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Introducing land use and agriculture in
TIAM to assess land demand of future
bioenergy deployment scenarios
Alexandre Köbe...
Bioenergy: linking energy markets and agriculture
“Massive production of energy, mainly liquid fuels, from agricultural re...
TIAM: linking bioenergy, agriculture and land use
• Ideally, gridded, spatially explicit land use and crop models should b...
Method used successfully
Theses: http://ppe.ufrj.br/
Journal paper submitted
Crop 1
Process
Bioenergy
carrier 1
Bioenergy
carrier 2
Crop
Prod 1
Bioenergy
carrier 3 w/ CCS
Crop
Prod 2
Bioenergy
carrie...
Ethanol in BLUES (Köberle, 2018)
2nd Gen EtOH
prod
Biomass
processing
Grassy
prod
Woody
prod
BECCS elec
Bioelectricity
gen...
Proposed Land Use transitions matrix
Forest
Low Cap
Pasture
High Cap
Pasture
Cropland
Planted
Forest
Savanna
Managed
Fores...
Example of a land use conversion process in BLUES,
showing deforestation to create low-capacity pastures.
3/9/2018 Alexand...
3/9/2018 Alexandre Koberle DSc defense 9
Example of a crop production process
Methods
FAO data aggregated to TIAM regions
But what about costs?
Highly influenced by:
• Local conditions (grid cell level)
• Management systems (challenging to mode...
Cost calculation model: proxy inputs
Travel time to nearest city
• Source: ESA 2008
• 14 classes (0 to 100 hours)
• Proxy ...
Cost classes within each region
Relative production costs map
G
F
E
D
C
B
A
Set to NA (ignored)
Multiply values in each gr...
Global cost class distribution
# of grid cells
Each cost class a step in a cost-supply curve
More steps is possible (depends on data
resolution)
will be actual
cost in $$
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,...
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
Benchm...
Cost supply curves for regions in TIAM
Example: high input cereal suitability
Cost supply curves for regions in TIAM
Example: Canada high input cereal suitability
# of grid cells
Next steps: generating cost supply curves for regions in TIAM
Example: India high input cereal suitability
# of grid cells
Thank you!
akoberle@imperial.ac.uk
@alexkoberle
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Introducing land use and agriculture in TIAM to asses land demand of future bioenergy deployment scenarios

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Introducing land use and agriculture in TIAM to asses land demand of future bioenergy deployment scenarios

  1. 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. 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. 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. 4. Method used successfully Theses: http://ppe.ufrj.br/ Journal paper submitted
  5. 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. 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. 7. Proposed Land Use transitions matrix Forest Low Cap Pasture High Cap Pasture Cropland Planted Forest Savanna Managed Forests Deforestation
  8. 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. 9. 3/9/2018 Alexandre Koberle DSc defense 9 Example of a crop production process Methods
  10. 10. FAO data aggregated to TIAM regions
  11. 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. 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. 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. 14. Global cost class distribution # of grid cells
  15. 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. 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. 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. 18. Cost supply curves for regions in TIAM Example: high input cereal suitability
  19. 19. Cost supply curves for regions in TIAM Example: Canada high input cereal suitability # of grid cells
  20. 20. Next steps: generating cost supply curves for regions in TIAM Example: India high input cereal suitability # of grid cells
  21. 21. Thank you! akoberle@imperial.ac.uk @alexkoberle

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