Spatially optimizing the distribution of sustainable
sugarcane production in Goiás, Brazil, based on GHGemissions and costs
Master thesis
Sanne Hettinga
3922332
s.hettinga@students.uu.nl
Bron 14, 1511 JK, Oostzaan
Energy Science
37,5 ECT’s
Supervisors: Judith Verstegen and Floor van der Hilst
Second reader: Martin Junginger
ii
Abstract
The production of bioethanol from sugarcane in Brazil has increased steadily over the last decade,
and the surface area used to grow sugarcane has expanded. The aim of this study is to find the
optimal spatial configuration of new sugarcane fields in Goías, Brazil, created between 2005 and
2010, optimizing for both costs and GHG-emissions, keeping the ethanol production equal to the
demand in 2010. The analysis is performed by modelling both the costs and GHG-emissions in a
spatially explicit way, based on both spatial and non-spatial variables. The spatial variables that are
inputs for the costs are cultivation and transportation and for the GHG-emissions these are land use
change and transportation. The optimal location is determined by applying a multi-criteria analysis
(MCA) while applying different weight factors. It has to be taken into account that there is a large
uncertainty in the data and land availability and the research has to be improved by taking into
account the capacity of the mills and making newly constructed mills a spatial variable before the
actual optimal configuration of sugarcane fields can be determined. Results show that when
optimizing only for GHG-emissions, the GHG-emissions can be reduced with 58% compared to the
actual configuration of sugarcane fields in 2010, yet, the costs increase with 45%. When optimizing
only costs, the costs can be reduced with US$5,-/GJethanol compared with the actual configuration in
2010, and the GHG-emissions are still almost 10% lower than for the actual sugarcane fields in 2010.
When distributing the weight factors equally over costs and GHG-emissions in the MCA, the costs are
equal to when optimizing only costs, while decreasing the GHG-emissions with 23%, mainly by
avoiding sugarcane cultivation on forest land. It can be concluded that optimizing the location of
sugarcane fields can decrease both costs and GHG-emissions. Finally, it can be concluded that the
results of this study can aid in the finding a suitable location of sugarcane fields for ethanol
production.
iii
Index
Abstract ...................................................................................................................................................iii
Index ........................................................................................................................................................ iv
List of tables .............................................................................................................................................v
List of figures ........................................................................................................................................... vi
List of abbreviations ............................................................................................................................... vii
1. Introduction ......................................................................................................................................... 1
2. Method ................................................................................................................................................ 3
2.1 GHG-emissions .............................................................................................................................. 4
2.1.1 Land use change ..................................................................................................................... 4
2.1.2 Lifecycle analysis..................................................................................................................... 6
2.2 Costs .............................................................................................................................................. 7
2.3 Multi-criteria Analysis.................................................................................................................... 9
2.4 Spatial Optimization .................................................................................................................... 11
3. Case study.......................................................................................................................................... 13
4. Results ............................................................................................................................................... 13
5. Discussion .......................................................................................................................................... 22
6. Conclusion ......................................................................................................................................... 25
7. References ......................................................................................................................................... 27
Appendix A: Data ................................................................................................................................... 31
A1: GHG-emissions ............................................................................................................................ 31
A2: Costs ............................................................................................................................................ 34
iv
List of tables
Table 1: The three types of optimizations have been acted out in this study with the weight factors
assigned to it
Table 2: The potential GHG-emissions of bioethanol in Goiás (in kg CO2-eq/GJethanol)
Table 3: The potential costs of bioethanol in Goiás (in US$/GJethanol)
Table 4: The total actual costs, total actual GHG-emissions and the surface area calculated for the
actual new sugarcane cells and for the three options as defined in table 1
Table 5: Parameters involved with calculating the land use change emissions (ELUC)
Table 6: Ratio belowground biomass/aboveground biomass for different land use types
Table 7: Parameters involved with calculating the cultivation emissions (ECultivation)
Table 8: Parameters involved with calculating the transportation emissions (ETransportation)
Table 9: Parameters involved with calculating the cultivation emissions (EConversion)
Table 10: Parameters involved with calculating the avoided emissions (EAvoided)
Table 11: Parameters involved with calculating the cultivation costs (CCultivation)
Table 12: Parameters involved with calculating the transportation costs (CTransportation)
Table 13: Parameters involved with calculating the conversion costs (CConversion)
Table 14: Parameters involved with calculating the avoided costs (CAavoided)
v
List of figures
Figure 1: A visual representation of the data flow to optimize the sugarcane field location.
Figure 2: A visual representation of the spatial optimization method.
Figure 3: Map showing the states of Brazil, with in yellow the state Goiás.
Figure 4: Maps showing the potential of (a) LUC emissions and (b) transportation emissions
contributing to the GHG-emissions of ethanol production (in kg CO2-eq/GJethanol).
Figure 5: Maps showing the potential of (a) cultivation costs and (b) transportation costs contributing
to the total costs of ethanol production (in US$/GJethanol).
Figure 6: Maps showing the potential of (a) total GHG-emissions (in kg CO2-eq/GJethanol) and (b) total
costs (in US$/GJethanol) of ethanol production, containing both the spatial and non-spatial variables.
Figure 7: Histogram of the yield of the optimized configuration of option A. Only the low yield options
in Goiás are present.
Figure 8: Histogram of the yield of region Goiás.
Figure 9: Histogram of the yield of the optimized configuration of option A. Only the low yield options
in Goiás are present.
Figure 10: Map showing the existing sugarcane cells in 2005 (black), the actual new sugarcane cells in
2010 (pink), the optimized configuration of sugarcane cells from option A (yellow) and the overlap
between the actual sugarcane cells in 2010 and the optimized sugarcane cells from option C
(orange).
Figure 11: Map showing the existing sugarcane cells in 2005 (black), the actual new sugarcane cells in
2010 (pink), the optimized configuration of sugarcane cells from option B (light green) and the
overlap between the actual sugarcane cells in 2010 and the optimized sugarcane cells from option B
(dark green).
Figure 12: Map showing the existing sugarcane cells in 2005 (black), the actual new sugarcane cells in
2010 (pink), the optimized configuration of sugarcane cells from option C (light blue) and the overlap
between the actual sugarcane cells in 2010 and the optimized sugarcane cells from option C (dark
blue).
vi
List of abbreviations
EU
GHG-emissions
GIS
IPCC
LCA
LUC
MCA
SOC
European Union
Greenhouse Gas emissions
Geographic Information Systems
Intergovernmental Panel for Climate Change
Life Cycle Analysis
Land Use Change
Multi-Criteria Analysis
Soil Organic Carbon
vii
1. Introduction
The global production of bioethanol has increased significantly over the last decade (Walter et al.,
2011). The raised interest can mainly be explained by a desire for energy security and in part by an
increase in concern of individual countries regarding climate change, causing a growth in interest in
reducing Greenhouse Gas emissions (GHG-emissions) (European Commission, 2013). The European
Union (EU) has set standards for a minimum of bioethanol blended in with the regular fuel. In 2010
5.75% bioethanol has to be added to regular fuel, and by 2020 this must be 10% (EU, 2011). This
target can increase the future demand for bioethanol (IEA, 2013; Leite et al., 2009). Bioethanol can
only be imported as a part of this target into the EU if the GHG-emissions during the lifecycle are at
least 35% less than for fossil fuels and meets specific criteria regarding the previous land use type of
the biofuel crop fields (EU, 2011). To counteract disadvantages of future land use change (LUC) - such
as deforestation, competition with food, threats to hydrology and decreasing biodiversity (IEA, 2010;
IPCC, 2011; Scharlemann & Laurance, 2008) - evaluating the location of future crop fields that are
converted to accommodate the future bioethanol demand can decrease GHG-emissions emitted
during ethanol production. Furthermore, by for instance shortening transportation distance, both
GHG-emissions and costs along the production chain can be reduced. This can enable the reduction
of costs and GHG-emissions of future biofuel crop fields without additional investments, by choosing
a different location for the biofuel crop fields.
Research has been done regarding the optimization of both costs and GHG-emissions of different
stages of the production of bioethanol. Up until now, research has focused on the optimization of
efficiency of the biofuel crop processing (Efe et al., 2005), and the infrastructure and transport of
bioethanol production (Kang et al., 2010). However, no research has yet been done as to how the
spatial configuration of biofuel crop fields can be optimized. Therefore the aim of this study is to
optimize the configuration of biofuel crop fields, considering GHG-emissions and costs for a case
study. When optimizing the biofuel crop field configuration, the ethanol production of the optimal
configuration has to be equal to the actual ethanol demand. Because the production depends on the
yield (which is spatially variable), the total biofuel crop field surface area to produce the bioethanol
can vary, depending on the configuration of the fields. A time window in the past is selected to
evaluate the surface area that is converted to biofuel crop field. The location of only the new biofuel
crop fields are spatially variable and thereby only the location of these fields can be optimized.
Currently, the United States are the largest producer of bioethanol, a position held by Brazil up until
2006, which currently remains the second largest producer of bioethanol. Bioethanol from sugarcane
has both the lowest price and GHG-emissions of all bioethanol products (Walter et al., 2011).
Therefore bioethanol production from sugarcane is considered in this study. A region in Brazil has
been selected as a case study in this research, because of the long history in and significant
experience with the production process of sugarcane bioethanol and the optimal climate, soil type
and precipitation for sugarcane cultivation in the South-Central region (Walter et al., 2011). In Brazil,
the sugarcane produced for bioethanol production has grown with 9,9 % between 2002 and 2008 to
560 million tonnes which corresponds to 8,1 Mha growth of sugarcane fields (Walter et al., 2011).
The change in sugarcane fields is studied between 2005 and 2010. The case study is set in Goiás, a
state in the South-Central region of Brazil, because in this region the production of bioethanol from
sugarcane has occurred for a long time, a significant growth between 2005 and 2010 is observed
(Rudorff et al., 2010) and a significant future growth is predicted (Walter et al., 2011).
1
Two criteria are optimized in this study: costs and GHG-emissions. These criteria have been chosen to
comply with the targets of the EU (EU, 2011), and to make the price of ethanol more competitive
with regular fossil fuels. These criteria are both assessed spatially. The costs are all costs associated
with the production of bioethanol over the lifecycle until the gate (both feedstock production as
industrial costs). The GHG-emissions are determined using two methods: an evaluation of the GHGemissions from land use change (LUC) (IEA, 2010; IPCC, 2011; Scharlemann & Laurance, 2008) and
the lifecycle emissions along the production chain (LCA) (Baumann & Tillman, 2004; de Bruin et al.,
2001). These two decision criteria have different units, namely kg CO2-equivalent for the GHGemissions and USUS$ for the costs. To enable the evaluation of a problem with a combination of two
variables with different units, a multi-criteria analysis (MCA) is applied, using the costs and GHGemissions as decision criteria. Finally, the optimal configuration is selected. It has to be kept in mind
when analysing costs and GHG-emissions of bioethanol production along its lifetime that a part of
the sugarcane produced is not for the production of bioethanol, but for other purposes, such as
sugar production.
The research in this study is carried out using Geographic information Systems (GIS). GIS and energy
science are not self-evident partners in science. In the field of bio-energy some research has been
done using GIS (Freppaz et al., 2004; Velazquez-Marti & Annevelink, 2009), but Horner et al. (2011)
emphasizes that these two disciplines can aid one another in even more research areas. In this
research it is attempted to evaluate and optimize the spatial components of several stages (land use
change, cultivation and transportation) of the production process of bioethanol using GIS to answer
the research questions of this study:
How can sugarcane fields, converted between 2005 and 2010, in the Brazilian state Goiás be
distributed optimally (while keeping the ethanol production equal to the demand in 2010) considering
GHG-emissions and the cost over time?
Subquestions:
- How can the GHG-emissions and costs of bioethanol, used to optimize the location of
sugarcane fields, be quantified?
- What characterizes suitable locations for new sugarcane fields created between 2005 and
2010, considering the spatial variables individually?
- What are the optimal configurations of the expansion (between 2005 and 2010) of sugarcane
fields in 2010, considering both costs and GHG-emissions, while keeping the ethanol
production equal to the demand in 2010, with different weights assigned to each variable?
- What are the average GHG-emissions and costs of the optimized configuration of new
sugarcane fields?
- How does the optimized configuration for 2010 compare to the actual configuration of
sugarcane fields in 2010?
2
2. Method
To obtain the optimal location of sugarcane fields in Goiás, considering the decision criteria GHGemissions and costs, several steps are taken (Figure 1). Firstly, a map showing the potential GHGemissions of the bioethanol production chain is calculated, using the potential LUC emissions and the
potential LCA emissions as input. The inputs for the LCA are cultivation, transportation, conversion
and avoided emissions. The land use map is based on the land use in 2005, determined by Verstegen
et al. (2014), and is compared to the sugarcane fields in 2010. The map by Verstegen et al. (2014), a
raster map with a cellsize of 5x5 km2, is the starting point of the analysis. All maps are converted to
this format. For instance, the sugarcane fields in 2010, which are polygons, are converted to raster
cells with a cellsize of 5x5 km2. Because the actual sugarcane field polygons can cross the boundaries
of these cells, in the method and result section they are referred to as sugarcane cells. Secondly, a
map for the potential costs is calculated, using potential cultivation, transportation, conversion and
avoided costs as input. The map showing the potential GHG-emissions and potential costs are called
potential for they are a prediction for the GHG-emissions and costs for all cells in Goiás, also those
that are not converted to sugarcane fields and therefore are not involved in the bioethanol
production chain. The GHG-emissions and costs of the actual sugarcane cells in 2010 are called actual
costs, for these are GHG-emissions and costs that are actually generated by sugarcane cells in Goiás
in 2010. Thirdly, the potential GHG-emissions and potential costs are summed using a MCA with
different weight factors, creating ranking maps that can be used to optimize the location of
sugarcane cells. Fourthly, the optimal configuration of sugarcane cells in 2010 is derived using several
sets of weight factors (only varying the sugarcane cells that are converted between 2005 and 2010),
keeping the ethanol production equal to the demand in 2010. The sugarcane mills that are
considered are all sugarcane mills that are located and functional in Goiás in 2010. The mills that
have been constructed between 2005 and 2010 are not moved in this study (so they are at a fixed
location). The GHG-emissions and costs associated with these optimal configurations are called
optimal GHG-emissions and optimal costs. Finally, optimized GHG-emissions and optimized costs are
compared to the actual configuration in 2010.
3
MCA
Potential LUC
emissions
LCA
Potential GHGemissions
Potential
cultivation
Ranking map
Spatial
Optimization
Potential
transportation
Potentially
conversion
Optimal
configuration
Potential costs
Potential
avioded
Figure 1: A visual representation of the data flow to optimize the sugarcane field location.
In this chapter, first the two methods that are used to model the potential GHG-emissions (LUC and
LCA) are explained. Second, the method for modelling of the potential costs is elaborated upon.
Variables that vary over space are indicated in the equations in bold. Third, the method for applying
the multi-criteria analysis is clarified. And finally, the optimization procedure is explained.
2.1 GHG-emissions
In this study the map showing the potential for the total GHG-emissions (ETotal) is calculated using
two methods: emissions due to the change in carbon stock due to LUC and the emissions from the
production chain of sugarcane ethanol (LCA). The total GHG-emissions are calculated using equation
1:
𝑬𝑻𝒐𝒕𝒂𝒍 = 𝑬𝑳𝑼𝑪 + 𝑬𝑳𝑪𝑨
ETotal
ELUC
ELCA
Total GHG-emissions
Land use change emissions
GHG-emissions made along production chain
(1)
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
All GHG-emissions are expressed in a functional unit. The functional unit is kg CO2-eq/GJethanol. This
unit is chosen because it enables an easy comparison to other fuels, such as gasoline.
2.1.1 Land use change
When the use of the land changes, the inputs and outputs of the land become different, and
therefore the carbon stock changes (Guo et al., 2002; van der Hilst et al., 2014). The difference in
carbon stock in the above ground biomass is most visible, but the main amount of carbon is stored in
4
the soil, where it has been transported by roots and dead matter (IPCC, 2006).The amount of carbon
stock present in soils, as well as the above and belowground biomass depends on several spatial
components, such as soil type, climate, land use and agricultural management (van der Hilst et al.,
2014). In this study only land use is a heterogeneous spatial component and the others are assumed
to be constant for the entire case study area.
The change in carbon stock (Eluc) can be caused by changes in one or more of the following four
components: residues (dead material and litter), Soil Organic Carbon (SOC), aboveground biomass
(visible biomass) and belowground biomass (roots) (IPCC, 2006). As in the TIER 1 approach from IPCC
(2006), only SOC, above and belowground biomass are considered in this study. Emissions caused by
indirect land use change are not considered. The potential LUC emissions are calculated using
equation 2:
𝑬𝑳𝑼𝑪 =
(𝑺𝑶𝑪𝒔𝒖𝒈𝒂𝒓𝒄𝒂𝒏𝒆 −𝑺𝑶𝑪𝒐𝒍𝒅 )+((𝑨𝑮𝑩𝒔𝒖𝒈𝒂𝒓𝒄𝒂𝒏𝒆 +𝑩𝑮𝑩𝒔𝒖𝒈𝒂𝒓𝒄𝒂𝒏𝒆 )− ( 𝑨𝑮𝑩𝒐𝒍𝒅 +𝑩𝑮𝑩𝒐𝒍𝒅 ))
𝑡
(2)
=𝑇
∑𝑡𝑙𝑢𝑐 =1 𝑷𝒓𝒐𝒅𝑬𝒕𝒉𝒂𝒏𝒐𝒍/𝒉𝒂
𝑙𝑢𝑐
tluc = 1,2,3,…,T
ELUC
SOCsugarcane
AGBsugarcane
BGBsugarcane
SOCold
AGBold
BGBold
tluc
Land use change emissions
SOC after LUC to sugarcane
Aboveground biomass after LUC to sugarcane
Belowground biomass after LUC to sugarcane
SOC before LUC
Aboveground biomass before LUC
Belowground biomass before LUC
Time required for the system to reach equilibrium
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
years
𝑷𝒓𝒐𝒅𝑬𝒕𝒉𝒂𝒏𝒐𝒍/𝒉𝒂 = 𝒚𝒊𝒆𝒍𝒅 × 𝜂𝑚𝑖𝑙𝑙 × 𝐸𝐷𝑒𝑛𝑠𝑖𝑡𝑦
ProdEthanol/ha
yield
ηmill
EDensity
Ethanol production in one hectare
Sugarcane production
Conversion efficiency
Energy density of ethanol
(3)
GJethanol/ha/year
tonne/ha/year
m3ethanol/tonne
GJethanol/m3ethanol
The sugarcane yield is determined by equation 4:
𝒚𝒊𝒆𝒍𝒅 = 𝑦𝑖𝑒𝑙𝑑𝑚𝑎𝑥 × 𝑭𝒚𝒊𝒆𝒍𝒅
yield
yieldmax
Fyield
Sugarcane production per hectare
Maximum yield in Brazil
Fraction of maximum yield
(4)
tonne/ha
tonne/ha
tonne/tonne
The fractional yield (Fyield) is the fraction of the maximum yield that is obtained in Brazil. Because the
yield is different for each cell in Goiás, the ethanol production is a spatial variable. Specific data
regarding the SOC, above and belowground biomass, mill efficiency, energy density and maximum
yield in Brazil are provided in appendix A1.
5
2.1.2 Lifecycle analysis
An LCA is a “compilation and evaluation of the inputs, outputs and potential environmental impacts
of a product system throughout its lifecycle” (de Bruin et al., 2001). The LCA in this study is done
following the ISO 14040-14049 guidelines as is done by for instance Macedo et al. (2004), Luo et al.
(2009) and Seabra et al. (2011) who also researched the LCA of bioethanol from Brazilian sugarcane.
However, only GHG-emissions and no other sustainability criteria are considered. In the GHGemissions of the LCA (ELCA), the four common stages are included: the cultivation stage, the
transportation stage, the conversion stage and the avoided emissions stage (these are credits that
can be claimed for a surplus of electricity that would otherwise have to be produced elsewhere)
(figure 1). Equation 5 shows how the potential lifecycle GHG-emissions (ELCA) are calculated:
𝑬𝑳𝑪𝑨 = 𝐸𝐶𝑢𝑙𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛 + 𝑬𝑻𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕𝒂𝒕𝒊𝒐𝒏 + 𝐸𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 − 𝐸𝐴𝑣𝑜𝑖𝑑𝑒𝑑
ELCA
ECultivation
ETransportation
EConversion
EAvoided
Potential GHG-emissions made along production chain
Potential GHG-emissions in the cultivation stage
Potential GHG-emissions in the transportation stage
Potential GHG-emissions in the conversion stage
Potential GHG-missions in the avoided emissions stage
(5)
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
Potential cultivation emissions (ECultivation) are GHG-emissions caused by preparing the land for
sugarcane production (fertilizers and limestone, which require fossil fuels to produce), pre-harvest
burning (35% of the cells are still subject to burning (Seabra et al., 2011)), machines used while
planting and harvesting the sugarcane (this includes the fuel use of the machines, but not the GHGemissions from producing the machines) and production of agrochemicals (JRC, 2011; Seabra et al.,
2011). Specific data regarding the cultivation emissions can be found in table 7 in appendix A1.
The potential transportation emissions (ETransportation) are the potential GHG-emissions made while
transporting the sugarcane from the fields to the mills. No return trip is considered, just as by for
instance Seabra et al. (2010). This is not modelled because the load of the truck on the return trip is
different from the trip to the mill, and no data is available regarding this. The potential
transportation emissions are calculated using equation 6:
𝑬𝑻𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕𝒂𝒕𝒊𝒐𝒏 =
ETransportation
Captruck
DPath
EPath
DRoad
ERoad
ηmill
EDensity
1
𝐶𝑎𝑝𝑡𝑟𝑢𝑐𝑘
×(𝑫𝑷𝒂𝒕𝒉 ×𝐸𝑃𝑎𝑡ℎ +𝑫𝑹𝒐𝒂𝒅 ×𝐸𝑅𝑜𝑎𝑑 )
(6)
𝜂𝑚𝑖𝑙𝑙 ×𝐸𝐷𝑒𝑛𝑠𝑖𝑡𝑦
Potential transportation emissions
Capacity of a truck
Distance travelled by truck on a path
Truck emission on a path
Distance travelled by truck on a road
Truck emission on a road
Conversion efficiency
Energy density of ethanol
kg CO2-eq/GJethanol
tonne
km
kg CO2-eq/truck*km
km
kg CO2-eq/truck*km
m3ethanol/tonne
GJethanol/m3ethanol
6
It is assumed that the transportation of the sugarcane takes place by truck. The distance that is
travelled on the path (DPath) and on the road (DRoad) is the shortest route with the least GHGemissions (in kg/km) from the sugarcane cell to a mill. This is calculated using an algorithm that
models the shortest distance between a field and a mill, while also monitoring whether a slightly
longer route can cause lower GHG-emissions. Specific data regarding the location of the mills, the
emissions on a road and a path can be found in appendix A1. The transportation distance and the
division of traveling on- and off road that influences the production of ethanol make these value a
spatial variable.
The potential conversion emissions (EConversion from equation 5) in the mill are GHG-emissions created
by the use of heat and electricity in the conversion stage of the sugarcane. The heat that is supplied
is recycled within the conversion process, yet it is not sufficient to maintain the temperatures
required to sustain the conversion process. The required heat and electricity are generally generated
by burning bagasse in the mill, producing GHG-emissions. Specific data regarding the non-spatial
conversion emissions can be found in table 9 in appendix A1.
In the potential avoided emissions stage (EAvoided) GHG-emissions from credits can be obtained for
replacing electricity that otherwise would have to be produced elsewhere. The GHG-emissions that
are avoided by replacing the electricity can be subtracted from the GHG-emissions from the ethanol
production process. In most mills, heat and electricity are produced by burning bagasse – a waste
product from the conversion process – when bioethanol is produced. This is done in a combined heat
and power unit, from which the electricity can be sold to the electricity grid and used to supply the
heat and electricity for the conversion process. The potential avoided emissions are calculated using
equation 7:
𝐸𝐴𝑣𝑜𝑖𝑑𝑒𝑑 =
𝑃𝑂𝑢𝑡𝑝𝑢𝑡 × 𝑇𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 × ∑8𝑘=1 𝑀𝐸𝑛𝑒𝑟𝑔𝑦,𝑘 × 𝐹𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛,𝑘
(7)
𝐴𝑣𝑃𝑟𝑜𝑑𝐸𝑡ℎ𝑎𝑛𝑜𝑙
k=1,2,3,…,8
EAvoided
POutput
TOperation
k
MEnergy
FEmission,k
AvProdEthanol
Avoided GHG-emission from replaced fossil fuels
Average power output per hour of a sugarcane mill
Operation time
fuel type
fraction fuel j in the total energy matrix in 2005
GHG-emission of fuel type k
Average ethanol production in one mill in Goiás
kg CO2-eq/GJethanol
MW/hour
hour
%
kg CO2-eq/MW
GJethanol
The power output (POutput) is the average power output of one year of the mills in the Goiás region
produced over the entire operation time (TOperation). The energy matrix of Brazil (MEnergy) is used
together with the GHG-emission factors for each fuel type (FEmission,k) to determine the GHG-emissions
per MW. Specific data regarding the power output, operation time, energy mix and ethanol
production can be found in appendix A1.
2.2 Costs
In this section, the method for calculating the map showing the potential for the total costs over all
stages of the bioethanol production chain is described. The potential total costs are all potential
costs associated with producing feedstock (potential cultivation costs and potential transportation
7
costs) and all potential costs associated with the ethanol production (potential conversion costs and
potential avoided costs from selling electricity to the grid). Equation 8 shows how the potential total
costs can be:
𝑪𝑻𝒐𝒕𝒂𝒍 = 𝑪𝑪𝒖𝒍𝒕𝒊𝒗𝒂𝒕𝒊𝒐𝒏 + 𝑪𝑻𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕𝒂𝒕𝒊𝒐𝒏 + 𝐶𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 − 𝐶𝐴𝑣𝑜𝑖𝑑𝑒𝑑
(8)
CTotal
CCultivation
CTransportation
CConversion
CAvoided
Potential cost of ethanol
Potential cultivation cost made for ethanol production
Potential transportation cost made for ethanol production
Potential conversion cost of ethanol
Potential avoided costs of ethanol production
US$/GJethanol
US$/GJethanol
US$/GJethanol
US$/GJethanol
US$/GJethanol
Considering these potential costs are not all spread equally over time, all potential costs and benefits
are converted to annuities (van den Broek et al., 2000; van der Hilst et al., 2010). It is unusual to
make annuities of products that are not expressed in a monetary unit. Van den Broek et al. (2000)
describe how these physical units can be reduced to an annuity as well. In this study, the produced
ethanol is a physical unit, yet still represents a monetary value and can be considered one of the
benefits in the production chain. Therefore it has to be reduced to an annuity as well.
All potential costs are expressed in a functional unit. The functional unit US$/GJethanol is chosen
because it enables easy comparison to other fuels, such as gasoline.
The potential cultivation costs (CCultivation) are all potential costs related to the growing and harvesting
of the sugarcane crops and include the potential costs for: land rent/acquisition, machine
rent/acquisition, maintenance and lubricants, labour, diesel, phosphor, nitrogen and potassium
fertilizers, agrochemicals, seedlings and administration. Equation 9 shows how to obtain the
potential cultivation costs:
𝐶
𝐹𝑒𝑒𝑑𝑠𝑡𝑜𝑐𝑘
𝑪𝑪𝒖𝒍𝒕𝒊𝒗𝒂𝒕𝒊𝒐𝒏 = 𝑷𝒓𝒐𝒅
(9)
𝑬𝒕𝒉𝒂𝒏𝒐𝒍/𝒉𝒂
CCultivation
CFeedstock
Prodethanol/ha
Potential cultivation cost made for sugarcane production
Potential feedstock cost per hectare
Potential ethanol production per hectare
US$/GJethanol
US$/ha
GJethanol/ha
Specific data regarding the potential feedstock costs per hectare (CFeedstock) can be found in appendix
A2. The potential cultivation costs per hectare are not spatially explicit, which is a simplification, but
the ethanol production for each sugarcane cell depends on the yield (as described in equation 3) and
is therefore the potential cultivation costs are a spatial variable.
The potential transportation costs (CTransportation) are the potential costs associated with the
transportation of sugarcane from the cell to the mill, including truck rent/acquisition and fuel costs.
No return trip is considered, just as explained for the potential transportation emissions (ETransportation).
The potential transportation costs are calculated using equation 10:
𝑪𝑻𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕𝒂𝒕𝒊𝒐𝒏 =
CTransportation
DPath
(𝑫𝑷𝒂𝒕𝒉 ×𝐶𝑃𝑎𝑡ℎ +𝑫𝑹𝒐𝒂𝒅 ×𝐶𝑅𝑜𝑎𝑑 )
(10)
𝜂𝑚𝑖𝑙𝑙 ×𝐸𝐷𝑒𝑛𝑠𝑖𝑡𝑦
Potential transportation costs
Distance travelled by truck on a path
US$/GJethanol
km
8
CPath
DRoad
CRoad
ηmill
EDensity
Potential truck costs on a path
Distance travelled by truck on a road
Potential truck costs on a road
Conversion efficiency
Energy density of ethanol
US$/tonne*km
km
US$/tonne*km
m3ethanol/tonne
GJethanol/m3ethanol
The assumptions for calculating the potential transportation costs (Ctransportation) are the same as the
assumptions for the potential transportation emissions (Etransportation) in equation 6 and can be found
in appendix A1. The distance that is travelled on the path (Dpath) and on the road (Droad) is the
shortest route with the lowest potential costs (in US$/tonne*km) from the sugarcane cell to a mill.
Specific data regarding the costs for traveling on the road (CRoad) and on the path (CPath) can be found
in appendix A2. Both the transportation distance, the division of traveling on- and off road and the
yield that influences the production of ethanol make the potential transportation costs a spatial
variable.
The potential conversion costs (CConversion) are all potential costs associated with the building,
operating and maintaining of the sugarcane mill and all the equipment in the mill (van den Wall Bake
et al., 2009). The conversion costs are not a spatial variable and is therefore an actual cost that is the
same for all cells. Specific data regarding the conversion costs can be found in appendix A2.
The potential avoided costs (CAvoided) are the potential benefits that come from selling the electricity
produced at the mill to the electricity grid. The potential total avoided costs can be calculated using
equation 11:
𝐶𝐴𝑣𝑜𝑖𝑑𝑒𝑑 =
CAvoided
POutput
TOperation
CElectricity
AvProdEthanol
𝑃𝑂𝑢𝑡𝑝𝑢𝑡 × 𝑇𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 × 𝐶𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦
𝐴𝑣𝑃𝑟𝑜𝑑𝐸𝑡ℎ𝑎𝑛𝑜𝑙
Potential avoided costs from replaced fossil fuels
Average power output per hour for Goiás
Operation time
Cost of electricity
Average ethanol production in one mill in Goiás
(11)
t=1,2,3,…,T
US$/GJethanol
MW/hour
hour
US$/MWh
GJethanol
The method for calculating the potential avoided costs (CAvoided) are calculated in a similar way as the
potential avoided emissions (EAvoided) in equation 7, using the same assumption that are described in
appendix A1. Specific data and assumptions regarding the avoided costs can be found in appendix
A2. The potential avoided costs are a simplification and therefore not a spatial variable and is the
same for all cells.
2.3 Multi-criteria Analysis
After calculating the decision criteria (the potential total GHG-emissions (ETotal) and the potential
total costs (CTotal)), an MCA is applied to be able to combine the two (figure 1) and rank the values.
The rank of a cell is determined by the GHG-emissions and costs. The cell with the highest rank is the
cell with the lowest normalized total of GHG-emissions and costs, or the cell with the best
performance. To perform the multi-criteria analysis, all input variables have to be normalized. The
MCA method chosen is known as one of the simplest: the weighted sum model (Triantaphyllou,
2000). This is described by equation 12:
9
𝑗=𝐾
𝑽 = ∑𝑗=1 𝑤𝑗 𝒏𝒋
(12)
j=1,2,3,…..K
V
j
wj
nj
Rank of each cell in Goiás
Decision criterion
Weight factor applied to decision criterion j
Normalized performance variable in of decision criterion j
To apply the MCA, all potential GHG-emissions and potential costs for all cells in Goiás are
normalized according to equation 13 and 14:
𝒆𝒕𝒐𝒕𝒂𝒍 =
ETotal
ETotalmax
eTotal
𝑬𝒕𝒐𝒕𝒂𝒍
𝑬𝒕𝒐𝒕𝒂𝒍 𝒎𝒂𝒙
Potential total GHG-emissions
Maximum potential total GHG-emissions in Goiás
Normalized potential GHG-emissions in Goiás
𝒄𝒕𝒐𝒕𝒂𝒍 =
Ctotal
Ctotalmax
ctotal
(13)
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
(-)
𝑪𝒕𝒐𝒕𝒂𝒍
𝑪𝒕𝒐𝒕𝒂𝒍 𝒎𝒂𝒙
Potential total costs
Potential maximum total costs in Goiás
Normalized potential costs in Goiás
(14)
US$/ GJethanol
US$/ GJethanol
(-)
Second, the weighted sum model is applied to calculate several ranking maps with different weight
factors. The number of decision criteria j (equation 1, section 2.3) is 2 in this study (GHG-emissions
and costs). Equation 15 shows how the ranking maps can be calculated:
𝑽 = 𝑤1 𝒆𝒕𝒐𝒕𝒂𝒍 + 𝑤2 𝒄𝒕𝒐𝒕𝒂𝒍
(15)
𝑤1 + 𝑤2 = 1
V
w1
w2
etotal
ctotal
Rank of each cell in Goiás
Weight factor for the normalized GHG-emissions
Weight factor for the normalized costs
Normalized potential total GHG-emissions for all cells in Goiás
Normalized potential total costs for all cells in Goiás
(-)
(-)
(-)
(-)
(-)
In this study, three different sets of weight factors are considered, generating three ranking maps.
These three options are listed in table 1. The ranking maps created with these three sets of weight
factors enable the optimization of the actual new sugarcane cell configuration while only considering
GHG-emissions (option A), only considering costs (option B) or both equally (option C).
Table 1: The three types of optimizations have been acted out in this study with the weight factors assigned to it
w1 (GHG-emissions) w2 (costs)
Option A
1
0
10
Option B
0
1
Option C
0.5
0.5
2.4 Spatial Optimization
Frequency of occurrence of rank (-)
With the maps ranking the locations according to the selected option, the optimal configuration of
new sugarcane cells is calculated, following the procedure described in figure 2. The first step to
obtain the optimal configuration is to determine the ethanol production of the actual new cells in
2010 using equation 3. This step is required, for the optimal configurations have to be able to
produce the same amount of ethanol (in GJ) as the actual new sugarcane cells, rather than have the
same surface area of cells. Secondly, the rankings of all cells (V from equation 16) are sorted, to
enable the detection of the lowest scores. By an iterative process, the correct surface area of cells to
meet the actual ethanol production in 2010 of the new sugarcane cells is determined. This is done by
starting with the cell with the highest rank (i.e. the best performance) and calculating the ethanol
production of this cell using equation 3. Then cells with slightly worse ranks are added to the
selection, and the ethanol production is calculated, until the actual ethanol production has been
reached. Thus, if the ethanol production is higher than the production of the new cells in 2010, less
cells are selected, and if the production is lower, more cells are selected until the ethanol production
is has been reached. These cells that are finally selected are the optimal configuration of sugarcane
cells for this option. This process is then repeated for the different options as described in table 1.
Score (-)
Figure 2: A visual representation of the spatial optimization method. First the map ranking the cells using MCA is made.
Second, a histogram is made, sorting the ranks of the cells in Goiás. The cells with the highest score (left of the vertical
line) are selected and the ethanol production is calculated. If the ethanol production matches, these cells are shown in
the third figure.
When the optimal configuration has been determined, the optimized average total emissions and the
optimized average total costs that are generated by this configuration (while applying different
weight factors) are calculated using equation 16 and 17:
𝐸𝑇𝑜𝑡𝑎𝑙 = ∑𝑖=𝑁
(16)
𝑖=1 𝑬𝑳𝑼𝑪 + 𝑬𝑻𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕𝒂𝒕𝒊𝒐𝒏 + 𝐸𝐶𝑢𝑙𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛 + 𝐸𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 + 𝐸𝐴𝑣𝑜𝑖𝑑𝑒𝑑
i=1,2,3,…,N
ETotal
ELUC
ETransportation
i
ECultivation
Optimized total GHG-emissions of the Goiás region
Optimized land use change emissions
Optimized transportation emissions
Sugarcane cells in Goiás
Optimized GHG-emissions in the cultivation stage
kg CO2-eq/GJethanol
kg CO2-eq/ GJethanol
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
11
EConversion
EAvoided
Optimized GHG-emissions in the conversion stage
GHG-missions in the avoided emissions stage
kg CO2-eq/GJethanol
kg CO2-eq/GJethanol
𝐶𝑇𝑜𝑡𝑎𝑙 = ∑𝑖=𝑁
𝑖=1 𝑪𝑪𝒖𝒍𝒕𝒊𝒗𝒂𝒕𝒊𝒐𝒏 + 𝑪𝑻𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕𝒂𝒕𝒊𝒐𝒏 + 𝐶𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 + 𝐶𝐴𝑣𝑜𝑖𝑑𝑒𝑑
CTotal
CCultivation
CTransportation
i
CConversion
CAvoided
Optimized total costs of the Goiás region
Optimized cultivation cost for sugarcane production
Optimized transportation cost for sugarcane production
Sugarcane cell in Goiás
Optimized conversion cost of ethanol
Optimized avoided costs from ethanol production
(17)
i=1,2,3,…,N
US$/GJethanol
US$/GJethanol
US$/GJethanol
US$/GJethanol
US$/GJethanol
12
3. Case study
The selected case study area is Goiás, a state in Brazil of 340086
km2, located in the so called highlands (called highlands because it
is located on an elevated plateau) (figure 3) (Governo de Goiás,
2014). In 2012 over 6 million people lived in the state, of which
88.6% lives in the urban areas. Up until recently Goiás used to be
covered in forest land, yet in recent years both cropland and
pasture for livestock have greatly diminished the forest covered
surface area, with a peak land conversion in 2004. In 2005,
approximately 21% of the Goiás area has been forest area, 0.5%
sugarcane, 11.5% other crops, 61% livestock pasture and the Figure 3: Map showing the states of
remainder is mainly water and urban. The livestock that is held Brazil, with in yellow the state Goiás.
mainly consists of cows. The main crops that are produced are soy
beans and sugarcane (Governo de Goiás, 2014). The tropical climate is very suitable to producing
these crops. The temperature only varies between 22 and 26⁰C, with an annual average rainfall of
1700 mm. The climate can differ at the local scale, due to the different levels of elevation compared
to the sea level (up to 1,676 m) (Governo de Goiás, 2014).
The surface area used for sugarcane production has increased significantly in Goiás, with a factor 3.6
between 2003 and 2010 to over 650.000 ha. In 2010, 36 mills that at least in part produced
bioethanol from sugarcane (if not 100% for bioethanol, a part of the sugarcane is used for sugar
production) (Rudorff et al., 2010). The oldest mill has been opened in 1965. Over half of the mills
located in Goiás in 2010 are opened between 2005 and 2010 (Anuário da cana 2011: Brazilian sugar
and ethanol guide.2011).
4. Results
The models applied to the case study of Goiás are focused on finding optimized configurations of
sugarcane cells based on GHG-emissions and costs. In this chapter, firstly, maps showing the
potential for the individual spatial variables contributing to the potential total GHG-emissions and
potential total costs are shown. Secondly, the maps showing the potential total GHG-emissions and
potential total costs are presented. Thirdly, the optimized configurations of sugarcane cells with
three sets of weight factors are shown. Fourthly, the optimized average GHG-emissions and the
optimized average costs for the optimized configurations (as described in equation 16 and 17) for the
region of Goiás are presented. Finally, the optimized configurations are compared to the actual
configuration of the new cells in 2010.
The potential LUC emissions (ELUC) are generally positive, indicating carbon emissions (figure 4(a)).
However, the cells that are crop cells in 2005 have potential negative emissions, indicating carbon
sequestration. The potential emissions are highest in the north of Goiás, for here the most forests
are located and the yield is lowest. This means that that the potential LUC emissions are high, and
the burden of these potential emissions has to be carried by a low ethanol production, indicating
high carbon emissions for each cell. The main conclusions that can be drawn from the potential LUC
emission map is that changing land from forest to sugarcane cells is highly unfavourable when one
takes GHG-emission into consideration and that cropland to sugarcane is the most favourable
change, though also pasture land can be favourable location for sugarcane production.
13
It can be observed that the transportation emissions (ETransportation) of the cells located close to a mill
as well as those close to a road are lower than those located further away (figure 4 (b)). The first is
because a shorter travel distance naturally decreases the GHG-emissions. The latter can be explained
by the fact that the GHG-emissions on the road are twenty-five times lower than off the road. The
potential average transportation emissions in this study are similar to the ones described by Seabra
et al. (2011), where Seabra et al. (2011) assume a fixed transportation distance of 21 km and make
no differentiation between on and off road emissions (table 3).
(a)
(b)
Figure 4: Maps showing the potential of (a) LUC emissions and (b) transportation emissions contributing to the GHGemissions of ethanol production (in kg CO2-eq/GJethanol). The green regions have the lowest GHG-emissions for sugarcane
production, while the red regions have the highest GHG-emissions. The potential LUC emissions have been categorized
by land use type in 2005, showing the correlation between land use type in 2005 and potential GHG-emissions. The white
cells, i.e. the NoData cells, are the locations where either water or urban areas are located, and therefore cannot be
changed to sugarcane. The white zone in the eastern part of Goiás is known as “distrito federal” and is not a part of the
Goiás region.
A strong dependency of the potential cultivation costs (CCultivation) on the yield can be observed in
figure 5(a), as expected from equation 9. The potential cultivation costs are approximately 60%
higher than the actual cultivation costs derived by van den Wall Bake et al. (2009) (table 3). This can
be explained by the fact that this study, as Jonker et al. (2014) does, considers additional costs that
van den Wall Bake et al. (2009) do not, such as different types labour of wages, machinery lubricants
and chemicals to prepare the soil for cultivation.
The potential transportation costs (CTransportation) shows the same spatial pattern as the potential
transportation emissions (figure 5(b)), even though the potential costs are only a factor six larger
when travelling off the road, compared to the factor twenty-five that is considered in the potential
transportation emissions. This limitation to the study is caused by the fact that the values are taken
from different studies (Clark et al. (2009) for the emissions and van der Hilst & Faaij (2012) for the
costs) The average optimal transportation costs are four times higher than those described by van
den Wall Bake et al. (table 2). This can be explained because van den Wall Bake et al. (2009) consider
a transportation distance of 20 km, while in the entire region the transportation distance can vary
significantly and they do not consider differences between on and off road costs.
14
(a)
(b)
Figure 5: Maps showing the potential of (a) cultivation costs and (b) transportation costs contributing to the total costs of
ethanol production (in US$/GJethanol). The green regions have the lowest costs for sugarcane production, while the red
regions have the highest costs. The white cells, i.e. the NoData cells, are the locations where either water or urban areas
are located, and therefore cannot be changed to sugarcane. The white zone in the eastern part of Goiás is known as
“distrito federal” and is not a part of the Goiás region.
A strong correlation between the potential total GHG-emissions (ETotal) with the potential LUC
emissions can be observed (figure 6(a)). The potential total emissions are highest in forested areas,
for here the carbon stock is largest, while the most favourable locations are those where cropland is
located. As can be observed, a strong correlation of the potential total costs (CTotal) with the potential
cultivation costs and yield can be observed (figure 6(b)). The main result is that locating sugarcane
cells in the north of Goiás is unfavourable from a cost perspective, because the yield is low.
Table 2: The potential GHG-emissions of bioethanol in Goiás (in kg CO2-eq/GJethanol)
Minimum Average
ELUC
Maximum
-16.87
36.56
1225
21
21
21
0
1.00
5.17
EConversion
3.60
3.60
3.60
EAvoided
1.13
1.13
1.13
ETotal
6.89
61.03
1252
ECultivation
ETransportation
15
Table 3: The potential costs of bioethanol in Goiás (in US$/GJethanol)
Minimum Average
CCultivation
Maximum
7.14
17.37
158.20
0
4.12
18.93
CConversion
6.25
6.25
6.25
CAvoided
1.41
1.41
1.41
12.99
26.33
171.62
CTransportation
CTotal
(a)
(b)
Figure 6: Maps showing the potential of (a) total GHG-emissions (in kg CO2-eq/GJethanol) and (b) total costs (in
US$/GJethanol) of ethanol production, containing both the spatial and non-spatial variables. The green regions have the
lowest costs or GHG-emissions for sugarcane production, while the red regions have the highest costs or GHG-emissions.
The white cells, i.e. the NoData cells, are the locations where either water or urban areas are located, and therefore
cannot be changed to sugarcane. The white zone in the eastern part of Goiás is known as “distrito federal” and not a part
of the Goiás region.
The MCA has been carried out using three different sets of weight factors, resulting in different
optimized configurations after optimizing the new sugarcane cells in 2010. Some overlap between
the actual configuration in 2010 and the optimized configuration occurs, yet the overlap is very small.
For option A only 0.5% of the actual sugarcane cells in 2010 is used, for option B this is 2.5% and
option C 2%, indicating that a large share of the actual new sugarcane cells in 2010 are not located
on an optimal location and significant improvements can be made. The optimization using option A
has resulted in a configuration as can be observed in figure 10. The cells are located both in the north
and the south of the region Goiás, at locations where mainly cropland used to be located in 2005 and
the yield is low. As can be observed in figure 7 and 8, the yields occurring in the optimized
configuration of option A (figure 7) is only the lowest yields that occur in Goiás (figure 8). This can be
explained for the negative emissions (per GJethanol) from cropland are maximized when it has to be
distributed over a small production of ethanol (i.e. at locations with low yield). This does lead to a
15% higher surface area required to produce the same amount of bioethanol as the actual new cells
in 2010 (table 4). The total actual costs and actual GHG-emissions can also be found in table 4. The
optimized GHG-emissions are lower than in all the other optimizations (12.03 kg CO2-eq/GJethanol),
16
which is 58% lower than the actual configuration in 2010 (26.76 kg CO2-eq/GJethanol). But the
optimized costs are 45% higher (US$30.87/GJethanol) compared to the actual configuration which are
US$19.51/GJethanol.
The optimization using option B is shown in figure 11. The cells are located mainly to the west of
Goiás, where the yield is highest as can be observed when comparing figure 8 and 9. The yield
observed in the optimized configuration of option B (figure 8) is only in the highest region of the yield
of entire Goiás. Interesting to note is that when considering only costs, optimized sugarcane cells are
also located on cells that used to be forest in 2005. The surface area required to produce the
required amount of ethanol is 64% lower than in the actual situation in 2010 due to the high yield, as
can be observed in table 4. Table 4 furthermore shows that the optimized costs of ethanol are 25%
lower than for the actual cells, and 53% lower than those modelled for option A. Furthermore, the
actual GHG-emissions of option B is still 10% lower than that of the actual configuration in 2010,
even though it is not optimized considering GHG-emissions.
The optimized configuration generated using option C displays a distribution very similar to option B,
as can be observed in figure 12. This can be explained by the fact that when considering both costs
and GHG-emissions equal, the yield is a dominant determinant. When using option B, sugarcane cells
are still assigned to forest area, while in option C this no longer occurs, because the GHG-emissions
are taken into account, i.e. forest areas become unfavourable for sugarcane production. The surface
area required to meet the ethanol demand in 2010 is 65% lower than for the actual new sugarcane
fields in 2010. Interesting to note is that the optimized costs have not increased compared to option
B when considering option C (which does not allow sugarcane production on former forest cells),
while the optimized GHG-emissions are 23% lower than for option B (5.57 kg CO2-eq/GJethanol), and
30% lower than the actual configuration in 2010.
When comparing the optimized GHG-emissions to those of gasoline, it shows that all options show
significantly less GHG-emissions than gasoline does. According to IPCC (2006) the GHG-emissions for
gasoline are approximately 69 kg CO2-eq/GJgasoline. This is 83% higher than the optimized GHGemissions calculated for option A (only optimizing GHG-emissions), and still 65% lower than the
option C (only optimizing costs). It has to be noted that the actual GHG-emissions are also
significantly lower (61%) and would still match the European standards. Comparing the optimized
costs to those of gasoline proves more difficult, for the costs of ethanol are volatile and change
significantly. Demirbas (2011) shows that the gasoline price in 2008 has been 25US$/GJgasoline, where
the price in 2009 are only 10US$/GJgasoline. When comparing the costs of bioethanol to the costs of
gasoline in 2008, bioethanol is approximately 40% less expensive than gasoline for option B and C,
where option A is 23% more expensive. When comparing the costs of bioethanol to the cost of
gasoline in 2009, bioethanol all optimized scenarios are far more expensive. Option B and C are 45%
more expensive, and option A is over 200% more expensive.
17
Frequency of occurance of
yield (%)
Histogram of yield of option A
20%
15%
10%
5%
0%
5 14 24 34 43 53 62 72 82 91 101 110 120 130
yield (tonne/ha)
Figure 7: Histogram of the yield of the optimized configuration of option A. Only the low yield options in Goiás are
present.
8%
6%
4%
2%
0%
5
12
19
26
34
41
48
55
62
70
77
84
91
98
106
113
120
127
134
Frequency of occurance of yield
(%)
Histogram of yield of Goiás
yield (tonne/ha)
Histogram of fractional yield of
option B
25%
20%
15%
10%
5%
0%
5
12
19
26
34
41
48
55
62
70
77
84
91
98
106
113
120
127
134
Frequency of occurance of yield
(%)
Figure 8: Histogram of the yield of region Goiás.
yield (tonne/ha)
Figure 9: Histogram of the yield of the optimized configuration of option B. Only the low yield options in Goiás are
present.
18
Table 4: The total actual costs, total actual GHG-emissions and the surface area calculated for the actual new sugarcane
cells and for the three options as defined in table 1
Actual new cells
Option A Option B
Option C
Value
w1
(-)
1
0
0.5
(-)
w2
(-)
0
1
0.5
(-)
Ctotal
19.51
30.87
14.52
14.52
US$/GJethanol
Etotal
26.76
12.03
24.20
18.63
kg CO2-eq/GJethanol
Area
505000
580000
182500
175000
ha
Figure 10: Map showing the existing sugarcane cells in 2005 (black), the actual new sugarcane cells in 2010 (pink), the
optimized configuration of sugarcane cells from option A, where the full weight has been assigned to the GHG-emissions
(yellow) and the overlap between the actual sugarcane cells in 2010 and the optimized sugarcane cells from option C
(orange).
19
Figure 11: Map showing the existing sugarcane cells in 2005 (black), the actual new sugarcane cells in 2010 (pink), the
optimized configuration of sugarcane cells from option B, where the full weight has been assigned to costs (light green)
and the overlap between the actual sugarcane cells in 2010 and the optimized sugarcane cells from option B (dark
green).
20
Figure 12: Map showing the existing sugarcane cells in 2005 (black), the actual new sugarcane cells in 2010 (pink), the
optimized configuration of sugarcane cells from option C, where the weight factors have been distributed equally over
both GHG-emissions and costs (light blue) and the overlap between the actual sugarcane cells in 2010 and the optimized
sugarcane cells from option C (dark blue).
21
5. Discussion
While modelling the GHG-emissions and costs of bioethanol production in Brazil in a spatially explicit
way, assumptions have been made, for instance to solve the absence of data or to overcome a
methodological difficulty. In this chapter these assumptions are discussed and the qualitative
consequences of these assumptions is indicated.
In this study it has been attempted to obtain data that is specific for the state of Goiás, or at least for
Brazil, yet is not always available. The largest uncertainty in the data collection can be found in the
data used to calculate the LUC emissions. First of all, the data on the land use types in Goiás has not
been gathered in large detail (Verstegen et al., 2014). For instance, it is known at which cells (of a
resolution of 5x5 km2) crop fields are located. However, what type of crops are grown (besides for
the specific sugarcane fields) is not known. Therefore in this study, the SOC and above and
belowground biomass are taken as an average for all crop types. This also influences the
quantification of the belowground biomass (table 6), for these are also taken as an average for all
types of crop fields, or forests. Secondly, there are no data available on the SOC an above and
belowground biomass specifically for the land use types in Goiás. Therefore the data are obtained
from IPCC (2006), which provided average data for land use types on a global scale. This indicates
that besides an average for all categories within the land use type, the carbon stock is also a global
average, rather than specific for Goiás, or even Brazil. In this study only land use is differentiated, not
soil types or climate regions. Finally, the equilibrium time (the time in which the carbon stock has
reached new equilibrium after the occurrence of LUC) that has been considered in this study is an
average for all land use types in the world. This while the equilibrium time can differ between only a
year and hundreds of years (IPCC, 2006). All these assumptions can have a significant impact on the
results in this study. For instance, the negative emissions that occur when sugarcane fields are placed
on fields that have been crop fields in 2005 are not necessarily negative, for there are certain types
of crops that have a higher SOC and/or above and belowground biomass than sugarcane. However,
in general, it can be stated that the carbon stock of forests is significantly higher than that of all other
land use types, even though the exact amount can be both higher or lower than assumed in this
study. Yet, having more thorough knowledge of the carbon stock, and doing a sensitivity analysis can
provide more clear insight in the finding of the optimal location for sugarcane fields.
In this study, the indirect land use change that might occur when crop field is converted to sugarcane
field is not taken into account, for there are no sufficient amount of data is available. The crop that
has been grown can be grown at another location, which for instance used to be forest. This is not
taken into account in this study.
The assumptions for the potential transportation costs and emissions are listed in appendix A1 and
A2. Another point of uncertainty in the data is the fact that literature gives a very different ratio
between traveling on the road and on a path. For the potential transportation costs this is 1:6, while
for the potential transportation emissions this is 1:25. This might be explained by the fact that the
transportation costs are calculated using data from Mozambique (van der Hilst & Faaij, 2012), while
the transportation emissions are for the US (Clark et al., 2009). Another limitation to the calculation
of both the potential transportation emissions and costs can be zero when a sugarcane cell is located
in the same cell as the mill. In this case, the assumed potential transportation emissions and costs
are zero in the model, while in reality the sugarcane does have to be transported from the sugarcane
cell to a mill, hence (few) GHG-emissions are emitted. This can have a minor impact on the total costs
and GHG-emissions, for the transportation costs and GHG-emissions are only a minor part of total
costs and total GHG-emissions.
22
Besides limitations due to the assumptions for the spatially explicit variables, the fact that the other
variables are not spatially explicit is in itself another assumption and limitation in this study. This is
both a limitation of the data (because the data is not available in a spatially explicit way) but also one
of methodology, for in this study no methodology has been developed to model the variables in a
spatially explicit way. An example is the land costs per hectare. The cultivation costs are variable, due
to the variable ethanol production. However, the cultivation costs per hectare are considered equal
in this study, while it is likely that different land uses, with different yields, which are located further
or closer to roads and towns, have different lease or acquisition costs. The fact that these variables
are not considered spatially influences both the total potential GHG-emissions and costs, because
spatial patterns that might exist when modelling all variables spatially explicit are not considered in
this study. Thus, the strong influence of LUC and yield can be enhanced, counteracted or balanced by
other spatial patterns that are so far unknown. Future research can focus on exploiting further
spatial components for the GHG-emissions and costs model.
Another large limitation to both the data and the methodology is the fact that the capacity of the
mills is not taken into account. This is in part because data on the capacity of over half of the mills in
Goiás is not available, and in part because it is methodologically difficult to model which sugarcane
fields provide sugarcane for which mill. This means that in this study only the most suitable fields are
selected, without taking into account that the sugarcane might have to be transported to another
mill (that is located further away) than the one that is currently considered in the model, increasing
transportation costs and emissions, and thereby the feasibility of the location of a field. It might be
that when the capacity of the mills is taken into account, other fields with similar, yet slightly worse
yield are more feasible, for they are located nearer to an existing mill that has not reached full
capacity. It is therefore crucial that in future research the capacity of the mills is determined and
taken into account in determining the optimal location of the new sugarcane fields. It would even be
more interesting to, besides the new sugarcane fields created between 2005 and 2010, also make
the mills that are constructed between 2005 and 2010 spatially variable in future research. However,
to optimize the location of sugarcane cells when both the cells and the mills are variable requires a
different method. A different method, such as simulated annealing (Aerts & Heuvelink, 2002) or a
genetic algorithm (Li & Yeh, 2005), is required because the number of solutions is too large and
therefore the computation time is too long. Furthermore, the method described by figure 2 would no
longer work because when cells are added or removed from the optimal configuration, the potential
transportation costs and emissions change as well.
Another limitation to the method is that it does not take into account the socio-economic context
and ownership of the land in Goiás. This study provides an indication of which fields are most
suitable to sugarcane cultivation. However, whether the owner is willing to transform his business to
sugarcane production, or is willing to sell his land is not considered. Therefore there is no guarantee
that knowing the optimal location can also lead to optimizing the location of sugarcane fields in Goiás
in reality in the future. Yet, it is interesting to have an indication of which locations can be considered
when future sugarcane fields are planned. For future research it would for instance be interesting
not only to look at the optimal location of sugarcane fields that match a certain demand, but to
locate all fields that adhere to the conditions set by the European Union, such that from this set of
locations can be selected fields that are both suitable considering these conditions as well as socioeconomic variables.
23
It also has to be taken into account that the calculations are acted out on cells of a grid size of 5x5
km2. The cells are aggregated, which means that a cell that is considered to be a sugarcane field can
contain other land use types.
In conclusion, the results presented in this study have several limitations, yet provide a rough insight
into the optimal location of sugarcane fields for ethanol production. This can enable policy-makers to
plan a good location for future sugarcane fields.
24
6. Conclusion
The aim of this study has been to find the optimal spatial configuration of new sugarcane fields in
Goiás, Brazil, created between 2005 and 2010, optimizing for both costs and GHG-emissions, keeping
the ethanol production equal to the demand in 2010. Firstly, the GHG-emissions and costs have been
quantified, in part with spatial variables. Secondly, the suitable locations for new sugarcane fields
have been determined for each spatial variable individually. Thirdly, the optimal configurations
considering costs and GHG-emissions with different weights have been determined and the average
GHG-emissions and costs for these configurations have been calculated. Finally, the optimized
configuration are compared to the actual configuration of sugarcane fields in 2010.
The first criterion, the GHG-emissions is quantified using two input values: the LUC emissions and the
emission occurring during the lifetime (LCA). The LCA emissions consist of four components:
cultivation emissions, transportation emissions, conversion emissions and avoided emissions. In this
study, the LUC emissions and the transportation emissions are calculated spatially. The avoided
emissions are lower than those found in for instance Seabra et al. (2011), because the actual Brazilian
electricity mix is considered in this study, rather than the emissions from a natural gas fired unit. The
second criterion, the costs, is quantified using four components: cultivation costs, transportation
costs, conversion costs and avoided costs. The cultivation and the transportation costs are calculated
spatially in this study.
Both the potential transportation emissions and the transportation costs show that the locations
with the lowest potential GHG-emissions are those near mills and roads. The average potential
transportation costs for Goiás is higher in this study (with calculated transportation distance) than for
instance van den Wall Bake et al. (2009) (with average transportation distance and road type). When
considering LUC emissions, the locations with the lowest emissions are mainly those that used to be
cropland, while those of the highest emissions are locations where forest is present in 2005. So,
when taking GHG-emissions into account in an optimization, forests are not considered favourable as
future sugarcane fields.
The optimized location is determined by doing a multi-criteria analysis (MCA) while applying different
weight factors. All three optimizations show little overlap with the actual configuration in 2010. The
optimized configuration of new sugarcane fields with the full weight on costs and when distributing
the weight factors equally over costs and GHG-emissions in the MCA are concentrated mainly to the
east of Goiás whereas the actual configuration in 2010 is spread out over the region. Both optimized
configurations display equal costs of ethanol production (US$14.52/GJethanol), yet they require a
different surface area (64% less than the actual new sugarcane fields in 2010 when optimizing only
costs, and 65% less than the actual new sugarcane fields in 2010 when distributing the weight factors
equally over costs and GHG-emissions). Furthermore, the GHG-emissions are lower than for the
actual configuration in 2010, 10% lower when optimizing only costs, and 30% lower while distributing
the weight factors equally over costs and GHG-emissions. The configuration of new sugarcane fields
when optimized only considering GHG-emissions has sugarcane fields concentrated both to the north
and the south of Goiás mainly in regions that used to be cropland in 2005 and have low yield,
whereas the actual configuration in 2010 is spread out of the region. This can be explained for the
negative emissions (per GJethanol) from cropland are maximized when it has to be distributed over a
small production of ethanol (i.e. at locations with low yield). Due to this low yield, 15% more fields
have to be converted to sugarcane to meet the required ethanol production than the actual new
fields in 2010. The GHG-emissions are 58% lower than in the actual configuration in 2010, yet the
25
costs are 45% higher. Due to these high costs, the configuration with the full weight on GHGemissions is not a reasonable solution.
When comparing the GHG-emissions of the optimized configurations to the GHG-emissions from
gasoline, all three options show significantly lower emissions, which are all below the 35% standard
set by the EU. The best performing configuration is (naturally) the one with the full weight on GHGemissions (83% lower) and the configuration with the highest GHG-emissions (full weight on costs)
still emits 61% less than gasoline fuel. When comparing the costs of the optimized configuration to
the costs of gasoline fuel it is more difficult to draw conclusions, for the costs of gasoline has varied
between US$25 in 2008 and US$10 in 2009. When comparing to the highest gasoline price only the
configuration with the full weight on GHG-emissions is more expensive than gasoline. Yet, when
taking the lower gasoline price in 2009, all configurations are far more expensive (45-200%).
This study has several limitations. The first is the quality of the data. An example is the data used to
model the LUC, which are all from IPCC (IPCC, 2006), are not specific to Goiás, or even Brazil, but
global averages. Another example is the absence of data, such as the actual capacity of the mills in
Brazil. This has resulted in the fact that the capacity of the mills has not been taken into account in
this study, a great limitation. Another limitation is that due to absent data and complex methodology
the location of the mill is not spatially variable. An additional limitation is that the indirect LUC is not
modelled in this study.
In future research the location and capacity of the mills has to be taken into account to obtain more
accurate results regarding the exact optimal location of the sugarcane fields for ethanol production.
Furthermore, the variables that are equal for the entire state of Goiás in this study should be
expressed as a spatial component to determine possible other relations between location and GHGemissions and costs. Additionally, the quality of the data, especially that of the LUC data should be
improved by conducting further research regarding the SOC, above and belowground biomass and
equilibrium time. Also, the socio-economic context, or the availability of land has to be determined in
future research. Finally, future research can focus on finding all fields in Goiás that can produce
sugarcane for bioethanol that adhere to the standards set by the EU.
In this study, some indications have been found that can help policy makers plan locations for future
sugarcane fields while reducing costs and GHG-emissions. Furthermore, a method has been
developed to optimize the location of bioenergy crop fields. This method can be applied not only to
Goiás and sugarcane bioethanol, but also to other case studies and feedstocks.
26
7. References References
Aerts, J. C. J. H., & Heuvelink, G. B. M. (2002). Using simulated annealing for resource allocation.
International Journal of Geographical Information Science, 16(6), 571-587.
doi:10.1080/13658810210138751
ANEEL. (2014). Electricity rates-end-consumers. Retrieved 01/02, 2014, from
http://www.aneel.gov.br/area.cfm?idArea=507&idPerfil=11
Anuário da cana 2011: Brazilian sugar and ethanol guide. (2011). ().
Baumann, H., & Tillman, A. M. (2004). The hitch hiker's guide to LCA: An orientation in life cycle
assessment methodology and application (3rd ed.). Lund: Studentlitteratur AB.
Clark, N., Thompson, G., & Delgado, O. (2009). Modeling heavy‐duty vehicle fuel economy based on
cycle properties. (). Morgantown: West Virginia University: Center for Alternative Fuels, Engines
and Emissions.
de Bruin, H., van Duin, R., & Huijbregts, M. A. J. (2001). LCA an operational guide to the ISO
standards. (). Leiden: Leiden University.
de Souza Soler, L., & Verburg, H. (2010). Combining remote sensing and household level data for
regional scale analysis of land cover change in the brazilian amazon. Reg Environ Change, 10,
371-386.
Demirbas, A. (2011). Competitive liquid biofuels from biomass. Applied Energy, 88(1), 17-28.
Efe, Ç, Straathof, A. J. J., & van der Wielen, L. A. M. (2005). Technical and economical feasibility of
production of ethanol from sugarcane and sugarcane bagasse. (Unpublished TU Delft, Delft.
EU. (2011). Certification schemes for biofuels (Memo ed.)
European Commission. (2013). What is the EU doing about climate change? Retrieved 08/02, 2013,
from http://ec.europa.eu/clima/policies/brief/eu/index_en.htm
Freppaz, D., Minciardi, R., Robba, M., Rovatti, M., Sacile, R., & Taramasso, A. (2004). Optimizing
forest biomass exploitation for energy supply at a regional level. Biomass and Bioenergy, 26(1),
15-25.
Goldemberg, J., & Guardabassi, P. (2010). The potential for first generation ethanol production from
sugarcane. Biofuels, Bioprod. Bioref., 4, 17–24.
Governo de Goiás. (2014). Goiás. Retrieved 12/28, 2013, from http://www.goias.gov.br/
Guo et al. (2002). Soil carbon stocks and land use change: A meta analysis. Global Change Biology, 8,
345-360.
Hamelinck, C. N., Suurs, R. A. A., & Faaij, A. P. C. (2005). International bioenergy transport costs and
energy balance. Biomass and Bioenergy, 29, 114–134.
27
Horner, M. W., Zhao, T., & Chapin, T. S. (2011). Toward an integrated GIScience and energy research
agenda. Annals of the Association of American Geographers, 11(4), 764-774.
IEA. (2010). World energy outlook 2010. (). Paris: OECD.
IEA. (2013). World energy outlook 2013. (). Paris: OECD.
IPCC. (2006). IPCC guidelines for national greenhouse gas inventories, prepared by the national
greenhouse gas inventories programme. (). IGES, Japan: Eggleston, H.S., Buendia, L., Miwa, K.,
Ngara, T., Tanabe, K., (Eds.).
IPCC. (2011). Bioenergy. in IPCC special report on renewable energy sources and climate change
mitigation. (). Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.
Jonker, G. J. (2014). Economic outlook for ethanol production in brazil. Forthcoming, Economic
outlook for ethanol production in Brazil
JRC. (2011). Well-to-wheels analysis of future automotive fuels and powertrains in the european
context. ( No. 3c). Luxemburg: Publications Office of the European Union.
Kang, S., Önal, H., Ouyang, Y., Scheffran, J., & Tursun, Ü D. (2010). Optimizing the biofuels
infrastructure: Transportation networks and biorefinery locations in illinois. Handbook of
bioenergy economics and policy (33rd ed., pp. 151-173). New York: Springer.
Lapig. (2014). Lapig base de dados raster. Retrieved April, 2013, from
http://www.lapig.iesa.ufg.br/lapig/
Leite, R. C. C., Leal, M. R. L. V., Cortez, L. A. B., Griffin, W. M., & Scandiffio, M. I. G. (2009). Can brazil
replace 5% of the 2025 gasoline world demand with ethanol? Energy, 34(5), 655-661.
doi:http://dx.doi.org.proxy.library.uu.nl/10.1016/j.energy.2008.11.001
Luo, L., van der Voet, E., & Huppes, G. (2009). Life cycle assessment and life cycle costing of
bioethanol from sugarcane in brazil. Renewable and Sustainable Energy Reviews, 13(6-7), 16131619.
Macedo, I. C., Leal, M. R. L. V., & da Silva, J. E. A. R. (2004). Assessment of greenhouse gasemissions in
the production and use of fuel ethanol in brazil. (). São Paulo: Government of the State of São
Paulo.
OECD. (2005). OECD economic survey of brazil, 2005: REGULATION OF THE ELECTRICITY SECTOR.
().OECD Publishing.
Pecege. (2010). Custos de produção de cana-de-açúgar, açúgar e etanol no brazil: Fechamento de
safra 2009/2010. ( No. 1).
Pecege. (2011). Custos de produção de cana-de-açúgar, açúgar e etanol no brazil: Fechamento de
safra 2010/2011. ( No. 1).
Picoli, M. (2013). Brazilian sugarcane mills map. Retrieved April, 2013, from
http://www2.bioetanol.org.br/siis/mapas/
28
Rudorff, B. F. T., de Aguiar, D. A., da Silva, J. E. A. R., Sugawara, L. M., Adami, M., & Moreira, M. A.
(2010). Studies on the rapid expansion of sugarcane for ethanol production in são paulo state
(brazil) using landsat data. Remote Sensing, 2, 1057-1076. doi:10.3390/rs2041057
Scharlemann, J. P. W., & Laurance, W. F. (2008). How green are biofuels? Science, 139(43), 43-44.
Seabra, J. E. A., Macedo, I. C., Chum, H. L., Faroni, C. E., & Sarto, C. A. (2011). Life cycle assessment of
brazilian sugarcane products: GHG emissions and energy use. Biofuels, Bioproducts and
Biorefining, 5(5), 519–532.
Seabra, J. E. A., Tao, L., Chum, H. L., & Macedo, I. C. (2010). A techno-economic evaluation of the
effects of centralized cellulosic ethanol and co-products refinery options with sugarcane mill
clustering. Biomass and Bioenergy, 34, 1065–1078.
Tóth, G., Kozlowski, B., Prieler, S., & Wiberg, D. (2012). Global agro-ecological zones. ( No. v3.0).
Triantaphyllou. (2000). Multi-criteria decision making methods: A comparative study. Dordrecht:
Kluwer Academic Publishers.
van den Broek, R., van den Burg, T., van Wijk, A., & Turkenburg, W. (2000). Electricity generation
from eucalyptus and bagasse by sugar mills in nicaragua: A comparison with fuel oil electricity
generation on the basis of costs, macro-economic impacts and environmental emissions.
Biomass and Bioenergy, 19, 311-335.
van den Wall Bake, Junginger, M., Faaij, A. P. C., Poot, T., & Walter, A. (2009). Explaining the
experience curve: Cost reductions of brazilian ethanol from sugarcane. Biomass and Bioenergy,
33, 644–658.
van der Hilst, F., Dornburg, V., Sanders, J. P. M., Elbersen, B., Graves, A., Turkenburg, W., . . . Faaij, A.
P. C. (2010). Potential, spatial distribution and economic performance of regional biomass
chains: The north of the netherlands as example. Agricultural Systems, 103, 403-417.
van der Hilst, F., & Faaij, A. P. C. (2012). Spatiotemporal cost-supply curves for bioenergy production
in mozambique. Biofuels, Bioproducts and Biorefining, 6, 405-430.
van der Hilst, F., Verstegen, J. A., Zheliezna, T., Drozdova, O., & Faaij, A. P. C. (2014). Integrated
spatiotemporal analysis of agricultural land use, bioenergy production potentials and related
GHG balances in ukraine. Biofuels, Bioproducts and Biorefining,
Velazquez-Marti, B., & Annevelink, E. (2009). GIS application to define biomass collection points as
sources for linear programming of delivery networks. Transactions of the ASABE, 52(4), 10691078.
Verstegen, J. A., Karssenberg, D., van der Hilst, F., & Faaij, A. P. C. (2014). Land use projections for
brazil under increasing demand for bioenergy with uncertain data. Forthcoming,
Walter, A., Dolzan, P., Quilodran, O., de Oliveira, J. G., da Silva, C., Piacente, F., & Segerstedt, A.
(2011). Sustainability assessment of bio-ethanol production in brazil considering land use
change, GHG emissions and socio-economic aspects. Energy Policy, 39, 5703–5716.
29
30
Appendix A: Data
A1: GHG-emissions
This section provides more information regarding the input data for the GHG-emission model
described in section 2.1. Both assumptions and the origin of both the spatial and non-spatial input
data data is provided.
When modelling the LUC emissions (ELUC) six categories of land use types are defined in this study:
urban, water, forest, cropland, pasture land and sugarcane cells, which are an aggregation of land
use types described by IPCC (2006). It is assumed that the land use types urban or water cannot be
converted sugarcane. Each land use type has its own value for SOC, aboveground biomass and
belowground biomass as can be found in table 5. The belowground biomass is proportional to the
aboveground biomass, the rate for each different land use type can be found in table 6 (IPCC, 2006).
These data are global averages, so not specific to Brazil. Furthermore, there is a variance in the SOC
and above and belowground biomass for each land use type, such as for different forest types or
different crops at a crop field. These are agglomerated in this study to one category, and therefore
the carbon stock is taken as an average over all these types. The land use type in 2005 is obtained
from the map from Verstegen et al. (2014). The LUC emissions of all cells in 2010 are divided by the
ethanol production of these cells. The ethanol production has to be summed over the equilibrium
time, the time for the land use type to reach equilibrium (tLUC). The equilibrium time has been
obtained from IPCC (2006) and can be found in table 5. This is an average global equilibrium time and
therefore not specific for Goiás, or even Brazil and can differ with each land use type, as well as
within subcategories of the land use types defined in this study. The efficiency of the mill (ηmill) is an
average of the mills in Goiás, for it is not known which sugarcane cell delivers to which mill and
therefore an average is taken, and is obtained from Anuário da cana 2011: Brazilian sugar and
ethanol guide (2011) and can be found in table 5. The fractional yield (Fyield) is the fraction of the
maximum yield that is obtained in Brazil and is obtained from Tóth et al. (2012) The maximum yield is
obtained from Goldemberg and Guardabassi (2010) and can be found in table 5.
Table 5: Parameters involved with calculating the land use change emissions (ELUC)
Parameter
Carbon stock in soil (SOC)
Value
Unit
Reference
Forest
71000 kg C/ha
(IPCC, 2006)
Cropland
44000 kg C/ha
(IPCC, 2006)
Pasture
52000 kg C/ha
(IPCC, 2006)
Sugarcane
41000 kg C/ha
(IPCC, 2006)
Urban
0 kg C/ha
(IPCC, 2006)
Water
Forest
0 kg C/ha
(IPCC, 2006)
(IPCC, 2006)
200000 kg C/ha
(IPCC, 2006)
Aboveground biomass (AGB)
Cropland
2000 kg C/ha
Pasture
5000 kg C/ha
(IPCC, 2006)
(IPCC, 2006)
Sugarcane
17000 kg C/ha
(IPCC, 2006)
Urban
0 kg C/ha
31
(IPCC, 2006)
Water
0 kg C/ha
Forest
80000 kg C/ha
(IPCC, 2006)
(IPCC, 2006)
Cropland
2000 kg C/ha
Pasture
20000 kg C/ha
Sugarcane
17000 kg C/ha
(IPCC, 2006)
Belowground biomass (BGB)
(IPCC, 2006)
(IPCC, 2006)
Urban
0 kg C/ha
Water
0 kg C/ha
(IPCC, 2006)
24 GJ/m3ethanol
Energy density of ethanol (EDensity)
(Goldemberg &
0,0738 m3ethanol/tonne Guardabassi, 2010)
Efficiency of the mill (ηmill)
Equilibrium time (tLUC)
20 years
Maximum sugarcane yield in Brazil (yieldmax)
240 tonne/ha
(IPCC, 2006)
(Anuário da cana 2011:
Brazilian sugar and
ethanol guide.2011)
Table 6: Ratio belowground biomass/aboveground biomass for different land use types
Parameter
Value
Forest
Unit
Reference
(IPCC, 2006)
0.4 kg/kg
(IPCC, 2006)
Cropland
1 kg/kg
Pasture
4 kg/kg
Sugarcane
1 kg/kg
(IPCC, 2006)
(IPCC, 2006)
The data regarding the cultivation emissions (ECultivation) are shown in table 7.
Table 7: Parameters involved with calculating the cultivation emissions (ECultivation)
Parameter
Cultivation emissions (ECultivation)
Value
Unit
21 Kg CO2-eq/GJethanol
Reference
(JRC, 2011; Seabra et al., 2011)
For the transportation emissions (ETransportation), three things are assumed about the truck
transportation. First of all, it is assumed that American trucks are similar to Brazilian trucks, for the
emission values EPath and ERoad are for an average American truck. Secondly, trucks can travel on two
types of roads: paved roads (at which a velocity of 100 km/h is possible (de Souza Soler & Verburg,
2010) and unpaved roads. Unpaved roads are all locations where no paved roads are present (at
which a velocity of 15 km/h is possible (de Souza Soler & Verburg, 2010)). Finally, it is assumed that
there is no blending of bioethanol with the fuel by the trucks that transport the sugarcane, i.e. the
use of 100% fossil fuel is assumed (Seabra et al., 2011). The capacity of the truck (Captruck) determines
the amount of runs the truck has to make from the sugarcane cells to the mill. The data for the
emission on the road and on the path, as well as the truck capacity can be found in table 8.
32
The GHG-emissions (Epath and Eroad) are extrapolated from Clark et al. (2009) who measured the
emissions of American trucks at several velocities. A linear relation is assumed between velocity and
GHG-emissions. The location of the mills in 2010 is obtained from Picoli (2013).The location of roads
in 2010 is obtained from Lapig (2014).
Table 8: Parameters involved with calculating the transportation emissions (ETransportation)
Parameter
Value
Unit
Reference
Emissions path (EPath)
2.09 Kg CO2-eq/truck *km
(Clark et al., 2009)
Emission road (ERoad)
0.105 Kg CO2-eq/truck *km
(Clark et al., 2009)
Truck Capacity (Captruk)
40 tonne
(Hamelinck et al., 2005)
The data regarding the conversion emissions (EConversion) can be found in table 9. This value was taken
from literature for entire Brazil, rather than for the individual mills in Goiás because data is not
available for all mills.
Table 9: Parameters involved with calculating the cultivation emissions (EConversion)
Parameter
Value Unit
Conversion emissions (EConversion)
3.6 Kg CO2-eq /GJethanol
Reference
(Seabra et al., 2011)
For the avoided emissions (EAvoided), it is assumed that electricity mix of Goiás is similar to the
electricity mix in Brazil. The electricity matrix is obtained from OECD (2005), the GHG-emissions per
fuel type (FEmission) from IPCC (2006), the average power output and ethanol production of a mill in
Goiás (AvProdEthanol) (also for one year of production) from Anuário da cana 2011: Brazilian sugar and
ethanol guide (2011) and the operation time from (Seabra et al., 2011) and can be found in table 10.
The avoided emissions are taken as an average of the known mill data in Goiás, because there is no
data available on all mills in Goiás.
Table 10: Parameters involved with calculating the avoided emissions (EAvoided)
Parameter
Fraction of energy matrix in Brazil
(MEnergy)
Value
Reference
Oil and derivatives
0.05 %
(OECD, 2005)
Natural gas
0.08 %
(OECD, 2005)
Coal
0.01 %
(OECD, 2005)
Nuclear
0.02 %
(OECD, 2005)
Hydroelectricity
0.80 %
(OECD, 2005)
Biomass
0.03 %
(OECD, 2005)
Other renewables
0.01 %
(OECD, 2005)
Oil and derivatives
700 kg CO2-eq/MWh
(IPCC, 2006)
Natural gas
450 kg CO2-eq/MWh
(IPCC, 2006)
1000 kg CO2-eq/MWh
(IPCC, 2006)
10 kg CO2-eq/MWh
(IPCC, 2006)
Hydroelectricity
0 kg CO2-eq/MWh
(IPCC, 2006)
Firewood and charcoal
0 kg CO2-eq/MWh
(IPCC, 2006)
Cane products
25 kg CO2-eq/MWh
(IPCC, 2006)
Other renewables
25 kg CO2-eq/MWh
(IPCC, 2006)
Coal
Emission of fuel type (global average)
(FEmission)
Unit
Nuclear
33
Operation time of a sugarcane mill in
Brazil (TOperation)
4000 hours/year
Power Output of a sugarcane mill in
Goiás (POutput)
7.4 MW/h
Av. Ethanol Production of a sugarcane
mill in Goiás in a year (AvProdethanol)
2237392 GJethanol
(Seabra et al., 2010)
(Anuário da cana
2011: Brazilian sugar
and ethanol
guide.2011)
(Anuário da cana
2011: Brazilian sugar
and ethanol
guide.2011)
A2: Costs
This section provides more information regarding the input data for the costs model described in
section 2.2. Both assumptions and the origin of both the spatial and non-spatial input data data is
provided.
The input data regarding the cultivation costs (CCultivation) are the potential feedstock costs per hectare
(CFeedstock) are obtained from Jonker (2014), Pecege (2010) and Pecege (2011) and can be found in
table 11.
Table 11: Parameters involved with calculating the cultivation costs (CCultivation)
Parameter
Feedstock cost (CFeedstock)
Value
Unit
Reference
1782 US$/ha/year
(Jonker, 2014)
The assumptions regarding the transportation costs (CCultivation) can be found in appendix A1. The
values for the potential costs on a path (Cpath) and for the potential costs on a road (Croad) are
obtained from van der Hilst & Faaij (2012), using the value for “other roads” for Cpath and “Primary
roads; good condition; paved” for Croad. These values can be found in table 12.
Table 12: Parameters involved with calculating the transportation costs (CTransportation)
Parameter
Value
Unit
Reference
(van der Hilst & Faaij, 2012)
Costs path (CPath)
0.12 US$/tonne*km
Costs road (CRoad)
0.02 US$/tonne*km
(van der Hilst & Faaij, 2012)
The conversion costs are obtained from van den Wall Bake (2009), who has discounted them over
the lifetime of the mill and can be found in table 13. This value was taken from literature for entire
Brazil, rather than for the individual mills in Goiás because data is not available for all mills.
Table 13: Parameters involved with calculating the conversion costs (CConversion)
Parameter
Conversion costs (CConversion)
Value
Unit
6.25 US$/GJethanol
Reference
(van den Wall Bake et al., 2009)
When modelling the avoided costs, assumptions are made. The electricity costs (CElectricity) are the
average costs for different consumer groups as is described by ANEEL (2014) for Brazil in 2005, such
as residential, commercial, rural or industrial consumers. The average power output and ethanol
34
production (AvProdEthanol) (for one year of production) from Anuário da cana 2011: Brazilian sugar
and ethanol guide (2011) and the operation time (TOperation) from (Seabra et al., 2011) and can be
found in table 14. The avoided costs are taken as an average of the known mill data in Goiás,
because there is no data available on all mills in Goiás.
Table 14: Parameters involved with calculating the avoided costs (CAavoided)
Parameter
Cost of electricity in Brazil (CElectricity)
Value
Unit
106.54 US$/MWh(2005)
Reference
(ANEEL, 2014)
35
36