0 to 100 hours)
โข Proxy for transportation costs
GAEZ crop suitability index
โข Source: FAO-IIASA
โข 8 classes (not suitable to very suitable)
โข Indicator of land productivity
โข Proxy for crop production costs
Both at 5-arc minute resolution (10 km at equator)
13. Cost classes within each region
Relative production costs map
G
F
E
D
C
B
A
Set to NA (ignored)
Multiply values in each grid cell of input rasters and reclassify into 7 cost classes
15. Each cost class a step in a cost-supply curve
More steps is possible (depends on data
resolution)
will be actual
cost in $$
16. Cost classes within each region
Relative production costs map
๐ฟ ๐,๐
G
F
E
D
C
B
A
Cost class 0 is Set to NA (ignored)
๐ฟ ๐,๐
= a coefficient to multiply benchmark production cost of crop c in region r๐ฟ ๐,๐
17. Benchmark costs: the case of Brazil
Cost
Class
dr,c
A 0.8
B 0.9
C 1
D 1.2
E 1.7
F 2.5
G 3.5
๐ฃ๐๐๐,๐,๐ = ๐ฟ ๐,๐ โ ๐๐,๐
Benchmark regional costs (๐๐,๐ ) are adjusted by a cost multiplier
๐ฟ ๐,๐ to account for land productivity and distance to demand centers:
US$/t NO NE SE SU CO
Wheat 9999 9999 334.1 208.3 213.1
Fruits 923.4 923.4 923.4 923.4 923.4
Soybeans 252.5 340.5 254.3 261.0 252.5
Maize 234.6 653.4 252.2 182.5 234.6
Cereal 223.5 622.6 240.3 173.9 223.5
Vegetables 560.1 560.1 560.1 560.1 560.1
Roots 1229 1229 1229 1229 1229
Rice 334.8 334.8 297.3 259.8 334.8
Pulses 618.2 618.2 618.2 618.2 618.2
Oilseed 55.2 74.4 55.5 57.0 55.2
Nuts 1637 1637 1637 1637 1637
Sugarcane 28.1 28.1 27.3 32.4 28.1
Coffee 1724.3 1724.3 1724.3 1724.3 1724.3
Fiber 3111.7 8668.2 3345.8 2420.9 3111.7
Woody 33.0 42.9 30.8 33.0 33.0
Source: Koberle 2018
18. Cost supply curves for regions in TIAM
Example: high input cereal suitability
19. Cost supply curves for regions in TIAM
Example: Canada high input cereal suitability
# of grid cells
20. Next steps: generating cost supply curves for regions in TIAM
Example: India high input cereal suitability
# of grid cells