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 Köberle
Grantham Institute, Imperial College London
ETSAP Workshop
Göteborg, 18 June 2018

2. Bioenergy: linking energy markets and agriculture
“Massive 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.”
Tyner and Taheripour (2008)
“Massive 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.”
Tyner and Taheripour (2008)
• Currently, TIAM lacks adequate representation of both land use and agriculture
• This undermines the credibility of resulting high bioenergy scenarios

3. TIAM: linking bioenergy, agriculture and land use
• Ideally, gridded, spatially explicit land use and crop models should be used
• This is resource-intensive (funds, person-hours, computing power)
• Mostly done via soft-link approach:
• Models exchange key variables in iterated runs
• This method may result in sub-optimal solutions
• LU and agriculture can also be represented as commodity flows and processes
• Introduce a methodology to include it in TIAM

4. Method used successfully
Theses: http://ppe.ufrj.br/
Journal paper submitted

5. Crop 1
Process
Bioenergy
carrier 1
Bioenergy
carrier 2
Crop
Prod 1
Bioenergy
carrier 3 w/ CCS
Crop
Prod 2
Bioenergy
carrier 3
CO2
storage
Challenges:
• Agricultural data availability
• Modelling variables as aggregated averages
• Yields, costs, inputs

6. Ethanol in BLUES (Köberle, 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
𝑖,𝑟
𝐿𝐴𝑁𝐷𝑖,𝑟 = 𝑇𝑂𝑇_𝐿𝐴𝑁𝐷𝑖,𝑟

9. 3/9/2018 Alexandre Koberle DSc defense 9
Example of a crop production process
Methods

10. FAO data aggregated to TIAM regions

11. But what about costs?
Highly influenced by:
• Local conditions (grid cell level)
• Management systems (challenging to model)
• Dominated by short-term decisions (agriculture)
• 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
• Source: ESA 2008
• 14 classes (0 to 100 hours)
• Proxy for transportation costs
GAEZ crop suitability index
• Source: FAO-IIASA
• 8 classes (not suitable to very suitable)
• Indicator of land productivity
• 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

14. Global cost class distribution
# of grid cells

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
𝛿 𝑟,𝑐
G
F
E
D
C
B
A
Cost class 0 is Set to NA (ignored)
𝛿 𝑟,𝑐
= a coefficient to multiply benchmark production cost of crop c in region r𝛿 𝑟,𝑐

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
𝑣𝑜𝑚𝑖,𝑟,𝑐 = 𝛿 𝑟,𝑐 ∗ 𝑐𝑖,𝑟
Benchmark regional costs (𝑐𝑖,𝑟 ) are adjusted by a cost multiplier
𝛿 𝑟,𝑐 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

21. Thank you!
akoberle@imperial.ac.uk
@alexkoberle