Life Cycle Environmental and Economic Impact of Using Switchgrass-derived Bioethanol as Transport Fuel

Full text

Life Cycle Environmental and Economic
Impact of Using Switchgrass-derived
Bioethanol as Transport Fuel
Master program graduation thesis
Author: Yu Bai
Student No.: 0716332 (Leiden)/ 1398504 (Delft)
Program: Industrial Ecology
Supervisors: Ester van de Voet
Adrie Straathof
Lin Luo
30th June 2009

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Acknowledgements
I wish to express my gratitude to all those who gave me the possibility to complete this
thesis. I own my deepest gratitude to my supervisors Ester van der Voet, Lin Luo and
Adrie Straathof for their guidance, suggestion, and support. I would like to thank Reinout
Heijungs for giving me help on methodology, LCA software and database. My thanks
also go to PDEng Trainees Olalla Guerra Miguez, Dhinakaran Siva siddarthan and Diego
Suarez Zuluaga for answering me the questions about ethanol production process. Then I
am indebted to the whole IE research group, colleagues at CML and my classmates for
their stimulating discussions and conversations, and nice working environment at CML.
Special thanks to my parents, without whom none of this would even be possible. Thanks
for their love and support for these years and giving me the chance to study in the
Netherlands. Thanks to all my friends, in the Netherland and in my homeland, for their
encouragement and support always.

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Table of Content
Acknowledgements ........................................................................................................... 1
Table of Content................................................................................................................ 2
List of Figures.................................................................................................................... 4
List of Tables ..................................................................................................................... 4
Abbreviations .................................................................................................................... 5
Summary............................................................................................................................ 6
1. Introduction............................................................................................................... 7
1.1 Background ............................................................................................................... 7
1.2 Context of study........................................................................................................ 8
1.3 Goal and Research Questions ................................................................................... 9
1.3.1 Goal.................................................................................................................... 9
1.3.2 Research questions........................................................................................... 10
2. Literature Review ................................................................................................... 11
3. Methodologies.......................................................................................................... 16
3.1 Life Cycle Assessment (LCA)................................................................................ 16
3.1.1 Goal and scope definition ................................................................................ 17
3.1.2 Inventory analysis ............................................................................................ 18
3.1.3 Impact assessment............................................................................................ 20
3.1.4 Interpretation.................................................................................................... 20
3.2 Life Cycle Costing (LCC)....................................................................................... 21
4. Life Cycle Modeling................................................................................................ 22
4.1 Goal and Scope Definition...................................................................................... 22
4.2 Function Unit and Alternative Systems .................................................................. 22
4.3 System Boundary.................................................................................................... 22
4.4 Data Collection and Calculation ............................................................................. 26
4.4.1 Data collection of switchgrass agriculture....................................................... 26
4.4.2 Data collection of bioethanol production......................................................... 27
4.4.3 Data of vehicle operation (end use) ................................................................. 27
4.5 Allocation................................................................................................................ 27
4.6 Environmental Impact Assessment Method ........................................................... 28
5. Life Cycle Environmental Impact ......................................................................... 29
5.1 General Results ....................................................................................................... 29
5.2 WTT Analysis......................................................................................................... 32
5.3 Contribution Analysis ............................................................................................. 33
5.4 Sensitivity Analysis ................................................................................................ 35
5.4.1 Allocation methods .......................................................................................... 35
5.4.2 Excluding soil establishment ........................................................................... 36
5.4.3 Transport distance............................................................................................ 38
5.5 Comparison with Other Studies.............................................................................. 39
5.5.1 Comparison with other switchgrass-ethanol study .......................................... 39
5.5.2 Comparison with other feedstocks ethanol studies.......................................... 40
6. Life Cycle Economic Impact .................................................................................. 41
6.1 Life Cycle Costing .................................................................................................. 41
6.1.1 Switchgrass-ethanol life cycle and functional unit of LCC............................. 41

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6.1.2 Data collection ................................................................................................. 41
6.2 Life Cycle Cost of Bioethanol ................................................................................ 41
6.3 The Influence of Energy Tax .................................................................................. 44
6.3 Possibilities of Cost Reduction ............................................................................... 45
7. Conclusions.............................................................................................................. 47
Reference ......................................................................................................................... 48
Appendix 1 Data Collection of Switchgrass Agriculture............................................. 54
Appendix 2 Data Collection of Bioethanol Production Process.................................. 58
Appendix 3 Data Collection of Final Vehicle Driving ................................................. 62
Appendix 4 Summary of LCIA Results ........................................................................ 63

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List of Figures
Figure 2.1 Alternative metrics for evaluating ethanol based on the intensity of primary
energy inputs (MJ) per MJ of fuel and of net green house gas emissions (kg CO
2
-
equivalent) per MJ of fuel. (Farrell et al., 2006) .............................................................. 12
Figure 3.1 Stages of an LCA (ISO, 2006a)....................................................................... 17
Figure 3.2 Concept of category indicators (ISO, 2006b).................................................. 20
Figure 4.1 Life cycle of switchgrass –ethanol (base scenario) ......................................... 23
Figure 4.2 Units in switchgrass agriculture ...................................................................... 24
Figure 5.1 Climate change potential of vehicle driving 1km by petrol, E10, E85 and E100
........................................................................................................................................... 30
Figure 5.2 Overall comparison results of the environmental impact of petrol, E10, E85
and E100 ........................................................................................................................... 31
Figure 5.3 Comparison results of WTT analysis of all fuel alternatives, using largest
value as reference.............................................................................................................. 32
Figure 5.4 Contribution analysis result of ADP................................................................ 33
Figure 5.5 Contribution analysis result of POCP.............................................................. 33
Figure 5.6 Contribution analysis result of ODP................................................................ 34
Figure 5.7 Contribution analysis result of EP................................................................... 34
Figure 5.8 Contribution analysis result of AP .................................................................. 34
Figure 5.9 Contribution analysis result of HT .................................................................. 35
Figure 5.10 Contribution analysis result of ET................................................................. 35
Figure 5.11 Comparison of including with excluding soil establishment of GHG
emissions reduction........................................................................................................... 37
Figure 6.1 Future change trend of crude oil price globally (Source: EIA) ....................... 43
Figure 6.2 Future increasing trend of petrol price (Source: EIA)..................................... 43
Figure 6-3 Cost breakdown of biothonal production from switchgrass (Source: Guerra
Miguez et al.) .................................................................................................................... 45
List of Tables
Table 1 Comparison results of global warming potential_100 years of E10 and E85 based
on different allocation methods ........................................................................................ 36
Table 2 Comparison results of environmental impacts for 1 km driving by E100 in two
scenarios............................................................................................................................ 38
Table 3 Comparison of environmental impacts of E85 driving in varied transport
distances............................................................................................................................ 38
Table 4 Comparison of life cycle GHG emissions for E85 driving 1 km with Spatari et
al.’s studies........................................................................................................................ 39
Table 5 Costs of all the fuel alternatives in 2008 and 2030 (excluding taxes) (unit: €) ... 42
Table 6 The comparison results of costs of bioethanol including taxation in varied levels
and cost of petrol including taxation (unit: €/kg )............................................................. 44
Table 7 Costs of driving 1 km by all fuel types including taxation (based on data in year
2008) ................................................................................................................................. 45

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Abbreviations
1, 4-DCB: 1, 4-Dichlorobenzene
ADP: Abiotic resource depletion potential
AFEX: Ammonia Fiber Explosion
AP: Acidification potential
CFC: Chlorofluorocarbons
DM: Dry mass
E10: mixture with 10% ethanol and 90% petrol by volume
E85: mixture with 85% ethanol and 10% petrol by volume
E100: Pure ethanol as fuel
Eq.: Equivalent
ETP: Ecotoxicity potential
EP: Eutrophication potential
GHG: Greenhouse gas
GWP: Global warming potential
HTP: Human toxicity potential
K-: Potassium (fertilizer)
LCA: Life cycle assessment
LCC: Life cycle costing
LCIA: Life cycle impact assessment
LHV: Low heating value
N-: Nitrogen (fertilizer)
NOx: Nitrogen oxides
ODP: Ozone depletion potential
P-: Phosphate (fertilizer)
POCP: Photochemical oxidant formation potential
SSCF: Simultaneous Saccharification and co-Fermentation
TU Delft: Delft University of Technology
WTT: Well to tank
w/w: percent by weight
v/v: percent by volume

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Summary
Increasing petrol price, reduction of fossil fuels reserve and negative environmental
consequences of driving by petroleum fuels have driven alternative transport fuels.
Different from other renewable energy, such as wind and solar energy, biomass can be
converted to biofuels, as the liquid fuel. Thus, traditional petroleum transport fuels would
be replaced by biofuels. The aim of GHG emissions reduction from Kyoto Protocol is 8%
for EU, and additionally, EU also has its own target of energy policy, which is renewable
energy should account for 20% of the EU’s final energy consumption by 2020.
Bioethanol as one kind of biofuels has some advantages to attract global attention, like
less GHG emissions, reduction the use of fossil fuel and blending with petrol as transport
fuels.
This study assessed the environmental and economic impacts of bioethanol from
switchgrass as transport fuel, and compared the results with the ones of traditional petrol,
to analyze whether it is environmental friendly and economic feasibility. Life cycle
approaches were employed in this study.
A comprehensive life cycle assessment (LCA) study of using switchgrass-derived
bioethanol as transport fuel is done, to evaluate the environmental impact, and to
compare to life cycle environmental impact of petrol. The comparison results indicated
that bioethanol fuels had significant benefit in the reduction of GHG emissions,
especially E85. In terms of other impact categories (excluding abiotic resource depletion),
however, ethanol fueled driving contributed to larger environmental impacts than petrol
driving.
Furthermore, a life cycle costing (LCC) was performed to calculate the total cost of
bioethanol fuels from switchgrass. The results suggested that the cost of switchgrass-
derived bioethanol as transport fuel was still too high, which would not be attractive at
this moment without subsidies. The drop of the cost would be possible in the future,
considering the elevation of switchgrass yield and improvement of bioethanol production
technologies. These developments also could help the decrease of environmental impacts.

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1. Introduction
1.1 Background
The renewable biofuels can be used as alternative fuels for transportation, to reduce the
reliance on fossil fuels. The rapid increasing demand of energy caused the increasing
price of crude oil and petrol and decreasing reserve of fossil fuels, which drove new
development of transport fuels. Furthermore, the great negative environmental
consequence of using of fossil fueled automobiles, especially GHG emissions, is another
reason for improvement of transport fuels. According to the Kyoto Protocol, a reduction
of GHG emissions by 5.2% compared to the year 1990 will be achieved averaged over
the period of 2008 to 2012. The EU should decrease its GHG emissions by 8% to share
the responsibility to total target (UNEP, 1997). In addition, based on the Energy Policy
for European in 2007, EU commitment has to achieve at least a 20% reduction of GHG
by 2020 compared to 1990 (CEC, 2007a). Moreover, European heads of State or
Government agreed in 2007 on binding targets to increase the share of renewable energy.
Renewable energy should account for 20% of the EU’s final energy consumption by
2020, compared with 8.5% in 2005 (CEC, 2007b).
Biofuels as a kind of renewable energy, which are obtained by processing or fermenting
non-fossil biological source, therefore, could be contributed to the objective of Kyoto
Protocol. The usage of fossil fuel increases the GHG emissions substantially and
subsequently contributes to negative environmental impacts, such as global warming and
air pollutions. Apart from biofuels, other renewable energy, like wind and solar energy
can not be generated to liquid fuel. So biofuels are good options to instead of or in a
mixture with conventional fossil fuels. Generally, biofuels are derived from plants, which
absorb CO
2
from atmosphere as they grow. This is why the biofuels have the potential to
offset CO
2
emissions and mitigate climate change.
Hence, this research focuses on one type of the biofuels, and relevant environmental and
economic analyses are evaluated.

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1.2 Context of study
Bioethanol is considered as one of the main biofuels for application in the European
transport sector by Dutch Agency for Energy and the Environment (SenterNovem)
(2001). In Europe, Sweden is a leader of using bioethanol as transport fuel and all petrol
contains 5% bioethanol. E85 is also widely used in Sweden as a fuel first and there are
around 120 gas stations spread across the country where motorists can get E85. Blending
of up to 5% bioethanol in petrol is also used in France. Furthermore, in Stockholm,
ethanol has also enabled to be directly added to vehicles-converted diesel buses have
been running on 100% ethanol for some time and the cost of operating these special
buses has almost reached the level of standard diesel buses. Meantime, car manufacturers
such as Ford, Volvo and Saab are introducing so-called flexi-fuel vehicles, which are able
to run on petrol, E85, and everything in between (Anon, 2008).
Bioethanol is alcohol fuel made from biomass, such as woods, grasses, much of the
material in municipal soil waste, and forestry and agricultural residues. And as one of the
biofuels, bioethanol shares many characters with other biofuels; it is often considered as
green energy, for it is combusted relatively clearly, less or no net CO
2
contribution. Until
now, the hot spots in this research field are the ethanol from corns, sugar cane and other
food sourced biomass (Dias De Oliveria et al., 2005; Kadam, 2002; Kim and Dale, 2004;
Kim and Dale, 2005b and Sheehan et al., 2004; Luo et al., 2008), which are usually
named first generation bioethanol. Although the benefits described above can be
achieved, it still has a hotly conflict for first generation bioethanol, which is the feud of
surge sources used for bioethanol fermentation are also the source of food and sugar as
consumer goods. A large number of using first generation bioethanol might be increasing
the demand pressure of food, and there is a fear that it will cause the rise of the price of
these essential foods (Tan et al., 2008).
To avoid this conflict, second generation bioethanol which is produced from cellulosic
sources is being piloted. Switchgrass (panicum virgatum), a warm season grass, not food
supply, is able to be a good option to produce second generation bioethanol. Switchgrass
can grow in different lengths of growing seasons, soil types and land conditions, on
marginal lands and little of fertilizer to thrive, so it is very adaptable for bioenergy
feedstock. A recent energy model indicated that switchgrass could produce above 700%

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more output than input energy, and GHG emissions has been assumed to be near zero or
estimated to be slightly positive for ethanol from switchgrass (Schmer et al., 2008). It is
acknowledged, however, that comprehensive environmental impacts of switchgrass
ehtanol should undergo further research and development before it would perform at the
levels of large scale production.
Switchgrass is the native grass in North American, and has long been used for soil
conservation and as a fodder crop. A cooperative research (Elbersen, 2001) by several
organizations in Europe has suggested that switchgrass could be grown in Europe and it
can be used to produce inexpensive biomass under low input conditions and at a very low
environmental impact. The experimental sites included the Netherlands, UK, Germany,
Greece and Italy.
In Europe, current production of bioethanol is largely based on fermentation of sugar and
starch crops. In 2005, 721,000 tons of bioethanol for transportation were produced, and it
was assumed that the demand for fuel ethanol in 2010 would be about 14.5 million tons
(EuroObserv’ER, 2006). In order to fulfill this rapidly growing demand, several new
production processes which are using cellulosic biomass as feedstock have been
developed. Switchgrass is a very suitable objective, and a group of designers in Delft
University of Technology designed a technical process of switchgrass-ethanol. In their
program, an operational plant producing ethanol from switchgrass was designed
conceptually to assess the technical and economical feasibility (Guerra Miguez O. et al.,
2009). This design of the ethanol production process is applied to this study.
1.3 Goal and Research Questions
1.3.1 Goal
The first objective of this study is to assess the environmental impact of using
switchgrass-derived bioethanol as transport fuel in life cycle perspective, compared with
petrol and to compare the environmental impact of bioethanol from switchgrass with the
ones from other feedstocks, such as corn and sugar cane. The results of ethanol from
other feedstocks come from published literatures, which are also in life cycle perspective.

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The second objective is to study the economic impact of switchgrass-derived bioethanol
as transport fuel, over the entire life cycle.
1.3.2 Research questions
Correlating with the goal, there are two research questions for this study and some sub-
questions related to research questions:
1. What are the environmental impacts of using switchgrass-derived bioethonal as
transport fuel?
• What are the environmental impacts of using switchgrass ethanol as transport fuel?
• Is it environmental benefits of using switchgrass ethanol as transport fuel in place of
petrol?
• What are by-products in the switchgrass ethanol life cycle and how can be they used?
• Which units will contribute the most GHG emissions and are there possible solutions
to improve them?
• What is environmental performance of ethanol from switchgrass compared with
ethanol from other feedstocks (sugar cane, sugar beet, corn and wheat)?
2. What is the cost of using switchgrass-derived bioethonal as transport fuel?

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2. Literature Review
Blottnitz and Curran (2007) analyzed the comparative results of various bioethanol
systems with traditional fuel on a life cycle basis, or using life cycle assessment (LCA)
from 47 published papers during 1996-2004. They concluded that there were strong
evidences that all bioethanol productions were mildly to strongly beneficial from climate
protection and a fossil fuel conservation perspective, depending on the studies which had
been done in recent years.
However, one paper which was included in Blottnitz and Curran’s analysis, done by
Pimentel (2003), reported negative results – about 29% more energy was used to produce
a gallon of ethanol than the energy in a gallon of ethanol, and its overall production
system caused serious environmental degradation. Similar negative result was also from
Patzek’s paper (2004) and their cooperated work (Pimentel and Patzek, 2005). In these
two papers, they both argued their opposition to a net energy positive return of 34% in
the study by Shapouri et al. (2002), and the main reason was that the corn data used in
Shapouri et al.’s study was incorrect. Besides, Pimentel and Patzek doubted the doubled
number in Shapouri’s another report (2004), in which it said a net energy positive return
of 67% adjusted for byproducts, since dried-distillers grain (DDG) could be used to feed
cattle. Similar question was figured out by Hill et al. (2006) also; although ethanol
yielded 25% more energy than the energy invested in, almost all of this net energy value
was attributable to the energy credit for its DDG co-product.
These ‘negative’ conclusions were mainly given by the studies of ethanol produced from
corn (Hill et al., 2006; Pimentel, 2003; Pimentel and Patzek, 2005 and Shapouri et al.,
2002). Except for these, major part of studies came out that bioethanol had positive
results in energy value and GHG emissions (Beer et al., 2002; Blottnitz and Curran,
2006; Blottnitz and Curran, 2007; Halleux et al., 2008; Kadam, 2002; Kim and Dale,
2005a; Kim and Dale, 2005b; Kim and Dale, 2006; Kim and Dale, 2008; Luo et al.,
2008; Nguyen et al., 2007; Nguyen and Gheewala, 2008; Sheehan et al., 2004; Spatari et
al., 2005; Shapouri et al., 2002 and Shapouri et al., 2004), and because of the difference
of feedstocks, ethanol production processes and analytical methods, the results from these
studies were not same. Farrell et al. (2006) figured out that net energy ratios were

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extremely sensitive to specifications and assumptions, and comparing across published
studies to evaluate how these assumptions affect outcomes was difficult owing to the use
of different units and system boundaries across studies. To deal with this problem, they
developed the Energy and Resources Group (ERG) Biofuel Analysis Meta-Model
(EBAMM).
Figure 2.1 Alternative metrics for evaluating ethanol based on the intensity of primary energy inputs
(MJ) per MJ of fuel and of net green house gas emissions (kg CO
2
-equivalent) per MJ of fuel.
(Farrell et al., 2006)
They used best data from six studies to create three cases in EBAMM. Figure 2-1 gives a
general overview of the result of EBAMM. For all three cases, Ethanol Today, CO
2
Intensive, Cellulosic, producing one MJ of ethanol requires far less petroleum than is
required to produce one MJ of petrol (Farrell et al., 2006).
And from this figure, it can be found that Cellulosic ethanol is far better than others,
expected to have an extremely low intensity for all fossil fuels. One of the suggestions
from Hill et al. (2006) also demonstrated that biofuels would provide greater benefits if
their biomass feedstocks were producible on land with low agricultural value and
required low input energy to convert feedstocks to biofuel. And nonfood feedstocks could
offer advantages for energetic, environmental and economic criteria, such as switchgrass.
Spatari et al. (2005) has studied the life cycle environmental impact of switchgrass

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derived ethanol fueled automobiles, in which 57% GHG emissions avoided for 1 km
driving by E85 from switchgrass, compared with same driving by petrol.
Beside of energy balance and GHG emissions, there are other environmental impacts
which should be considered in, to show a comprehensive concept of environmental
performance of bioethanol fuel. Some studies (Blottnitz and Curran, 2006; Fu et al.,
2003; Halleux et al., 2008; Hill et al., 2006; Kim and Dale, 2005b; Kim and Dale, 2006;
Kim and Dale, 2008; Luo et al., 2008; Nguyen et al., 2007; Nguyen and Gheewala,
2008a; Nguyen and Gheewala, 2008b; Niven, 2005 and Sheehan et al., 2004) did fully
life cycle assessment of bioethanol, and impact categories, like acidification,
eutrophication, human toxicity, ecotoxicity, water use, were taken into account. Niven’s
(2005) review article examined the results from different reports, and found that E10 had
negative environmental impacts in most categories, and offered a marginal reduction in
GHG emissions over petrol. Kim and Dale (2005b) focused on agricultural stage of
various cropping systems, demonstrating that utilization of biomass for biofuels would
increase acidification and eutrophication, primarily because of large nitrogen from soil
during cultivation. The similar results were presented in another paper by same authors
(Kim and Dale, 2008). Nguyen and Gheewala gave out a positive result that E10,
compared with petrol, reduced environmental impacts in acidification and nutrient
enrichment (2008a), but a negative result in another paper (2008b). The reason of this
difference is the feedstocks were different. Fu et al. (2003) also showed that feedstock
cultivation had significantly contribution to environmental impacts. Moreover, Luo et
al.’s (2008) paper indicated that compared with petrol, bioethanol was better in abiotic
depletion, ozone layer depletion and photochemical oxidation, but worse in human
toxicity, ecotoxicity, acidification and eutrophication. And in this paper, a suggestion was
given, which was weighting factors needed to be established for an overall evaluation of
environmental impact.
Many studies chose E85 and E10 as final fuel to do comparison with petrol. Kim and
Dale (2006) did a research to compare these two final fuels which are both from ethanol.
The result was that based on traveling distance perspective, which was usually chosen as
the functional unit in fuel LCA studies, E85 application provided less environmental
impact than E10 application; however, considering the current situation of constrained

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ethanol fuel supply, E10 application offered better environmental performance than E85,
unless the fuel economy of an E85 fueled vehicle was close to that of an E10 fueled
vehicle.
Major studies paid attention to the methods used to deal with multi-functional problems
in life cycle assessment. Malca and Freire (2006) analyzed four different allocation
approaches in their report and showed that different allocation approaches had a major
influence in the result. It is found that partition method based on economic price of multi-
functions, for example, by-product and co-product (Luo et al., 2008), and system
expansion were the preferable options (Halleux et al., 2008; Kim and Dale, 2002; Kim
and Dale, 2005a; Kim and Dale, 2005b; Kim and Dale, 2006 and Sheehan et al., 2004).
To make bioethanol an attractive option for energy policy, only improving GHG
emissions reduction is not enough; the incremental cost of ethanol over petrol also should
be kept to be a minimum. Some researches and studies did economic analysis of
bioethanol beside of environmental analysis. Connecting with life cycle assessment, life
cycle perspective can be also applied for economic analysis. However, it has to be noted
that the ‘life cycles’ being addressed by LCA and life cycle cost analysis are different
(Norris, 2001). In 2005, ethanol net production cost was $0.46 per energy equivalent liter
of petrol, while wholesale petrol process averaged $0.44 per liter, so bioethanol was
unprofitable unless subsidies (Hill et al., 2006). Nguyen and Gheewala (2008c) had
similar conclusion in their paper, and the main reason of this unprofitable price was the
high cost of cassava cultivation. While Luo et al. (2008) indicated that driving with
ethanol fuels from sugarcane was more economical than petrol, and this outcome
depended very much on the assumed price for crude oil.
As mentioned before, switchgrass has attracted people’s attention to be an energy
feedstock. In last century, there have been studies of considering switchgrass as biomass
feedstock (Ranney and Mann, 1994; McLaughlin and Walsh, 1998; Sanderson et al.,
1999). There are upland varieties and lowland varieties of switchgrass in North America,
Sanderson et al. evaluated different types of switchgrass and recommended that Alamo
switchgrass was the best-adapted commercially available switchgrass cultivar for
biomass feedstock production in Texas. In the study of adapting switchgrass as an

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bioenergy feedstock in Europe, it concluded that it was possible to find switchgrass
varieties that were adapted to most regions of Europe (Elbersen, 2001). Lewandowski et
al. (2003) reviewed the studied about experience with switchgrass for bioenergy
production in both the US and Europe, and gave some reasons of switchgrass chosen as
the bioenergy feedstock, such as high yields on relatively poor quality sites, positive
environmental attributes, less limitations of establishment and production compared with
other grasses and so on. For the purpose of development switchgrass as bioenergy
feedstock, some studies about agriculture management of switchgrass in large scale have
done. Optimal agriculture management is performed to enhance the productivity for large
scale application (McLaughlin and Kszos, 2005; Mitchell et al., 2008; Sokhansanj et al.,
2009). And the economical and environmental performance of large scale production and
usage of switchgrass for bioethanol is also studied (Smeets et al., 2008). Furthermore,
researches started to focus on the qualification and quantification of convert switchgrass
to bioenergy, mainly bioethanol. Laser et al. (2009) evaluated three processes for
producing ethanol and electricity from switchgrass and the ethanol yields were from 318
L/DM feedstock to 440L/DM switchgrass.

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3. Methodologies
Life cycle thinking has become a key to the development of sustainability in recent years,
to avoid shifting problem from one place to another, from one geographic region to
another and from one environmental medium to another (UNEP, 2004). Life cycle
assessment (LCA) is a part of life cycle thinking, which is a quantitative tool to measure
environmental impact, based on accounting the material and energy input and output in
entire life cycle. Thus, LCA is applied in this study for assessing the environmental
impact of switchgrass-derived ethanol, and comparing the environmental benefit with
petrol. The life cycle is from raw material origin, through production process, to the end
use and final disposal, including transport inside every sub-system and among sub-
systems. LCA, however, typically does not address the economic or social aspects of a
product (ISO 2006), thus for evaluating the economic impact of switchgrass-derived
ethanol as transport fuels, life cycle costing (LCC) is performed in this study.
3.1 Life Cycle Assessment (LCA)
LCA is a systematic tool for assessing the environmental aspects and impacts of product
systems or service throughout its life cycle or in another word from “cradle-to-grave”.
Generally, the life cycle starts from raw material acquisition, goes through all production
steps and the use of the product, to final disposal. It includes all the necessary
transportation, recycle and reuse. LCA can provide the quantitative and scientific basis,
and a structured framework has been improved by ISO (International Organization for
Standardization) with four standardized procedures.
The method of this study will mainly follow the ISO framework of LCA (ISO, 2006a),
which are:
• Goal and scope definition
• Inventory analysis;
• Impact assessment;
• Interpretation.
The relationship between the phases is illustrated in Figure 3.1, as well as the direct
application of the result of LCA or LCI study.

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Figure 3.1 Stages of an LCA (ISO, 2006a)
3.1.1 Goal and scope definition
This is the first phase of an LCA study, in which the initial choices that determine the
working plan of the entire LCA are made (Guidebook). The goal and scope should be
clearly defined, since the depth of detail and time frame of an LCA may vary to a large
extent. In the scope definition, the items that shall be considered and clearly described
mainly including functional unit and alternatives, the system boundary, data requirement,
allocation procedures, the impact assessment methodology and categories of impacts,
assumptions and limitations etc. The key issues of LCA will be detailed described in the
following context. Furthermore, for a comparative study, the scopes and systems should
be defined equivalently that can be compared, as well as the functional unit and
equivalent methodological considerations. (ISO, 2006a)
3.1.1.1 Function and functional unit
A key aspect of the scope definition is definition of the functional unit, which enables
subsequent results to be interpreted correctly and in a meaningful manner. The clear

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definition of a functional unit is to provide a reference to which the input and output data
a re normalized.
On the base of the functional unit, the reference flows will be determined. And for
comparative study, the comparisons between systems should be based on the same
functions.
3.1.1.2 System boundary
The system boundary determines which unit processes to be included in the LCA study.
Definition of system boundary should be consistent with the goal of the study, and
relevant input and output should be included. Usually, description of the system uses a
process flow diagram showing the unit processes and their inter-relationships. Dividing a
product system into its component unit processes facilitates identification of the inputs
and outputs of the product system.
One key point in system boundary is cut-off. In principle, an LCA should track all the
process in the life cycle of the product system. However, it is impossible in practice, for
the lack of readily accessible data. The cut-off criteria used in the LCA should be
described clearly.
Data collection and quality, allocation procedure and LCIA methodology will be
described detailed in the following relevant sub-chapters.
3.1.2 Inventory analysis
The definition of the goal and scope of a study provides the initial plant for conducting ht
life cycle inventory phase of an LCA.
3.1.2.1 Data collection and calculation
A key task of the inventory phase is the collection of process data. The qualitative and
quantitative data should be collected for each unit process inside of the system boundary.
The data for each unit process are mainly including following categories, energy inputs,
raw material inputs and other environmental inputs; emissions to air, water and soil;
products enter in and come out the system boundary, as well as co-products/by-products,
and other economic flows.

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The sources of the data used in an LCA study should be clearly declared. Since data
quality has a major influence on results, evaluation of data quality is an important step in
an LCA study.
After data are collected, it is necessary to check the validity of the data in order to
confirm the data quality, and then relate data to unit process and functional unit.
Aggregating the inputs and outputs in the product system is to get the completed
inventory.
3.1.2.3 Allocation
Allocation is always an important issue in LCA studies, since multi-functionality happens
in many production processes. The output of the product system comprises more than one
production, and raw material inputs often include recycled intermediate or discarded
product. An appropriate decision has to be made that which economic flows and
environmental interventions inside the system should be allocated and what allocation
procedure will be used.
There is a stepwise procedure for allocation in LCA, according to Handbook on Life
cycle assessment (Guinée, 2002). If possible, avoid allocation by modeling within the
inventory analysis at first. Then if allocation cannot be avoided, there are several
allocation options. One is to expand the systems, in order to include the additional
functions related to the co-product. However, in this research, a comparative study will
be done between bioethanol and petrol. System expansion might not be a good choice, for
the production processes of bioethanol and petrol are not similar. According to ISO
14044, the second option is to separate the exchanges in a way which reflects the
underlying physical relationships between main product and co-production. And the third
option is parallel to the second one in concept, which is to partition the exchanges
between the productions and functions in a way which reflects other relationships
between them. Usually, this partition is based on economic factors. (Rebitzer et al., 2004)
Since choosing different allocation method will have big influence on final result, it is
important to remember that to use same option in same stages between different systems,
to make sure the meaning of comparison. And usually, a sensitivity analysis of allocation
is performed in LCA studies.

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3.1.3 Impact assessment
Impact assessment has been developed to broaden the information and context of life
cycle inventory data. In LCIA, the set of the inventory table is processed and interpreted
in terms of environmental impacts and societal preferences. The impact categories,
category indicators and characterization models which are employed in the LCA study
has to be determined in the scope definition, and the selection should be justified and
consistent with the goal and scope of the LCA. Figure 3.2 illustrates the concept of
category indicators, and the example in the figure explains how the LCIA is performed to
get the LCIA results from LCI.
Figure 3.2 Concept of category indicators (ISO, 2006b)
3.1.4 Interpretation
The results of the LCI or LCIA phase should be interpreted according to the goal and
scope of the study. Usually, the contribution analysis is applied to identify which
environmental flows or processes are the significant contributors to the results of LCI or
LCA. A sensitivity check of the significant inputs, outputs and methodological choice is
necessary to understand the uncertainty of the results.

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3.2 Life Cycle Costing (LCC)
Since LCA typically does not address the economic or social aspect of a product, another
method is needed to assess the economic impact. Generally, life cycle costing (LCC) is a
method for calculating the total cost throughout a product’s life, including acquisition,
installation, operation, maintenance, refurbishment and disposal costs (NSW Treasury,
2004). In Carlsson Reich’s report (2005), LCC could be divided into two different types:
financial LCC and environmental LCC. Environmental LCC is to weight environmental
impact of an LCA system in monetary terms, and financial LCC is to assess the present
values of all monetary cost of a product or function in life cycle perspective. Due to the
aims of this study, financial LCC is used to analysis the life cycle economic impact of
ethanol fuel.
LCC can offer a related perspective with LCA, but it emphasizes on the monetary
dimension, while LCA is used to analysis physic flows. Unlike LCA, LCC does not have
an agreed framework or methods to follow. However, the life cycle approach and
methodologies from ISO 14040 for LCA can be applied to these other aspect (ISO,
2006a). Rebitzer et al. (2003) indicated that to relate LCC to life cycle inventories, the
LCC must be based on the physical life cycle of the good or service. Moreover, in order
to combine with LCA in this study, the analysis will be conducted with the same system
specification as LCA. The life cycle inventory of the LCA would be an excellent basis for
identifying and allocating all of the aforementioned costs in an efficient manner. Using a
LCC, however, combined with an LCA can provide several difficulties, since traditional
economic system usually do not follow environmental life cycle. The challenges include
estimating and discounting future cost and revenues and aligning data per functional unit
with financial data (Rebitzer et al., 2003).

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4. Life Cycle Modeling
4.1 Goal and Scope Definition
The goal of this LCA study is to assess the environmental impacts of using switchgrass-
derived bioethanol as transport fuel, compared with traditional transport fuel petrol.
Although the conceptual bioethanol plant from TU Delft’s design is located in Greece, it
can be applied in north American and other part of Europe, based on the same conditions
and assumption, such as the design of ethanol plant and the distance between switchgrass
farm and ethanol production plan, since switchgrass is a native grass in north American
and also can be grown in the most part of Europe.
4.2 Function Unit and Alternative Systems
Functional unit in this study is defined as “power to wheels for one kilometer driving of a
midsize car”. Not the production and disposal of the car is taken into account but only its
energy requirements in driving (Luo et al., 2008). On the basis of the functional unit, a
number of alternative product systems can be declared functionally equivalent and
reference flows will be determined for these systems. The power in functional unit to
drive one kilometer on a midsize car is transport fuel. As the progress of vehicle and
transport fuel using bioethanol in the future, E10, E85 are considered into this study, and
100% ethanol is also taken into account as reference; and traditional transport fuel petrol
is considered into for comparative purpose. So the alternative systems in this study
consist of E100, E85, E10 and petrol.
4.3 System Boundary
In system boundary, it defines which unit processes are part of a product system. Figure
4.1 shows the product system including all relevant processes, from agriculture of
switchgrass, throughout the production of pure ethanol, E85, and E10, to the end use for
vehicle driving, and all transport sections. According to the method of LCI, the product
system in this study is separated into seven unit processes, which are the smallest
elements considered in the LCI for which input and output data are quantified (ISO,
2006a).

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Figure 4.1 Life cycle of switchgrass –ethanol (base scenario)

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The details of each unit process, including the input and output required, are described as
following.
a) Agriculture of switchgrass as feedstock.
This unit process includes land preparation, planting, post planting treatment, post
planting pest, disease and weed control, fertilizer requirement, harvesting, and storage.
This process is going on basically according to the base case scenario of switchgrass set
in the report by Bullard and Metcalfe (2001), but there is one modification through the
reviews of switchgrass agriculture managements. For soil establishment, 3,000 kg lime is
applied to control soil acidity. After one pass sub-soiling, ploughing and disking, a rate of
10 kg/ha of seed is drilled. One pass of rolling is following planting. In first year, there
are N, P, K fertilizers applied, but only N fertilizer is applied in the rest years. Manganese
sulphate is used to buffer soil supply problem. After harvesting, switchgrass is cut and
baled to store. Figure 3.2 shows the flow sheet of agriculture of switchgrass.
Figure 4.2 Units in switchgrass agriculture
b) Transport of switchgrass from farm field to ethanol production factory.
The baled switchgrass with assumed 25% moisture is transported to ethanol production
plant from the storage in farm by lorry. In this unit process, the average transport distance
is assumed to be 20 km.

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c) Ethanol production process.
The conversion of lignocellulosic feedstock to bioethanol used in this study consists of
four major steps: pretreatment, hydrolysis, fermentation and product
separation/purification. In Guerra Miguez et al.’s (2009) design project, they selected
optimized option for every step, and finished an entire conversion process, due to the
characters of switchgrass and the development of technologies.
In the pretreatment, an AFEX (Ammonia Fiber Explosion) operation was chosen. During
this step, the physical conformation of the switchgrass was changed in order to make it
more accessible to enzymes. The system was optimized in order to recover 99% ammonia
that is used for pretreatment
For the fermentation, it was decided to choose SSCF (Simultaneous Saccharification and
co-Fermentation), operation in which only one reactor instead of two (as the classical
approach) was used, thereby lowering the operation and investment costs.
For downstream section, ordinary distillation, followed by molecular sieve adsorption,
was chosen in order to obtain the specifications for the final product (99.5% w/w). The
plant was designed in such a way that most of the water used is recovered and recycled
back into the process which lowers the operation costs and the environmental impact.
Moreover, the different “wastes” produced during the process are used in a cogeneration
unit in order to supply all the electricity needed in the plant and part of the required
steams.
The co-product is electricity from final step. The co-generated electricity firstly can be
used inside the ethanol production factory, covering the demand of electrical energy. The
rest would be sold to grid or other industrial processes, which are need extra electricity.
About 3.35*10
4
KWh electricity is generated, followed with 2.13*10
4
kg ethanol
obtained per hour.
Besides, during the ethanol conversion, enzyme is an important material. Although the
quantity of enzyme is small, the enzyme production is an electricity consumption process,
which might have large environmental impacts.

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d) Transport of pure bioethanol to refinery
In this unit process, the refinery is assumed nearby the bioethanol production plant, and
the transport distance is 20 km on average.
e) Ethanol fuel manufacture
Unleaded petrol is used to be blended with 99.5% ethanol. E85 is obtained through
blending 85% pure bioethanol with 15% petrol (v/v), and E10 is gained by mix 10% pure
bioethanol with 90% petrol (v/v).
f) Transport of ethanol fuel from refinery and petrol to regional storage
This unit is assumed followed the similar processes as Luo’s researches. The distance
from refinery to regional storage is 0.0337 km.
g) Vehicle operation
The end-use stage of ethanol fuel life cycle is fuel combustion via vehicle operation. In
this unit process, a midsize car is chose and only tailpipe emissions are considered in, for
the objective of this study is transport fuel.
4.4 Data Collection and Calculation
4.4.1 Data collection of switchgrass agriculture
The data of switchgrass agriculture are mainly from a report by Bullard and Metcalfe
(2001). In the report, a base-case scenario of switchgrass production is built, including
planting, post-planting and harvesting operations. The fertilizers and other chemicals
which are used for switchgrass production were described, as well as the data of
harvesting and the approach to storage. In this scenario, the life time was assumed 20
years, from soil establishment to end of a planted area. There is a modification of this
base case, through the review of other literatures about switchgrass agricultures. In
Bullard and Metcalfe’s base case, the fertilizers are only performed in first year.
Optimizing switchgrass biomass yield and maintaining quality stands, however, require
the N nutrients, otherwise, biomass production declined over years without applied N.
The quantiity of N demand is studied by some research, and based on different conditions,
the suggestions of N application are from 100-168 kg N per ha per year. Finally, around
10 kg per ha per year of applied N is needed for each 1 ton per ha switchgrass yield is

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used. So the input of N fertilizer is 100 kg per ha per year, the same as the quantities for
soil establishment. The P and K fertilizers are still only applied in first year, since
researches about the switchgrass response to phosphorus have been variable, and to other
mineral elements need more study. (Mitchell et al, 2008). The emissions during
cultivation were calculated by using the input data from the base-case scenario, via the
methods from Ecoinvent report (Nemecek and Kägi, 2007).
4.4.2 Data collection of bioethanol production
Biological conversion of cellulosic feedstocks to produce ethanol is receiving increased
attention, and several reports and papers described this technical process. Due to the
development biotechnology, every sub-process has varied options. In this study, the
production process is performed from the report of the TU Delft’ design program, and the
data used in this unit process are also from the same report, including the material and
energy input and output, as well as the usage of equipments.
4.4.3 Data of vehicle operation (end use)
The tailpipe emissions in the practice are very complex, since the different vehicle types,
road situations and driving behaviors etc. The data of tailpipe emissions of vehicle
driving are from Luo’s research, for the purpose of comparison study.
The other data are mainly from Ecoinvent database version 1.3, since the result of this
study will be used to compare with other studies in Luo’s PhD work, which employed
Ecoinvent database version 1.3.
The software CMLCA (Chain Management Life Cycle Assessment) developed by CML
is used for data calculation, and further analysis.
4.5 Allocation
When bioethanol is produced, there is electricity co-generated as co-product. For multi-
functionality in LCA, allocating the material and energy inputs and environmental
emissions between the main product and co-product/by-product is a vital issue. There is a
stepwise allocation procedure in ISO 14044 (ISO, 2006b). To deal with the allocation
which can not be avoided, partition method based on physical relationships between
multi-functions is first option. And if not, the partitions based on other relationships are
used, for example, economic relationships.

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In this study, allocation based on energy value of bioethanol and electricity is performed.
However, economical method based on market value is also studied in sensitivity
analysis, to compare with physical partition, discussing the influence of different
allocation methods.
4.6 Environmental Impact Assessment Method
The method which is applied in this study is the NOGEPA (Netherlands Oil and Gas
Exploration and Production Association) panel method, which is in the context of
implementing environmental policy in its sector. The effects in this method are mainly in
line with the effects contemplated by the SETAC LCA guidelines (Notarnicola et al.,
1998). In this study, the following environmental impacts have been assessed: abiotic
resource depletion potential (ADP), global warming potential (GWP), ozone depletion
potential (ODP), acidification potential (AP), eutrophication potential (EP),
photochemical oxidant formation potential (POCP), human toxicity potential (HTP) and
ecotoxicity potential (ETP). The method selected and impact categories included are the
same as Luo’s research.

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5. Life Cycle Environmental Impact
5.1 General Results
The comparative results of the LCA study of three types of bioethanol fuels and petrol
are shown in Fig. 5.1 and 5.2. For 1 km vehicle driving, E100 here is only as a relevant
reference, since E100 is hardly to be used as transport fuel directly. The analysis mainly
focuses on E85 and E10, comparing with petrol.
GHG mitigation is one of the most attractive issues globally. From the perspective of
global warming potential in this study, switchgrass-derived ethanol as transport fuel
produces less GHG than traditional petrol, and driving by E85 reduces much more GHG
than by E10 obviously, compared with petrol. The primary reason of this significant
decrease is the agriculture of switchgrass. Switchgrass takes up lots of CO
2
from
atmosphere during growing, and in E85, there is more ethanol mixed in than in E10.
During the vehicle driving, more CO
2
emission by petrol than by E10 and E85, so the life
cycle GHG emissions of driving 1 km using ethanol fuel is lower than using petrol,
although the quantities of fuels used for 1 km driving are different. For using E10, there
is only 5.49% reduction of GWP, since 90% (v/v) of E10 is still from petrol. Driving by
E85, however, it is 64.9% decrease of GHG emissions compared to petrol, even though
the amount of driving 1km by E85 is 0.099 kg, which is larger than 0.0665kg by petrol.
In terms of abiotic resource depletion, replacing petrol by bioethanol is able to reduce the
use of crude oil, which is the main source of petrol.
Except from GWP and ADP, driving by ethanol fuels from switchgrass does not have
less environmental impacts in other categories. The large environmental impact in
photochemical oxidation by using ethanol fuels is mainly because of acetaldehyde
emission during bioethanol fermentation, which contributes 77% to POCP of vehicle
driving by E85. Switchgrass agriculture is the largest contributor to eutrophication of
ethanol fueled driving, especially the nitrate to the ground water and NOx to the air from
N-fertilizer performance. Since the productions of industrial equipments used in ethanol
conversion emit abundant of CFC-11 to air, driving by bioethanol fuels causes more
stratospheric ozone depletion potential than by petrol.

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-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
Petrol E10 E85 E100
Driving 1 km by different fuels
Comparison of contributions to GHG emission
Other
Switchgrass agriculture
Wooden materials
Transport systems
Fossil fuel
Agricultural means of
production
Waste management
Chemicals
Final vehicle driving
Bioethanol production
Figure 5.1 Climate change potential of vehicle driving 1km by petrol, E10, E85 and E100
The comparison result of acidification impact of driving by different fuels is a bit
different from others. Driving 1 km by E10 leads to the smallest effect in all alternative
fueled driving. Acidification potential of driving by petrol is a little larger than by E10.
However, E85 and E100 fueled driving both make greater acidification impact than E10
and petrol fueled. The main reason is that the differences of AP of 1 kg all alternative
fuels are small, and this impact of petrol is a little larger than ethanol fuels. However,
driving 1 km by E85 and E100 use much more quantities of fuels, causing the results of
driving 1 km by varied fuels changed.
The agriculture of switchgrass contributes largely to human toxicity and ecotoxicity also.
It is because the production processes of mineral fertilizers and chemicals used in
agriculture are related to large metal emissions to the air, which are one of the main
sources of human toxicity. The increasing heavy metal ions (Cu, Ni, and Vanadium) to
fresh water can lead to ecotoxicity, which is primary from waste disposals relevant to
heavy metal productions.

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Figure 5.2 Overall comparison results of the environmental impact of petrol, E10, E85 and E100
Ecotoxicity
0
0,001
0,002
0,003
0,004
0,005
0,006
0,007
0,008
Fuel type
kg 1,4-dichlorobenzene eq
Human toxicity
0
0,005
0,01
0,015
0,02
0,025
0,03
0,035
0,04
Fuel type
kg 1,4-dichlorobenzene eq
Abiotic depletion
0
0,0002
0,0004
0,0006
0,0008
0,001
0,0012
0,0014
0,0016
0,0018
Fuel type
kg antimony eq.
Petrol E10 E85 E100
Climate change GWP100a
0
0,05
0,1
0,15
0,2
0,25
0,3
Fuel type
kg CO2-eq.
Ozone layer depletion
0,00E+00
1,00E-08
2,00E-08
3,00E-08
4,00E-08
5,00E-08
6,00E-08
Fuel type
kg CFC-11 eq.
Photochemical oxidation
0
0,0001
0,0002
0,0003
0,0004
0,0005
0,0006
0,0007
0,0008
Fuel type
kg ethylene eq.
Eutrophication
0
0,00005
0,0001
0,00015
0,0002
0,00025
0,0003
0,00035
Fuel type
kg PO4 eq.
Acidification
0,0007
0,00071
0,00072
0,00073
0,00074
0,00075
0,00076
0,00077
0,00078
0,00079
0,0008
Fuel type
kg SO2 eq.

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5.2 WTT Analysis
Due to E100 as transport fuel is an assumption, only for reference, ethanol products are
also analyzed, excluding final use. This is also known as well-to-tank (WTT) analysis.
Figure 5.3 illustrates the comparison results of 1 kg petrol, E10, E85 and E100 in all
environmental impact categories. Expect from ODP and AP, the differences between
production of 1 kg petrol and production of 1 kg ethanol are large, from 50% to 180%.
For global warming potential, E85 and ethanol have significant negative results, since the
switchgrass absorbs a lot of CO
2
from atmosphere during growing period.
WTT comparison result of all alternative fuels
-2
-1.5
-1
-0.5
0
0.5
1
a
b
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y
e
c
o
t
o
x
i
c
i
t
y
Environm ental impacts
Petrol
E10
E85
E100
Figure 5.3 Comparison results of WTT analysis of all fuel alternatives, using largest value as
reference
In Figure 5.3, the comparison of acidification impact of all alternative fuels in terms of 1
kg is showed. The impacts of ethanol fuels are smaller than petrol, but ranges are not too
large. The reduction of acidification impact of E100 and E85, compared with petrol, are
only 17% and 15%, respectively. However, the quantities of driving by E85 and E100
are much more than by petrol, about 1.5 times, so the acidification impacts of final
driving by E85 and E100 are larger than by petrol. For E10, the reduction is 6%.
Although the quantities of driving 1 km by E10 is also a bit more than by petrol, only
3.7%, the final vehicle driving by E10 still causes smaller acidification impact than by
petrol.

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The primary emissions which cause ozone layer depletion are CFCs emission. The crude
oil production onshore contributes large part of CFCs emission for petrol production. The
replacement by bioethanol can reduce the CFCs emission from crude oil production.
However, nonwoven fabrics are involved in the production of major machineries, which
are used in bioethanol production, and enough CFCs emit from the production of this
nonwoven fabrics to compensate the reduction from less use of crude oil.
5.3 Contribution Analysis
To understand which processes cause the largest effect in the entire life cycle of using
switchgrass ethanol as transport fuels, a series of contribution analysis are made. The
detailed results are presented in figure 5.4-5.10, and the contribution results of ethanol
fuels are compared with petrol.
Contributions to ADP of all alte rnative fuels
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Petrol E10 E85 E100
Others
Chemicals
Plastics
Fossil fuel
Figure 5.4 Contribution analysis result of ADP
Contributions to ODP of all alternative fuels
0%
20%
40%
60%
80%
100%
Petrol E10 E85 E100
Natural gas
Oil
Production
Figure 5.5 Contribution analysis result of POCP

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Contributions to POCP of all alternative fuels
0%
20%
40%
60%
80%
100%
Petrol E10 E85 E100
Others
Matel
Agricultural means of production
Construction processes
Plastics
Fossil fuel
Transport systems
Final vehicle driving
Bioethanol production
Figure 5.6 Contribution analysis result of ODP
Figure 5.7 Contribution analysis result of EP
Figure 5.8 Contribution analysis result of AP
Contributions to EP of all alternative fuels
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Petrol E10 E85 E100
Others
Fossil fuel
Construction processes
Transport systems
Agricultural means of production
Chemicals
Bioethanol production
Final vehicle driving
Switchgrass agriculture
Contributions to AP of all alternative fuels
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Petrol E10 E85 E100
Others
Construction
Plastics
Metals
Transport systems
Chemicals
Agricultural means of production
Fossil fuel
Final vehicle driving
Bioethanol production
Switchgrass agriculture

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Figure 5.9 Contribution analysis result of HTP
Figure 5.10 Contribution analysis result of ETP
5.4 Sensitivity Analysis
5.4.1 Allocation methods
As mentioned in previous chapter, physical allocation based on the energy values of the
process outputs is used in this study as the base study. The results from these studies and
researches from literatures indicated that the choice of allocation methods is an important
factor of final results. Here economic allocation method, which is based on market values
of the process outputs, is used as an optional case to compare with base case (physical
allocation method).
Contributions t
o ETP of all alternative fuels
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Petrol
E10 E85
E100
Others
Switchgrass agriculture
Electricity
Transport systems
Fossil fuel
Waste management
Chemicals
Metals
Contributions to HTP of all alternative fuels
0%
20%
40%
60%
80%
100%
Petrol
E10
E85 E100
Others
Final vehicle driving
Production
Construction materials
Transport systems
Fossil fuel
Waste management
Chemicals
Metals

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The main multi-functionality is occurred in the ethanol production process, which
electricity is a co-production with bioethanol. In the base case, the bioethanol/electricity
ratio is 0.855/0.145. The low heating value (LHV) in 1 kg 99.5% ethanol is 28.8 MJ,
meaning total ethanol in this process contains 615052.8 MJ in every hour. The energy
value of electricity is 3.6 MJ per KWh, so total output electricity value is 78725 MJ per
hour. For allocation based on market value, the average price of bioethanol and electricity
are 0.697 €/kg and 0.065 €/KWh, respectively, so the allocation factors 0.896 and 0.104
are applied (bioethanol/electricity ratio 0.896/0.104).
Table 1 Comparison results of global warming potential_100 years of E10 and E85 based on different
allocation methods
Factors GWP100a of 1 km driving (kg CO
2
-Eq.)
ethanol electricity by E10 by E85
Energy allocation
0.855 0.145 0.241 0.0896
Economic allocation
0.894 0.106 0.242 0.0958
Table 1 presents the results of GWP100a impacts of E10 and E85 by using two allocation
methods. It indicated that allocation methods have influence of final LCA results. And
because the allocation happens in bioethanol production process, the influence in
GWP100a of E85 is larger than E10. However, since the change of partition factors from
energy allocation to economic allocation is small, the differences of GWP100a of driving
1 km by E10 and E85 are not that significant by different allocation methods, and the
analogous results are presented in other impact categories.
5.4.2 Excluding soil establishment
Switchgrass is a new developed bioenergy feedstock, so for the base scenario in this
study, one assumption is that it is necessary to do soil establishment before planting and
harvesting for large scale production currently. For some food-sourced feedstocks, such
as corn and sugar cane, however, these agriculture products are produced every year, for
food demand or energy demand, so in some biofuels LCA studies in which agricultural
processes are involved in, the land preparation is not taken into account, and only
conventional input and output annually contained in. In the future, if switchgrass was
well improved as a usual feedstock for bioenergy, and lands had been already available,
no soil preparation would be required in every LCA study. Therefore, here is an

Master Thesis-Yu Bai
37
alternative scenario that soil establishment is excluded, and the yield of switchgrass is
16t/ha (with 25% moisture) in one year.
In this alternative scenario, 100 kg per/ha N-fertilizer is used as one important input
every year, and the herbicides for weeding and manganese for soil buffer are applied
annually. The soil establishment year without harvesting of switchgrass is not included in
system boundary.
Figure 5.11 presents the comparison results of GHG emissions reduction of driving 1 km
replacing petrol by E100, E85 and E10. It indicates that excluding soil establishment
could lead to better results in terms of GHG emissions reduction than base case scenario.
The main reason is that some farming activities that are only performed for soil
establishment, like hoeing and plouging, and these farming activities could cause some
CO
2
and N
2
O emissions. The increasing of GHG emissions avoid of 1 km driving by
E100 can achieve to 21%, and by E85 is 9%. The change of driving by E10 is quite
small, since only 10% (v/v) bioethanol is mixed with petrol.
Figure 5.11 Comparison of including with excluding soil establishment of GHG emissions reduction
The reduction happens in other impact categories, as showed in table 2. The largest
reduction appears in human- and eco-toxcitiy, since the reduction of heavy metal
involved in fertilizers and limes for switchgrass agriculture. For base scenario, in
establishment year and initial two production years, the yield of switchgrass can not
The differences of GHG emissions reduction between including and excluding
soil establishment
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
E100 E85 E10
Fuel type
GHG emissions reduction
percents
Base case scenario Excluding soil establishment

Master Thesis-Yu Bai
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achieve to 16 t/ha, so the replacement of abiotic resources by biomass is less. The main
reason of acidification potential reduction is that NOx and SO
2
emissions from
agriculture in alternative scenario are much less than in base scenario. The primary
emission in eutrophication is the P- emissions, and in alternative scenario, no P- fertilizer
applied in production years. The changes of ODP and POCP are very small since the
main contributors to these two categories are from bioethanol production process.
Table 2 Comparison results of environmental impacts for 1 km driving by E100 in two scenarios
Impact categories Unit Base scenario Alternative scenario Range
Abiotic resources depletion kg antimony eq. 0.000679 0.000652 -3.98%
Global warming GWP100 kg CO
2
eq. 0.0896 0.0814 -9.15%
Ozone layer depletion kg CFC-11 eq. 4.99E-08 4.95E-08 -0.80%
Photochemical oxidation kg ethylene eq. 0.000591 0.000586 -0.85%
Acidification potential kg SO
2
eq. 0.000752 0.000703 -6.52%
Eutrophication potential kg PO
4
eq. 0.000269 0.000251 -6.69%
Human toxicity kg 1,4-DCB eq. 0.0322 0.0289 -10.25%
Ecotoxicity kg 1,4-DCB eq. 0.00633 0.005753 -9.12%
5.4.3 Transport distance
The transportations inside of the system boundary are vital part in the LCA study, since
the truck is the major transport vehicle and diesel is used for driving, which both
contribute large environmental impacts. This is not a fixed-site case study, so the
distances of transport from switchgrass farm to bioethanol plant and from bioethanol
plant to refinery are both assumed to be 20 km. The sensitivity of transport distance is
analyzed, to see how great influence of the transport section in this LCA study. To be
compared with 20 km, 40 km and 80 km transport distances are studied.
Table 3 Comparison of environmental impacts of E85 driving in varied transport distances
Impact categories 40KM 80KM
Abiotic resource depletion 2.65% 7.66%
Climate change GWP100a 2.23% 6.81%
Ozone depletion potential 0.80% 2.00%
Photochemical oxidation 0.93% 2.03%
Acidification potential 1.23% 4.52%
Eutrophication potential 0.94% 2.23%
Human toxicity 0.68% 2.17%
Ecotoxicity 1.46% 3.93%

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Table 3 presents the differences of final environmental impacts of transport distances
change in the entire life cycle. The results of all environmental impacts are increased,
especially the depletion of abiotic resources, climate change and acidification potential.
The increasing of transport distances makes the more usage of diesel for trucks, enlarging
the impact in ADP. And other consequence of more use of diesel is more CO
2
emissions,
leading to the increasing of GWP, and more SO
2
emissions contributing to ADP. All
these changes are based on E85 driving. For E10 driving, there is hardly change in every
impact category, and the largest range is only about 1%.
5.5 Comparison with Other Studies
5.5.1 Comparison with other switchgrass-ethanol study
In Spatari et al.’s study, the near-term results demonstrated that driving by E85 from
switchgrass could reduce 57% GHG emissions compared with driving by petrol. And the
reduction could reach to 69% in the mid-term scenario (year 2020). These illustrated that
using ethanol fuels derived from switchgrass, especially E 85, is able to reduce the GHG
emissions significantly, compared with traditional fuel petrol.
Table 4 Comparison of life cycle GHG emissions for E85 driving 1 km with Spatari et al.’s studies
Spatari’s near-term Spatari’s mid-term This study
Biomass yield 8 DM/ha/yr 11 DM/ha/yr 12 DM/ha/yr
Ethanol yield 330 L/DM 470 L/DM 345 L/DM
Life cycle GHG emissions 107.4 g CO2-eq/km 78.5 g CO2-eq/km 81.4 g CO2-eq/km
Life cycle GHG emissions of
petrol (baseline)
252 g CO2-eq/km 252 g CO2-eq/km 255 g CO2-eq/km
GHG emissions reduction 57% 69% 68%
The data used to compare with Spatari et al.’s in table 4 are from the alternative scenario
excluding soil establishment, since there was no soil establishment period in system
boundary from Spatari et al.’s study. It suggests that increasing of switchgrass
productivity and enhancing the ethanol yield could contribute to the reduction of GHG
emissions.

Master Thesis-Yu Bai
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5.5.2 Comparison with other feedstocks ethanol studies
Corn is always a common bioenergy feedstock for ethanol production, especially in
North America and Brazil. Through the analysis by Hill et al. (2006), corn grain ethanol
could cause12% reduction of GHG emissions in an energetically equivalent amount of
petrol. A report from Wu et al. (2006) assessed the different bioethanol production
pathways in USA, in which corn ethanol was selected as one feedstock, and they
suggested GHG emissions avoid of corn ethanol was 21-24%.
Wheat is also able to be used for producing bioethanol, different from corn, which is
mainly applied in Europe. A well-to-wheel study evaluated the GHG emissions of
ethanol from wheat. Because of the byproducts inside the life cycle could be used in
different ways, so the credits were varied in different scenarios, and final results showed
that the GHG emissions avoid can achieve to 72% in optimal scenario. In another
scenario, however, it was only about 12% reduction (Punter et al., 2004).
In Luo et al.’s (2008) research, using sugarcane could result in 24% reduction of GHG
emissions in base case, and 81% reduction in future case. The differences of two cases
are the electricity generation and bagasse is only used in future case. If only taking into
account of base case, the reduction of GHG emissions from sugarcane, 24%, is similar
with the ones from corn in Wu et al.’s report.
The advantage of bioethanol from switchgrass, over the ones from food-sourced biomass,
comes from lower agricultural inputs and more efficient conversion of switchgrass to
bioethanol.

Master Thesis-Yu Bai
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6. Life Cycle Economic Impact
6.1 Life Cycle Costing
6.1.1 Switchgrass-ethanol life cycle and functional unit of LCC
LCC method is applied in this study with the same system specification as LCA, to
evaluate the cost (tax excluded) of driving 1 kilometer of a midsize car using bioethanol
fuels (E10, E85 and E100), and to compare with driving 1 kilometer of a midsize car
using petrol. The life cycle in the system boundary of LCC analysis includes switchgrass
agriculture, ethanol production, ethanol distribution and all transport sections.
The details of switchgrass agriculture and ethanol production are showed in Chapter
3.1.2. Except the direct material and energy input, labor, deprecation, maintenance and
fuels for the operations of machines should be covered into the total cost.
The important difference from LCA study is that the vehicle driving (end-use stage) is
not included into the system, since the functional unit of LCC study is the cost of fuels,
and the manufacture and the deprecation of vehicle are excluded.
6.1.2 Data collection
In this LCC analysis, a steady-state cost model is used, which means no discounting and
depreciation are taken into account. The life time of ethanol plant is assumed to be 15
years, so the capital investment is divided over 15.
Since electricity as by-product is produced in the switchgrass-derived bioethanol life
cycle, the fixed capital investment and operating costs are allocated between bioethanol
and electricity based on market values.
The cost data of ethanol production are from Guerra Miguez et al.’s (2009) design report.
The data of petrol are from Energy Information Administration (EIA).
6.2 Life Cycle Cost of Bioethanol
In Smeets’ report (2008), he calculated the cost of switchgrass production in euro per
tone DM of five regions in Europe. The cost included the cost of agricultural machinery,
land rent, establishment, fertilizing, weeding, herbicide application, harvesting, storage,
pelletising and transport. The final results showed that the costs of switchgrass were
estimated to be 30-81 €/tone DM in 2004, and were expected to be roughly stable in year

Master Thesis-Yu Bai
42
2030. Another report from Sokhansanj et al. (2009) analyzed the cost of switchgrass in
USA. The cost input to produce and deliver switchgrass is 80.64$/ton (57.09 €/tone) with
baling system for harvest and storage and transport in baled.
In Guerra Miguez et al.’s report, they also calculated the economical feasibility of
ethanol production from swtichgrass. The cost of swtichgrass used to assess the annual
operation cost is 0.053 €/kg, in the range of 0.03-0.081€/kg. Using steady-state cost
model, production of 1 kg pure ethanol is costing 0.722 euro. This number would be
reduced to 0.639 euro if the price of switchgrass dropped to 0.03 €/kg.
The annual retail price of petrol in 2008 was 0.728 €/kg (2.80 $/gallon) on average,
including taxes. Meantime, the price of crude oil was 98.52 dollars per barrel (EIA,
2009). From history data, the prices of crude oil and petrol achieved the highest in year
2008, which were almost twice than the ones in 2007. However, the prices have dropped
in 2009, which is similar to the prices in 2007. Figure 6.1 and 6.2 show the change trends
of the prices of crude oil and petrol in the future. The price of crude oil will be doubled in
year 2030, and the price of petrol will have the consistently change. The source of these
data is Energy Information Administration.
A crude cost of petrol is calculated by reducing 20% tax on the base of price in year 2008
(EIA, 2009), which is about 0.582 €/kg. The cost of bioethanol 0.722 €/kg is used to
evaluate the costs of E85 and E10, which are 0.597 €/kg and 0.703 €/kg separately. The
costs of 1 km driving by all the fuel alternatives (excluding taxes) in this study are
presented in table 5. It indicates that, based on the petrol price in year 2008, driving 1 km
by E10 is only a bit higher than driving by petrol, driving by E85, however, the cost is
much higher than by petrol, about 66% more.
Table 5 Costs of all the fuel alternatives in 2008 and 2030 (excluding taxes) (unit: €)
petrol E10 E85 E100
1 kg
0,582 0.586 0,613 0,618
2008
driving 1km
0,0387 0.0404 0,0561 0,0611
1 kg
0,811 0,782 0,584 0,547
2030
driving 1km
0,0539 0,0540 0,0535 0.0541

Master Thesis-Yu Bai
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0,00
20,00
40,00
60,00
80,00
100,00
120,00
140,00
2000 2005 2010 2015 2020 2025 2030 2035
Year
Dollar per barrel
Figure 6.1 Future change trend of crude oil price globally (Source: EIA)
0,0
50,0
100,0
150,0
200,0
250,0
300,0
350,0
400,0
450,0
2000 2005 2010 2015 2020 2025 2030 2035
Year
Cents per gallon
Figure 6.2 Future increasing trend of petrol price (Source: EIA)
In year 2030, it is assumed that the cost of petrol will be doubled, then the cost of petrol
will be about 0.81 €/kg. And the assumption of switchgrass is that the cost will be
decreased to 0.03 €/kg on average. The investment and operation cost (except from cost
of switchgrass) of bioethanol production are not changed. The results in the table 5 show
that the cost of driving 1 km by ethanol fuels in 2030 would be similar as driving by
petrol.

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6.3 The Influence of Energy Tax
Although the production of biofuels is more expensive than the production of
conventional fuels at present, the energy policies increase the competitive of biofuels,
especially in Europe. Energy tax is used to reduce CO2 emissions and energy
consumption (Ministry of Finance, 2009). From the report from EU, the minimum level
of taxation applicable to automotive petrol is 0.5€/L from 2002 (Enguidanos et al., 2002).
The EU Commission set the target that the market share for renewable energy should
account for 20% of the EU’s final energy consumption by 2020. For this purpose, time-
limited exemption or reduction of taxes on renewable fuels is performed to accelerate the
use of renewable fuels. In the Netherlands, there is an excise duty levied on petrol and
other mineral oils. In year 2007, the excise duty of one liter unleaded petrol is € 0.68
(Ministry of Finance, 2009). In UK, the fuel tax is £ 0.54 per liter for conventional
unleaded petrol, and only £ 0.30 per liter for bioethanol (Petrolprices.com, 2009).
Similar, producing bioethanol in France is able to obtain partial exemption of the excise
tax on petroleum products for an amount of 0.503 €/L (Enguidanos et al., 2002).
Table 6 compares the bioethanol price with the petrol price, depending on the tax level
applied. In the EU, minimum level of taxation 0.5 €/L (0.634 €/kg) is applied.
Table 6 The comparison results of costs of bioethanol including taxation in varied levels and cost of
petrol including taxation (unit: €/kg )
Levels of bioethanol taxation
Full taxation 10% taxation 50% taxation total exemption
Petrol
1.216 1.216 1.216 1.216
Bioethanol
1.252 1.1886 0.936 0.618
Because the different taxation policies on ethanol and petrol, the price of 1 kg bioethanol
from switchgrass is lower than 1 kg petrol when the total or partial exemption of taxation
for transport fuel petrol.
However, considering the functional units in this study, comparisons of the costs of
driving 1 km by bioethanol fuels with by petrol are not positive until the total taxation is
exempted for bioethanol production. When the reduction of taxes is only 10% or 50%,
the costs of 1 km driving by ethanol fuels are still higher than by petrol now.

Master Thesis-Yu Bai
45
Table 7 Costs of driving 1 km by all fuel types including taxation (based on data in year 2008)
Driving 1 km Petrol E10 E85 E100
Bioethanol 10% taxation
0,081 0,084 0,109 0,118
Bioethanol 50% taxation
0,081 0,082 0,089 0,093
Bioethanol 100% taxation
0,081 0,080 0,064 0,061
6.3 Possibilities of Cost Reduction
From the previous analysis, using bioethanol from switchgrass as transport fuel at this
moment is not attractive from economic perspective, unless there is total taxation
exemption for bioethanol production. However, in Laser et al.’s report, a production costs
of switchgrass bioethanol from a mature technology design could be reduced to $ 0.19-
$0.20/L (Laser et al., 2009), which would be able to compete with petrol in economy.
The cost breakdown for ethanol production from switchgrass is presented in Figure 6.3.
The three key contributors of bioethanol cost are cost of switchgrass, utilities and capital
charge.
27%
7%
21%
2%
0%
6%
1%
17%
2%
1%
4%
4%
8%
Switchgrass
Other material
Utilities
Labour cost
Laboratory charges
Maintenance and repairs
Patents and loyalities
Capital charge
Local taxes
Insurances
Plant Overhead cost
General expenses
Average investment
Figure 6.3 Cost breakdown of biothonal production from switchgrass (Source: Guerra Miguez et al.)
The drop of switchgrass cost can contribute to a significant decrease of bioethanol. From
breakdown figure, switchgrass cost takes 27% of total cost of bioethanol. If the cost of

Master Thesis-Yu Bai
46
switchgrass cost dropped from 0.053 €/kg to 0.030 €/kg, the total cost of bioethanol
would decrease by 0.083 €/kg. The increase of switchgrass yield can reduce the cost. One
reason of this is that the fixed costs of production are spread over more tones and the cost
per ton decrease as the switchgrass yields increase. Through the analysis by Iowa State
University, increasing yield would have the biggest impact on reducting the costs of
switchgrass for energy use (Duffy, 2007). An increase in yield of 10% would decrease
the cost per dry tone by about 1.5% to 3.5%, and for a 25% increase, production cost
decreases range from 2.2% to 7.5% (Kszos et al., 2002). When the yield of switchgrass
become 30 tones per ha from 10 tones per ha, the cost of switchgrass production is less
15.12 €/tone (Sokhansanj et al., 2009).
In the bioethanol production process applied in this study, approximately 4 kg
switchgrass can be converted to 1 kg bioethanol. The ethanol yield of this process design
is about 345 L/tone DM switchgrass. In Laser et al.’s report, there is a mature technology
developed, which the ethanol yield could achieve to 440 L/tone DM. Thus, the reduction
of switchgrass usage can make the total cost drop. And because of the high yield of
bioethanol, the cost of utilities, which is the second largest contributor, and unit
investment and operational cost could be divided over more kg bioethanol, causing the
unit cost of bioethanol reduction.
The average price of E85 is $ 2.13 per gallon (AAA, 2009), about 0.51 €/kg. Obviously,
present cost of switchgrass-ethanol is hard to compete with other ethanol, unless there are
subsidies to support.

Master Thesis-Yu Bai
47
7. Conclusions
In this study, LCA method is used to evaluate the ethanol from switchgrass to assess the
environmental impacts. The results of this LCA study of using switchgrass bioethanol as
transport fuels indicates that switchgrass-ethanol fuels have great contributions to the
reduction of GHG emissions, mainly because the CO
2
uptake from atmosphere in the
period of switchgrass growing. Driving 1 km by E85 is able to reduce by 64.9% GHG
emissions compared with driving by petrol. Besides, bioethanol fueled driving
contributes to less impact in abiotic resource depletion than petrol fueled driving, since
the reduction dependence on fossil fuels. E85 has more benefit than E10. However,
switchgrass ethanol fuels present worse results considering other impact categories,
including photochemical oxidation, eutrophication and stratospheric ozone depletion,
ecotoxicity and human toxicity. For acidification, driving by E10 presents the lowest
impact result. In this case, the difference of allocation method option has affect on final
results of the LCA study, but the influence is not distinct, especially on driving by E10.
Compared the GHG emissions reduction from switchgrass-ethanol with the one from
other food-sourced ethanol, replacing petrol by switchgrass ethanol fuels have greater
advantages than by bioethanol from corn and sugarcane, because of the low demand of
fertilizers in switchgrass agriculture and the combustion of waste biomass that contains
lignin fractions to power bioethanol production plant.
Switchgrass-ethanol fuels do not have an advantage in economy. From the LCC study,
the costs (tax excluding) of ethanol fuels are higher than traditional petrol at this moment,
even though the increasing price of crude oil. In Europe, there are policies to promote the
share of renewable energy. The taxation exemption can help the distinction of cost
between switchgrass ethanol and petrol. Only when the total taxes are exempted,
however, the cost of driving by bioethanol fuels from switchgrass could be competed
with petrol.
On one hand, enhancing the yield of switchgrass in the future by optimal agricultural
management would help to reduce the environmental impact and cost. Ethanol production
technology improvement, to increasing the ethanol yield from switchgrass, on the other
hand, also could contribute to lessen of environmental and economic impacts.

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Reference
American Automobile Association. AAA’s daily fuel gauge report (online). Available
from: http://www.fuelgaugereport.com/ [Accessed 4 June 2009]
Anonymous. 2008. Biofuels [online]. VROM.
Available from: http://www.vrom.nl/pagina.html?id=37483 [Assessed 4 November
2008]
Beer T., Grant T., Williams D. and Watson H. 2002. Fuel-cycle greenhouse gas
emissions from alternative fuels in Australian heavy vehicles. Atmospheric Environment
36: 753-763.
Blottnitz H. and Curran M.A. 2006. A comparison of the environmental benefits of
bagasse-derived electricity and fuel ethanol on a life cycle basis. Energy Policy, 34:
2654-2661
Blottnitz H. and Curran M.A. 2007. A review of assessments conducted on bio-ethanol as
a transportation fuel from a net energy, greenhouse gas, and environmental life cycle
perspective. Journal of Cleaner Production, 15: 607-619
Bullard M. and Metcalfe P. 2001. Estimating the energy requirement and CO2 emissions
from production of the perennial grasses miscanthus, switchgrass and reed canary grass.
ETSU B/U1/00645/REP
Carlsson Reich M. 2005. Economic Assessment of municipal waste management system-
case studies using a combination of life cycle assessment (LCA) and life cycle costing
(LCC). Journal of Cleaner Production, 13: 253-263
CML. 2008. CMLCA. Available from: http://www.leidenuniv.nl/cml/ssp/index.html
[Assessed 4 November 2008]
Commission of the European Communities. 2007a. An energy policy for European.
Brussels, 10.1.2007
Commission of the European Communities. 2007b. Renewable energy road map.
Brussels, 10.1.2007
Delucchi, Mark A. 2005. A Multi-Country Analysis of Lifecycle Emissions From
Transportation Fuels and Motor Vehicles. Institute of Transportation Studies, University
of California, Davis, Research Report UCD-ITS-RR-05-10
Dias De Oliveria M. E., Vaughan B.E. and Rykiel E.J. 2005. Ethanol as fuel: energy,
carbon dioxide balances, and ecological footprint. BioScience, 55(7): 593–602

Master Thesis-Yu Bai
49
Duffy M. 2007. Estimated costs for production, storage and transportation of switchgrass.
Iowa State University.
Available online: http://www.econ.iastate.edu/research/webpapers/paper_12917.pdf
Ecoinvent. 2008. Ecoinvent database. Available from: http://www.ecoinvent.ch/
[Assessed 20 December 2008]
Elbersen H.W. 2001. Switchgrass (Panicum virgatum L.) as an alternative energy crop in
Europe [online]. Final report FAIR 5-CT97-3701.
Available from: http://www.switchgrass.nl/archive.htm [Assessed 4 November 2008]
Energy Information Administration (EIA).
Online: http://www.eia.doe.gov/ [Accessed 5 June 2009]
Erich M.S. 1991. Agronomic effectiveness of wood ash as a source of phosphorus and
potassium. Journal of Environmental Quality. 20: 576-581
EuroObserv’ER. 2006. Biofuels barpmeter. EuroObserv’ER 29, May 2006
Farrell A.E., Plevin R.J., Turner B.T., Jones A.D., O’Hare M. and Kammen D.M. 2006.
Ethanol can contribute to energy and environmental goals. Science, 311: 506-508
Frishknecht R., Jungbluth N., Althaus H.-J., Bauer C., Doka G., Dones R., Hischier R.,
Hellweg S., Humbert S., Kollner T., Loerincik Y., Margni M. and Nemecek T. 2007.
Implementation of Life Cycle Impact Assessment Methods. Ecoinvent report No. 3, v2.0.
Swiss Centre for Life Cycle Inventories, Dubendorf, 2007
Fu G.Z., Chan A.W. and Minns D.E. 2003. Life cycle assessment of bio-ethanol derived
from cellulose. Int J LCA, 8: 137-141
Green N. 2004. Growing energy: how biofuels Can help end America’s oil dependence.
Natural Resources Defense Council, New York, December, 78 pp
Guerra Miguez O., Siva siddarthan D. and Suarez Zuluaga D.A. 2009. Technical and
Economical Feasibility of Production and Ethanol from Switchgrass. Faculty of Applied
Sicence, Delft University of Technology.
Guinée J.B. 2002. Handbook on Life Cycle Assessment Operational Guide to the ISO
Standards. Dordrecht: Kluwer Academic Publishers.
Halleux H., Lassaux S., Renzoni R. and Germain A. 2008. Comparative life cycle
assessment of two biofuels: ethanol from sugar beet and rapeseed Methyl Ester. Int J
LCA, 13 (3): 184-190

Master Thesis-Yu Bai
50
Hill J., Nelson E., Tilman D., Polasky S. and Tiffany D. 2006. Environmental, economic,
and energetic costs and benefits of biodiesel and ethanol biofuels. PNAS, 103 (30):
11206-11210
ISO. 2006a. ISO 14040: Environmental management: Life cycle assessment- principles
and framework. Second edition. Geneva: ISO
ISO. 2006b. ISO 14044: Environmental management: Life cycle assessment-
Requirements and guidelines. Second edition. Geneva: ISO
Kadam K.L. 2002. Environmental benefit on a life cycle basis of using bagasse-derived
ethanol as gasoline oxygenate in India. Energy Policy, 30: 371-384
Kim S. and Dale B.E. 2002. Allocation procedure in ethanol production system from corn
grain. I. system expansion. Int J LCA, 7 (4): 237-243
Kim S. and Dale B.E. 2004. Global potential bioethanol producing from wasted crops
and crop residues. Biomass and Bioenergy, 26: 361-375
Kim S. and Dale B.E. 2005a. Environmental aspects of ethanol derived from no-tilled
corn grain: nonrenewable energy consumption and greenhouse gas emission. Biomass
and Bioenergy, 28: 475-489
Kim S. and Dale B.E. 2005b. Life cycle assessment of various cropping systems utilized
for producing biofuels: bioethanol and biodiesel. Biomass and Bioenergy, 29: 426-439
Kim S. and Dale B.E. 2006. Ethanol fuels: E10 or E85- life cycle perspective. Int J LCA,
11 (2): 117-121
Kim S. and Dale B.E. 2008. Life cycle assessment of fuel ethanol derived from corn
grain via dry milling. Bioresource Technology, 99: 5250-5260
Kszos L.A., McLaughlin S.B., Walsh M.E. 2002. Bioenergy from switchgrass: reducing
production costs by improving yield and optimizing crop management. Bioenergy 2002,
Bioenergy for the environment, Boise, ID, September 22–26, 2002.
Lewandowski I., Scurlock J.M.O, Lindvall E., and Christou M. 2003. The development
and current status of perennial rhizomatous grasses as energy crops in the US and
Europe. Biomass and Bioenergy, 25:335-361
Luo L., van der Voet E. and Huppes G. 2008. Life cycle assessment and life cycle costing
of bioethanol from sugarcane in Brazil. Renewable and Sustainable Energy Reviews, doi:
10. 1016/j.rser.2008.09.024

Master Thesis-Yu Bai
51
Malca J. and Freire F. 2006. Renewability and life-cycle energy efficiency of bioethanol
and bio-ethyl tertiary butyl ether (bioETBE): assessing the implications of allocation.
Energy, 31: 3362-3380
McLaughlin S.B. and Walsh M.E. 1998. Evaluating environmental consequences of
producing herbaceous crops for bioenergy. Biomass and Bioenergy, 14(4): 317-324
McLaughlin S.B.and Kszos L.A. 2005. Development of switchgrass (Panicum virgatum)
as a bioenergy feedstock in the United States. Biomass and Bioenergy, 28:515-535
Mitchell R., Vogel K.P. and Sarath G. 2008. Managing and enhancing switchgrass as
bioenergy feedsock. Biofuels, Bioproducts &Biorefining, 2: 530-539
Naylor L.M., Schmidt E.J. 1986. Agricultural use of wood ash as a fertilizer and liming
material. Tappi Journal, 69(10): 114-119
Nemecek T and Kägi T. 2007. Life cycle inventories of Swiss and European Agricultural
production systems. Ecoinvent report No. 15. Zurich and Dübendorf, December 2007
New South Wales Treasury. 2004. Life Cycle Costing Guideline.
Available online:
www.treasury.nsw.gov.au/__data/assets/pdf_file/0005/5099/life_cycle_costings.pdf
Nguyen TLT, Gheewala SH, Garivait S. 2007. Energy balance and GHG-abatement cost
of cassava utilization for ethanol in Thailand. Energy Policy, 35: 4585-4596
Nguyen TLT, Gheewala SH. 2008a. Life cycle assessment of fuel ethanol from cassava
in Thailand. Int J LCA, 13(2): 147-154
Nguyen TLT, Gheewala SH. 2008b. Life cycle assessment of fuel ethanol from cane
molasses in Thailand. Int J LCA, 13: 301-311
Nguyen TLT, Gheewala SH. 2008c. Life cycle cost analysis of fuel ethanol produced
from cassava in Thailand. Int J LCA, 13: 564-573
Niven R.K. 2005. Ethanol in gasoline: environmental impacts and sustainability review
article. Renewable and Sustainable Energy Reviews, 9: 535-555
Norris G.A. 2001. Integrating life cycle cost analysis and LCA. Int J LCA, 6 (2): 118-121
Notarnicola B., Huppes G., and van de Berg N.W. 1998. Evaluating options in LCA: the
emergence of conflicting paradigms for impact assessment and evaluation. Int J LCA, 3
(5): 289-300
Patzek T.W. 2004. Thermodynamics of the corn-ethanol biofuel cycle. Critical Reviews
in Plant Sciences, 23(6): 519-567

Master Thesis-Yu Bai
52
Petrolprices.com. Fuel tax.
Online: http://www.petrolprices.com/fuel-tax.html [Accessed 23 June 2009]
Pimentel D. 2003. Ethanol fuels: energy balance, economics, and environmental impacts
are negative. Natural Resources Research, 12 (2):127-134
Pimentel D. and Patzek T. W. 2005. Ethanol production using corn, switchgrass, and
wood; biodiesel production using soybean and sunflower. Natural Resources Research,
14: 65-76
Punter G., Rickeard D., Larive J., Edwards R., Mortimer N., Horne R., Bauen A. and
Woods J. 2004. Well-to-wheel evaluation for production of ethanol from wheat. LowCVP
Fuels Working Group. Report FWG-P-04-024.
Ranney J.W. and Mann L.K. 1994. Environmental considerations in energy crop
production. Biomass and Bioenergy, 6(3): 211-228
Rebitzer G. and Hunkeler D. 2003. Life cycle costing in LCM: ambitions, opportunities,
and limitations. Int J LCA, 8(5): 253-256
Rebitzer G., Ekvall T., Frischknecht R., Hunkeler D., Norris G., Rydberg T., Schmidt W.
P., Suh S., Weidema B. P. and Pennington D. W. 2004. Life cycle assessment: Part 1:
Framework, goal and scope definition, inventory analysis, and applications. Environment
International, 30 (5): 701-720
Sanderson M.A., Reed R.L., Ocumpaugh M.A., Hussey M.A., Van Esbroeck G., Read
J.C., Tischler C.R. and Hons F.M. 1999. Switchgrass cultivars and germplasm for
biomass feedstock production in Texas. Bioresource Technology, 67: 209-219
Schmer M.R., Vogel K.P., Mitchell R.B., and Perrin R.K. 2008. Net energy of cellulosic
ethanol from switchgrass. PNAS, 105 (2): 464-469
SenterNovem. 2001. Bioethanol in Europe- overview and comparison of production
process. Report 2GAVE0601.
Shapouri H., Duffield J.A. and Wang M. 2002. The energy balance of corn ethanol: an
update. USDA, Office of Energy Policy and New Uses, Agricultural Economics. Rept.
No. 813. 14p.
Shapouri H., Duffield J., McAloon A. and Wang M. 2004. The 2001 net energy balance
of corn-ethanol (preliminary). US Dept. Agriculture, Washington, DC.
Sheehan J., Aden A., Paustian K., Killian K., Brenner J., Walsh M. and Nelson R. 2004.
Energy and environmental aspects of using corn stover for fuel ethanol. Journal of
Industrial Ecology, 7 (3-4): 117-146

Master Thesis-Yu Bai
53
Smeets E. 2008. Possibilities and limitations for sustainable bioenergy production
systems. Thesis. Utrecht University.
Spatari S., Zhang Y. and Maclean H.L. 2005. Life cycle assessment of switchgrass- and
corn stover-derived ethanol-fueled automobiles. Environmental Science & Technology,
39: 9750-9758
Sokhansanj S., Mani S., Turhollow A., Kumar A., Bransby A., Lynd L. and Laser M.
2009. Large-scale production, harvest and logistics of switchgrass (Panicum virgatum L.)
–current technology and envisioning a mature technology. Biofuels, Bioprod, Bioref. 3:
124-141
Tan K.T., Lee K.T. and Mohamed A.R. 2008. Role of energy policy in renewable energy
accomplishment: The case of second-generation bioethanol. Energy Policy, 36(9):3360-
3365
United Nations Environment Programme. 1997. "Industrialized countries to cut
greenhouse gas emissions by 5.2%" [online].
Available from: http://unfccc.int/cop3/fccc/info/indust.htm [Assessed 4 November 2008]
United Nations Environment Programme. 2004. Why take a life cycle approach? [online].
Available from:
http://jp1.estis.net/sites/lcinit/default.asp?site=lcinit&page_id=138F5949-6997-4BE6-
A553-585E92C22EE4#lcmwhy [Assessed 12 May 2009]
Wu M., Wang M., and Huo H. 2006. Fuel-cycle assessment of selected bioethanol
production pathways in the United States. Energy System Division, Argonne National
Laboraory.

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Appendix 1 Data Collection of Switchgrass Agriculture
The base case scenario is from Bullard and Metcalfe’s (2001) report, and in their case, a
clay soil is selected for switchgrass agriculture. Their base case scenario included the
operations, material inputs and yield. However, there is an important difference from
their base case scenario in this study, which is the N fertilizer input. In their base case, the
fertilizers are only applied in first year. In this study, N fertilizer is applied every year,
and assumption of quality is 100 kg per ha per year. Table A1.1 summarizes the
economic input of switchgrass production in 20 years, in which establishment year and
production years are separated. And in table A1.2, CO2 and solar energy uptake during
switchgrass growing are showed, based on the C content and upper heating value in dry
biomass.
Table A1.1 Economic input and output in 20 years period.
Establishment year
Seed 10 kg/ha
Fertilizer- Ammonium Nitrate 100 kg/ha
Fertilizer- P2O5 acide 40 kg/ha
Fertilizer- K2O Manufactured 80 kg/ha
Lime 3000 kg/ha
Manganese (MnSO4)
Herbicide- Advance (bromoxynil/ioxynil/fluroxypyr) 2 kg/ha
Herbicide- Trifolex-Tra (MCPA + MCPB) 7,7 kg/ha
Herbicide- Isoproturon 2 kg/ha
Yield in first year 0
Production Years
Fertilizer- Ammonium Nitrate 100 kg/ha
Manganese (MnSO4) 4 kg/ha
Herbicide- Advance (bromoxynil/ioxynil/fluroxypyr) 2 kg/ha
Herbicide- Trifolex-Tra (MCPA + MCPB) 7,7 kg/ha
Herbicide- Isoproturon 2 kg/ha
12 t/ha in year 2 and 3
Yield in production years ((25% moisture))
16 t/ha in rest years
Total yields in 20 years(25% moisture) 296000 t/ha
Table A1.2 Nature input per unit dry biomass
Nature input Per unit remark
CO2, in air (input) 1,54 kg/kg DM C-content is 42% w/w
Energy 17 MJ/kg DM The energy content corresponds to the upper heating
value of the dry biomass

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Table A1.3 summarizes assumptions of planting, post-planting and harvesting operations.
Table A1.3 Relevant farming activities of 20 years switchgrass production
Agricultural field work processes unit
Work in 20
years
In establishment
year
In productive
years
Hoeing ha 1 1 0
tillage, plounging ha 1 1 0
tillage, rotary cultivator ha 1 1 0
Sowing ha 1 1 0
tillage, rolling ha 1 1 0
fertilising, by broadcaster ha 20 1 19
application of plant protection
products, by field sprayer (herbicide)
ha 60 3 3*19
application of plant protection
products, by field sprayer (lime)
ha 1 1 0
mowing, by rotary mower ha 19 0 19
Baling ba 740 0 19
loading bales ba 740 0 19
Transport, 16t lorry tkm 474 0 19
Building, multi-storey m3 59.2
Table A1.4 shows the environmental emissions during switchgrass production in 20 years.
The quantities of emissions are calculated based on the economic inputs, using the
methods of emissions calculation from Ecoinvent report.
Table A1.4 Environmental emissions in 20 years switchgrass production
In establishment year In production years(per year) Total
Emissions to air (kg/ha)
Nitrogen oxides 1.02
1.02
20.5
Dinitrogen monoxide 4.88
4.88
97.5
Ammonia 2.43
2.43
48/6
Emissions to water (kg/ha)
NO3- 48
48
960
P to ground water 0.06
0.06
1,2
P to surface water 0.275
0.25
5,025
P from erosion to surface
water 0.0707
0.0707
1.41
Cd 3.9E-5
1.72E-6
7.16E-5
Cu 0.00321
0.00185
0,0384
Zn 0,0152
0.00553
0,120
Pb 0,00026
2.14E-5
0,000663
Cr 0,0211
0.00604
0,136
Emissions to soil (kg/ha)
Cd 1,79E-05
7.93E-7
3.29E-5
Cu 0,00121
0.000701
0,0145
Zn 0,00222
0,000805
0,0175
Pb 0,000783
6.522E-5
0,00202
Ni 0,00148
0.000769
0,0161
Cr 0,00178
0.000509
0,0114

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Here are some important emissions calculation details showed.
Direct Field Emissions:
Ammonia to the air:
In establishment year, the total NPK-fertilizers is 220 kg per ha, so in a unit field,
4%*220 kg = 8.8 kg N is emitted in form of NH3. In the production years, only
ammonium nitrate is applied in 100 kg/ha, and there is 2%* 100 kg = 2 kg N emitted in
form of NH3 per ha per year. In total, the N emissions in form of NH3 per ha in 20 years
is 46.8 kg, meaning 56.8 kg NH3 kg/ha emission.
NO3- to the ground water:
Here is the assumption that the switchgrass plant is using intensive way. The
accumulation of the monthly values of nitrate mineralization, nitrate uptake by the plants
and the nitrate from fertilizing for switchgrass is from March to September, and assuming
the fertilizer is applied in March. So the nitrate mineralization is 280 kg N/ha yr, and
nitrate uptake is 270 kg N/ha yr, and the nitrate from fertilizer is 50%*100=50 kg N/ha
yr. Accumulation result is 0.8*60=48 kg NO3-/ha yr, in both establishment year and
production years.
Nitrous Oxide to the air:
The N2O emissions from fertilizers were calculated on the basis of available nitrogen
(100% of the nitrogen was available for mineral fertilizers). The factor o 1.25% N lost as
N2O was used. The ammonia emissions have two impacts: on one hand, the quantity of
available nitrogen was reduced by losses in the form of ammonia; one the other hand,
N2O emissions induced by ammonia. And 2.5% of the nitrogen which is leached in the
form of nitrate is converted to N2O.
Finally, N2O emissions are calculated by:
N20= 44/28* (0.0125*(100-14/17*NH3) +0.01*14/17*NH3+0.025814/62*48.
The emissions of NH3 are different in establishment year and in production years. Using
the figures for calculation, the N2O emissions are 4.28 kg/ha/yr and 4.31 kg/ha/yr
respectively, and 86.2 kg/ha in 20 years

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NOx to the air
These emissions were estimated from the emissions of N2O, which is
0.21*86.2=18.1kg/ha
Phosphorus to the water:
Assumption: From the paper of Bullard and Metcalfe, the annual erosion rates for lands
with perennial energy crops will probably be in the range of 0.2 to 0.3 t/ha. So soil
erosion from the production of switchgrass 200 kg/ha is used.
Phosphorus leaching to ground water: 0.06kg P/ha yr
P run-off to surface waters:
Fro= 1+0.2/80*14+0.7/80*0+0.4/80*0=1.035
Pro=0.25*1.035 kg P/ha yr=0.26 kg P/ha yr
P emissions trough erosion by water to surface water:
200 kg/ha yr*0.00095 kg/kg *1.86*0.2=0.0707

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Appendix 2 Data Collection of Bioethanol Production
Process
The whole production process can be divided into four stages: size reduction,
pretreatment, fermentation & downstream and waste treatment and recovery. Table A2.1-
2.4 summarized the compositions inflows and outflows of every stage.
The equipments of production are expressed using cash. A database of industries is
combined in LCA module to express and calculated the impacts of production and
operation of equipments in bioethanol production process. The equipments are designed
for 15 years use. In this study, the total cost of equipments will be divided by 24 hours *
330 days * 15 years, since the function in this process is the bioethanol production in one
hour. The summary of equipments which are adapted to software module is present in
table A2.5.
Table A2.5 The summary of equipments in bioethanol production process (per hour)
Code Equipments Cost average per hour (€)
Size reduction
I48 Tank 3.58
I288 Conveyor and conveying equipment 3.09
I295 Rolling mill machinery and equipment 7.77
I310 General industrial machinery and equipment 11.7
Pretreatment
I288 Conveyor and conveying equipment 0.429
I310 General industrial machinery and equipment 20.5
I322 Refrigeration and heating equipment 1.04
Fermentation and downstream
I305 Pumps and compressor 0.674
I310 General industrial machinery and equipment 75.2
I322 Refrigeration and heating equipment 4.84
Waste treatment and recovery
I280 Turbines and turbines generator sets 88.2
I288 Conveyor and conveying equipment 0.386
I307 Blowers and fans 2.68

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Table A2.1 Mass balance of size reduction stage in bioethanol production process (Unit:kg/hr)
1. Size reduction
Input composition: Output composition:
1) Switchgrass feed
84,300
4) Rocks out
371
2) Polymer feed
28
5) Rocks, sand and dust out
599
3) Makeup water
29019
6) Biomass to section 2
110,901
Total flow
113,347
Total flow
111,871
Input components:
1) 2) 3) Total
Output components:
4) 5) 6) Total
Water
5,842
29019
34,861
Water
120
33264
33,384
Rocks
169
169
Rocks
160
8
168
Sand and dust
253
253
Sand and dust
240
13
253
Switchgrass
78,037
78,037
Switchgrass
211
211
77,616
78,038
Polymer
28
28
Polymer
28
28
Total flow
84,301
28
113,348
Total flow
371
599
110901
111,871
Table A2.2 Mass balance of pretreatment stage in bioethanol production process (Unit:kg/hr)
2. Pretreatment
Input composition: Output composition:
7) Biomass from section 1
110,901
11) To section 3
169,652
8)Stripping steam
21,988
9) Water input
36,416
10) NH3L input
347
Total flow
169,652
Total flow 169,652
Input components:
7) 8) 9) 10) Total
Output components:
11) Total
Cellulose
Cellulose 28960
28,960
Hemicellulose
Hemicellulose 22,116
22,116
Lignin
Lignin 14,856
14,856
Water 33,264
21,988
36416
178
91,846
Water 91,846
91,846
Insoluble biomass
Insoluble biomass 7,511
7,511
Ash
Ash 4,173
4,173
Rocks 8
8
Rocks 8
8
Sand and dust 13
13
Sand and dust 13
13
Switchgrass 77,616
77,616
Switchgrass
NH3L
122
122
NH3L 169
169
NH3G
47
47
NH3G
Total flow 110901
21988
36416
347
169,652
Total flow 169,652
169,652

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Table A2.3 Mass balance of fermentation and downstream stage in bioethanol production process (Unit:kg/hr)
3. Fermentation and Downstream
Input composition: Output composition:
12) From section 2
169,652
16) Off gas
20,964
13) Recycled water
231,570
17) Ethanol product
21,356
14) Yeast input
1,940
18) Water to recycle
260590
15) Enzyme input
35
19) Waste water
47985
20) Cake to section
40791
Total flow
403,197
Total flow
403,686
Input components:
12) 13) 14) 15) Total
Output components:
16) 17) 18) 19) 20) Total
Cellulose
28960
28,960
Cellulose
1448
1,448
Hemicellulose
22,116
22,116
Hemicellulose
4423
4,423
Lignin
14,856
14,856
Lignin
14856
14,856
Water
91,846
231,570
323,416
Water
559
57
260590.4
44215.2
6217
323,638
Insoluble biomass
7,511
7,511
Insoluble biomass
7511
7,511
Ash
4,173
4,173
Ash
4173
4,173
Ethanol
Ethanol
21,266
429
21,695
Yeast
1,940
1,940
Yeast
1559
1,559
Enzyme
35
35
Enzyme
34
1
35
CO2
CO2
20,223
73
20,296
NH3L
169
169
NH3L
Glycerol
Glycerol
1567
32
1,599
Acetic acid
Acetic acid
722.49
15
738
Acetaldehyde
Acetaldehyde
181
33
3
217
Lactic acid
Lactic acid
723.01
15
738
Solubilized glucose
Solubilized glucose
723
15
738
Total flow
169652
231,570
1940
35
403,197
Total flow
20,963
21,356
260590.4
47984.7
40791
403,685

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Table A2.4 Mass balance of waste treatment and recovery stage in bioethanol production process (Unit:kg/hr)
4. Waste treatment and recovery
Input composition: Output composition:
19) Waste water from section 3
47,985
23) Off gas
322,085
20) Cake from section 3
40,791
24) Ashes 56,031
21) Air
289,342
Total flow
378,118
Total flow
378,116
Input components: 19) 20) 21) Total Output components: 23) 24) Total
Cellulose 0
1448
1,448
Cellulose
Hemicellulose 0
4423
4,423
Hemicellulose
Lignin 0
14856
14,856
Lignin
Water 44215.2
6217
50,432
Water 18,688
18,688
Insoluble biomass
7511
7,511
Insoluble biomass
Ash
4173
4,173
Ash
Ethanol
429
429
Ethanol
Yeast
1559
1,559
Yeast
Enzyme 34
1
35
Enzyme
CO2 0
73
73
CO2 58,413
58,413
Glycerol 1567
32
1,599
Glycerol
Acetic acid 722.49
15
737
Acetic acid
Acetaldehyde 0
3
3
Acetaldehyde
Lactic acid 723.01
15
738
Lactic acid
Solubilized glucose 723
15
738
Solubilized glucose
O2
66,549
66,549
O2 22,123
22,123
N2
222,793
222,793
N2 222,793
222,793
SO2
SO2 7
7
NO2
NO2 61
61
Ashes 56,031
56,031
Total flow 47,985
40,791
289,342
378,118
Total flow 322,085
56,031
378,116
In this stage, a mass of steam is produced in company with off gas and ash to generate electricity. The amount of electricity which can be
generated is 33.5MWH, in which 6471.9 KWH is used to power the bioethanol production plant.

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Appendix 3 Data Collection of Final Vehicle Driving
For comparison study, the tailpipe emissions of final vehicle driving used the same data as the
ones in Luo’s researches.
Table A3.1 Tailpipe emissions of all fuel alternatives in this study
1 km driving By petrol By E10 By E85 By E100
Amount (kg) 0.0665 0.069 0.0916 0.099
Emissions (kg)
Acetaldehyde 2.09E-6 4.14E-6 5.11E-5 5.11E-5
Benzene 0 7.00E-9 5.3E-9 5.3E-9
Carbon dioxide 0.208 0.208 0.179 0.173
Carbon monoxide 0.00151 0.00143 0.00103 0.00103
Formaldehyde 2.3E-8 2.00E-8 2.16E-8 2.16E-8
Methane 2.17E-5 1.49E-5 0 0
Nitrogen oxides 0.000417 0.0004 0.000247 0.000247
Particulates 6.65E-6 4.25E-6 0 0
Hydrocarbons 7.45E-5 7.1E-5 5.4E-5 5.4E-5
Butadiene 4.76E-7 2.89E-7 2.47E-8 2.47E-8

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Appendix 4 Summary of LCIA Results
Table A4.1 LCIA results of driving 1 km by petrol, E10, E85 and E100 in base case scenario
Unit Petrol E10 E85 E100
Abiotic depletion
kg antimony eq.
0.00169 0.0016 0.000679 0.000438
Global warming GWP100
kg CO2 eq. 0.255 0.241 0.0896 0.0497
Ozone layer depletion
kg CFC-11 eq. 3.14E-08 3.31E-08 4.99E-08 5.52E-08
Human toxicity
kg 1,4-DCB eq. 0.0141 0.0153 0.0322 0.0375
Freshwater aquatic ecotoxicity
kg 1,4-DCB eq. 0.00247 0.00266 0.00569 0.00659
Terrestrial ecotoxicity
kg 1,4-DCB eq. 0.000281 0.000306 0.00064 0.00074
Photochemical oxidation
kg ethylene eq. 0.000161 0.000198 0.000591 0.000699
Acidification
kg SO2 eq. 0.000744 0.000735 0.000752 0.000787
Eutrophication
kg PO
4-
eq. 7.50E-05 9.26E-05 0.000269 0.000325
Table A4.2 LCIA results of driving 1 km by petrol, E10, E85 and E100; economic allocation
Unit Petrol E10 E85 E100
Abiotic depletion
kg antimony eq.
0.00169 0.00162 0.000683 0.000445
Global warming GWP100
kg CO2 eq. 0.255 0.242 0.0958 0.0575
Ozone layer depletion
kg CFC-11 eq. 3.14E-08 3.32E-08 5.06E-08 5.60E-08
Human toxicity
kg 1,4-DCB eq. 0.0141 0.0153 0.0326 0.0379
Freshwater aquatic ecotoxicity
kg 1,4-DCB eq. 0.00247 0.00267 0.00574 0.00665
Terrestrial ecotoxicity
kg 1,4-DCB eq. 0.000281 0.000307 0.000647 0.000747
Photochemical oxidation
kg ethylene eq. 0.000161 0.000199 0.000596 0.000706
Acidification
kg SO2 eq. 0.000744 0.000736 0.000763 0.0008
Eutrophication
kg PO
4-
eq. 7.50E-05 9.29E-05 0.000272 0.000329
Table A4.3 LCIA results of driving 1 km by petrol, E10, E85 and E100; excluding soil
establishment
Unit Petrol E10 E85 E100
Abiotic depletion
kg antimony eq. 0.00169 0.00162 0.000652 0.0004
Global warming GWP100
kg CO2 eq. 0.255 0.24 0.0814 0.0394
Ozone layer depletion
kg CFC-11 eq. 3.14E-08 3.31E-08 4.95E-08 5.46E-08
Human toxicity
kg 1,4-DCB eq. 0.0141 0.015 0.0289 0.0332
Freshwater aquatic ecotoxicity
kg 1,4-DCB eq. 0.00247 0.00261 0.00512 0.00586
Terrestrial ecotoxicity
kg 1,4-DCB eq. 0.000281 0.000306 0.000633 0.000728
Photochemical oxidation
kg ethylene eq. 0.000161 0.000198 0.000586 0.000693
Acidification
kg SO2 eq. 0.000744 0.00073 0.000698 0.000718
Eutrophication
kg PO
4-
eq. 7.50E-05 9.08E-05 0.00025 0.000301
Table A4.4 LCIA results of driving 1 km by petrol, E10, E85 and E100; 40 km transport distance
Unit Petrol E10 E85 E100
Abiotic depletion
kg antimony eq. 0.00169 0.00162 0.000697 0.000457
Global warming GWP100
kg CO2 eq. 0.255 0.241 0.0916 0.0522
Ozone layer depletion
kg CFC-11 eq. 3.14E-08 3.32E-08 5.03E-08 5.56E-08
Human toxicity
kg 1,4-DCB eq. 0.0141 0.0153 0.0325 0.0377
Freshwater aquatic ecotoxicity
kg 1,4-DCB eq. 0.00247 0.00267 0.00576 0.00667
Terrestrial ecotoxicity
kg 1,4-DCB eq. 0.000281 0.000307 0.000646 0.000746
Photochemical oxidation
kg ethylene eq. 0.000161 0.000199 0.000595 0.000704
Acidification
kg SO2 eq. 0.000744 0.000736 0.000763 0.000801
Eutrophication
kg PO
4-
eq. 7.50E-05 9.28E-05 0.000271 0.000327

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Table A4.5 LCIA results of driving 1 km by petrol, E10, E85 and E100; 80 km transport distance
Unit Petrol E10 E85 E100
Abiotic depletion
kg antimony eq. 0.00169 0.00161 0.000731 0.000496
Global warming GWP100
kg CO2 eq. 0.255 0.242 0.0957 0.0573
Ozone layer depletion
kg CFC-11 eq. 3.14E-08 3.32E-08 5.09E-08 5.64E-08
Human toxicity
kg 1,4-DCB eq. 0.0141 0.0153 0.0329 0.0383
Freshwater aquatic ecotoxicity
kg 1,4-DCB eq. 0.00247 0.00268 0.00592 0.00688
Terrestrial ecotoxicity
kg 1,4-DCB eq. 0.000281 0.000308 0.000659 0.000763
Photochemical oxidation
kg ethylene eq. 0.000161 0.0002 0.000603 0.000713
Acidification
kg SO2 eq. 0.000744 0.000738 0.000786 0.000829
Eutrophication
kg PO
4-
eq. 7.50E-05 9.32E-05 0.000275 0.000333
Table A4.5 LCIA results of driving 1 kg petrol, E10, E85 and E100 (WTT)
Unit Petrol E10 E85 E100
Abiotic depletion
kg antimony eq. 0.0262 0.0239 0.00739 0.00444
Global warming GWP100
kg CO2 eq. 0.7 0.47 -0.976 -1.25
Ozone layer depletion
kg CFC-11 eq. 4.72E-07 4.80E-07 5.45E-07 5.57E-07
Human toxicity
kg 1,4-DCB eq. 0.189 0.205 0.348 0.375
Freshwater aquatic ecotoxicity
kg 1,4-DCB eq. 0.0371 0.0385 0.0621 0.0665
Terrestrial ecotoxicity
kg 1,4-DCB eq. 0.00423 0.00444 0.00699 0.00746
Photochemical oxidation
kg ethylene eq. 0.00178 0.00227 0.00579 0.00645
Acidification
kg SO2 eq. 0.00805 0.00775 0.00686 0.0067
Eutrophication
kg PO
4-
eq. 0.000313 0.000588 0.00258 0.00296