Kindergarten Teacher Directions C ommon F ormative A ssessment
Environmental Sustainability ssessment of iomass and B A...
Transcript of Environmental Sustainability ssessment of iomass and B A...
i
Environmental Sustainability Assessment of Biomass and
Biorefinery Production Chains: Using a Life Cycle
Assessment Approach
Ranjan Parajuli
PhD Dissertation
Department of Agroecology, Science and Technology
October 2016
a
b
ENVIRONMENTAL SUSTAINABILITY ASSESSMENT OF BIOMASS AND
BIOREFINERY PRODUCTION CHAINS: USING A LIFE CYCLE
ASSESSMENT APPROACH
PhD Thesis
RANJAN PARAJULI
Submitted: October 23, 2016
Department of Agroecology
Faculty of Science and Technology
Aarhus University
Blichers Allé 20
P.O. Box 50
8830 Tjele
Denmark
October 2016
c
Main Supervisor
Professor Tommy Dalgaard
Department of Agroecology
Aarhus University, Denmark
Co-supervisor
Marie Trydeman Knudsen
Researcher
Department of Agroecology
Aarhus University, Denmark
Assessment Committee
Lars Elsgaard (Associate Professor)
Department of Agroecology
Aarhus University, Denmark
Göran Berndes (Associate Professor)
Department of Energy and Environment
Chalmers University of Technology
SE-412 96 Göteborg, Sweden
Lorie Hamelin (Senior Scientist)
Department of Bioeconomy and System Analysis
Institute of Soil Science and Plant Cultivation
Lublin District, Pulawy County, Poland
i
Acknowledgements
My sincere gratitude goes to my Supervisors Tommy Dalgaard and Marie Trydeman Knudsen
for their continuous supports and encouragement throughout my PhD research. I also would
like to thank to all the co-authors who have supported me with their constructive inputs
while preparing the manuscripts related to this thesis. I also would like to make a special
thanks to Ib Sillebak Kristensen and Lisbeth Mogensen for their special supports to the study
that I co-authored with them. Sylvestre, thank you for all the discussions we have had during
different time and in the contexts of LCA studies. Thank you, Jesper Overgård Lehmann for
helping me on the Danish translation. Thanks to Margit Schacht for partly cleaning my
thesis.
I must thank to the Graduate School of Science and Technology (GSST) of Aarhus University
for the PhD Grant. The support and coordination from the BioValue SPIR is also highly
acknowledged. I am highly indebted with my office colleagues for all those direct and indirect
supports that I received during my study. All the friends at the Department of Agroecology
are also acknowledged for all those social events and interactions that made my stay in
Denmark enjoyable. I also would like to thank to my Nepalese friends for all the enjoyable
moments that we shared together.
Love to my wife Sheela, to my daughter Kritika and son Shashank. Special thanks also go to
my parents and brothers back home in Nepal for their support and encouragements for my
education, because of which today I am writing this thesis. All the direct and indirect help to
me are also acknowledged.
Thanks to all
Photo courtesy:
Biomasses: Uffe Jørgensen and Jesper Overgård Lehmann
Ranjan Parajuli
October 2016
ii
Preface
The principal objective of this PhD study was to assess environmental performance of
biomass and biobased products production chains. Biomass production chains represented
processes related to growing lignocellulosic biomasses, as selected in the study. Likewise,
biobased products production chains indicated the conversion of biomasses in a biorefinery
to produce marketable biobased products. This study was carried out in coordination with
the BIOVALUE SPIR PLATFORM funded under the SPIR initiative by The Danish Council
for Strategic Research and The Danish Council for Technology and Innovation. This PhD
research, in particular was associated with the project on “Socioeconomics, sustainability and
ethics (SeSE)” under Bio-Value SPIR.
The general flow of the thesis is that it first introduces the importance of biorefineries in the
context of satisfying the increasing demand of food, feed, fuels and chemicals. Afterward the
thesis is structured in a way to reflect environmental sustainability assessment studies in the
production chains of biomass and biobased products. The PhD thesis is based on the work
presented in the following five papers: three of them are published and two are submitted to
respective journals.
Paper-I: Parajuli R, Dalgaard T, Jørgensen U, Adamsen APS, Knudsen MT, Birkved M, et al.
Biorefining in the prevailing energy and materials crisis: a review of sustainable pathways for
biorefinery value chains and sustainability assessment methodologies. Renewable and
Sustainable Energy Reviews. 2015; 43(0):244-63.
Paper-II: Parajuli, R., Knudsen, M.T., Dalgaard, T. Multi-criteria assessment of yellow,
green, and woody biomasses: pre-screening of potential biomasses as feedstocks for
biorefineries. Biofuels, Bioproducts and Biorefining. 2015; (9): 545-566.
Paper-III: Parajuli, R., et al. (in-press). Environmental life cycle assessments of producing
maize, grass-clover, ryegrass and winter wheat straw for biorefinery. Journal of Cleaner
Production (2016):1-13.
Paper-IV: Parajuli, R., et al. (submitted). Environmental Life Cycle Assessment of willow,
alfalfa and straw from spring barley as feedstocks for bioenergy and biorefinery systems.
Paper-V: Parajuli, R., et al. (submitted). Evaluating the environmental impacts of
standalone and integrated biorefinery systems using consequential and attributional
approaches: cases of bioethanol and lactic acid production.
iii
Summary
The increasing demand for biomass for biofuels has stimulated the food vs fuels debates.
Furthermore, the exploitation of biomass sources for biofuels has impacts on soil carbon
change, nitrous oxide emissions, biodiversity and human health. Biorefinery is an evolving
technology, bringing new value chains for biomass conversions. Meanwhile agriculture is the
important primary sector that biorefineries depend. In addition to the innovations on the
conversions of biomass to biofuels, the concept of a green biorefinery is particularly
interesting. It provides not only alternative options for using green biomass, but also aims to
reduce the import dependency on livestock feed (e.g. protein, energy feed) and to produce
other biochemicals and renewable fuels.
This PhD study aimed at evaluating the production of lignocellulosic biomasses and biobased
products using the Life Cycle Assessment (LCA) method. Seven biomasses were evaluated,
and they scored differently in terms of their specific environmental performance. For
instance, ryegrass, grass-clover, maize and straw from spring barley and winter wheat scored
higher carbon footprints than willow and alfalfa. Soil C debits/credits, N2O emissions and
emissions from diesel use during the farm operations were the principal sources for the
carbon footprint. On the other hand, grasses were found with lower potential biodiversity
damage and freshwater ecotoxicity than straw produced from cereals. The study highlighted
the importance of considering a wide range of environmental impact categories rather than
based on a single indicator to avoid inconsistency in the decision support for screening of
biomasses and biorefining policies and to minimize the risk of flawed decision support.
With regard to the production of biobased products, the environmental evaluation was made
on using both attributional (ALCA) and consequential (CLCA) approaches. The evaluation
was carried for the two standalone biorefinery systems: (i) conversion of straw to bioethanol
and (ii) alfalfa to biobased lactic acid. These two standalone systems were then combined to
co-produce bioethanol and lactic acid in an integrated system. The results obtained from the
ALCA and CLCA approaches for biobased products arrived at the same conclusions in terms
of savings in GHG emissions and fossil fuel use compared to conventional products. The
integrated system performed much better for producing bioethanol than the standalone
system, e.g. the savings in terms of greenhouse gas emissions, non-renewable energy use and
eutrophication potential were higher compared to the standalone system. The study
highlighted that the extent of material processing and recycling of residual products was
beneficial by progressively offsetting the environmental impacts. Finally, assessing the
economic viability of producing biobased products is crucially important for an overall
systemic evaluation and to promote biobased products in a fossil-fuel-based market.
iv
Sammendrag
Den stigende efterspørgsel efter biobaserede brændstoffer har påvirket debatten omkring
brug af jord og andre begrænsede ressourcer til at producere mad eller brændstof. Nogle af
de vigtigste konsekvenser ved at benytte biomasseressourcer i forhold til brugen af fossile
ressourcer vedrører kulstoffiksering, udledning af kvælstofoxider, biodiversitet og
menneskers sundhed. Omdannelsen af biomasse gennem et bioraffinaderi afføder en
udvikling af nye værdikæder, men de baseres stadig på landbruget som den primære sektor.
Selvom bioethanol er det mest undersøgte blandt de biobaserede produkter, så er
produktionsoptimering stadig relevant også på dette felt. Endvidere er konceptet omkring et
grønt bioraffinaderi (dvs. et bioraffinaderi der baserer sig på grønne råvarer såsom græs)
specielt interessant, fordi det giver alternative udnyttelsesmuligheder for græsområdernes
biomasse, og der er kun et begrænset antal studier af denne værdikæde.
Formålet med denne ph.d. var at evaluere biomasse fra ligninholdige cellulosedele og
værdikæden for biobaserede produkter ved hjælp af metoden livscyklusvurdering (LCA). Syv
forskellige biomasser blev evalueret, og resultaterne viste forskellige miljøpåvirkninger.
Eksempelvis havde rajgræs og kløvergræs det højeste klimaaftryk, men samtidig havde de en
mindre påvirkning på biodiversiteten og ferskvandsmiljøet, når de sammenlignes med halm
fra korn. Effekten afhang primært af ændringer i jordens kulstofpuljer, emission af
kvælstofdioxid og emission fra dieselforbruget i forbindelse med brugen af maskiner i
marken. Studiet understreger derfor vigtigheden af at inddrage en bred vifte af effekter, når
den miljømæssige bæredygtighed skal evalueres, og at evalueringen bør indeholde mere end
blot klimagas-udledningen.
To forskellige LCA-metoder blev brugt til at evaluere den afledte miljøpåvirkning ved
produktion af henholdsvis bioethanol fra halm og mælkesyre fra lucerne. Evalueringen blev
foretaget særskilt for hvert produkt og efterfølgende i et scenarie, hvor begge produkter blev
produceret samtidigt. Forskellen mellem de to LCA-metoders beregnede miljøpåvirkning var
minimal, og de resulterede derfor i den samme konklusion. En samtidig produktion af både
bioethanol fra halm og mælkesyre fra lucerne havde en væsentligt lavere miljøpåvirkning end
en særskilt produktion af begge produkter på grund af dels en lavere eutroficeringsrisiko og
et mindre dieselforbrug. Studiet viste, at graden af materialernes forarbejdning og
muligheden for recirkulering af restprodukterne påvirkede produkternes miljøaftryk, og her
ligger en mulighed for reduktion i aftrykket. Endeligt vil en økonomisk vurdering af de
biobaserede produkter kunne indgå i en overordnet systemisk evaluering og dermed
understøtte en proces, hvor biobaserede produkter bliver promoveret i et marked domineret
af fossile brændstoffer.
v
Table of Contents
Acknowledgements ..................................................................................................................... i
Preface ........................................................................................................................................ ii
Summary ................................................................................................................................... iii
Sammendrag ............................................................................................................................. iv
Figures ...................................................................................................................................... vii
Table ......................................................................................................................................... vii
Acronyms and abbreviations................................................................................................... viii
Appended Papers ...................................................................................................................... ix
1. Introduction .........................................................................................................................1
1.1. Background...................................................................................................................1
1.2. Biorefinery systems ..................................................................................................... 2
1.3. Sustainability of biorefinery production chains .......................................................... 6
2. Research design .................................................................................................................. 9
2.1. Research question and objectives ............................................................................... 9
2.2. Introduction to the thesis structure ............................................................................ 9
2.3. Introduction to the thesis .......................................................................................... 10
2.4. Introduction to Papers ............................................................................................... 11
3. Reflections on the materials and methods ........................................................................13
3.1. Types and choice of biomasses and biorefinery platforms ........................................13
3.2. Life Cycle Assessment methods ................................................................................ 14
3.3. Environmental LCA of the agriculture system ...........................................................15
3.4. Environmental LCA of the biorefinery systems .........................................................17
4. Reflections on the results...................................................................................................21
4.1. Types and choice of biomasses and biorefinery platforms ........................................21
4.2. Environmental LCA of the agricultural system......................................................... 24
4.3. Environmental LCA of the biorefinery systems ........................................................ 28
5. Discussions ........................................................................................................................31
5.1. Environmental hotspot assessments on biomass production....................................31
5.2. Environmental hotspot assessments on biobased products ......................................31
5.3. Uncertainties and methodological dilemmas............................................................ 32
6. Conclusions ....................................................................................................................... 41
7. Perspectives ...................................................................................................................... 45
8. References ......................................................................................................................... 49
vi
9. Supporting Papers ............................................................................................................ 69
9.1. Paper I ....................................................................................................................... 69
9.2. Paper II .......................................................................................................................91
9.3. Paper III ................................................................................................................... 115
9.4. Paper IV ................................................................................................................... 130
9.5. Paper V ..................................................................................................................... 181
10. Additional Paper ......................................................................................................... 232
vii
Figures
Figure 1: A schematic diagram for the conversion of biomass to bioethanol and other co-
products (italics and dotted lines show the processing alternatives), modified after Parajuli et
al. (2015a) and Larsen et al. (2012). .......................................................................................... 4
Figure 2: A schematic diagram for the conversion of green biomass to feed protein and lactic
acid. ............................................................................................................................................ 5
Figure 3: Dissertation outline and overall approach of the research. ....................................... 9
Figure 4: Process flow diagram of the standalone system for straw conversion to bioethanol
(System A). Electricity produced represents net values of the system (i.e., plant’s own
consumptions are subtracted). The dotted lines indicate the avoided products considered in
the CLCA approach, adapted from Paper-V..............................................................................18
Figure 5: Process flow diagram for the standalone system producing biobased lactic acid from
alfalfa (System B). Electricity produced represent net values of the individual system (i.e.,
plant’s own consumptions are subtracted). The dotted lines indicate the avoided products
considered in the CLCA approach. The index for material flow lines are as shown in Figure 1,
adapted from Paper-V. ..............................................................................................................18
Figure 6: Process flow and energy balance of the integrated biorefinery system (System C).
The index for material flow lines are as shown in Figure 1, adapted from Paper-V. ................19
Figure 7: Environmental burdens of producing the selected biomass types. Percentages are
indexed to the maximum values (based on Paper-III and Paper-IV)...................................... 26
Figure 8: Process-wise contributions to net GWP1 00 (kg CO2 eq per t DM) related to the
production of the selected biomass types (based on Paper-III and Paper-IV)........................ 26
Figure 9: Process-wise contributions to NRE use use (MJ eq per t DM) related to the
production of the selected biomass types (based on Paper-III and Paper-IV)........................ 27
Table Table 1 Environmental impacts of bioethanol and biobased lactic acid, obtained relying on
CLCA approach (FU = functional unit) (adapted from Paper-V) ............................................ 28
viii
Acronyms and abbreviations
ALCA Attributional Life Cycle Assessment
ALO Agricultural Land Occupation
CHP Combined Heat and Power
CLCA Consequential Life Cycle Assessment
DM Dry matter
EP Eutrophication Potential
EtOH Ethanol hydroxide
GHG Greenhouse Gas
GWP1 00 Global Warming Potential in 100 years
GBR Green Biorefinery
iLUC Indirect Land Use Change
LA Lactic acid
LCA Life Cycle Assessment
LCI Life Cycle Inventory
M t Million ton
NRE Non-Renewable Energy
OM Organic Matter
PFWTOx Potential Freshwater Ecotoxicity
SOC Soil Organic Carbon
Note for the readers: In the thesis, the words: GWP, GHG and carbon footprints are used to
reflect the same meaning on the effect climate change.
ix
Appended Papers
1: Parajuli R, Dalgaard T, Jørgensen U, Adamsen APS, Knudsen MT, Birkved M, et al.
Biorefining in the prevailing energy and materials crisis: a review of sustainable pathways for
biorefinery value chains and sustainability assessment methodologies. Renewable and
Sustainable Energy Reviews. 2015; 43(0):244-63.
2: Parajuli, R., Knudsen, M.T., Dalgaard, T. Multi-criteria assessment of yellow, green,
and woody biomasses: pre-screening of potential biomasses as feedstocks for biorefineries.
Biofuels, Bioproducts and Biorefining. 2015; (9): 545-566.
3: Parajuli, R., et al. (in-press). Environmental life cycle assessments of producing
maize, grass-clover, ryegrass and winter wheat straw for biorefinery. Journal of Cleaner
Production. (2016):1-13.
4: Parajuli, R., et al. (submitted). Environmental Life Cycle Assessment of willow, alfalfa
and straw from spring barley as feedstocks for bioenergy and biorefinery systems, submitted
to Science of the Total Environment.
5: Parajuli, R., et al. (submitted). Evaluating the environmental impacts of standalone
and integrated biorefinery systems using consequential and attributional approaches: cases
of bioethanol and lactic acid production, prepared to be submitted to Journal of Cleaner
Production.
Additional Paper
Parajuli R, Løkke S, Østergaard PA, Knudsen MT, Schmidt JH, Dalgaard T. Life Cycle
Assessment of district heat production in a straw fired CHP plant. Biomass and Bioenergy.
2014;68(0):115-34.
x
1
1. Introduction
1.1. Background
The global population is expected to rise to more than 9 billion and the demand of food is
expected to increase by two-fold from the current level (Kremen et al., 2012). Currently fossil
fuels contribute about 80% of the global energy demand. Even with the numerous political
commitments and strategies to tackle the issues of climate change and energy security the
energy demand of fossil fuel is still projected to rise by 40% by 2035. Of this expected
demand the contribution of fossil fuel was projected to be 75% (IEA, 2013). On the other
hand interactions among climate change effects, increasingly exploited fossil fuel reserves
and also the biotic stresses on plants and animals (Chakraborty & Newton, 2011, Dukes &
Mooney, 1999) are exacerbating vulnerabilities in different production sectors, including
agriculture (Gelfand et al., 2013). The alarming consequences of over exploiting biomass
resources, particularly for grain based biofuel production can be on soil carbon sequestration
(Fargione et al., 2008), nitrous oxide emissions (Crutzen et al., 2008), nitrate pollution
(Donner & Kucharik, 2008), biodiversity (Landis et al., 2008) and human health (Hill et al.,
2009). Furthermore, consequences of higher dependency on fossil fuels in the agriculture
sector has resulted hikes in the prices of the raw ingredients for food and feedstuffs (Lange,
2007), since fossil fuel is one of the principal raw material in the modern agriculture
(Krausmann, 2016) and one of the largest commodities that are produced and consumed
(Gielen et al., 2016). The challenge of agriculture sector in the context of climate change is
thus to sustainably address two mechanisms: reducing the emissions and adapting to a
changing climate (Smith & Olesen, 2010). Additionally, in biofuel and biobased products
value chains impacts of indirect land use change (iLUC) is also one of the dominating issues
that are often discussed and debated (Khanna et al., 2011, Templer & van der Wielen, 2011,
Tonini et al., 2016, Tonini et al., 2012). The essence was that any utilization of a productive
land can be claimed for increasing overall pressure on the frontier between “nature” and the
land use, hence therefrom inducing unintended consequences of GHG emissions due to land
use changes around the world (Audsley et al., 2009, Schmidt & Brandao, 2013, Schmidt &
Muños, 2014). These consequences exerted due to occupation of productive land in one
region/place and the impact occurring elsewhere in the world is defined in terms of iLUC
(Schmidt et al., 2015, Schmidt. J. H. et al., 2012). During such consequences, the capacity of
growing crops, in general is argued can be created in different ways e.g. expansion of the area
of arable land (deforestation), intensification of land already in use and crop displacement
(reduction in the consumption) e.g., due to influences of the commodities prices. Based on
such considerations, different methods can be used to quantify the impact of iLUC in terms of
GHG emissions (Flysjö et al., 2012, Schmidt et al., 2015).
2
The current driving force for a sustainable agroecological system is the need to facilitate the
development of more sustainable agricultural systems (Dalgaard et al., 2003), mainly by
introducing new value chains in the conversion of available biomass with higher
environmental benefits (Harvey & Pilgrim, 2011). The European Biorefinery Vision and
Roadmap for 2030 (Kircher, 2012) emphasized on the need to diversifying biomass
production and also the development of biorefineries for generating new biomass conversion
value chains. Biomass is one of the main raw material input to biorefineries, hence it is
important that their production system is also sustainable (Ragauskas et al., 2006). It is also
relevant as the current attempts towards bioeconomy has aimed to replace fossil-based
products and energy by biobased products (IEA, 2011). However, in majority of the countries
working to ensure energy security and have bioenergy and biofuels policies, there is either no
policy support for bio-based materials (e.g. mainly biochemicals) or it is limited to research
and development incentives (Palgan & McCormick, 2016, Philp, 2015). Moreover, evaluating
general peformance of biorefinery value chains considering wider environmental and socio-
economic paradigms would contribute to knowledge creations (Van Lancker et al., 2016) and
also supports in making decisions when various value chains have to be screened for devising
necessary policy measures (Fritsche & Iriarte, 2014).
1.2. Biorefinery systems
In spite of biomasses are important source of bioenergy options, issues related to their
environmental impacts, security and stability and the need to diversify their usage is
inevitably important (Cherubini et al., 2009a, Clapp et al., 2000, Elghali et al., 2007).
Diversifications in the conversion processes and in the product value chains are now possible
in the form of biorefinery technology. The technology utilizes one or more types of biomasses
to produce spectrum of biobased products (René & Bert, 2007), including both food and
non-food products (Chen & Zhang, 2015). The classification of biorefinery, until now, have
been made according to (i) types of raw material inputs (e.g. green biorefinery, lignocellulosic
biorefinery and whole crop biorefinery), (ii) types of products (syngas platform, sugar
platform, lignin platform), and/or (iii) status of technology (1st and 2nd generation
biorefinery) (René & Bert, 2007).
The 7th Framework Program of the European Union (EU) advocated for a joint European
Biorefinery Vision and Roadmap for 2030, and has targeted to substantially cover the
conventional market by biobased products (e.g. 30% biochemicals, 25% biofuel, 30% heat
and power). It also stressed on the need to diversifying biomass supply chains and
integrating bio-based industrial sectors (Kircher, 2012). Furthermore, biorefineries are
regarded important not only to tackle the energy insecurity issues, but additionally also to
sustainably meet the increasing demand of high value chemicals, proteins for livestock
production and food ingredients (IEA, 2011). The relevancy of such can also be explained by
3
the status and trend of net import of protein sources in Europe (Parajuli et al., 2015a), e.g.
the average net import of soybean cake and soybean from South America for the period of
2006-2011 was about 22 million ton (Mt) and 14.5 Mt respectively (FAOSTAT, 2013). The
increasing internal consumptions in Brazil and the demand from other countries, e.g., China
has stressed to look into potential alternatives, particularly in European countries so that
vulnerabilities in their supply can be mitigated (Parajuli et al., 2015a). Another important
concern was also on the sustainable grassland management of the European countries. and
hence on the utilization of available biomasses to alternatively produce products besides
conventional animal feeds (Mandl, 2010). Green biorefinery (GBR) is thus regarded as one of
the important technology in the cross-road of managing the surplus grass land and for the
production of alternative products (protein and other chemical-building blocks, e.g. lactic
acid, lysine) (Kamm et al., 2009).
1.2.1. Conversion of biomass in a lignocellulosic biorefinery
The conversion of biomass, as discussed here mainly focus on the two production chains: (i)
bioethanol and (ii) feed protein and biochemicals. These pathways are chosen to reflect the
biobased products that have been considered in the study, as were also outlined among the
research perspectives in Paper I.
In a typical lignocellulosic biorefinery, the conversion of biomass occurs mainly in four steps:
(i) pretreatment of the raw biomass, (ii) hydrolysis, (iii) fermentation, and (iv) product
recovery (FitzPatrick et al., 2010). A schematic flow for biomass conversion to bioethanol is
shown in Figure 1. After the biomasses are pre-processed (e.g. chopping of the baled stock)
pretreatment is important to convert its strong lignocellulosic structure into reactive
cellulosic intermediates (Galbe et al., 2007). The cellulose is structurally strong with a long
chains of glucose molecules, giving a crystalline structure which is difficult to break down
compared to starch (Zhang, 2008). The compositions of biomass are significantly changed
after undergoing a pretreatment process (as also illustrated from the case of bioethanol
conversion discussed in Paper-V). After the pretreatment process, the C5 sugars (mainly
pentose, xylose and arabinose) are immediately liberated and the C6 sugars (cellulose) are
subjected to hydrolysis.
The hydrolysis process can be either acid hydrolysis or enzymatic hydrolysis. The limitations
of the acid hydrolysis process were related to a lower bioethanol yield, corrosion problem and
hence requiring of resistant materials for the hydrolysis chamber. The acid hydrolysis also
needs acid neutralization process to avoid formation of large amounts of gypsum, calcium
sulphate and other disposable compounds (Galbe et al., 2007). Enzymatic hydrolysis is thus
suitable to work even at the higher temperature and hence the fermentation process is less
susceptible to contamination (DSM, 2012). The fermentation process is normally performed
4
either in a separate fermenting tank, the process generally being referred to as “separate
hydrolysis and fermentation” (SHF), or simultaneously with the hydrolysis of the cellulose
chains, also called “simultaneous saccharification and fermentation” (SSF) (Galbe et al.,
2007). Fermentation of C6 sugars is in general practice, but are reported for a lower
bioethanol yield (Mosier et al., 2005). In recent innovations, with the recirculation of C5
sugar and with the exposure to C5 yeast the yield of bioethanol was reported to increase by
20-40% per ton of biomass (Inbicon, 2013, Losordo et al., 2016). After the fermentation
process, the distillation process maintains the concentration of bioethanol (above 4-6 w/w %)
(Larsen et al., 2008). The residual products from the bottom of the distillation are collected.
The collected lignin can be pelletized and can be used as fuel (e.g. co-firing in a CHP plant)
and the liquid particle can be used to produce biogas.
Figure 1: A schematic diagram for the conversion of biomass to bioethanol and other co-
products (italics and dotted lines show the processing alternatives), modified after Parajuli et
al. (2015a) and Larsen et al. (2012).
1.2.2. Conversion of green biomass in a green biorefinery
Figure 2 shows the schematic process of processing green biomass in a green biorefinery. The
green biorefinery generally utilizes fresh or wet grasses to produce different biobased
products e.g. feed protein and chemical building blocks (lactic acid, lysine etc) (Kamm &
Kamm, 2004). The processing of biomass initiates with the wet fractionation process, as the
primary step to isolate the green biomass substances into a fibre-rich cake/pulp and a
5
nutrient-rich juice (Figure 2). The pulp consists of celluloses and starch along with the
valuable organic pigments (Kamm & Kamm, 2004). The press cake can be used for the
production of green feed pellets, and also as a raw material for the production of organic
acids or for the conversion to hydrocarbons (synthetic biofuels) (Kamm et al., 2009).
Depending on the water content of the raw feedstock, additional water can be used to reduce
overheating of the fiberizing plates; water is used normally at the ratio of 0.55:1 (grass:water)
(Hansen & Grass, 2000). Furthermore, recirculation of water at different stages of biomass
processing can be done to meet the plant’s water demand (O’Keeffe et al., 2011). Double
pressing of the biomass can be carried out to optimize the juice extraction. The process is
then followed with washing of the press cake. In general, most of the pilot scale GBR plant
reported that press juice have a DM content of 7%, and the protein content around 25% of the
juice dry matter (O’Keeffe et al., 2011) (see Paper-V). In the case of a system producing both
lactic acid and feed-protein, the juice is distributed into two streams, e.g. the larger stream
(70-90%) can be used for protein extraction and the smaller stream (10-30%) can be used to
produce lactic acid (Kamm et al., 2009, O’Keeffe et al., 2011), depending on the need of
processing green biomasses in the technology.
Figure 2: A schematic diagram for the conversion of green biomass to feed protein and lactic
acid.
The aqueous residual flows (brown juice) can be added to the fermenting medium for lactic
acid production In addition, to increase the value of processing the biomass and to utilize the
raw material; press cake can also be subjected to enzymatic hydrolysis in order to
6
monomerize the carbohydrate to produce readily available glucose. Glucose produced after
the hydrolysis can then be treated with full fermentation medium to produce lactic acid. In
general, based on the fermenting agent the fermentation of biomass substrate to lactic acid
can be put into different categories (Bayitse, 2015, John et al., 2006). Regarding the recovery
of the lactic acid, the processes including ultrafiltration, reverse osmosis (Patel et al., 2006),
bipolar electro-dialysis (Kim & Moon, 2001) and distillation (Kamm et al., 2009) are
followed. During the process, the protein from the fermentation broth can be separated using
an ultrafiltration membrane (Li et al., 2006). Sodium hydroxide can be used as a base
material to the fermentation process, which results into sodium lactate. The reported
recovery of lactic acid from the sodium lactate fermentation broth is about 90%. The residual
content in the broth that is left after separation from lactic acid and single-cell biomass can
be used in a biogas plant (Kamm et al., 2009, O’Keeffe et al., 2011).
Normally, lactic acid are produced in two optically active isomers d(−)- lactic acid and l(+)-
lactic acid. The l(+)-lactic acid is the preferred isomer for food and pharmaceutical industries
(Ghaffar et al., 2014). However, the combinations of both isomers are preferable for
producing the polylactic acid (PLA), which is produced after further processing of biobased
lactic acid (Zhang et al., 2016).
1.3. Sustainability of biorefinery production chains
Sustainability of biorefinery production chains primarily depends on the types of feedstock
supply (Lange, 2007). Generally about 40-60% of the total operating costs of a typical
biorefinery are spent on the feedstocks, and this makes the choice of feedstocks even more
important (Caputo et al., 2005). Numerous studies on bioethanol claimed for environmental
savings in terms of fossil fuel use and Greenhouse Gas (GHG) emissions, however such
claims are with inconsistent findings , particularly on the reported range (Borrion et al.,
2012). But, it appeared that the savings from the conversion of lignocellulosic biomasses to
bioethanol was significant compared to petrol (Sheehan et al., 2003). The variations on such
claims were partly due to different feedstocks used (Muñoz et al., 2013). For example, the
ratio of net energy input to the net energy output was lower for corn grain based bioethanol
production chains, which was mainly due to number of factors, such as differences on: yields,
fertilizer application rates, fertilizer manufacturing efficiencies, conversion technologies, and
methods of evaluating co-products and energy inputs (Shapouri et al., 2002, Wang et al.,
1999).
The major question in the sustainability assessment of biorefinery is to investigate how
biobased products (biofuels, biochemicals and protein) could demonstrate themselves as
rationale alternatives compared to conventional fossil fuel based products. With regard to the
sustainability assessment of a biorefinery system, it can be categorized in three important
7
value chains (i) feedstock supply: requiring the assessment of suitability and adequacy of
biomass for the conversion process (Ghatak, 2011, Thorsell et al., 2004) (ii) biorefinery
process performances: stressing on the optimization of refining process and the upgrading
the conversion efficiency of the system (Kudakasseril Kurian et al., 2013) , and (iii)
productivity of biobased products: as a measure for assessing environmental and economic
footprints of the biobased products (IEA, 2011, Ragauskas et al., 2006) Likewise, careful
considerations on the issues of agro-environmental management, e.g. land use, soil nutrient
losses, soil quality, eco-toxicological impacts, fossil fuel depletion (Arshad & Martin, 2002,
Brandão et al., 2011) and the interconnected wider environmental concerns, i.e. climate
change (Watson, 2011) are also important. These are relevant, since there exists synergy
between agriculture system and industrial processing of biomass in biorefinery value chains
(Jenkins & Alles, 2011).
For the comparative assessments of environmental sustainability of producing different types
of biomasses and biobased products a Life Cycle Assessment (LCA) (Rebitzer et al., 2004) is
widely used method (Cherubini & Jungmeier, 2010, Cherubini & Ulgiati, 2010, Luo et al.,
2009, Modahl & Vold, 2011). LCA is an analytical tool to calculate environmental impacts of
different production system and processes. It is one of the highly recommended tools that
have been practiced in EU for the sustainability assessment of different production sectors
including agriculture (European Commission, 2015). Regardless of a wider use of the LCA
method for evaluating different biomasses (Berndes & Hansson, 2007) and biofuel
production systems (Cherubini, 2010, Cherubini & Ulgiati, 2010, Kim & Dale, 2005) very
few studies have compared environmental impacts of producing several biomass feedstocks.
Furthermore, most of the LCA studies mostly focussed on GHG and energy balance; however
other impact categories are also relevant (Rødsrud et al., 2012, Wagner & Lewandowski,
2016). Wider selection of environmental impact categories are also important in order to
avoid single indicator based decision support for biorefining policies and to minimize the
flawed decision support (Finkbeiner, 2009).
In order to maximize the benefits of biorefinery and hence for its sustainability it is also
relevant to explore the possible interactions between agricultural and biorefinery systems,
mainly by looking into the opportunities of recycling nutrients that can be recovered from the
residual products of biorefinery system and utilizing them back in farmers’ field, and
eventually offsetting the use of synthetic fertilizers (Ahring & Westermann, 2007, Langeveld
et al., 2010). The benefits that one system can owe to another and vice-versa (Dale et al.,
2011) is thus among the prime perspectives to be explored and answered in this sector.
8
9
2. Research design
2.1. Research question and objectives
The overall research question of this project is: how does the utilization of biomasses for a
biorefinery process affect the environmental sustainability?
In relation to the outlined research question, this study was designed to work with the
following objectives through the subsequent papers.
1. Objective-I: To get an overview of biorefinery processes in relation to sustainability
aspects and to carry out an overall evaluation of different biomass feedstocks.
2. Objective-II: To assess the environmental impacts of producing biomasses for
biorefineries.
3. Objective-III: To assess the environmental impacts of producing biobased products
from a biorefinery and relate them to a wider sustainability perspective.
2.2. Introduction to the thesis structure
The thesis is structured in seven main chapters. The overall structure of the dissertation is
shown in Figure 3, along with the assessment outputs through each sets of objectives and
delivered Papers.
Figure 3: Dissertation outline and overall approach of the research.
10
Chapter 1 highlights the importance of biorefinergy technologies, on the basis of expected
interactions between the agriculture system and industrial processing of biomasses in a
biorefinery. It makes discourses on the issues of climate change, fossil fuel reserves depletion
and on the need of systemic evaluation of biorefinery systems for long term sustainability.
Chapter 2 illustrates the research design and within it describes the research question and
the specific objectives of this study, on the basis of which the entire research was structured.
In Chapter 3, materials and methods that were employed throughout the study are reflected.
The results of the study are reported in Chapter 4. In Chapter 5, the study makes overall
discussions on the results along with the synopsis of the major environmental hotspots.
Methodological dilemmas and identified uncertainties related to environmental
sustainability assessment are also discussed in Chapter 5. Finally in the Chapter 6, the study
concludes along make some discourses on the perspectives in Chapter 7.
2.3. Introduction to the thesis
This PhD dissertation has primarily aimed to assess environmental sustainability of biomass
and biorefinery production chains. For the assessment, at first the biomass types were
classified as: “yellow” covering agricultural residues (e.g straw), “green” (grasses) and
“grey/woody” (e.g. short rotation coppice), in accordance to Gylling et al. (2013). Regarding
the selection of biomasses, among the different criteria were the chemical compositions of
biomasses and their energetic properties. In general higher cellulose: lignin ratio is regarded
favourable for biochemical conversion pathways, e.g. straw (McKendry, 2002); likewise the
crude protein content and the carbohydrate content (Kamm et al., 2009) makes grasses
suitable for green biorefinery. From environmental perspectives, bioenergy options, e.g.
Short Rotation Coppice (SRC) including willow is suited for climate change mitigation and to
reduce import dependency on fossil fuels (Berndes & Hansson, 2007). It is also important
because of its effective nutrient withdrawal potential from soil and with better fossil fuel
energy balance (Murphy et al., 2014). These scoping and also the formulation of overall
methodological framework was made on the basis of detail literature review (Paper-I) and
latter based on the pre-screening of biomasses, which was carried out in Paper-II . The
review aimed to summarize the factors affecting the sustainability of biomass production. A
framework of multicriteria decision analysis was thus utilized in Paper-II to prescreen
aforementioned biomass feedstocks for detail LCAs. In the latter part, a method of LCA was
used to quantify and evaluate the environmental impacts of producing biomasses (Paper-III
and Paper-IV) and biobased products (Paper-V). The environmental impacts assessment
of biomass production system was limited at the farm level and included the following
biomasses: straw from spring barley and winter wheat, maize, ryegrass, grass-clover, alfalfa
and willow. Likewise, the selected biorefinery production chains were the conversion of straw
to bioethanol and alfalfa to biobased lactic acid.
11
2.4. Introduction to Papers
This PhD dissertation consists of five papers, of which three are published. Paper-I in
Renewable & Sustainable Energy Reviews (2015) and Paper II in Biofuels Bioproducts &
Biorefining-Biofpr (2015). Paper-III is published in the Journal of Cleaner Production
(2016). Paper-IV is submitted to the journal Science of the Total Environment
(http://www.journals.elsevier.com/science-of-the-total-environment/) and Paper-V is
prepared and aimed for submitting to the Journal of Cleaner Production
(http://www.journals.elsevier.com/journal-of-cleaner-production/).
Paper-I aimed to discuss the sustainability features of biorefinery system focusing at: (i)
farming system level, (ii) biomass conversions platforms and (iii) methodological aspects to
be included in the sustainability assessment of biorefinery value chains. It outlined the
research perspective depending on which the entire research was framed.
Paper-II aimed to pre-screen available biomass types for a system wide sustainability
assessment in related biorefinery value chains. The study describes the use of multiple-
criteria decision making techniques, as a tool for assessing criteria and to draw preferences
among the biomass alternatives.
Paper-III and Paper-IV aimed to assess environmental impacts of producing biomasses.
These papers are thus expected to act as bridge to connect the agriculture system with the
biorefinery production system, in terms of sharing the environmental footprints.
Paper-V aimed to assess environmental impacts of the conversion of yellow and green
biomasses to biobased products. It enunciated on the environmental footprints of producing
straw based bioethanol and producing biobased lactic acid from alfalfa.
12
13
3. Reflections on the materials and methods
3.1. Types and choice of biomasses and biorefinery platforms
The overall framework and steps applied to work with the stated research question and the
research objectives of this thesis (see section 2.1) are shown in Figure 3. In order to make
overall environmental evaluations of the selected biomass types, the first step adopted was
the identification of potential biomasses for different biorefinery technologies. For the
purposes, a review study was carried out, primarily explaining: the features of sustainability
assessment, indicators of the assessment and the related methodological frameworks. The
study also made a review of classified biorefinery systems. Based on the characteristics of
biomasses (yield, chemical properties, and environmental aspects) it also aimed at outlining
the research perspectives, which were the key guidelines to make the next episodes of the
research and designing the framework for this thesis. These assignments were made in
Paper-I (Parajuli et al., 2015a). The key literatures, but not limited, which were reviewed
were grouped in terms of the information to be deployed on: (i) sustainability assessment
methods: e.g. Gasparatos and Scolobig (2012), Sammons Jr et al. (2008), Afgan and
Carvalho (2002) and Ness et al. (2007); (ii) environmental burdens of biomass production,
e.g. Hamelin et al. (2012), Blengini et al. (2011), Börjesson (1996), Tsoutsos et al. (2010) and
Uellendahl et al. (2008); (iii) biorefinery pathways: e.g., Larsen et al. (2012), Kaparaju et al.
(2009) and Kamm and Kamm (2004); (iv) diversifications of biobased products and
sustainability of biorefinery systems, e.g. Carole et al. (2004), Koutinas et al. (2008), Wright
and Brown (2007), Schaidle et al. (2011), Unnasch (2005) and IEA (2011).
The second step was to prioritize or rank the biomass types from the available different types
of lignocellulosic biomasses for detailed environmental sustainability assessments. The
biomasses were selected considering their suitability in Danish and similar agro-climatic
conditions. The overall approach of the pre-screening and setting-up the criteria are
explained in Paper-II (Parajuli et al., 2015b). The evaluation process was classified mainly
to cover two levels of biorefinery value chains: (a) agricultural system and (b) biorefinery
system. For the agriculture system emphasis were given to: the environmental sustainability
index (ESI) (Sands & Podmore, 2000), monetary and energy values (Tellarini & Caporali,
2000) and farming system managements (e.g. nutrient management, land productivity, pest
management and crop yield) (Chandre Gowda & Jayaramaiah, 1998). Apart from other
classifications of biorefineries (René & Bert, 2007) the discussed biorefinery platforms that
were included in the evaluations and also in this thesis were sugar-based platform (IEA,
2011) and the green biorefinery technology (Kamm et al., 2009). The properties of the
selected thirteen biomasses were aligned with respect to the aforementioned types of
biorefineries. Since an integrated biorefinery system can include different biomasses and
conversion processes (IEA, 2011), both thermal and biochemical properties of biomasses
14
(Galbe & Zacchi, 2007, Höltinger et al., 2014) were taken into considerations during the
prioritization process. The evaluation parameters were classified as: (i) supply potential, (ii)
biomass properties and (iii) potential environmental gain/losses. Pre-screening of the
selected biomass was carried out by using a Multicriteria Decision Analysis (MCDA) tool. The
ratings on biomass types were given on the basis of information collected for the stated
evaluation parameters. The weighing factors was calculated by using the approach of the
Analytical Hierarchy Process (AHP) process (Macharis et al., 2004). Furthermore, as an out-
ranking method of MCDA (Wang et al., 2009) the method “Preference Ranking Organization
Method for Enrichment of Evaluations” (PROMETHEE) was chosen. This method was used
because it was useful to determine the preferences among the alternatives. The method is
also applicable whenever the evaluations of the alternatives are to be made on the basis of
qualitative and quantitative information. The PROMETHEE-II was used in the decision
making process, as it enabled a complete ranking of preferences by involving the net
outranking flows (Brans & Mareschal, 2005), i.e. higher the net flow the better is the
alternative. The detail mathematical iterations of the method are shown in Appendix 1.b of
the Paper-II (Parajuli et al., 2015b).
3.2. Life Cycle Assessment methods
LCA is one of the most established methods to undertake systemic evaluation of a product
and the processes of a production system (ISO, 1997). According to ISO (2006) the LCA
method is divided into four main steps: (i) goal and scope definition, (ii) inventory analysis,
(iii) impact assessment and (iv) interpretations. At the first phase of LCA studies the system
under the assessment, functionalities and the assessment boundaries are explained. The
inventory analysis basically collects and analyses information on the resource use for the
expected product outputs, including the emissions. The interpretation step evaluates the
results of life cycle inventory analysis and or life cycle impact assessment. The evaluation of
different production system is comparable if the system under the evaluation is expressed in
a common functional unit, which is basically defined as the main function of the assumed
production system or process (ISO, 2006).
The LCA method considers a life cycle of a product system, starting from the extraction of
required resources, production, use of resources and products, and recycling up to the
disposal of remaining waste (European Commission, 2010). Furthermore, another requisite
aspect in the assessment is the system boundary. The system boundaries in LCA studies have
been defined in several dimensions, e.g. boundaries between the technological system and
nature; system delimitations of geographical coverage and time-horizon; and the boundaries
between the life cycle of the products assessed and corresponding life cycles of the other
products (ISO, 2006, Tillman et al., 1994). Despite there is presumptions that the LCA has
covered all the stages of product system, some processes, inputs and life cycle stages which
15
do not significantly change to the overall assessment can be deleted (ISO, 2006, Sinden,
2009).
With regard to the LCA approaches, two distinct approaches are debated from time to time,
particularly when evaluations for a system involving two or more products are to be made,
and when these products possess multi-functional characteristics (Cherubini et al., 2011). For
example, combustion of straw in a Combined Heat and Power (CHP) plant producing heat
and electricity, and if the assessment requires defining as such, the “main” and the “co-
products” (Cherubini et al., 2009b, Parajuli et al., 2014). For dealing with such issues,
generally following approaches are used: (i) Attributional LCA (ALCA) and (ii) Consequential
LCA (CLCA) (Ekvall & Weidema, 2004, Guinée et al., 2004, ISO, 2006). In general, ALCA
approach provides the ways of assessing impacts of different processes used for producing
and consuming a product (Brander et al., 2009). In ISO (2006) and Sinden (2009) avoiding
the allocation of impacts are recommended by sub-dividing or expanding the product system.
However, PAS 2050 (Sinden, 2009) suggested that the system expansion may be applicable if
it is possible to identify the potential products that are able to be displaced and the displaced
product can be defined with their average emissions. In addition, in any situations, if such
conditions are difficult to meet Sinden (2009) suggested that economic allocation to
coproducts can be done, which is computed based on their economic values. ALCA approach
is basically based on average data and the relative values of the products produced from a
system (Rehl et al., 2012). It uses physical properties, e.g. mass, heating value, economic
values or revenues of production system that determines the ratio of products’ shares on the
resource demand and on the emissions (Ekvall & Finnveden, 2001, Rehl et al., 2012). In
CLCA approach, system expansion can be carried out whenever avoided products are
identified, and for such marginal technologies are used, hence also their marginal emissions
(Brander et al., 2009, Ekvall & Weidema, 2004, Schmidt, 2008, Styles et al., 2015,
Thomassen et al., 2008). These principles were kept in mind when preparing the LCA studies
in relation to this PhD dissertation.
The sections hereafter make reflections on the methods applied for assessing the LCA of
producing the biomasses and the biobased products.
3.3. Environmental LCA of the agriculture system
3.3.1. Life cycle inventory, data sources and system boundaries
The Life Cycle Inventory (LCI) for the selected yellow and green biomasses, e.g., winter
wheat-straw and maize, grass-clover and ryegrass respectively are descried in Paper-III
(Parajuli et al., 2016). Likewise, the LCI described in Paper-IV covered the similar
classification, but different biomasses, which were spring barley-straw, alfalfa and
additionally covered the “grey/woody” biomass, i.e. willow. The LCI included both the
16
background system (emissions covering the production of material inputs and during their
supply to the farm) and the foreground system (accounting the materials inputs, resources
used and farm based emissions). The LCIs of background processes were mainly based on the
default allocated unit process values, as reported in Ecoinvent v3 (Weidema et al., 2013). For
the foreground processes crop production data were mainly used, which were based on
different sources, as elaborated in the related papers and are also briefly discussed in the
sections below.
3.3.2. Environmental impact categories
Environmental impact categories commonly considered in the assessment were: Global
Warming Potential (GWP100), (ii) Eutrophication Potential (EP), (iii) Non-Renewable Energy
(NRE) use (iv) Agricultural Land Occupation (ALO) and (v) Potential Freshwater Ecotoxicity
(PFWTox). A consistency was made in the selection of environmental impact categories in
both the studies (Paper-III and IV). There were some differences in the selected impact
categories, e.g. in Paper-III additional consideration was on the Potential Biodiversity
Damage (PBD). The PBD was calculated based on the loss of plant “species richness” (de
Baan et al., 2012), and the characterization factors were adapted from Knudsen et al. (2016).
Likewise, in Paper-IV “Soil quality” was additionally included. The change in Soil Organic
Carbon (Δ SOC) stock due to the transformation and occupation of land during the
production of the selected biomasses was regarded as one of the indicator of the soil quality.
The assumption was in accordance to IPCC (2000) and Milà i Canals et al. (2007). The
method that was used to calculate the Δ SOC stock was based on Brandão et al. (2011) and
Milà i Canals et al. (2007). Necessary land use parameters and methods are detailed in
Paper-IV. Regarding PFWTox, in both papers, it was calculated at two levels: (i) considering
the emissions from the applied pesticides at the farm and (ii) total impact covering both the
emissions at the farm and the indirect emissions occurring at the background system. Data
on the types and amount of the applied pesticides (active ingredients (a.is)) were based on
the pesticides application practices of Denmark (Ørum & Samsøe-Petersen, 2014, SEGES,
2010, SEGES, 2015). For the emissions at the farm level, it was assumed that emissions to
soil can occur indirectly (Birkved & Hauschild, 2006), hence distribution patterns to air and
freshwater were simulated for the selected pesticides. In Paper-III , the emission
distribution were calculated based on the model PestLCI 2.0.6 (Birkved & Hauschild, 2006)
after applying the different field scenarios (e.g. months of the pesticide application, crops
development stages and application technique) (see Supporting Information of the Paper-
III). In the case of Paper-IV, average emission distribution fractions, as calculated in Paper-
III for the most commonly used a.is, for cereal crops and grasses were considered. The reason
for considering the average fractions was that by the time of preparing this study for many
a.is, inventory was not included in PestLCI 2.0.6. The approach for the calculation of
17
PFWTox related to pesticides application was according to the method suggested by the
characterization model USEtox-Default” (Fantke et al., 2015).
3.3.3. Input-output and system modelling
The selected crops were assumed to be cultivated on Danish arable farm with sandy and
loamy soils (NaturErhvervstyrelsen, 2015). The rate of Synthetic fertilizer (N, P, K)
application followed the Danish regulation (NaturErhvervstyrelsen, 2015). The detailed
input-output on material flows (energy, fuel, agro-chemicals, emissions etc.) entering into the
agriculture system and the emissions are reported in Paper-III and Paper-IV.
With regard to the method used to calculate the SOC change, in both studies the net turnover
of the organic matter derived from the net non-harvestable biomass residues was considered.
This was calculated in relative to a reference crop, and was selected as spring barley (Parajuli
et al., 2016). The SOC change was calculated in 100 years’ time frame (Petersen et al., 2013).
In both the papers, the method to calculate the net C assimilation was in accordance with
Taghizadeh-Toosi et al. (2014a), however for willow (Paper-IV) the non-harvestable biomass
was quantified in accordance with Hamelin et al. (2012). Temporal variations on the soil C
sequestration (e.g. in 20 years) were also reported in the analysis in the respective papers.
With regard to calculation of N-leaching, both papers adopted the N-balance method
(Brentrup et al., 2000, Hansen et al., 2000), after accounting the N input-output and losses.
Direct and indirect nitrous-oxide emission (N2O) were based on the factors reported in IPCC
(2006). The emission factors for NH3 emission from N-fertilizer was taken after the reports
(EEA, 2013, Nemecek & Kägi, 2007), as reported in Paper-III.
3.4. Environmental LCA of the biorefinery systems
3.4.1. Life cycle inventory, data sources and system boundaries
This study dealt with the conversion of the selected biomasses in two different biorefinery
systems: the conversion of straw based on winter wheat to bioethanol (System A) and the
conversion of alfalfa in a green biorefinery technology (System B). These two systems were
regarded as “Stand alone system” on the basis of producing identified key main product. In
the next evaluation, these two individual systems were combined to utilize the resources,
primarily the useful energy and the system was termed as “Integrated system” (System C).
The integrated system was co-producing bioethanol and biobased lactic acid along with other
biobased products (Figure 4, 5 and 6).
The production database for the conversion of straw to bioethanol (System A) was taken by
averaging the mass balances, as reported in Bentsen et al. (2006), Kaparaju et al. (2009) and
Wang et al. (2013). For the conversion of alfalfa to lactic acid (System B) it was based on the
mass and energy balance reported in O’Keeffe et al. (2011) and Kamm et al. (2009).
18
Figure 4: Process flow diagram of the standalone system for straw conversion to bioethanol
(System A). Electricity produced represents net values of the system (i.e., plant’s own
consumptions are subtracted). The dotted lines indicate the avoided products considered in
the CLCA approach, adapted from Paper-V.
Figure 5: Process flow diagram for the standalone system producing biobased lactic acid from
alfalfa (System B). Electricity produced represent net values of the individual system (i.e.,
plant’s own consumptions are subtracted). The dotted lines indicate the avoided products
considered in the CLCA approach. The index for material flow lines are as shown in Figure 1,
adapted from Paper-V.
19
For the integrated system (System C), the details on the energy flows within the biorefinery
systems and the energy exchanges between the two standalone systems are shown in Figure 6
and the notations presented in the Figure 6 are: Gross Ei n-T = total energy required in the
biorefinery systems; Eout = Energy produced from the CHP plants (after deducting the self-
demand, e.g. to burn the fuel); Ei n-GBR = Energy input to System B; Ei n-EtOH = Energy input to
System A; E*out= co-produced energy from the CHP plants; Net Ei n-Total = Energy required in
the biorefinery after accounting all internal consumptions and Net Eout-surplus = surplus energy
production from the system.
Figure 6: Process flow and energy balance of the integrated biorefinery system (System C).
The index for material flow lines are as shown in Figure 1, adapted from Paper-V.
3.4.2. Environmental impact categories
The selected environmental impact categories were: (i) Global Warming Potential (GWP1 00),
(ii) Eutrophication Potential (EP), (iii) Non-Renewable Energy (NRE) use and (iv)
Agricultural Land Occupation (ALO).
3.4.3. Handling of co-products
Decision to assume main and co-products was based on the potential revenues of each
biorefinery systems. Prices of the products were based on the available market (documented
as basic assumptions in Paper-V). The products’ spectrums are illustrated in Figure 4, 5 and
6. Handling of co-products during the LCA was carried out by using both ALCA and CLCA
approaches. Economic allocation was made for the designed biorefinery system scenarios
when the evaluation was relying on ALCA approach.
20
3.4.4. Consequences of producing biomass and the biobased products
In the case of CLCA approach to the consequential effects related to the biomass production
were accounted in the following two manners:
a. Straw removal:
Consequences related to straw removal from the field (Petersen & Knudsen, 2010) were
estimated in relative to the situation if straw was ploughed back into the field. The approach
included: (i) emissions from soil C change (ii) compensation of displaced nutrients by
applying equivalent amount of synthetic fertilizers and (iii) related N emissions with respect
to the application of the compensated nutrients. The consequences of straw removal was thus
amounted to 143 kg CO2 eq/ t straw (85% DM) (Parajuli et al., 2014).
b. Indirect land use change (iLUC):
Several methods have been used with regard to quantifying impact of iLUC, e.g. in Audsley et
al. (2009), Cederberg et al. (2011) and Schmidt et al. (2015). In Audsley et al. (2009), a
generic iLUC factor was suggested to be 1.4 t CO2 eq per ha. Likewise, the alternative iLUC
factors are, e.g. 1.9 t CO2 eq per ha for Denmark and for the world average arable land was 1.7
t CO2 eq per ha, as reported in Schmidt and Muños (2014). In this study, 1.4 t CO2 eq per ha
was used as iLUC factor.
21
4. Reflections on the results
4.1. Types and choice of biomasses and biorefinery platforms
Objective-I: To get an overview of biorefinery processes in relation to sustainability aspects
and to carry out an overall evaluation of different biomass feedstocks.
Conclusions from Paper-I:
About 30 different types of lignocellulosic biomasses, agricultural residues and wastes were
reviewed on their chemical compositions. The proportion of cellulose, hemicellulose and
lignin were reported varying in different biomasses, e.g. animal waste had 6% and 28% of
cellulose and hemicellulose respectively of the total dry matter; and hardwood stem was with
higher proportion of cellulose, hemicellulose and lignin (e.g., 40-50, 24-40 and 18-25 % of
DM) (Ghatak, 2011, McKendry, 2002, Nanda et al., 2013, Parajuli et al., 2015a). A
comparison between the bioethanol production based on wheat straw and grass-clover
showed that straw had a yield of 270 kg t DM-1 and grass-clover had 241 kg t DM-1 (Thomsen
& Haugaard-Nielsen, 2008), which was partly due to the respective carbohydrate contents
(Jenkins & Alles, 2011). Moreover, there are other alternative pathways, e.g. for producing
biochemicals and energy from such biomasses, but the market value of the alternate biobased
products is important in such decisions (IEA, 2011). A study of International Energy Agency
(IEA)-Bioenergy (Task 42 Biorefinery) reported on massive demand of biobased chemicals
and also highlighted on their growing future market (IEA, 2011). The demand of special
biobased-chemicals (enzymes, bio-pesticides, essential amino acids, vitamins, etc.) in the
global market was reported currently being several billion US dollars per year and was
growing at a rate of 10–20% per year (Dale, 2003). Furthermore, annually about 8 million
tons (Mt) of fermentation products (e.g. lactic acid, amino acids, enzymes) are produced
(IEA, 2011). The market for lactic acid in the year 2009 covered about 19% of the total market
of the fermentation derived chemicals, which was after the market of amino acid and enzyme.
With regard to primary task of processing the biomasses in a lignocellulosic biorefinery, a
review on techno-economic performance of the different pretreatment process (e.g. lime
pretreatment, dilute sulphuric acid pretreatment and steam pretreatment) was made, and
concluded them on the basis of their advantages and disadvantages (Eklund et al., 1995,
Klein-Marcuschamer et al., 2011, Sierra et al., 2009, Talebnia et al., 2010, Uihlein &
Schebek, 2009, Zhao et al., 2009). Hydrothermal pretreatment is generally regarded suitable
to breakdown strong lignocellulosic structures into cellulosic medium (Galbe et al., 2007).
This type of pretreatment was suitable compared to the other pretreatment process, e.g. acid
pretreatment, based on the issues to deal with unwanted waste compounds and degradation
of the pretreatment chamber and hence increasing the cost (Galbe & Zacchi, 2007).
Likewise, hydrolysis is another important task to yield sugar from both hemicellulose and
22
cellulose, and is one of the critical parameters for ensuring better economic returns from the
bioethanol production (Öhgren et al., 2007). Hydrolysis of biomass can be carried out in
different ways, e.g. acid hydrolysis and enzymatic hydrolysis. Acid hydrolysis was reported
with a limitation of yielding lower bioethanol and issues related to waste disposals were also
reported serious to the environment. These disadvantages related to acid hydrolysis was
argued to increase the production cost of bioethanol (Galbe et al., 2007). Enzymatic
hydrolysis is thus generally recommended to facilitate the production of glucose, as enzymes
can work at mild process condition (Verardi et al., 2012).
With regard to environmental sustainability assessments of biorefinery systems, the foremost
issues that were discussed were about the complexities of biorefinery system, primarily
related to multiple material flows. This was also argued mainly for the purpose of handling
the main and co-products that are produced from a typical biorefinery (Cherubini &
Jungmeier, 2010, Cherubini et al., 2011). The review also stressed on exploring possibilities
for enhancing the production efficiency of biorefinery system. Technological innovations on
biorefinery plants to make them capable of processing versatile biomasses and producing
wide range of high value products were emphasized (Mickwitz et al., 2011). Hence, it was
highlighted that environmental and economic evaluations of producing different biomasses
and also along their conversion routes to different biobased products is relevant to come-up
with necessary decision supports for ensuring energy security and reducing import
dependencies on petro-commodities and other conventional products. For instance,
conversion of biomasses for diversifying and contributing to the future stake of renewable
fuels and also reducing import dependency on livestock feeds, e.g. in Denmark were
specifically highlighted.
Biomass prioritization was also argued to be relevant so that bulk volume of biomass can be
supplied with minimum negative ecological impacts and also without inducing competitions
among the different relying demand sectors (feed, food, fibers etc) (Lin et al., 2006, Watson,
2011). This showed a perspective of pre-screening potential biomasses, e.g. that are available
in Danish and similar agro-climatic environment. Concerning the methodological aspects on
pre-screening and overall sustainability assessment, multi-criteria assessment was
recommended as a suitable method. This was because of the fact that it facilitates to collect
both bio-physical and socio-economic parameters during the process of evaluation (Afgan &
Carvalho, 2002, Akash et al., 1999, Boufateh et al., 2011, Dalgaard et al., 2012,
Haralambopoulos & Polatidis, 2003, Lahdelma et al., 2000, Macharis et al., 2004, Tommy
Dalgaard et al., 2012). Hence, as the first step towards making detail environmental
sustainability assessment was to pre-screening potential biomass types that are relevant in
Denmark and also to the northern European climate. This was carried out in Paper-II.
23
Conclusions from Paper-II:
Prioritization of biomass types was mostly influenced by parameters such as: availability of
land, biomass yields and environmental performance of their production. The ranking of
biomass types based on the outranking flows of the preference functions (Behzadian et al.,
2010) among the selected thirteen types of biomasses were: grass-clover, pure grass, alfalfa,
switchgrass, wheat straw, willow, maize, ryegrass, miscanthus (autumn harvest), barley
straw, miscanthus (spring harvest), oil seed rape (straw) and poplar.
The advantage of growing alfalfa, grass-clover and in general grasses for biorefinery was
argued from the standpoints of: their high yields and higher supply potential in Denmark and
other EU member states (Berndes & Hansson, 2007, Gylling et al., 2013, Statistics Denmark,
2014, Statistics Denmark, 2016). They were also argued from the agricultural management
practices, e.g. growing in rotation with the cereal crops, thereby having potential benefits, e.g.
on the soil nutrient management (Høgh-Jensen & Schjoerring, 1997) by providing majority
of the nitrogen fertilizer requirements to the corresponding crops (Eriksen et al., 2014) .A
review on the potential contribution to soil C stock by growing different crops (Azeez, 2009,
Bransby et al., 1998, Fortier et al., 2015, Hamelin et al., 2012, Mogensen et al., 2014) also
reflected that grasses and in general perennial crops had positive contributions (Azeez, 2009,
Bransby et al., 1998, Mogensen et al., 2014). Furthermore, based on the chemical
composition, particularly based on crude protein content grasses were reported favourable
for the protein extraction (Bals et al., 2007, Dale et al., 2009, Fiorentini & Galoppini, 1981).
The advantages of considering straw, particularly from winter wheat were argued based on:
availability to supply and to maintain bulk demand, as the cereal crops (e.g. wheat, barley)
cover most part of the agriculture area of Denmark (i.e. about 55.5% of the arable land in
2010), and currently also represents more or less in the similar range (Statistics Denmark,
2010, Statistics Denmark, 2014). Moisture content of straw is generally favourable for the
both thermo and bio-chemical conversion processes (McKendry, 2002). Higher
concentrations of carbohydrates was favourable for prioritizing the biomass in a sugar based
platform, e.g. to produce bioethanol. In spite of the stated advantages of straw, drawbacks for
it, particularly looking into the current demand were listed to be: e.g. impact on soil fertility
(Petersen & Berntsen, 2003, Petersen et al., 2013), competition among the contemporary
demands, e.g. as fuel for energy conversion (Danish Energy Agency, 2012, Parajuli, 2012) and
as animal feed (Statistik Danmarks, 2013). Despite such potential competitions, to some
degree the conversion of straw in biorefinery can addresses such issues, e.g. by partially
maintaining soil health by recoverable nutrients from waste streams and utilizing the fibers
as a source of animal feed to address partially on the loses in the feed-values of straw (Larsen
et al., 2012, Larsen et al., 2008). Among the recommendations outlined from this study, one
was also to analyze the consequential effect of utilizing straw, whenever sustainability
24
assessment of a lignocellulosic biorefinery has to be made. These aspects were also pertinent
to explore the best possible ways of resource utilization for improving the system
performance of a typical biorefinery.
With regard to biomass quality, optimistic features related to willow and other SRC were
reported as: their higher yield (Aylott et al., 2008, McKendry, 2002), total sugar contents
((Nanda et al., 2013, Zamora et al., 2014) and positive contribution to the SOC change and to
the SOC stock (Brandão et al., 2011, Fortier et al., 2015, Hamelin et al., 2012). The important
characteristics related to SRC were also on the soil nutrient management (Jørgensen et al.,
2005, Simmelsgaard, 1998). The disadvantage of woody biomass was however related to the
higher lignin content, but depending on the conversion pathways and extent of material
processing lignin can be regarded as valuable raw material for secondary processing and also
to produce other high value chemicals (Uihlein & Schebek, 2009). In addition, increasing
demand of biochemicals (e.g. binder elements and bio oil) (IEA, 2011) can make the selection
of this biomass more suitable, provided that these bio-chemicals can be produced
sustainably. Likewise, the study also stressed to look into the potential consequences of
indirect land use change (iLUC) effects during the occupation of available land (Schmidt. J.
H. et al., 2012) resulting due to the production of biomasses (Bourguignon, 2015).
Considering the diverse physical and chemical characteristics of biomasses and also since
environmental implications of growing these biomasses depend on the specific agro-climatic
condition the study realized to compare the pre-screened biomass using more systemic
evaluation procedures, e.g. using the method of life cycle assessment. This led to work with
the Objective-II.
4.2. Environmental LCA of the agricultural system
Objective-II: To assess the environmental impacts of producing biomasses for biorefineries.
Conclusions from Paper-III and Paper-IV:
The general conclusions that were drawn from the two sets of LCA studies made at the farm
level are listed below and further discussed after the below lists:
• Environmental impacts of producing the selected biomasses ranged differently for the
annual and perennial sources (Figure 7). Alfalfa and willow performed better for the
selected impact categories compared to other biomasses.
• The obtained carbon footprint for producing 1 t DM of biomass was highest for grass-
clover, ryegrass and maize, straw from spring barely and winter wheat compared to
alfalfa and willow. The obtained environmental impacts were mainly as a result of
impacts induced from the agro-chemicals and fuel used, e.g. indirect emissions during
their production, and emissions at the farm level. These characterized to contribute
substantially to the impacts, such as GWP1 00, EP and also to NRE use.
25
• The contribution from the agro-chemicals production ranged from 25% to 71% of the
specific net GWP1 00 obtained for the selected biomasses.
• Likewise, the contribution of N2O emissions to net GWP1 00 ranged from 16% to 62%.
Lower contribution was for alfalfa and the higher was for grass-clover and ryegrass.
• Direct emission from diesel used during the farm operations was also the principal
contributor to the obtained carbon footprint (Figure 8).
• Soil C credits for ryegrass, grass-clover, alfalfa and willow were mitigating, respectively
35%, 36% 44% and 66% of the obtained net GWP1 00 (Figure 8).
• The EP was lowest for straw from winter wheat and highest for grass-clover.
• It was as a result of higher nutrients used, N-leaching and other losses at field and at
the background system.
• Willow and alfalfa also had an accumulation of SOC to the soil pool, in particular
compared to the initial SOC stock. This was opposite in the case of annual crop (e.g.
barley) and hence for straw. Hence, the production of willow was with better soil quality
based on the change in the SOC stock (Paper-IV).
• The study also demanded to understand whether the biomass production system is a net
energy producer or a consumer. On such, it was concluded that willow favour among the
selected biomasses, as it was with a higher total energy output to input ratio.
• The total PFWTox was highest for straw from spring barley, grass-clover, maize, ryegrass,
and straw from winter wheat; and was lowest for alfalfa and willow (Figure 7). Likewise,
based on the emissions from the pesticides application only, the impact turned to be
highest for straw from spring barley and winter wheat, alfalfa and maize; and the lowest
for ryegrass, grass-clover and willow.
With regard to ecotoxicological measures, it should be noted that because of absence of
complete list of pesticides in PestLCI2.0.6, particularly that were considered for willow,
barley and alfalfa, average emission distribution fractions (see supporting information in
Paper-III) were used for their impact induced at the farm level.
The environmental impacts obtained for the biomass production also highlighted that
assessments based on a single set of environmental impact category (e.g. GWP) would not be
sufficient to conclude on the suitability of biomasses, thus it is relevant to consider wider set
of environmental impact categories (Figure 7).
26
Figure 7: Environmental burdens of producing the selected biomass types. Percentages are
indexed to the maximum values (based on Paper-III and Paper-IV).
The environmental footprints as shown in Figure 7 are indexed to the maximum
values of each impact category and are shown in percentage in relative to those
maximum values.
Figure 8: Process-wise contributions to net GWP1 00 (kg CO2 eq per t DM) related to the
production of the selected biomass types (based on Paper-III and Paper-IV).
27
Figure 9: Process-wise contributions to NRE use use (MJ eq per t DM) related to the
production of the selected biomass types (based on Paper-III and Paper-IV).
These two specific studies also stressed to look into the opportunities of minimizing the use
of synthetic fertilizer, e.g. by recycling/reusing organic matter available in waste streams of
biorefinery, as was also highlighted in Paper-I. For this, some of the potential opportunities
that were discussed in these two studies were: recovering potassium chloride from the liquid
fraction of the lignocellulosic biorefinery (Larsen et al., 2008) and recirculating the digestate
slurry from a biogas production system. The chemical properties of the selected biomass in
these two LCA studies further rationalize them to be used in different biorefinery platforms.
The overall evaluation on their specific environmental footprints and biomass qualities
further recommended evaluating them for related biobased products value chains. This
emphasized on the need of integrating agriculture system and biorefinery system and also
exploring the means of resources exchanges between these two value chains so that
environmental burdens of biobased products can be quantified. This was carried out when
working with the next objective.
28
4.3. Environmental LCA of the biorefinery systems
Objective-III: To assess the environmental impacts of producing biobased products from a
biorefinery and relate them to a wider sustainability perspective.
Conclusions from Paper-V:
• The main conclusion from this study was that the integrated system (System C)
performed better than the standalone system (System A) for producing bioethanol. For
example, the obtained GWP1 00 and NRE use for System C was 70% lower than System A.
• The EP and NRE use from System C were lower by 15% and 140% respectively compared
to System A.
• Both ALCA and CLCA approaches resulted to net savings in terms of GHG emissions and
NRE use for both bioethanol and biobased lactic acid. Despite there were some
differences in the net impacts obtained from CLCA and ALCA approaches, when
compared on the basis of the gross values of the respective environmental impacts they
were close to each other.
• CLCA results showed relatively lower impacts in the case of System B and System C;
where the avoided impacts were offering higher credits to reduce the environmental
impact.
Table 1 Environmental impacts of bioethanol and biobased lactic acid, obtained relying on
CLCA approach (FU = functional unit) (adapted from Paper-V)
Impact
Categories Units System C
(per MJEtOH)
Standalone
System A
(per MJEtOH)
System B
(per kgLA)
GWP1 00 kg CO2 eq/FU 0.03 0.1 -1.24
EP kg PO4 eq/FU 1*10-4 1.3*10-4 -9.4 *10-3
NRE use MJ eq/FU -0.2 0.5 12
ALO m2a/FU 0.16 0.02 6
The trade-off in the process of converting straw and alfalfa, respectively to bioethanol and
biobased lactic acid was on the net savings obtained on the environmental footprints
compared to their conventional counterparts. The net savings in terms of GHG emissions and
NRE use from bioethanol compared to petrol was 67% and 90% by for System A and System
C respectively.
29
Likewise, the net savings in terms of GHG emissions for producing biobased lactic acid was
127%. Savings in terms of NRE use from the production of biobased lactic acid was 93%
compared to conventional lactic acid.
The specific environmental impacts obtained relying on ALCA and CLCA approaches
concluded that the results were not differing in terms of deriving conclusion to support in the
decision making process. Hence the recommendations would be more or less the same for the
both approaches. Both approaches concluded that for bioethanol system integration yielded
higher environmental savings for most of the impact categories. Also both approaches yielded
with results showing net environmental benefits compared to their alternatives available in
the market.
On top of the above conclusion, the consequential effects of utilizing biomasses were
articulated in the following manner:
• the consequences of utilizing straw for bioethanol production was on the additional
burden, e.g. emitting 0.03 kg CO2eq per 1 MJEtOH on the obtained impact for both
bioethanol producing systems.
• the impact of iLUC induced due to the production of alfalfa resulted with net GWP1 00 of
0.06 kg CO2 eq per kgLA for System B. The net impact was 105% higher compared to the
impact obtained for lactic acid excluding iLUC (Table 1).
• the impact of iLUC induced due to the production of alfalfa and included to System C
resulted with net GWP1 00 of 0.05 kg CO2 eq per MJEtOH The impact was 41% lower in the
case when iLUC was excluded (Table 1)compared to the stated result.
• Even after the inclusion of iLUC effects, the net savings from the production of biobased
lactic acid from System B was better than conventional lactic acid. However, the savings
in terms of GHG emissions for biobased lactic acid was 29% smaller than the savings
obtained excluding the impacts of iLUC.
• The net savings in terms of GHG emissions for bioethanol production from System C was
8% smaller compared to petrol after including the iLUC than the result obtained
excluding it.
30
31
5. Discussions
5.1. Environmental hotspot assessments on biomass production
When working with objective-II, it was found that soil C sequestration for different crops
ranged differently. In the case of willow, it was mitigating -66% of the net GWP1 00 (Paper-
IV), whereas for winter wheat, which was latter allocated to straw was mitigating to net
GWP1 00 by -12% (Paper-III). However, in the case of straw, general understanding is that
removing straw would possess consequences, e.g. in terms of SOC change compared to the
situation it is ploughed back into the field (Petersen & Knudsen, 2010). If such
considerations were taken into account then the effect of removing straw was emitting 143 kg
CO2 eq per t straw (Parajuli et al., 2014). With regard to the green biomasses, the soil C
change credited -35% to -44% of the respective GHG emissions; the higher values on the
range were represented by grass-clover and ryegrass and the lower was for alfalfa (Figure 8).
The variation was due to the amount of net C input to the soil that was available from non-
harvestable residues in relative to the reference crop. Furthermore, N2O emission was also
among the major contributors to the GHG emissions, covering about 16%-62% of the net
GWP1 00 obtained for the selected biomasses (Figure 8). The lower range was for alfalfa with
no synthetic fertilizer application. The higher range was for grass-clover and ryegrass with a
higher level of direct N2O emissions coupled with relatively higher level of N-fertigation
(Parajuli et al., 2016). In line with the presented results on cereal crops, Knudsen et al.
(2014) also reported that the effects of soil C sequestration and N2O-N emission were the two
main hotspots in the total carbon footprint of the cereal crops. Likewise, similar aspects were
argued for the similar types of biomasses, e.g. in Mogensen et al. (2014) for grasses; and for
the cereals it was comparable with studies reported by Kramer et al. (1999), Korsaeth et al.
(2012) and Roer et al. (2012). Likewise, the contribution from the production of agro-
chemicals was ranging from 29% to 65% of the net GWP1 00 (Figure 8), however the absolute
values were not so modest for the specific biomasses (see Paper-III and Paper-IV). From the
sensitivity analysis and after analyzing the emission factors of producing the fertilizers, it was
found that the GWP1 00 can be partly varied by choosing an ammonium or urea based N
fertilizer. With regard to NRE use a similar trend of contribution to the impact was found
(Figure 9).
5.2. Environmental hotspot assessments on biobased products
A general overview on the environmental hotspots was drawn from Paper-V. It should be
noted that for below evaluations, particularly on GWP1 00, the impact of iLUC change due to
the production of alfalfa is not taken into consideration. The effect of such can be found in
section 4.3.
32
From the evaluation, it was found that regardless of the approach for most of the impact
categories the pattern of the contribution from different biorefinery value chain to the gross
impacts followed similar trend. For example, in System A it was the production of straw
contributing the most to the gross GWP1 0o, and the contribution ranged from 27% to 34% of
the gross impact, representing the results from ALCA and CLCA approaches.
Likewise, the biorefining processes contributed 62% of the gross GWP1 00 obtained using
ALCA approach, which it was 66% in the case of CLCA approach. Furthermore, of the stated
range the contribution due to primary energy input ranged from 17-20%. The contribution
from the enzyme production ranged from 25% to 28% of the gross impact obtained using the
ALCA and CLCA approach. In System C the contribution from the biorefining processes
ranged from 54% to 58% for the results obtained using ALCA and CLCA approaches. Here
also both approaches yielded a similar pattern.
It also followed the similar trend for NRE use. For example, the contribution from biomass
production to the gross NRE use was 49% in System A, 82 % and 67% in System B and
System C respectively, based on the results obtained using ALCA approach. The contribution
from the same value chain obtained after the use of CLCA approach ranged from 37% in
System A to 98% in System B.
5.3. Uncertainties and methodological dilemmas
Uncertainty, as discussed in this study are in accordance to variations on the results that were
obtained in the current study and also can be with other studies; mainly considering due to
the differences in the (i) assumed parameters; (ii) used models; (iii) used methods, (iv)
spatial variability of the processes; and (iv) temporal-variability of the processes etc. These
parameters were suggested as among the means to define uncertainty in LCA studies
(Huijbregts, 1998, Payraudeau et al., 2007).
5.3.1. Uncertainties related to SOC changes
First of all, there are limited LCA studies that have made distinctions on the emissions from
SOC change, mainly occurring due to use of different timings of emissions, particularly when
calculating the carbon footprints (Kløverpris & Mueller, 2013, Petersen et al., 2013, Schmidt
& Brandao, 2013). With regard to SOC change it was suggested that agricultural system
reaches a certain ‘steady state’ level of soil C, mostly when agricultural practices are changed
(Petersen et al., 2013). The rate of SOC change is however higher in the first few years and
then the gains/losses of carbon therefrom will decline over time to reach a new equilibrium
(Petersen et al., 2013).
In this study, for the emissions due to SOC change two temporal horizons (100 and 20 years)
were considered. The soil C sequestration or emissions from SOC change in 100 years was
33
9.7% of the net C input, whilst in 20 years it was 19.8% (Petersen et al., 2013). The range
reported for the current study covered the two methods Petersen et al. (2013) and IPCC
(2000) for 20 years. Upon the calculation on such for the selected crops, it was found that the
annual soil C sequestration for willow in 20 years ranged from -0.4 to -0.9 t C/ha/y,
depending on the methods (Paper-III and Paper-IV). The obtained range was comparable to
reported values for SRC (Brandão et al., 2011, Dawson & Smith, 2007, Grogan & Matthews,
2002, Murphy et al., 2014, Rowe et al., 2009), (e.g. -0.3 to -2.8 t C/ha/y under different land
use conversion scenarios). For example it was -0.5 -0.75 t C/ha/y, as reported occurring
during the land use change from arable to willow (Rowe et al., 2009, Tonini & Astrup, 2012).
Soil C sequestration for willow however was argued also depending on the genotypes of SRC
(Cunniff et al., 2015, Dimitriou et al., 2011) and other factors such as net primary production,
rates of soil organic matter decompositions, initial SOC content, agricultural management
practices etc (Grogan & Matthews, 2002). For alfalfa the estimated soil C sequestration
ranged from -0.25 to -0.62 t C/ha/y, the range reported in Dawson and Smith (2007) for
perennial grasses and ley rotations was from -0.5 to -0.62 t C/ha/y.
Furthermore, when the results obtained on soil C sequestration for 20 years were compared
to the ones obtained for 100 years (Paper-III and Paper-IV), the results for 20-years were
almost double; Knudsen et al. (2014) also coined in the similar line. These features indicated
that when C in the form of residues are applied in a year, partly will be remained in the soil,
whereas remaining are released to the atmosphere in different time frame (Petersen et al.,
2013).
Apart from the temporal perspective, some variations was also found occurring due to the
methods employed to estimate the non-harvestable residues and carbon assimilation (Sartori
et al., 2007). For instance, in the case of willow (as discussed in Paper-IV). shoot to root ratio
was used in Pacaldo et al. (2012) and Heller et al. (2003), and they had higher soil C
sequestration in their studies. Harvest index (Steduto et al., 2012) was used in the cases of
most of the biomasses selected in the current study. On contrary, for willow the non-
harvestable aboveground and below ground biomasses were calculated in accordance to the
method suggested in Hamelin et al. (2012) (see Paper IV).
5.3.2. Uncertainties related to soil quality
In Paper-IV, SOC stock change (Δ SOC stock) was used as one of the indicator of soil
quality, in accordance to Brandão et al. (2011), Milà i Canals et al. (2007) and IPCC (2000).
Brandão et al. (2011) suggested that it is however important to include a simple indicator,
such as SOC stock change than no assessment of soil quality in LCA studies. They suggested
that such assessments can be carried out by evaluating the land use change effects, which
normally takes place as a result of different agricultural management practices and eventually
34
would have effect on SOC stock (McClean et al., 2015, Taghizadeh-Toosi et al., 2014b). In this
study, the change in SOC stock was due to the effect of differences between potential SOC
stock (i.e. of the reference land use situation) and the initial SOC stock (of the current land
use). The period of recovering the soil quality to the natural relaxation was primarily found
depending on the difference between the rate of natural relaxation and soil C sequestration
taking place in the current land use (Paper-IV). Upon the sensitivity analysis, the variation
in the obtained Δ SOC stock was primarily occurring in the situation when soil C
sequestration and initial SOC stock were varied (as discussed in Paper IV). Furthermore, it
should be noted that soil organic matter does not cover all fundamental aspects of soil quality
(Milà i Canals et al., 2007), and requires additional indicators that may influence such
changes, e.g. soil erodibility, soil compactions (Arshad & Martin, 2002, Lal, 1993, Zalidis et
al., 2002). Despite there are numbers of biotic and non-biotic factors that affects the soil
quality, but in general, improvement of soil quality is followed by increasing SOC pool and
enhancing soil fertility. The risks of soil degradation and hence soil fertility can be mitigated
by such increment in SOC stock (Lal, 2015). These arguments may further support the
decision that was made for choosing the Δ SOC stock as an indicator of soil quality. However,
it also highlighted that LCA practitioners should analyze the specific pattern of soil C
sequestration and the initial SOC stocks of the current land use, particularly when dealing
with soil quality, and especially when there are cases where the results on soil C sequestration
may vary slightly to substantially depending on the different causes of the uncertainties.
5.3.3. Temporal scope of assessing Global Warming Potential
When the net GWP (in kg CO2 eq/t DM ) was assessed for 20 years and along with soil C
sequestration also calculated for the same time horizon, the order of biomasses in terms of
carbon footprint from a higher level to a lower level was: maize (approx. 356), followed by
spring barley-straw (approx. 308), ryegrass (255), grass-clover (approx. 215), winter wheat-
straw (approx. 132 ), willow (approx. 30) and alfalfa (approx. 45). These were calculated after
Paper-III and IV. In 100 years temporal scope, the obtained net GWP1 00 was highest for
ryegrass, grass-clover, maize, spring barley-straw and winter wheat-straw, and lowest for
alfalfa and willow (Paper-III and IV). The differences caused in the order of the biomasses
was mainly due to variation in global warming potential of N2O (IPCC, 2007) and also the
SOC change almost doubled for 20-years, as discussed in section 5.3.1. This indicated that
LCA practitioners may have different decisions when biomasses are compared on different
temporal scope when assessing the carbon footprints of biomass and biobased products.
5.3.4. LCA methods on handling the co-products
There have been wide attentions on the issues related to the use of LCA methods, particularly
in the cases when multi-functional co-products are to be handled (Cherubini et al., 2009b,
35
Cherubini et al., 2011). In such cases, despite there were general consensus on the methods to
be used, yet agreement on a common method is difficult to find (Wang et al., 2011). For
example, the displacement method was suggested by ISO (ISO, 1997, ISO, 2006), whilst there
are cases where energy-based allocation method was also used, such as in the European
Commission renewable energy directive (European Commission, 2009).
Depending on the approach of handling the co-products, the results on the environmental
footprints may be different (Rehl et al., 2012), but these two approaches are aimed for
answering different questions (Brander et al., 2009); e.g. assessing the unit environmental
footprint during the processing and use of resources is generally answered through ALCA
approach. On contrary, effects due to marginal changes in the output induced to the total
impacts is normally answered through CLCA approach (Brander et al., 2009) .
In this study, when the use of ALCA approach was compared for the different biorefinery
systems, variations in the results were mainly due to the economic allocation factors. For
example, allocation factors attributed to bioethanol in System A and System C was 73% and
38% respectively. The reason behind such variation was because of diverse co-products that
were accounted in the respective systems (Figure 4 and 6) with different economic values,
and were changing proportionately depending on the system configurations and numbers of
co-products producing from a particular biorefinery system (Paper-V). Likewise, if an
energetic allocation factor is to be attributed to bioethanol then the allocation factor was 87%
in the case of System A and 80% in the case of System C, which was calculated based on the
energetic outputs obtained for the system (Figure 4-6). Furthermore, mass-based allocation
factor was even higher (around 94% for bioethanol in System A), but was only 27% in the
integrated system depending on the mass balances under different biorefinery system
scenarios. Furthermore, uncertainties to economic allocation could also prevail due to surges
in the future prices of biobased products. From such variations, it can be concluded that if
allocation has to be done for LCA studies, it might be relevant to develop a simplified method
that can capture different functionalities of the products (Cherubini et al., 2011).
Likewise, uncertainty that may prevail when working with CLCA could be due to the selection
of marginal products, e.g., electricity and displaceable feed crops. There are debates on the
choices of marginal electricity (Lund et al., 2010, Mathiesen et al., 2009), however, in the
current study these uncertainties were tried to address through a sensitivity analysis (Paper-
V). For instance, if natural gas was assumed as the marginal fuel for electricity production,
the obtained GWP1 00 was 19% and 103% higher for System A and System C respectively for
producing bioethanol. This was due to less impacts were avoided when electricity generation
was based on natural gas compared to the basic scenario. Likewise, for the similar effect, in
System B, net GWP1 00 was found higher by 68% in the alternative scenario compared to the
basic scenario.
36
5.3.5. Benefits of co-products and consequences, including iLUC
The environmental impacts obtained for the selected biorefinery systems were largely
benefited due to the credits offered by displacing the marginal products. For example, about
29% and 87% of the obtained gross GWP1 00 for System A and System C were credited due to
avoided products of the respective systems. Likewise, for System B the co-products avoided
136% of the obtained GWP1 00. Reason behind the higher avoided impacts in the case of
System B and System C was mainly due to displacement of barley, soymeal and marginal
electricity, whilst it was mainly electricity for System A.
In the context of producing biomass for biofuels or for other alternative uses, impacts of iLUC
is among the widely discussed environmental concerns, and are more or less in consensus to
agree that such impact would occur (Gawel & Ludwig, 2011, Hamelin, 2013, Kløverpris &
Mueller, 2013, Sanchez et al., 2012, Schmidt. J. H. et al., 2012, Tonini et al., 2016). The
consensus, in general is in terms of occurrence of unintended consequences of releasing GHG
emissions due to land use changes, e.g. due the expansion or intensification of cropland
(Schmidt et al., 2015). Contrary to this, Brinkman et al. (2015) discussed on some measures
for mitigating impacts of iLUC, particularly for biofuel production pathways. Among the
measures, it was argued that “improved chain integration” in biofuel production system, e.g.
the use of suitable co-products as an alternative source of animal feed will increase the total
benefits (or output) per hectare and thus would reduce the demand for land. This argument
however can be opposed by other dissimilar urgings. For example, in spite of biobased
products are benefited by the credits offered by the co-products, particularly at mitigating
GHG emissions, it is also claimed that such avoidance can induce other chain effects of iLUC
(Berndes et al., 2013). During such situations, there would be a need to compensate the
effects of displacing the identified marginal products by other subsequent marginal products
(Schmidt & Brandao, 2013). For example, even though use of straw for bioethanol
conversion is claimed with no iLUC effect, but in the situation of avoiding marginal products,
such as by C5 molasses displacing the soymeal, there could be a state where other value
chains are affected (Bos et al., 2016, Schmidt & Brandao, 2013). To correlate this situation
with the case of current study producing feed protein and fodder silage from System B and
System C, the effect was that soymeal and barely as the marginal sources was displaced
respectively. For the case of displacing soy meal, if the chain effects of iLUC are to be
adapted, then the interpretations would start from the first argument claiming that soybeans
will be produced less. It then further continue with another claim, e.g. a reduction in the
production of soy oil would occur, as it was also argued for C5 molasses displacing the
soymeal (Schmidt & Brandao, 2013). Furthermore, the chain effects can advance ahead with
additional claims that such loss has to be compensated by increasing the production of
37
marginal oil, which may turned out to be rapeseed oil (Bos et al., 2016), and likewise the
effects move on.
Likewise, another argument was on the claim made for residual biomass also possessing
iLUC impact. The claim was in relation to the need of compensating the feed values that it
displaces by changing its route from animal feed option to another options, e.g. bioenergy
(Tonini et al., 2016). It eventually ended-up with an argument that it would be necessary to
produce equivalent amount of feed, which would be fulfilled by a marginal crop entering into
the agriculture system. However, in the current study the effects for straw was not accounted
in the form of iLUC effects but was rather on the consequences of removing it from the field.
In the current study, impact of iLUC induced during the production of alfalfa was accounted
in a way that the crop occupying a productive land in Denmark would have consequences
elsewhere, resulting mainly due to displacement of a marginal crop, e.g., soybean produced in
South America (Schmidt & Brandao, 2013). The method was used in such a way that the
effect would occur regardless of how it is used (Schmidt & Muños, 2014). For this different
methods have been used to derive “iLUC factor” with more or less in a close proximity, e.g.
1.4 to 1.9 (Audsley et al., 2009, Schmidt & Muños, 2014), and were with also with significant
differences, based on the assumptions made on land use conversion that would take place,
e.g. LUC-factors for soy meal production ranged from 1.5 to 10 t CO2 per ha per y. The highest
was reported if the conversion takes place in forest land the lowest was in grassland (Leip et
al., 2010).
Moreover, additional scenarios, particularly on the impact of iLUC may also be taken into
account by considering the chain effects that may occur due to co-products avoiding the
marginal products, as discussed above. This can also be in the context of using straw for
biorefinery, provided that alternative use of straw are evaluated based on their feed values,
e.g. as suggested in Tonini et al. (2016). Most importantly, double counting on any cases
should be avoided (Finkbeiner, 2013, Pawelzik et al., 2013).
Despite all these claims and perspectives, one of the major challenges while working with
iLUC models is the uncertainty on the methods to quantify the induced GHG emissions
(Broch et al., 2013, Di Lucia et al., 2012, Warner et al., 2014).
5.3.6. Extent of material processing in biorefineries
Sustainability of biorefineries also depend on extent of material processing, primarily
depending on the ways the residual products and intermediate chemicals are utilized (Uihlein
& Schebek, 2009). One of the examples on such variation can be discussed taking an
example of utilizing C5 sugars in bioethanol production chain. The first case was the basic
scenario, as reported in Paper-V, where the utilization of C5 molasses was to produce biogas.
38
The benefits obtained from the basic scenario was on the conversion to energy and eventually
crediting about 28% and 38% of the gross GWP1 00 obtained for System A and System B.
Apart from the above example, again in the current study, GHG emissions were also
accounted considering an alternative scenario of utilizing the C5 sugars, e.g. its fermentation
to boost the yield of bioethanol approximately by 23% higher than the initial situation
(Inbicon, 2013, Losordo et al., 2016) (see Paper-V for further details). In System A, with such
increment in the bioethanol yield the obtained net GWP1 00 was lower by 6% compared to the
basic scenario. In System C it was lower by more than eight-fold of the initial situation; and
eutrophication potential was also lower by 49% compared to the basic scenario. The savings
in the EP in the case of System A was about 8%.
However, another example of the utilization of C5 molasses can be a contribution to livestock
sector, assuming its feed values (Larsen et al., 2012). The potential impact of such alternative
utilization was argued in the form of displacing about 769 g of wheat per kg C5 molasses
(Bentsen et al., 2006). Hence, in this scenario the environmental footprints would be
different than aforementioned examples.
Likewise, another prospects of utilizing the available resources could be in the form of
utilizing glucose to produce both bioethanol and biobased lactic acid. In this case glucose
produced after hydrolysis processes in a lignocellulosic biorefinery, and even in the case of
System A as designed in the current study, it can be fractionated into two streams leading to
produce both bioethanol and lactic acid, as also reported in IEA (2011). Similarly, another
case of the extent of material processing could be the utilization of biobased lactic acid to
produce poly-lactic acid (Cosate de Andrade et al., 2016).
These arguments on the alternative ways of utilizing C5 sugars and also utilizing other
resources generated from a biorefinery might infer that the results on the environmental
impacts would vary significantly. Hence, comparison among such alternative might give
some ideas for deciding and concluding them for the most resource efficient biorefinery
system and further looking other opportunities in relation to such.
5.3.7. Global, regional or local impact categories
Another important concern on the LCA study is about the spatial differences with regard to
environmental impact categories (Hauschild, 2006), i.e. their representation to both spatial
scales: global and regional or local. For instance, GWP, is the effect of GHG, and wherever the
emissions are taking place their contributions are with the same effect; hence it is regarded to
be global (Stranddorf et al., 2005). Likewise, the effect of nutrient enrichments to the
terrestrial and aquatic ecosystem is caused by the atmospheric deposition of nitrogen
compounds and also due to nutrients leaching from a specific agricultural system. This makes
eutrophication potential to be regarded as a regional or local effect (Smith et al., 1999).
39
Likewise, ecotoxicity depend on the exposure of the emission to the environment, e.g. river,
sea or terrestrial, the impacts thus can be regional as well as local (Schulze et al., 2001,
Stranddorf et al., 2005). It was thus recommended in a way that the LCA practitioners can
decide considering relevant approaches and methods that can give the most likely suitable
results (Stranddorf et al., 2004). Furthermore, it was also suggested that the use of LCA
method may consider both local and global effects, in order to avoid the situation “the
unintended increase of global impact is avoided while trying to reduce local impact, or vice-
versa” (van der Werf & Petit, 2002). However, normalization with respect to the global and
regional references are recommended in Stranddorf et al. (2004), but still prevails
uncertainties.
5.3.8. Up scaled production capacity and the impact potentials
One of the most important messages that can be drawn from the production/conversion of
biomass and biobased products, particularly from the available experimental and pilot scale
databases is to outline potential trade-off in comparison to conventional fossil-fuel-based
products. In such feasibility assessments, usage of LCA results can be regarded as milestone
to look into a wider scope and for a long term sustainability assessments of a production
system, which is larger in context and capacity (Guillén-Gosálbez et al., 2008). In the
meantime, generally, without larger scale validation, it is difficult to assess such larger
systems. This thus may limit to attract potential small and medium enterprises and the
industrial players to invest on the technology and concept of such production systems. The
most important obligation, hence is to establish a proof of concept and test it under industrial
condition (Patel & Blok, 2013). The industrial operation often takes place amplification of
outputs compared to small/pilot level; and thus changes in the related environmental
burdens would occur accordingly. The issue is, only a limited amount of data could be known
for some production system because real plants or big-pilot lines are yet to exist (Caduff et
al., 2014). There are also chances of having limited access to full-scale manufacturing
facilities and or the pilot plants are not accessible to researchers. Despite these challenges,
one of the scientific way to address such challenges might be in terms of using “scaling
function” (Patel & Blok, 2013). In general the specific impacts decrease with the up-scaled
capacity of a plant or component. The procedure can be by adjusting the difference in the
impact per capacity between small plants and large plants using scaling functions (Caduff et
al., 2012, Junginger & van Sark, 2010). For instance, such practices were used to calculate
the environmental impact related to the up-scaled capacity of biogas conversion to electricity
via CHP plant (Whiting & Azapagic, 2014). However, one of the issues related to biomass
conversion plant, as reported was related to the cost or impacts of generating the final
products (e.g. materials, electricity, heat, fuel from bioreactors), as they are influenced by
input materials (e.g. fuel) and their unit production effects. In spite of such limitations, based
40
on the relationship found between fuel consumption and performance (or between mass and
performance), Patel and Blok (2013) suggested that scaling laws are indeed applicable to
assess energy use and environmental impacts. Furthermore, Junginger et al. (2006) argued
that since bioenergy systems often involve delivering more than one output (e.g. electricity
and heat from CHP plants), which may further complicate the process of determining
upscaling effects on the cost and environmental impacts. The suggested solution to deal with
such multiple products , however was the allocation (Junginger et al., 2006).
41
6. Conclusions
The main point of departure for this thesis were the issues and opportunities identified when
working with Objective-I. The study made in relation to the Objective-I stressed on the need
to satisfy the growing demands for food, feed, fibers, fuels and chemicals without
compromising the current demand, and with minimum environmental impacts.
In addition, the following conclusions were drawn from Objective-I, based on Paper-I and
Paper-II.
Objective-I: To get an overview of biorefinery processes in relation to sustainability aspects
and to carry out an overall evaluation of different biomass feedstocks
• Measuring sustainability of a biorefinery system required accompanying two systems: (i)
the agricultural system, which requires the judicious management of available resources
with minimum environmental damage, and (ii) the biorefinery system, which requires
process optimization to increase yield and reduce environmental impacts for producing
biobased products
• Choice of biomass was important to meet the bulk demand for biomass with minimum
negative ecological impacts.
• Choice of biomass was in general influenced by productivity, quality and their initial
environmental screening, e.g.:
• Straw from winter wheat was deemed suitable due to its higher carbohydrate content
making it more suitable for a sugar-based platform, e.g., for the production of
bioethanol. An important issue here is the consequential effects of removing straw
from the field if this has to be the principal input to biorefineries.
• Green biomasses, such as alfalfa, grass-clover, ryegrass, etc., were deemed suitable for
their use in a green biorefinery based on their yield, crude protein content and
carbohydrate content. They were also recommended for their positive contribution to
the soil C balance and to soil nutrient management.
• Woody biomasses such as willow was chosen on the basis of their chemical
composition making them suitable for a sugar-based platform, and recommended
also for their positive contribution to soil C change.
Message: Environmental impact assessments of the representative farming system are
necessary to draw a conclusion on their environmental footprints taking into consideration of
the biomass qualities as well.
42
Objective-II: To assess the environmental impacts of producing biomasses for biorefineries.
The thesis then prepared an overview of the environmental impacts of different biomass
production chains by using the LCA studies in Objective-II. The following conclusions were
drawn on the basis of Paper-III and Paper-IV.
• The selected environmental impact categories were in general higher for ryegrass, grass-
clover, maize, straw from spring barley and winter wheat compared to willow and alfalfa.
• As a result of a positive contribution to SOC change, willow was mitigating GHG
emissions, which was equivalent to -66% of its net GWP1 00, whereas for the green
biomasses this figure averaged -38%. In contrast, emissions from SOC change for spring
barley- straw were 17% of its net GWP1 00.
• Biodiversity impact was relatively expressed lower for grass-clover and ryegrass
compared to the maize and straw from winter wheat.
• If emissions from the applied pesticides were considered, grass-clover and ryegrass had a
lower freshwater ecotoxicity than the other biomasses.
• A critical negative effect on soil quality was found for spring barley production, and hence
for straw. Depending on the soil C sequestration rate and the initial SOC stock, a positive
contribution to soil quality was found for perennial crops (e.g. willow and alfalfa).
Message: It was clear that biomasses responded differently to the selected environmental
impact categories; hence comparisons based on a single environmental indicator would not
be sufficient to rank biomasses.
Objective-III: To assess the environmental impacts of producing biobased products from a
biorefinery and relate them to a wider sustainability perspective.
Two standalone systems, straw conversion to bioethanol and alfalfa to biobased lactic acid
and an integrated system co-producing bioethanol and lactic acid were evaluated. Analyses
relied on both ALCA and CLCA approaches. The following conclusions were drawn in relation
to Objective-III, based on Paper-V.
• The CLCA and ALCA approaches arrived at similar conclusions in favour of biobased
products; hence the decision to be made based on their impacts would be the same.
• The net savings from bioethanol in terms of GHG emissions and NRE use compared
to petrol were 67% and 88%, respectively for the standalone system, and the savings were
much higher in the integrated system.
• The net savings in terms of GHG emissions and NRE use from biobased lactic acid
were 127% and 93%, respectively, compared to conventional lactic acid.
Message: System integration was beneficial for the production of cascades of biobased
products and for minimizing their environmental burdens. For instance, the carbon footprint
43
of bioethanol production from the integrated system was 70% smaller than from the
standalone system, and EP and NRE use were also significantly lower in the integrated
system.
Finally, having stated the conclusions to the three research objectives, the answer to the main
research question of this thesis can be summarized.
Overall research question: How does the utilization of biomasses for a biorefinery process
affect the environmental sustainability?
• The significance of understanding the relationship between a biomass production system
and biorefinery systems in terms of environmental sustainability, as borrowed from
Paper-V after combining Paper-II and Paper-III, can be highlighted by way of
following examples:
• LCA of entire biorefinery value chains showed that environmental footprints were largely
determined by soil C credits from the agricultural system, whereas for biorefineries the
determining factors were energy input and impacts related to the enzyme production
• Soil C sequestration under alfalfa production resulted in -0.41 kg CO2 eq per kgLA.
This was reducing by 12% of the gross GHG obtained for lactic acid production.
• Soil C sequestration was even higher for ryegrass and grass-clover, and if they were
used as the feedstocks, the soil C credits assigned to the biobased products would be even
higher than for alfalfa, provided that other parameters do not have a significant impact
on such a presumption.
• On the other hand, consequence of straw removal resulted on emitting 0.03 kg CO2
eq/MJEtOH, which contributed approximately 18% to the gross GWP1 00 of the bioethanol
production chain.
Moreover, even after including the impacts of iLUC net carbon footprint of biobased products
were still lower than their conventional counterparts. But, the savings in terms of GHG
emissions was relatively smaller than the case excluding the impacts of iLUC.
Message: It highlights that biomasses should not be assumed in a way that it carries no
environmental drawbacks, e.g., straw, because it could be misleading when environmental
sustainability assessments are made for a wide range of lignocellulosic biomasses that are
used for different purposes, and if burdens on it are avoided. Likewise, impact of iLUC seems
one of the instrumental tools that may vary the carbon footprint label of biobased products.
The agriculture management practices played important roles in the obtained environmental
footprints.
44
45
7. Perspectives
In the current study, the biomasses used for calculating environmental footprints were those
that are used as fodder crops in the “cattle system”. However, the cattle system was not
included in the study, since the majority of the agricultural areas are used for roughage
production for cattle feeding, particularly in Denmark. Furthermore, the grasses considered
in the study are grown in temporary grasslands and in rotation, but not in permanent
grassland. It may be wise to make a comparative assessment of the environmental footprints
of a biomass production system by analysing all different agroecological changes, as
highlighted above, e.g., grasses produced in permanent grassland compared to grasses grown
as fodder crops (as temporary grassland and in rotation) and also including livestock
production value chains, e.g. in the green biorefinery system.
In addition, the main conclusions for the biobased products was drawn from the perspective
of analyzing the biorefinery value chains for their energy balances and carbon footprints,
showing a positive net gain compared to the petro-based products. Moreover, based on
results on the total freshwater ecotoxicity and from the results on biodiversity impacts
(assessed in Objective-II), it was revealed that the results of the studies may be further
interesting mainly if other biomass feedstocks and if additional impact categories are
included for the environmental impact assessments of the biobased products.
Likewise, looking into the complexities for accounting impacts of iLUC, it is important that
specific estimations on emissions from the biomass production system should be handled
carefully. One of the major issues identified in Bourguignon (2015) was that results on iLUC
were differing significantly between different studies than compared to the differences that
were reported between the different feedstocks. Despite these limitations, a general
consensus on the adverse impacts due to iLUC still prevails (Gawel & Ludwig, 2011,
Hamelin, 2013). In the current study, the impact of iLUC was accounted with an assumption
that occupation of productive land in Denmark would have environmental effects elsewhere
due to displacement of marginal crops, “regardless” of how it is used, and for the assessment
a generalized emission factor for iLUC was adapted. It is thus relevant to compute impact of
iLUC depending on other methods and compare the differences obtained therefrom. One of
the way could be the changing the scenario of consequences of straw utilization, e.g. taking
cases of their conventional utilization in the feed sector, or in the energy sector. This could be
interesting to assess on how co-products compensate the loss when they displace marginal
products, as argued in Tonini et al. (2016) and Schmidt et al. (2015). It is also highly relevant
in the context of a biorefinery, where there are many claims on the avoided products
crediting the environmental impacts, as was also claimed in this study.
46
A clear recommendation from IPCC (Pachauri et al., 2014) is that future bioenergy systems
should be sustainable, but it is very important that future energy and material production
systems should be designed in such a way that claims of net environmental savings are not
entirely made on the back of unfavourable effects of other contemporary petro-based
products. However, efficient and optimized production of biomasses, e.g., increased harvest
yield and better management of nutrients, could be self-driving measures for producing
biomasses with minimum environmental damage.
Likewise, renewable fuels and products are required to be produced in significant amounts,
in Europe and elsewhere, mainly to meet the demand for biobased products (Parajuli et al.,
2015a), as also projected in “IEA Bioenergy-Task 42 Biorefinery” (IEA, 2011). The production
of biobased products are also urgently required to meet the short- and long-term policy and
regulations made for the promotion of biofuels (Banse et al., 2008, Demirbas, 2008) and also
for balancing the bioeconomy (Philp, 2015). Moreover, a stringent policy for biobased
products and for the bioeconomy is still lacking (Palgan & McCormick, 2016) and might limit
the biobased products entering into the existing market dominated by fossil-fuel-based
products.
Apart from the above discussed perspectives, synopsis of the market of biobased products
and on the basis of the results obtained in this PhD study some of the specific
recommendations on technical aspects for the sustainability assessments of biomass and
biobased production value chains are as follows:
• Further studies can be selected to fill the critical gaps, e.g. full life cycle assessments are
needed on biobased products based on grasses harvested from permanent grassland and
other integrating other possible ways of optimizing biorefineries performances.
• Further studies can also be relevant to look at opportunities for a more optimal recycling
of resources, e.g. fractionation of the glucose produced after enzymatic hydrolysis into
two streams: fermentation into bioethanol and biobased lactic acid, as also reported in
IEA (2011) This would be interesting to compare with the current case of utilizing a grass-
based green biorefinery plant to coproduce bio-based lactic acid and other feed products.
• Within the biobased economy and operations of biorefineries, significant opportunities
are also found for the development of biobased chemicals, e.g. processing of lignin to
aromatic chemicals (IEA, 2011). Likewise, biobased lactic acid can be further processed to
produce secondary chemicals such as polylactic acid (Cosate de Andrade et al., 2016).
These high-value products can be assessed in terms of their environmental sustainability
by expanding the system boundary from where it stopped in the current study.
• A holistic study of biorefinery systems is especially relevant bearing in mind the Danish
Energy Strategy–2050 (Danish Energy Agency, 2011, Lund & Mathiesen, 2009). It could
47
be interesting to make a systemic evaluation by interlinking with the prospects of biomass
resource allocations for different purposes.
• The LCA of biobased products, as reported in the current study, was based entirely on
literature databases from pilot and experimental biorefinery setups. For future studies,
environmental sustainability assessments of new commercial-scale biorefineries will be
relevant.
Most importantly, a clear policy framework and regulations that can support the
development and promotion of biobased products is strongly needed. For the design of
sustainability assessment criteria and to support the design of policies conducive to long-
term bioeconomy development, some of the prerequisites would be: innovations in
biorefinery systems design, results in the socio-economic and environmental areas for
different biobased products, screening of suitable biomass feedstocks and appropriate
biobased production scenarios, etc. To such an end, this study might play an important role
by revealing how different biomasses respond to the environment and also how different
biobased products respond to conventional fossil-fuel based products in an environmental
paradigm. Last but not least, sustainability of a production system is also mainly affected by
their economic return. Hence, assessing the economic viability of producing the biobased
products is also assuredly important.
48
49
8. References
Reference List
Afgan NH, Carvalho MG (2002) Multi-criteria assessment of new and renewable energy
power plants. Energy, 27, 739-755.
Ahring B, Westermann P (2007) Coproduction of Bioethanol with Other Biofuels. In:
Biofuels. (ed Olsson L) pp 289-302. Springer Berlin Heidelberg.
Akash BA, Mamlook R, Mohsen MS (1999) Multi-criteria selection of electric power plants
using analytical hierarchy process. Electric Power Systems Research, 52, 29-35.
Arshad MA, Martin S (2002) Identifying critical limits for soil quality indicators in agro-
ecosystems. Agriculture, Ecosystems & Environment, 88, 153-160.
Audsley E, Brander M, Chatterton JC, Murphy-Bokern D, Webster C, Williams AG (2009).
How low can we go? An assessment of greenhouse gas emissions from the UK food
system and the scope reduction by 2050. Report for the WWF and Food Climate
Research Network. 1-80.http://dspace.lib.cranfield.ac.uk/handle/1826/6503
(accessed Oct 28, 2014).
Aylott MJ, Casella E, Tubby I, Street NR, Smith P, Taylor G (2008) Y ield and spatial supply
of bioenergy poplar and willow short-rotation coppice in the UK. New Phytol, 178,
358-370.
Azeez G (2009). A review of evidence on the relationship between agriculture and soil Carbon
sequestration , and how organic farming can contribute to climate change mitigation
and adaptation. Marlborough Street 1-
212.http://www.soilassociation.org/LinkClick.aspx?fileticket=SSnOCMoqrXs%3D&ta
bid=387 (accessed Nov 15, 2015).
Bals B, Teachworth L, Dale B, Balan V (2007) Extraction of proteins from switchgrass using
aqueous ammonia within an integrated biorefinery. Applied Biochemistry and
Biotechnology, 143, 187-198.
Banse M, van Meijl H, Tabeau A, Woltjer G (2008) Will EU biofuel policies affect global
agricultural markets? European Review of Agricultural Economics, 35, 117-141.
Bayitse R (2015) Lactic Acid Production from Biomass: Prospect for Bioresidue Utilization in
Ghana: Technological Review. International Journal of Applied, 5.
Behzadian M, Kazemadeh RB, Albadvi A, Aghdasi M (2010) PROMETHEE: A comprehensive
literature review on methodologies and applications. European Journal of
Operational Research, 200, 198-215.
Bentsen NS, Felby C, Ipsen KH (2006). Energy balance of 2 nd generation bioethanol
production in Denmark. 1-
45.http://www.tekno.dk/pdf/projekter/p09_2gbio/ClausFelby/p09_2gbio%20Bents
en%20et%20al%20(2006).pdf (accessed May 05, 2014).
50
Berndes G, Ahlgren S, Börjesson P, Cowie AL (2013) Bioenergy and land use change—state of
the art. Wiley Interdisciplinary Reviews: Energy and Environment, 2, 282-303.
Berndes G, Hansson J (2007) Bioenergy expansion in the EU: Cost-effective climate change
mitigation, employment creation and reduced dependency on imported fuels. Energy
Policy, 35, 5965-5979.
Birkved M, Hauschild MZ (2006) PestLCI - A model for estimating field emissions of
pesticides in agricultural LCA. Ecological Modelling, 198, 433-451.
Blengini GA, Brizio E, Cibrario M, Genon G (2011) LCA of bioenergy chains in Piedmont
(Italy): A case study to support public decision makers towards sustainability.
Resources, Conservation and Recycling, 57, 36-47.
Börjesson PII (1996) Energy analysis of biomass production and transportation. Biomass
and Bioenergy, 11, 305-318.
Borrion AL, McManus MC, Hammond GP (2012) Environmental life cycle assessment of
bioethanol production from wheat straw. Biomass and Bioenergy, 47, 9-19.
Bos HL, Meesters KPH, Conijn SG, Corré WJ, Patel MK (2016) Comparing biobased products
from oil crops versus sugar crops with regard to non-renewable energy use, GHG
emissions and land use. Industrial Crops and Products, 84, 366-374.
Boufateh I, Perwuelz A, Rabenasolo B, Desodt AMJ (2011) Multiple criteria decision-making
for environmental impacts optimisation. International Journal of Business
Performance and Supply Chain Modelling, 3, 28.
Bourguignon D (2015). EU Biofuels Policy. Dealing with Indirect Land Use Change.European
Parliament Research Service, EPRS Briefing. EPRS | European Parliamentary
Research Service.PE 548.993. 1-
10.http://www.europarl.europa.eu/RegData/etudes/BRIE/2015/548993/EPRS_BRI
(2015)548993_REV1_EN.pdf (accessed Oct 18, 2016).
Brandão M, Milà i Canals L, Clift R (2011) Soil organic carbon changes in the cultivation of
energy crops: Implications for GHG balances and soil quality for use in LCA. Biomass
and Bioenergy, 35, 2323-2336.
Brander M, Tipper R, Hutchison C, Davis G (2009). Consequential and Attributional
Approaches to LCA: a Guide to Policy Makers with Specific Reference to Greenhouse
Gas LCA of Biofuels. Technical Paper TP-090403-A. Ecometrica Press. 1-
14.http://ecometrica.com/assets/approachesto_LCA3_technical.pdf (accessed Jul
21,, 2015).
Brans J-P, Mareschal B (2005) Promethee Methods. In: Multiple Criteria Decision Analysis:
State of the Art Surveys. pp 163-186. Springer New York.
Bransby DI, McLaughlin SB, Parrish DJ (1998) A review of carbon and nitrogen balances in
switchgrass grown for energy. Biomass & Bioenergy, 14, 379-384.
51
Brentrup F, Küsters J, Lammel J, Kuhlmann H (2000) Methods to estimate on-field nitrogen
emissions from crop production as an input to LCA studies in the agricultural sector.
The International Journal of Life Cycle Assessment, 5, 349-357.
Brinkman M, Wicke B, Gerssen-Gondelach S, van der Laan C, Faaij A (2015) Methodology for
assessing and quantifying ILUC prevention options. Copernicus Institute of
Sustainable Development, Utrecht. http://www.uu.nl/sites/default/files/20150106-
iluc_methodology_report.pdf (accessed Oct 12, 2016). 1-28.
Broch A, Hoekman SK, Unnasch S (2013) A review of variability in indirect land use change
assessment and modeling in biofuel policy. Environmental Science & Policy, 29, 147-
157.
Caduff M, Huijbregts MA, Althaus HJ, Koehler A, Hellweg S (2012) Wind power electricity:
the bigger the turbine, the greener the electricity? Environ Sci Technol, 46, 4725-
4733.
Caduff M, Huijbregts MAJ, Koehler A, Althaus HJ, Hellweg S (2014) Scaling Relationships in
Life Cycle Assessment The Case of Heat Production from Biomass and Heat Pumps.
Journal of Industrial Ecology, 18, 393-406.
Caputo AC, Palumbo M, Pelagagge PM, Scacchia F (2005) Economics of biomass energy
utilization in combustion and gasification plants: effects of logistic variables. Biomass
& Bioenergy, 28, 35-51.
Carole T, Pellegrino J, Paster M (2004) Opportunities in the Industrial Biobased Products
Industry. In: Proceedings of the Twenty-Fifth Symposium on Biotechnology for Fuels
and Chemicals Held May 4–7, 2003, in Breckenridge, CO. (eds Finkelstein M,
Mcmillan J, Davison B, Evans B) pp 871-885. Humana Press.
Cederberg C, Persson UM, Neovius K, Molander S, Clift R (2011) Including Carbon Emissions
from Deforestation in the Carbon Footprint of Brazilian Beef. Environmental Science
& Technology, 45, 1773-1779.
Chakraborty S, Newton AC (2011) Climate change, plant diseases and food security: an
overview. Plant Pathology, 60, 2-14.
Chandre Gowda MJ, Jayaramaiah KM (1998) Comparative evaluation of rice production
systems for their sustainability1Part of the first author's Ph.D work.1. Agriculture,
Ecosystems & Environment, 69, 1-9.
Chen H-G, Zhang YHP (2015) New biorefineries and sustainable agriculture: Increased food,
biofuels, and ecosystem security. Renewable and Sustainable Energy Reviews, 47,
117-132.
Cherubini F (2010) The biorefinery concept: Using biomass instead of oil for producing
energy and chemicals. Energy Conversion and Management, 51, 1412-1421.
52
Cherubini F, Bird ND, Cowie A, Jungmeier G, Schlamadinger B, Woess-Gallasch S (2009a)
Energy- and greenhouse gas-based LCA of biofuel and bioenergy systems: Key issues,
ranges and recommendations. Resources, Conservation and Recycling, 53, 434-447.
Cherubini F, Bird ND, Cowie A, Jungmeier G, Schlamadinger B, Woess-Gallasch S (2009b)
Energy- and greenhouse gas-based LCA of biofuel and bioenergy systems: Key issues,
ranges and recommendations. Resources Conservation and Recycling, 53, 434-447.
Cherubini F, Jungmeier G (2010) LCA of a biorefinery concept producing bioethanol,
bioenergy, and chemicals from switchgrass. International Journal of Life Cycle
Assessment, 15, 53-66.
Cherubini F, Strømman AH, Ulgiati S (2011) Influence of allocation methods on the
environmental performance of biorefinery products—A case study. Resources,
Conservation and Recycling, 55, 1070-1077.
Cherubini F, Ulgiati S (2010) Crop residues as raw materials for biorefinery systems – A LCA
case study. Applied Energy, 87, 47-57.
Clapp CE, Allmaras RR, Layese MF, Linden DR, Dowdy RH (2000) Soil organic carbon and
13C abundance as related to tillage, crop residue, and nitrogen fertilization under
continuous corn management in Minnesota. Soil and Tillage Research, 55, 127-142.
Cosate de Andrade MF, Souza PMS, Cavalett O, Morales AR (2016) Life Cycle Assessment of
Poly(Lactic Acid) (PLA): Comparison Between Chemical Recycling, Mechanical
Recycling and Composting. Journal of Polymers and the Environment, 1-13.
Crutzen PJ, Mosier AR, Smith KA, Winiwarter W (2008) N<sub>2</sub>O release from
agro-biofuel production negates global warming reduction by replacing fossil fuels.
Atmos. Chem. Phys., 8, 389-395.
Cunniff J, Purdy SJ, Barraclough TJP et al. (2015) High yielding biomass genotypes of willow
(Salix spp.) show differences in below ground biomass allocation. Biomass &
Bioenergy, 80, 114-127.
Dale BE (2003) ‘Greening’ the chemical industry: research and development priorities for
biobased industrial products. Journal of Chemical Technology & Biotechnology, 78,
1093-1103.
Dale BE, Allen MS, Laser M, Lynd LR (2009) Protein feeds coproduction in biomass
conversion to fuels and chemicals. Biofuels Bioproducts & Biorefining-Biofpr, 3, 219-
230.
Dale VH, Kline KL, Wright LL, Perlack RD, Downing M, Graham RL (2011) Interactions
among bioenergy feedstock choices, landscape dynamics, and land use. Ecological
Applications, 21, 1039-1054.
Dalgaard T, Hutchings NJ, Porter JR (2003) Agroecology, scaling and interdisciplinarity.
Agriculture, Ecosystems & Environment, 100, 39-51.
53
Dalgaard T, Jørgensen U, Kristensen I et al. (2012) Concepts for a multi-criteria
sustainability assessment of a new more biobased economy in rural production
landscapes. In: International Farming Systems Association Conference. pp
Workshop 5.1. 9 p, Aarhus, Denmark. .
Danish Energy Agency (2011). Energy Strategy 2050– from coal, oil and gas to green energy.
The Danish Ministry of Climate and Energy, 1470 Copenhagen K, Denmark. 1-
66.http://www.ens.dk/Documents/Netboghandel%20-
%20publikationer/2011/Energy_Strategy_2050.pdf (accessed Aug 12, 2012).
Danish Energy Agency (2012) Annual Energy Statistics. Copenhagen, Danish Energy Agency.
Dawson JJC, Smith P (2007) Carbon losses from soil and its consequences for land-use
management. Science of The Total Environment, 382, 165-190.
de Baan L, Alkemade R, Koellner T (2012) Land use impacts on biodiversity in LCA: a global
approach. The International Journal of Life Cycle Assessment, 18, 1216-1230.
Demirbas A (2008) Biofuels sources, biofuel policy, biofuel economy and global biofuel
projections. Energy Conversion and Management, 49, 2106-2116.
Di Lucia L, Ahlgren S, Ericsson K (2012) The dilemma of indirect land-use changes in EU
biofuel policy – An empirical study of policy-making in the context of scientific
uncertainty. Environmental Science & Policy, 16, 9-19.
Dimitriou I, Baum C, Baum S et al. (2011) Quantifying environmental effects of short rotation
coppice (SRC) on biodiversity, soil and water. IEA Bioenergy Task 2011. pp 1-34.
Donner SD, Kucharik CJ (2008) Corn-based ethanol production compromises goal of
reducing nitrogen export by the Mississippi River. Proceedings of the National
Academy of Sciences, 105, 4513-4518.
DSM (2012) All you can eat yeast. pp 1-3, The Netherlands, DSM, Corporate
Communications.
Dukes JS, Mooney HA (1999) Does global change increase the success of biological invaders?
Trends in Ecology & Evolution, 14, 135-139.
EEA (2013). EMEP/EEA air pollutant emission inventory guidebook 2013. Copenhagen,
Denmark. 1-43.http://www.eea.europa.eu/publications/emep-eea-guidebook-2013
(accessed April 12, 2014).
Eklund R, Galbe M, Zacchi G (1995) The Influence of So2 and H2so4 Impregnation of Willow
Prior to Steam Pretreatment. Bioresource Technology, 52, 225-229.
Ekvall T, Finnveden G (2001) Allocation in ISO 14041—a critical review. Journal of Cleaner
Production, 9, 197-208.
Ekvall T, Weidema BP (2004) System boundaries and input data in consequential life cycle
inventory analysis. International Journal of Life Cycle Assessment, 9, 161-171.
Elghali L, Clift R, Sinclair P, Panoutsou C, Bauen A (2007) Developing a sustainability
framework for the assessment of bioenergy systems. Energy Policy, 35, 6075-6083.
54
Eriksen J, Askegaard M, Søegaard K (2014) Complementary effects of red clover inclusion in
ryegrass-white clover swards for grazing and cutting. Grass and Forage Science, 69,
241-250.
European Commission (2009) Directive 2009/28/EC of the European Parliament and of the
Council of 23 April 2009 on the promotion of the use of energy from renewable
sources and amending and subsequently repealing Directives 2001/77/EC and
2003/30/EC. Vol. 140. European Commission, Brussels, Belgium. Off J Eur Union L,
140, 16-47.
European Commission (2010). Joint Research Centre - Institute for Environment and
Sustainability: International Reference Life Cycle Data System (ILCD) Handbook -
General guide for Life Cycle Assessment - Detailed guidance. First edition March
2010. EUR 24708 EN. Luxembourg. Publications Office of the European Union;
2010. 1-
417.http://publications.jrc.ec.europa.eu/repository/bitstream/JRC48157/ilcd_handb
ook-general_guide_for_lca-detailed_guidance_12march2010_isbn_fin.pdf (accessed
May 15, 2015).
European Commission (2015). Product Environmental Footprint (PEF). News. European
Commission, Brussels,
Belgium.http://ec.europa.eu/environment/eussd/smgp/ef_news.htm (accessed Feb
4, 2016).
Fantke PE, Huijbregts M, Margni M et al. (2015). USEtox® 2.0 User Manual (Version 2).
UNEP/SETAC scientific consensus model for characterizing human toxicological and
ecotoxicological impacts of chemical emissions in life cycle assessment. USEtox®
Team.http://usetox.org (accessed Nov 15, 2015).
FAOSTAT (2013). Agri-environmental statistics.Food and Agriculture Organization of the
United Nations, Statistics Division. http://faostat.fao.org/ (accessed Dec 11, 2013).
Fargione J, Hill J, Tilman D, Polasky S, Hawthorne P (2008) Land clearing and the biofuel
carbon debt. Science, 319, 1235-1238.
Finkbeiner M (2009) Carbon footprinting—opportunities and threats. The International
Journal of Life Cycle Assessment, 14, 91-94.
Finkbeiner M (2013). Indirect Land Use Change (iLUC) within life cycle assessment (LCA)–
scientific robustness and consistency with international standards. Berlín,
OVID/VDEV. erband der Deutschen Biokraftstoffindustrie e. V.- Association of the
German Biofuel Industry, Berlin, Germany. . 1-
68.http://www.fediol.eu/data/RZ_VDB_0030_Vorstudie_ENG_Komplett.pdf
(accessed Oct 10, 2016).
Fiorentini R, Galoppini C (1981) Pilot-Plant Production of an Edible Alfalfa Protein-
Concentrate. Journal of Food Science, 46, 1514-&.
55
FitzPatrick M, Champagne P, Cunningham MF, Whitney RA (2010) A biorefinery processing
perspective: Treatment of lignocellulosic materials for the production of value-added
products. Bioresource Technology, 101, 8915-8922.
Flysjö A, Cederberg C, Henriksson M, Ledgard S (2012) The interaction between milk and
beef production and emissions from land use change – critical considerations in life
cycle assessment and carbon footprint studies of milk. Journal of Cleaner Production,
28, 134-142.
Fortier J, Truax B, Gagnon D, Lambert F (2015) Biomass carbon, nitrogen and phosphorus
stocks in hybrid poplar buffers, herbaceous buffers and natural woodlots in the
riparian zone on agricultural land. J Environ Manage, 154, 333-345.
Fritsche UR, Iriarte L (2014) Sustainability criteria and indicators for the bio-based economy
in Europe: state of discussion and way forward. Energies, 7, 6825-6836.
Galbe M, Sassner P, Wingren A, Zacchi G (2007) Process Engineering Economics of
Bioethanol Production. In: Biofuels. (ed Olsson L) pp 303-327. Springer Berlin
Heidelberg.
Galbe M, Zacchi G (2007) Pretreatment of Lignocellulosic Materials for Efficient Bioethanol
Production. In: Biofuels. (ed Olsson L) pp 41-65. Springer Berlin Heidelberg.
Gasparatos A, Scolobig A (2012) Choosing the most appropriate sustainability assessment
tool. Ecological Economics, 80, 1-7.
Gawel E, Ludwig G (2011) The iLUC dilemma: How to deal with indirect land use changes
when governing energy crops? Land Use Policy, 28, 846-856.
Gelfand I, Sahajpal R, Zhang X, Izaurralde RC, Gross KL, Robertson GP (2013) Sustainable
bioenergy production from marginal lands in the US Midwest. Nature, 493, 514-517.
Ghaffar T, Irshad M, Anwar Z et al. (2014) Recent trends in lactic acid biotechnology: A brief
review on production to purification. Journal of Radiation Research and Applied
Sciences, 7, 222-229.
Ghatak HR (2011) Biorefineries from the perspective of sustainability: Feedstocks, products,
and processes. Renewable and Sustainable Energy Reviews, 15, 4042-4052.
Gielen D, Boshell F, Saygin D (2016) Climate and energy challenges for materials science.
Nat Mater, 15, 117-120.
Grogan P, Matthews R (2002) A modelling analysis of the potential for soil carbon
sequestration under short rotation coppice willow bioenergy plantations. Soil Use and
Management, 18, 175-183.
Guillén-Gosálbez G, Caballero JA, Jiménez L (2008) Application of Life Cycle Assessment to
the Structural Optimization of Process Flowsheets. Industrial & Engineering
Chemistry Research, 47, 777-789.
Guinée J, Heijungs R, Huppes G (2004) Economic allocation: Examples and derived decision
tree. The International Journal of Life Cycle Assessment, 9, 23-33.
56
Gylling M, Jørgensen U, Bentsen NS, Kristensen IT, Dalgaard T, Felby C, Johannsen VK
(2013). The +10 Million Tonnes Study : Increasing the sustainable production of
biomass for biorefineries. Fødevareøkonomisk Institut, Københavns Universitet. 1-
32.http://curis.ku.dk/ws/files/47425822 (accessed Sep 15, 2013).
Hamelin L (2013). Carbon management and environmental consequences of agricultural
biomass in a Danish renewable energy strategy. PhD thesis. Department of Chemical
Engineering, Biotechnology and Environmental Technology, University of Southern
Denmark.
http://www.ceesa.plan.aau.dk/digitalAssets/114/114494_71029_thesis_lh.pdf
(accessed Mar 15, 2016). 1-104.
Hamelin L, Jørgensen U, Petersen BM, Olesen JE, Wenzel H (2012) Modelling the carbon
and nitrogen balances of direct land use changes from energy crops in Denmark: a
consequential life cycle inventory. Global Change Biology Bioenergy, 4, 889-907.
Hansen B, Kristensen ES, Grant R, Høgh-Jensen H, Simmelsgaard SE, Olesen JE (2000)
Nitrogen leaching from conventional versus organic farming systems — a systems
modelling approach. European Journal of Agronomy, 13, 65-82.
Hansen G, Grass S (2000) Method of continous separation of vegetable biomass into a fluid
phase and a solids containing phase of pulpy cosistence. Google Patents.
Haralambopoulos DA, Polatidis H (2003) Renewable energy projects: structuring a multi-
criteria group decision-making framework. Renewable Energy, 28, 961-973.
Harvey M, Pilgrim S (2011) The new competition for land: Food, energy, and climate change.
Food Policy, 36, Supplement 1, S40-S51.
Hauschild M (2006) Spatial Differentiation in Life Cycle Impact Assessment: A decade of
method development to increase the environmental realism of LCIA. The
International Journal of Life Cycle Assessment, 11, 11-13.
Heller MC, Keoleian GA, Volk TA (2003) Life cycle assessment of a willow bioenergy
cropping system. Biomass and Bioenergy, 25, 147-165.
Hill J, Polasky S, Nelson E et al. (2009) Climate change and health costs of air emissions
from biofuels and gasoline. Proceedings of the National Academy of Sciences, 106,
2077-2082.
Høgh-Jensen H, Schjoerring JK (1997) Interactions between white clover and ryegrass under
contrasting nitrogen availability: N2 fixation, N fertilizer recovery, N transfer and
water use efficiency. Plant and Soil, 197, 187-199.
Höltinger S, Schmidt J, Schönhart M, Schmid E (2014) A spatially explicit techno-economic
assessment of green biorefinery concepts. Biofuels, Bioproducts and Biorefining, 8,
325-341.
Huijbregts MAJ (1998) Application of uncertainty and variability in LCA. The International
Journal of Life Cycle Assessment, 3, 273-280.
57
IEA (2011). Bio-based Chemicals Value Added Products from Biorefineries. IEA Bioenergy
Task42,Wageningen UR - Food and Bio-based Research, The Netherlands. 42. 1-
36.http://www.qibebt.ac.cn/xwzx/kydt/201202/P020120223409482956847.pdf
(accessed Feb 25, 2015).
IEA (2013). Resource to Reserve 2013-Summary. Paris. 1-
252.https://www.iea.org/publications/freepublications/publication/WEO2015Specia
lReportonEnergyandClimateChange.pdf (accessed Nov 12, 205).
Inbicon (2013). DONG Energy and DSM prove cellulosic bio-ethanol fermentation on
industrial scale with 40% higher yield. Inbicon, Denmark.
http://www.inbicon.com/About_inbicon/News/Data/Pages/DONGEnergyandDSMp
rovecellulosicbio-ethanolfermentationonindustrialscalewith40higheryield.aspx
(accessed Aug 01, 2013).
IPCC (2000). Watson, R. T., Noble, IR., Bolin, B., Ravindranath, N. H., Verardo, DJ, Dokken,
DJ (Eds.). Land Use, Land-Use Change and Forestry. Intergovernmental Panel on
Climate Change. Available from Cambridge University Press, Cambridge, England. 1-
375.http://www.ipcc.ch/ipccreports/sres/land_use/index.php?idp=98 (accessed Jun
05, 2016).
IPCC (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by
the National Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L.,
Miwa K., Ngara T. and Tanabe K. (eds). Published: IGES, Japan. 4. 11.11-
11.24.http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html (accessed Sep 27,
2012).
IPCC (2007). IPCC Fourth Assessment Report: Climate Change
2007.http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch2s2-10-2.html
(accessed March 05, 2015).
ISO (1997). ISO 14040. Environmental Management – Life Cycle Assessment – Principles
and Framework.http://www.iso.org/iso/catalogue_detail?csnumber=37456
(accessed Nov 12, 2013).
ISO (2006). ISO14040: Environmental Management–Life Cycle Assessment–Principles and
Framework.International Organization for Standardization. London: British
Standards Institution. 1-
20.http://www.iso.org/iso/catalogue_detail?csnumber=37456 (accessed Jan 05,
2015).
Jenkins R, Alles C (2011) Field to fuel: developing sustainable biorefineries. Ecological
Applications, 21, 1096-1104.
John RP, Nampoothiri KM, Pandey A (2006) Solid-state fermentation for l-lactic acid
production from agro wastes using Lactobacillus delbrueckii. Process Biochemistry,
41, 759-763.
58
Jørgensen U, Dalgaard T, Kristensen ES (2005) Biomass energy in organic farming—the
potential role of short rotation coppice. Biomass and Bioenergy, 28, 237-248.
Junginger M, de Visser E, Hjort-Gregersen K, Koornneef J, Raven R, Faaij A, Turkenburg W
(2006) Technological learning in bioenergy systems. Energy Policy, 34, 4024-4041.
Junginger M, van Sark W (2010) Technological learning in the energy sector: lessons for
policy, industry and science, Edward Elgar Publishing.
Kamm B, Kamm M (2004) Principles of biorefineries. Applied Microbiology and
Biotechnology, 64, 137-145.
Kamm B, Schönicke P, Kamm M (2009) Biorefining of Green Biomass – Technical and
Energetic Considerations. CLEAN – Soil, Air, Water, 37, 27-30.
Kaparaju P, Serrano M, Thomsen AB, Kongjan P, Angelidaki I (2009) Bioethanol,
biohydrogen and biogas production from wheat straw in a biorefinery concept.
Bioresource Technology, 100, 2562-2568.
Khanna M, Crago CL, Black M (2011) Can biofuels be a solution to climate change? The
implications of land use change-related emissions for policy. Interface Focus, 1, 233-
247.
Kim S, Dale BE (2005) Life cycle assessment of various cropping systems utilized for
producing biofuels: Bioethanol and biodiesel. Biomass and Bioenergy, 29, 426-439.
Kim YH, Moon S-H (2001) Lactic acid recovery from fermentation broth using one-stage
electrodialysis. Journal of Chemical Technology & Biotechnology, 76, 169-178.
Kircher M (2012) The transition to a bio-economy: national perspectives. Biofuels,
Bioproducts and Biorefining, 6, 240-245.
Klein-Marcuschamer D, Simmons BA, Blanch HW (2011) Techno-economic analysis of a
lignocellulosic ethanol biorefinery with ionic liquid pre-treatment. Biofuels,
Bioproducts and Biorefining, 5, 562-569.
Kløverpris J, Mueller S (2013) Baseline time accounting: Considering global land use
dynamics when estimating the climate impact of indirect land use change caused by
biofuels. The International Journal of Life Cycle Assessment, 18, 319-330.
Knudsen MT, Dennis P, Hermansen JE et al. (2016) Characterization factors from direct
measures of plant species in European farmland for land use impacts on biodiversity
in life cycle assessment. Under review Science of the Total Environment.
Knudsen MT, Meyer-Aurich A, Olesen JE, Chirinda N, Hermansen JE (2014) Carbon
footprints of crops from organic and conventional arable crop rotations – using a life
cycle assessment approach. Journal of Cleaner Production, 64, 609-618.
Korsaeth A, Jacobsen AZ, Roer AG et al. (2012) Environmental life cycle assessment of cereal
and bread production in Norway. Acta Agriculturae Scandinavica, Section A —
Animal Science, 62, 242-253.
59
Koutinas AA, Du C, Wang RH, Webb C (2008) Production of Chemicals from Biomass. In:
Introduction to Chemicals from Biomass. pp 77-101. John Wiley & Sons, Ltd.
Kramer KJ, Moll HC, Nonhebel S (1999) Total greenhouse gas emissions related to the Dutch
crop production system. Agriculture, Ecosystems & Environment, 72, 9-16.
Krausmann F (2016) From Energy Source to Sink: Transformations of Austrian Agriculture.
In: Social Ecology: Society-Nature Relations across Time and Space. (eds Haberl H,
Fischer-Kowalski M, Krausmann F, Winiwarter V) pp 433-445. Cham, Springer
International Publishing.
Kremen C, Iles A, Bacon C (2012) Diversified Farming Systems: An Agroecological, Systems-
based Alternative to Modern Industrial Agriculture. Ecology and Society, 17, 1-19.
Kudakasseril Kurian J, Raveendran Nair G, Hussain A, Vijaya Raghavan GS (2013)
Feedstocks, logistics and pre-treatment processes for sustainable lignocellulosic
biorefineries: A comprehensive review. Renewable and Sustainable Energy Reviews,
25, 205-219.
Lahdelma R, Salminen P, Hokkanen J (2000) Using Multicriteria Methods in Environmental
Planning and Management. Environmental Management, 26, 595-605.
Lal R (1993) Tillage effects on soil degradation, soil resilience, soil quality, and sustainability.
Soil and Tillage Research, 27, 1-8.
Lal R (2015) Restoring Soil Quality to Mitigate Soil Degradation. Sustainability, 7, 5875.
Landis DA, Gardiner MM, van der Werf W, Swinton SM (2008) Increasing corn for biofuel
production reduces biocontrol services in agricultural landscapes. Proceedings of the
National Academy of Sciences, 105, 20552-20557.
Lange JP (2007) Lignocellulose conversion: an introduction to chemistry, process and
economics. Biofuels Bioproducts & Biorefining-Biofpr, 1, 39-48.
Langeveld JWA, Dixon J, Jaworski JF (2010) Development Perspectives Of The Biobased
Economy: A Review. Crop Sci., 50, 142-151.
Larsen J, Haven MØ, Thirup L (2012) Inbicon makes lignocellulosic ethanol a commercial
reality. Biomass and Bioenergy, 46, 36-45.
Larsen J, Østergaard Petersen M, Thirup L, Wen Li H, Krogh Iversen F (2008) The IBUS
Process – Lignocellulosic Bioethanol Close to a Commercial Reality. Chemical
Engineering & Technology, 31, 765-772.
Leip A, Weiss F, Wassenaar T et al. (2010). Evaluation of the livestock sector's contribution
to the EU greenhouse gas emissions (GGELS)–final report. European Commission,
Joint Research Centre. 1-
323.http://ec.europa.eu/agriculture/analysis/external/livestock-gas/full_text_en.pdf
(accessed Jun 15, 2014).
Li Y , Shahbazi A, Kadzere CT (2006) Separation of cells and proteins from fermentation
broth using ultrafiltration. Journal of Food Engineering, 75, 574-580.
60
Lin Y , Rujiang G, Chong S (2006) Food and energy of the collision alarm. Outlook Weekly,
50, 44-45.
Losordo Z, McBride J, Rooyen JV et al. (2016) Cost competitive second-generation ethanol
production from hemicellulose in a Brazilian sugarcane biorefinery. Biofuels,
Bioproducts and Biorefining, 10, 589-602.
Lund H, Mathiesen B, Christensen P, Schmidt J (2010) Energy system analysis of marginal
electricity supply in consequential LCA. International Journal of Life Cycle
Assessment, 15, 260-271.
Lund H, Mathiesen BV (2009) Energy system analysis of 100% renewable energy systems—
The case of Denmark in years 2030 and 2050. Energy, 34, 524-531.
Luo L, van der Voet E, Huppes G, Udo de Haes H (2009) Allocation issues in LCA
methodology: a case study of corn stover-based fuel ethanol. The International
Journal of Life Cycle Assessment, 14, 529-539.
Macharis C, Springael J, De Brucker K, Verbeke A (2004) PROMETHEE and AHP: The
design of operational synergies in multicriteria analysis - Strengthening
PROMETHEE with ideas of AHP. European Journal of Operational Research, 153,
307-317.
Mandl MG (2010) Status of green biorefining in Europe. Biofuels, Bioproducts and
Biorefining, 4, 268-274.
Mathiesen BV, Münster M, Fruergaard T (2009) Uncertainties related to the identification of
the marginal energy technology in consequential life cycle assessments. Journal of
Cleaner Production, 17, 1331-1338.
McClean GJ, Rowe RL, Heal KV, Cross A, Bending GD, Sohi SP (2015) An empirical model
approach for assessing soil organic carbon stock changes following biomass crop
establishment in Britain. Biomass and Bioenergy, 83, 141-151.
McKendry P (2002) Energy production from biomass (Part 1): Overview of biomass.
Bioresour Technol, 83, 37-46.
Mickwitz P, Hildén M, Seppälä J, Melanen M (2011) Sustainability through system
transformation: lessons from Finnish efforts. Journal of Cleaner Production, 19,
1779-1787.
Milà i Canals L, Romanyà J, Cowell SJ (2007) Method for assessing impacts on life support
functions (LSF) related to the use of 'fertile land' in Life Cycle Assessment (LCA).
Journal of Cleaner Production, 15, 1426-1440.
Modahl IS, Vold BI (2011). The 2010 LCA of cellulose, ethanol, lignin adn vanillin from
Borregaard, Sarpsborg. Ostfold Research, Gamle Beddingvei 2B, N-1671 Kråkerøy. 1-
78.http://ostfoldforskning.no/uploads/dokumenter/publikasjoner/658.pdf.
61
Mogensen L, Kristensen T, Nguyen TLT, Knudsen MT, Hermansen JE (2014) Method for
calculating carbon footprint of cattle feeds – including contribution from soil carbon
changes and use of cattle manure. Journal of Cleaner Production, 73, 40-51.
Mosier N, Wyman C, Dale B, Elander R, Lee YY, Holtzapple M, Ladisch M (2005) Features of
promising technologies for pretreatment of lignocellulosic biomass. Bioresource
Technology, 96, 673-686.
Muñoz I, Flury K, Jungbluth N, Rigarlsford G, i Canals LM, King H (2013) Life cycle
assessment of bio-based ethanol produced from different agricultural feedstocks. The
International Journal of Life Cycle Assessment, 19, 109-119.
Murphy F, Devlin G, McDonnell K (2014) Energy requirements and environmental impacts
associated with the production of short rotation willow (Salix sp.) chip in Ireland.
GCB Bioenergy, 6, 727-739.
Nanda S, Mohammad J, Reddy SN, Kozinski JA, Dalai AK (2013) Pathways of lignocellulosic
biomass conversion to renewable fuels. Biomass Conversion and Biorefinery, 4, 157-
191.
NaturErhvervstyrelsen (2015). Vejledning om gødsknings-og harmoniregler: Planperioden 1.
august 2014 til 31. juli 2015. Document number 6. Agriculture and Fisheries (in
Danish). Ministeriet for Fødevarer, Landbrug og Fiskeri,Copenhagen, Denmark.
http://www.nordfynskommune.dk/~/media/Files/Dokumenter/Teknik%20og%20M
iljoe/Natur%20og%20Miljoe/Landbrug/Vejledning%20om%20g%C3%B8dnings-
%20og%20harmoniregler.pdf (accessed May 15, 2015). 1-173.
Nemecek T, Kägi T (2007) Life cycle inventories of agricultural production systems.
Duebendorf, Switzerland, Swiss Centre for Life Cycle Inventories,.
Ness B, Urbel-Piirsalu E, Anderberg S, Olsson L (2007) Categorising tools for sustainability
assessment. Ecological Economics, 60, 498-508.
O’Keeffe S, Schulte RPO, Sanders JPM, Struik PC (2011) I. Technical assessment for first
generation green biorefinery (GBR) using mass and energy balances: Scenarios for an
Irish GBR blueprint. Biomass and Bioenergy, 35, 4712-4723.
Öhgren K, Bura R, Saddler J, Zacchi G (2007) Effect of hemicellulose and lignin removal on
enzymatic hydrolysis of steam pretreated corn stover. Bioresource Technology, 98,
2503-2510.
Ørum JE, Samsøe-Petersen L (2014). Bekæmpelsesmiddelstatistik 2013:
behandlingshyppighed og belastning.Orientering fra Miljøstyrelsen nr. 6, 2014.
Miljøstyrelsen, Copenhagen, Denmark. 1-
66.http://www2.mst.dk/Udgiv/publikationer/2014/12/978-87-93283-33-6.pdf
(accessed Dec 15, 2015).
62
Pacaldo RS, Volk TA, Briggs RD (2012) Greenhouse Gas Potentials of Shrub Willow Biomass
Crops Based on Below- and Aboveground Biomass Inventory Along a 19-Year
Chronosequence. BioEnergy Research, 6, 252-262.
Pachauri RK, Allen MR, Barros V et al. (2014) Climate change 2014: synthesis Report.
Contribution of working groups I, II and III to the fifth assessment report of the
intergovernmental panel on climate change, IPCC.
Palgan YV, McCormick K (2016) Biorefineries in Sweden: Perspectives on the opportunities,
challenges and future. Biofuels, Bioproducts and Biorefining, 10, 523-533.
Parajuli R (2012) Looking into the Danish energy system: Lesson to be learned by other
communities. Renewable & Sustainable Energy Reviews, 16, 2191-2199.
Parajuli R, Dalgaard T, Jørgensen U et al. (2015a) Biorefining in the prevailing energy and
materials crisis: a review of sustainable pathways for biorefinery value chains and
sustainability assessment methodologies. Renewable & Sustainable Energy Reviews,
43, 244-263.
Parajuli R, Knudsen MT, Dalgaard T (2015b) Multi-criteria assessment of yellow, green, and
woody biomasses: pre-screening of potential biomasses as feedstocks for
biorefineries. Biofuels Bioproducts & Biorefining-Biofpr, 9, 545-566.
Parajuli R, Kristensen IS, Knudsen MT et al. (2016) Environmental life cycle assessments of
producing maize, grass-clover, ryegrass and winter wheat straw for biorefinery (in-
press). http://dx.doi.org/10.1016/j.jclepro.2016.10.076. Journal of Cleaner
Production.
Parajuli R, Løkke S, Østergaard PA, Knudsen MT, Schmidt JH, Dalgaard T (2014) Life Cycle
Assessment of district heat production in a straw fired CHP plant. Biomass and
Bioenergy, 68, 115-134.
Patel M, Crank M, Dornburg V et al. (2006). Medium and Long-term Opportunities and
Risks of the Biotechnological Production of Bulk Chemicals from Renewable
Resources-The Potential of White Biotechnology. Final report. Prepared under the
European Commission’s GROWTH Programme (DG Research). Utrecht University,
Department of Science, Technology and Society (STS) / Copernicus Institute,
Heidelberglaan 2, NL-3584 CS Utrecht, Netherlands. 1-444.http://www.bio-
economy.net/applications/files/Brew_project_report.pdf /accessed April 23, 2016).
Patel MK, Blok K (2013). Experience curves for novel technologies: Scaling and Learning of
technologies - The effect of size and technological progress.
7.http://www.prosuite.org/c/document_library/get_file?uuid=9ae4b2a9-afb6-41c2-
8fdc-96d2eadfc4d0&groupId=12772.
Pawelzik P, Carus M, Hotchkiss J et al. (2013) Critical aspects in the life cycle assessment
(LCA) of bio-based materials – Reviewing methodologies and deriving
recommendations. Resources, Conservation and Recycling, 73, 211-228.
63
Payraudeau S, van der Werf HMG, Vertès F (2007) Analysis of the uncertainty associated
with the estimation of nitrogen losses from farming systems. Agricultural Systems,
94, 416-430.
Petersen B, Berntsen J (2003). The turnover of soil organic matter on different farm types.
DARCOFenews.http://orgprints.org/4665/ (accessed Feb 18, 2013).
Petersen BM, Knudsen MT (2010). Consequences of straw removal for soil carbon
sequestration of agricultural fields, Using soil carbon in a time frame perspective.
Faculty of Agricultural Sciences, Aarhus University, Aarhus, Denmark. 1-
49.http://pure.au.dk/portal/en/publications/consequences-of-straw-removal-for-
soil-carbon-sequestration-of-agricultural-fields(ab049e95-9471-463d-97b7-
69635ec81518).html (accessed Nov 15,, 2015).
Petersen BM, Knudsen MT, Hermansen JE, Halberg N (2013) An approach to include soil
carbon changes in life cycle assessments. Journal of Cleaner Production, 52, 217-224.
Philp J (2015) Balancing the bioeconomy: supporting biofuels and bio-based materials in
public policy. Energy & Environmental Science, 8, 3063-3068.
Ragauskas AJ, Williams CK, Davison BH et al. (2006) The path forward for biofuels and
biomaterials. Science, 311, 484-489.
Rebitzer G, Ekvall T, Frischknecht R et al. (2004) Life cycle assessment part 1: framework,
goal and scope definition, inventory analysis, and applications. Environ Int, 30, 701-
720.
Rehl T, Lansche J, Müller J (2012) Life cycle assessment of energy generation from biogas—
Attributional vs. consequential approach. Renewable and Sustainable Energy
Reviews, 16, 3766-3775.
René vR, Bert A (2007). Status Report Biorefinery 2007.Report 847. Agrotechnology and
Food Sciences Group. NL-6700 AA Wageningen. 1-110.
http://www.biorefinery.nl/uploads/media/StatusDocumentBiorefinery2007final2111
07.pdf (accessed Mar 11, 2013).
Rødsrud G, Lersch M, Sjöde A (2012) History and future of world's most advanced
biorefinery in operation. Biomass and Bioenergy, 46, 46-59.
Roer A-G, Korsaeth A, Henriksen TM, Michelsen O, Strømman AH (2012) The influence of
system boundaries on life cycle assessment of grain production in central southeast
Norway. Agricultural Systems, 111, 75-84.
Rowe RL, Street NR, Taylor G (2009) Identifying potential environmental impacts of large-
scale deployment of dedicated bioenergy crops in the UK. Renewable and Sustainable
Energy Reviews, 13, 271-290.
Sammons Jr NE, Yuan W, Eden MR, Aksoy B, Cullinan HT (2008) Optimal biorefinery
product allocation by combining process and economic modeling. Chemical
Engineering Research and Design, 86, 800-808.
64
Sanchez ST, Woods J, Akhurst M et al. (2012) Accounting for indirect land-use change in the
life cycle assessment of biofuel supply chains. Journal of The Royal Society Interface,
9, 1105-1119.
Sands GR, Podmore TH (2000) A generalized environmental sustainability index for
agricultural systems. Agriculture Ecosystems & Environment, 79, 29-41.
Sartori F, Lal R, Ebinger MH, Eaton JA (2007) Changes in soil carbon and nutrient pools
along a chronosequence of poplar plantations in the Columbia Plateau, Oregon, USA.
Agriculture, Ecosystems & Environment, 122, 325-339.
Schaidle JA, Moline CJ, Savage PE (2011) Biorefinery sustainability assessment.
Environmental Progress & Sustainable Energy, 30, 743-753.
Schmidt JH (2008) System delimitation in agricultural consequential LCA - Outline of
methodology and illustrative case study of wheat in Denmark. International Journal
of Life Cycle Assessment, 13, 350-364.
Schmidt JH, Brandao M (2013). LCA screening of biofuels-iLUC, biomass manipulation and
soil carbon. Copenhagen, Denmark. . 3-
97.http://concito.dk/files/dokumenter/artikler/biomasse_bilag1_lcascreening.pdf
(accessed May 12, 2013).
Schmidt JH, Muños I (2014). The carbon footprint of Danish production and consumption:
Literature review and model calculations. Energistyrelsen. Copenhagen, Denmark. 1-
119.http://vbn.aau.dk/files/196725552/_dk_carbon_footprint_20140305final.pdf
(accessed Feb 02, 2016).
Schmidt JH, Weidema BP, Brandao M (2015) A framework for modelling indirect land use
changes in Life Cycle Assessment. Journal of Cleaner Production, 99, 230-238.
Schmidt. J. H., Reinhard J, Weidema BP (2012) A Model of Indirect Land Use Change. In:
8th International Conference on LCA in the Agri-Food Sector. pp 1-6, Rennes,
France.
Schulze C, Jödicke A, Scheringer M, Margni M, Jolliet O, Hungerbühler K, Matthies M (2001)
Comparison of different life-cycle impact assessment methods for aquatic ecotoxicity.
Environmental Toxicology and Chemistry, 20, 2122-2132.
SEGES (2010). Growing instructions-Crops. SEGES, Agro Food Park, Aarhus, Denmark.
https://dyrk-
plant.dlbr.dk/Web/(S(pgsviibw4c1053wjgai5ni1p))/forms/Afgroeder.aspx?kategori=1
(accessed Sep 12, 2015).
SEGES (2015). Approved pesticides in Denmark ("positive list"). SEGES, Agro Food Park,
Aarhus, Denmark. https://www.middeldatabasen.dk/positiveList.asp (accessed Jan
19, 2015).
Shapouri H, Duffield JA, Wang MQ (2002). The Energy Balance of Corn Ethanol: An Update.
Agricultural Economics Reports. United States Department of Agriculture, Economic
65
Research Service.http://ideas.repec.org/p/ags/uerser/34075.html (accesed Feb 22,
2015).
Sheehan J, Aden A, Paustian K, Killian K, Brenner J, Walsh M, Nelson R (2003) Energy and
Environmental Aspects of Using Corn Stover for Fuel Ethanol. Journal of Industrial
Ecology, 7, 117-146.
Sierra R, Granda C, Holtzapple MT (2009) Short-term lime pretreatment of poplar wood.
Biotechnol Prog, 25, 323-332.
Simmelsgaard SE (1998) The effect of crop, N-level, soil type and drainage on nitrate
leaching from Danish soil. Soil Use and Management, 14, 30-36.
Sinden G (2009) The contribution of PAS 2050 to the evolution of international greenhouse
gas emission standards. The International Journal of Life Cycle Assessment, 14, 195-
203.
Smith P, Olesen JE (2010) Synergies between the mitigation of, and adaptation to, climate
change in agriculture. The Journal of Agricultural Science, 148, 543-552.
Smith VH, Tilman GD, Nekola JC (1999) Eutrophication: impacts of excess nutrient inputs
on freshwater, marine, and terrestrial ecosystems. Environmental Pollution, 100,
179-196.
Statistics Denmark (2010). Agricultural Statistics. Copenhagen. 182.
Statistics Denmark (2014). HST6: Harvest by crop and
unit.http://www.statistikbanken.dk/statbank5a/default.asp?w=1024 (accessed Mar
15, 2015).
Statistics Denmark (2016). FODER6: Value of feeding stuffs by quantity, average price and
value. Statbank Denmark.
http://www.statistikbanken.dk/statbank5a/SelectVarVal/Define.asp?Maintable=FO
DER6&PLanguage=1 (accessed Jun 12, 2016).
Statistik Danmarks (2013) Økonomien i landbrugets produktionsgrene 2013. In: Economics
of Agricultural activities 2013. pp 56, Denmark, Danmarks Statistik.
Steduto P, Hsiao TC, Fereres E, Raes D (2012) Crop yield response to water. FAO irrigation
and drainage paper. FAO, Rome, 2012.
Stranddorf H, Hoffmann L, Schmidt A (2004). Impact categories, normalisation and
weighting in LCA (Påvirkningskategories, normalisering og vægtning i LCA–in
Danish), Updated on selected EDIP97-data. Environmental News No. 77, The Danish
Ministry of the Environment. Environmental Protection Agency, Copenhagen. 1-
90.http://www2.mst.dk/udgiv/publications/2005/87-7614-574-3/pdf/87-7614-575-
1.pdf (accessed Sep 27, 2016).
Stranddorf HK, Hoffmann L, Schmidt A (2005). LCA technical report: impact categories,
normalization and weighting in LCA. Update on selected EDIP97-data. Copenhagen:
Danish Ministry of the Environment–Environmental Protection Agency. 1-
66
292.http://lca-center.dk/wp-content/uploads/2015/08/LCA-technical-report-
impact-categories-normalisation-and-weighting-in-LCA.pdf (accessed Sep 27, 2016).
Styles D, Gibbons J, Williams AP et al. (2015) Consequential life cycle assessment of biogas,
biofuel and biomass energy options within an arable crop rotation. GCB Bioenergy, 7,
1305-1320.
Taghizadeh-Toosi A, Christensen BT, Hutchings NJ, Vejlin J, Kätterer T, Glendining M,
Olesen JE (2014a) C-TOOL – A soil carbon model and its parameterisation.
Ecological Modelling, 292, 11-25.
Taghizadeh-Toosi A, Olesen JE, Kristensen K et al. (2014b) Changes in carbon stocks of
Danish agricultural mineral soils between 1986 and 2009. European Journal of Soil
Science, 65, 730-740.
Talebnia F, Karakashev D, Angelidaki I (2010) Production of bioethanol from wheat straw:
An overview on pretreatment, hydrolysis and fermentation. Bioresour Technol, 101,
4744-4753.
Tellarini V, Caporali F (2000) An input/output methodology to evaluate farms as sustainable
agroecosystems: an application of indicators to farms in central Italy. Agriculture
Ecosystems & Environment, 77, 111-123.
Templer R, van der Wielen L (2011) Biorenewables, the bio-based economy and
sustainability. Interface Focus, 1, 187-188.
Thomassen MA, Dalgaard R, Heijungs R, de Boer I (2008) Attributional and consequential
LCA of milk production. International Journal of Life Cycle Assessment, 13, 339-
349.
Thomsen M, Haugaard-Nielsen H (2008) Sustainable bioethanol production combining
biorefinery principles using combined raw materials from wheat undersown with
clover-grass. Journal of Industrial Microbiology & Biotechnology, 35, 303-311.
Thorsell S, Epplin FM, Huhnke RL, Taliaferro CM (2004) Economics of a coordinated
biorefinery feedstock harvest system: lignocellulosic biomass harvest cost. Biomass &
Bioenergy, 27, 327-337.
Tillman A-M, Ekvall T, Baumann H, Rydberg T (1994) Choice of system boundaries in life
cycle assessment. Journal of Cleaner Production, 2, 21-29.
Tommy Dalgaard, Uffe Jørgensen, Kristensen IT et al. (2012) Concepts for a multi-criteria
sustainability assessment of a new more biobased economy in rural production
landscapes. In: International Farming Systems Association Conference. pp
Workshop 5.1. 9 p, Aarhus, Denmar.
Tonini D, Astrup T (2012) LCA of biomass-based energy systems: A case study for Denmark.
Applied Energy, 99, 234-246.
67
Tonini D, Hamelin L, Astrup TF (2016) Environmental implications of the use of agro-
industrial residues for biorefineries: application of a deterministic model for indirect
land-use changes. GCB Bioenergy, 8, 690-706.
Tonini D, Hamelin L, Wenzel H, Astrup T (2012) Bioenergy Production from Perennial
Energy Crops: A Consequential LCA of 12 Bioenergy Scenarios including Land Use
Changes. Environmental Science & Technology, 46, 13521-13530.
Tsoutsos T, Kouloumpis V, Zafiris T, Foteinis S (2010) Life Cycle Assessment for biodiesel
production under Greek climate conditions. Journal of Cleaner Production, 18, 328-
335.
Uellendahl H, Wang G, Møller H, Jørgensen U, Skiadas IV, Gavala HN, Ahring BK (2008)
Energy balance and cost-benefit analysis of biogas production from perennial enrgy
crops pretreated by wet oxidation. . Water Science & Technology, 8, 1841-1847.
Uihlein A, Schebek L (2009) Environmental impacts of a lignocellulose feedstock biorefinery
system: An assessment. Biomass & Bioenergy, 33, 793-802.
Unnasch S (2005) Alcohol fuels from biomass: Well-to-wheel energy balance. In:
Proceedings of the 15th International Symposium on Alcohol Fuels (ISAF), San
Diego, California, United States. pp 26-28.
van der Werf HMG, Petit J (2002) Evaluation of the environmental impact of agriculture at
the farm level: a comparison and analysis of 12 indicator-based methods. Agriculture,
Ecosystems & Environment, 93, 131-145.
Van Lancker J, Wauters E, Van Huylenbroeck G (2016) Managing innovation in the
bioeconomy: An open innovation perspective. Biomass and Bioenergy, 90, 60-69.
Verardi A, Ricca E, De Bari I, Calabrò V (2012) Hydrolysis of lignocellulosic biomass:
current status of processes and technologies and future perspectives. INTECH Open
Access Publisher.
Wagner M, Lewandowski I (2016) Relevance of environmental impact categories for
perennial biomass production. GCB Bioenergy, n/a-n/a.
Wang J-J, Jing Y-Y, Zhang C-F, Zhao J-H (2009) Review on multi-criteria decision analysis
aid in sustainable energy decision-making. Renewable and Sustainable Energy
Reviews, 13, 2263-2278.
Wang L, Littlewood J, Murphy RJ (2013) Environmental sustainability of bioethanol
production from wheat straw in the UK. Renewable & Sustainable Energy Reviews,
28, 715-725.
Wang M, Huo H, Arora S (2011) Methods of dealing with co-products of biofuels in life-cycle
analysis and consequent results within the U.S. context. Energy Policy, 39, 5726-
5736.
Wang M, Saricks C, Santini D (1999). Effects of Fuel Ethanol Use on Fuel-Cycle Energy and
Greenhouse Gas Emissions. Other Information: PBD: 8 Feb 1999; PBD: 8 Feb 1999.
68
1-39.http://www.osti.gov/energycitations/servlets/purl/4742-Z5BevL/native/
(accessed Jan 15, 2014).
Warner E, Zhang Y, Inman D, Heath G (2014) Challenges in the estimation of greenhouse gas
emissions from biofuel-induced global land-use change. Biofuels, Bioproducts and
Biorefining, 8, 114-125.
Watson HK (2011) Potential to expand sustainable bioenergy from sugarcane in southern
Africa. Energy Policy, 39, 5746-5750.
Weidema BP, Bauer C, Hischier R et al. (2013). Overview and methodology. Data quality
guideline for the ecoinvent database version 3. Ecoinvent Report 1(v3). St. Gallen: The
ecoinvent Centre. Swiss Centre for Life Cycle Inventories. 1-
159.http://www.ecoinvent.org/files/dataqualityguideline_ecoinvent_3_20130506.pd
f (accessed Feb 12, 2015).
Whiting A, Azapagic A (2014) Life cycle environmental impacts of generating electricity and
heat from biogas produced by anaerobic digestion. Energy, 70, 181-193.
Wright MM, Brown RC (2007) Comparative economics of biorefineries based on the
biochemical and thermochemical platforms. Biofuels Bioproducts & Biorefining-
Biofpr, 1, 49-56.
Zalidis G, Stamatiadis S, Takavakoglou V, Eskridge K, Misopolinos N (2002) Impacts of
agricultural practices on soil and water quality in the Mediterranean region and
proposed assessment methodology. Agriculture, Ecosystems & Environment, 88,
137-146.
Zamora DS, Apostol KG, Wyatt GJ (2014) Biomass production and potential ethanol yields of
shrub willow hybrids and native willow accessions after a single 3-year harvest cycle
on marginal lands in central Minnesota, USA. Agroforestry Systems, 88, 593-606.
Zhang Y , Kumar A, Hardwidge PR, Tanaka T, Kondo A, Vadlani PV (2016) d-lactic acid
production from renewable lignocellulosic biomass via genetically modified
Lactobacillus plantarum. Biotechnology Progress, 32, 271-278.
Zhang YHP (2008) Reviving the carbohydrate economy via multi-product lignocellulose
biorefineries. Journal of Industrial Microbiology & Biotechnology, 35, 367-375.
Zhao X, Cheng K, Liu D (2009) Organosolv pretreatment of lignocellulosic biomass for
enzymatic hydrolysis. Applied Microbiology and Biotechnology, 82, 815-827.
69
9. Supporting Papers
9.1. Paper I
Status: Published.
Biorefining in the prevailing energy and materials crisis: a review of sustainable pathways for
biorefinery value chains and sustainability assessment methodologies.
Ranjan Parajuli, Tommy Dalgaard, Uffe Jørgensen, Anders Peter S. Adamsen, Marie
Trydeman Knudsen, Morten Birkved, Morten Gylling, Jan Kofod Schjørring
Renewable and Sustainable Energy Reviews 43 (2015) 244–263.
DOI: 10.1016/j.rser.2014.11.041
Reprinted with permission from Elsevier
70
91
9.2. Paper II
Status: Published.
Multi-criteria assessment of yellow, green, and woody biomasses: pre-screening of potential
biomasses as feedstocks for biorefineries
Ranjan Parajuli, Marie Trydeman Knudsen, Tommy Dalgaard
Biofuels, Bioproducts and Biorefining 9 (2015), 545-566. DOI: 10.1002/bbb.1567
Reprinted with permission from John Wiley and Sons
92
115
9.3. Paper III
Status: Published
Environmental life cycle assessments of producing maize, grass-clover, ryegrass and winter
wheat straw for biorefinery
Ranjan Parajuli, Ib Sillebak Kristensen, Marie Trydeman Knudsen, Lisbeth Mogensen,
Andrea Corona, Morten Birkved, Nancy Peña, Morten Graversgaard, Tommy Dalgaard
Journal: Journal of Cleaner Production
The Supporting information about the pesticides related emission distributions can be
accessed via the link below:
http://www.sciencedirect.com/science/article/pii/S0959652616316869.
Reprinted with permission from Elsevier
116
117
130
9.4. Paper IV
Status: Submitted
Environmental Life Cycle Assessment of willow, alfalfa and straw from spring barley as
feedstocks for bioenergy and biorefinery systems
Ranjan Parajuli, Marie Trydeman Knudsen, Sylvestre Njakou Djomo, Andrea Corona,
Morten Birkved, Tommy Dalgaard
Journal: Science of the Total Environment
131
Environmental Life Cycle Assessment of willow, alfalfa and straw from spring
barley as feedstocks for bioenergy and biorefinery systems
Ranjan Parajulia,*, Marie Trydeman Knudsena, Sylvestre Njakou Djomoa, Andrea Coronab,
Morten Birkvedb, Tommy Dalgaarda
aDepartment of Agroecology, Aarhus University, Blichers Allé 20, DK-8830 Tjele, Denmark
bDepartment of Management Engineering, Technical University of Denmark, Building 424,
DK-2800 Lyngby, Denmark
*Corresponding author, email: [email protected], Phone: +4571606831
Abstract:
The study focused on assessing the potential environmental impacts of producing biomasses
for bioenergy and biorefinery systems. A method of Life Cycle Assessment was used for the
evaluation. The assessment included the following impact categories: Global Warming
Potential (GWP1 00), Eutrophication Potential (EP), Non-Renewable Energy (NRE) use,
Agricultural Land Occupation (ALO), Potential Freshwater Ecotoxicity (PFWTox) and Soil
quality. The selected biomasses were willow, alfalfa and straw from spring barley. With
regard to the materials and methods, material inputs entering into the crop production
system were based on the crop production data representing the Danish agro-climatic
conditions. With regard to the methods, different tools were used that well suited to
represent the Danish biomass production system. For instance, estimated total dry matter
(DM) in above and belowground biomass were used for the calculation of soil carbon
changes. Likewise, to estimate freshwater ecotoxicity related to the applied pesticides,
emission distribution fractions to air and freshwater were derived from PestLCI 2.0.6 tool.
The results showed that the carbon footprints for willow and alfalfa were lower than for
straw, which was the result of higher soil C sequestration and lower N2O emissions. Likewise,
EP for willow and alfalfa turned out to be lower than for straw, with rate of fertilization and
nutrient uptake efficiency being the driving factors. The PFWTox was lower for willow and
alfalfa compared to straw, particularly considering emissions at farm level. A critical negative
effect on soil quality was found for spring barley production and hence for straw because of
losses in SOC stock. Finally, the results showed that willow and alfalfa performed better in
almost all impact categories.
Keywords: Energy crops, biorefinery feedstock, land use change, toxicity, environmental
sustainability
132
1. Introduction:
Increasing demands for food, feed, fibers and energy from the available agricultural land
resource put pressure on farmers to maximize productions from the land, balancing the need
for food with biomass production including biobased products via biorefineries (Freibauer et
al., 2011). One of the crucial challenges in providing feedstocks to biorefineries is
maintaining a year-round supply of biomass (Cherubini et al., 2007). The types of biomass
used are additionally important for their sustainable conversion to biofuels (Caputo et al.,
2005), since their chemical composition, e.g., carbohydrate content will differ, and this is one
of the main substrate for the biochemical conversions (Stephen et al., 2012).
There are also sustainability concerns associated with the production of biomass, and with
agriculture in general, such as the impact on the environment and human health due to direct
and indirect emissions from agro-chemicals used for the biomass production systems (von
Blottnitz and Curran, 2007), impacts related to land use changes as increasingly highlighted
in many sustainability studies (Milà I Canals et al., 2007a), and impacts on soil quality which
is crucial for the long-term productivity of agricultural soil and also for the provision of other
ecosystem services (Kibblewhite et al., 2008). Soil quality is often assessed on its organic
carbon change and fertility (Lal, 2015).
Life Cycle Assessment (LCA) has for a number of years been widely used as a tool for
assessing the environmental sustainability of different production systems (European
Commission, 2015). Most of the LCA studies related to the biomass production system have
mainly focused on greenhouse gas (GHG) balances (Wagner and Lewandowski, 2016). In
order to select the right biomasses and processing methods, it is also necessary to evaluate
other impact categories besides GHG and energy balances (Wagner and Lewandowski, 2016)
in order to avoid creating decision support tools for biorefining policies based on a single
indicator (Finkbeiner, 2009).
In most of the LCA studies, combinations of different crops including annual and perennial
grasses were partially covered and described. Mogensen et al. (2014) quantified the impacts
of producing different crops for livestock production, but mainly focussed on the carbon
footprint. Likewise, Pugesgaard et al. (2013) compared the energy balance and nitrate
leaching of annual crops and grasses in a rotation. Impacts of SOC changes on the GHG
balance were also partially addressed in most of the identified studies (Tonini et al., 2012). In
a study of Short Rotation Coppice (SRC), Dillen et al. (2013) focused on energy balance, but
assumed a less intensified farming system. Djomo et al. (2015b) compared the impact of
utilizing agricultural residues and also producing perennial crops for bioenergy options.
Similar studies on SRC include Goglio and Owende (2009), Pugesgaard et al. (2015) and
Sabbatini et al. (2015), but they were based on different assumptions with regard to the
133
farming system. Gallego et al. (2011) limited their study to the SOC change in the overall
GHG balances for alfalfa production. Godard et al. (2013) compared six feedstock supply
scenarios, but the emission factors and other basic assumptions adopted in their modeling
were less consistent with our study, particularly regarding system boundary and representing
the specific agro-climatic conditions. Wagner and Lewandowski (2016) included a wide range
of impact categories in their study, but it seemed that the system boundary for the related
emissions was differently used. For instance, for the calculation of freshwater ecotoxicity,
emission distributions to specific technological compartments (Birkved and Hauschild,
2006) (e.g. air and freshwater) were not considered. It was suggested that emissions of
pesticides to soil can occur indirectly, hence it is relevant to assess the relative emissions to
air and freshwater, particularly when impacts related to pesticide application have to be
included in LCA studies (Birkved and Hauschild, 2006). Furthermore, ecotoxicological
measures used for applied pesticides depend on the types of active ingredients used, timing
of the application and other agro-climatic features (Dijkman et al., 2012).
In this study, we have covered different types of biomasses, representing both annual and
perennial sources. The lignocellulosic biomasses selected for evaluating their environmental
footprints were willow, alfalfa and straw from spring barley, and the LCA method was used
for the evaluation. The biomasses were selected on the basis of their different physico-
chemical and environmental qualities (Parajuli et al., 2015). For example, the higher
cellulose:lignin ratio in straw and willow is an attribute that qualifies them for sugar-based
biorefinery platforms (Stephen et al., 2012). Likewise, the crude protein and carbohydrate
contents of alfalfa make this crop suitable for a green biorefinery (Parajuli et al., 2015). Straw
is regarded to induce a lower land use competition compared to other feedstocks (Kim and
Dale, 2004). Willow, in turn, is suited for cultivation on marginal land, reducing its
competition with food crops grown on fertile land (Helby et al., 2004). Willow also has an
effective nutrient uptake from soil, lower GHG emission and better fossil fuel energy balance
compared to fossil fuels (Murphy et al., 2014). This makes it relevant to diversify their uses
and their conversions into valuable biobased products, when estimates also show that about
10–20% of existing grassland (approximately 16.4 million ha) within the EU member states is
available for alternative uses to animal feed production (Mandl, 2010). To ensure the
sustainability of the alternative use in biorefinery value chains, environmental sustainability
assessments of the biomass production is one of the first steps to be taken (Nanda et al.,
2015). The current study hence aims to evaluate the biorefinery feedstocks taking into
account the important environmental impact categories.
134
2. Materials and methods:
2.1. Goal, System boundaries, functional unit, environmental impact categories and LCA
methods
The primary goal of this LCA is to provide a holistic view of resource requirements, emissions
and environmental impacts for the production of the selected biomasses as feedstocks to a
biorefinery. For this purpose, we take into account the system-wide effects of resource
utilization starting from material extraction, processing, production and their utilization in
an agricultural system. The effects are accounted in terms of related emissions and are
interpreted on the basis of the selected environmental impact categories. The system
boundaries for the production of the selected biomasses are shown in Figure 1. The functional
unit of the assessment is 1 tonne dry matter (t DM) of the selected biomasses. The results of
the environmental impacts are also shown in terms of energy in gigajoule (GJ) of the
harvested biomasses. Environmental impacts are assessed at farm level. The environmental
impact categories with the respective units, as covered in the current study, are: (i) Global
Warming Potential (GWP100) (kg CO2 eq), (ii) Eutrophication Potential (EP) (kg PO4 eq), (iii)
Non-Renewable Energy (NRE) use (MJ eq), (iv) Agricultural Land Occupation (ALO) (m2),
(v) Potential Freshwater Ecotoxicity (PFWTox) (CTUe) and (vi) Soil Quality.
The “EPD” method (Environdec, 2013) was used for the assessment of the first three impact
categories, while ALO was assessed using the ReCiPe method (Goedkoop et al., 2009).
PFWTox was calculated at two levels: (i) for applied pesticides at farm level and (ii) for
emissions from the processes involved in the background system, particularly for producing
the assumed material inputs entering into the agricultural system. It was calculated using the
ILCD method (European Commission, 2012), and emission distribution fractions used for
the calculation were taken from the simulation using the PestLCI2.0.6 model (Dijkman et al.,
2012). The choice of different impact assessment methods was mainly based on the following
two criteria: (i) methods that can cover most of the selected impact categories and (ii)
methods that interpret the results of the life cycle impact assessment, in the expressed units
as described above. For this, the EPD of the method that fulfilled the first criterion, as the
three impact categories were included in it. Likewise, freshwater ecotoxicity was interpreted
in terms of “comparative toxic units (CTUe)” in the ILCD method and the emission
distribution fractions that we derived from PestLCI2.0.6 and included in this method (see
section 2.2.5) were also reported with the same unit. The ILCD method has also implemented
all the USEtox factors (Rosenbaum et al., 2008) suggested for ecotoxicological measures
(European Commission, 2012). ISO (2006) does not recommend one above the other,
suggesting that the choice should be based on the specific requirements of the user
(European Commission, 2010).
135
Soil quality was also considered an environmental impact category, in accordance with
Brandão et al. (2011). For this, SOC stock change (Δ SOC stock) was used as an indicator
(IPCC, 2000; Milà I Canals et al., 2007a). The impact was defined as a carbon deficit (or
credit, indicated by negative values) with the unit ‘t C·year’, giving the amount of extra carbon
temporarily added to or removed from the soil compared to a reference system of a study
(Milà i Canals et al., 2007b).
Figure 1: System boundaries for the selected biomasses and related elementary flows.
(Figure 1a represents the general system boundary and Figure 1b represents the production
cycle of willow.)
2.2. Life Cycle Inventory Analysis
The system boundaries covered: (i) the background system, (upstream side processes) and
(ii) the foreground system (downstream side processes). The background system included the
product system of material inputs (e.g. fuel, chemicals, and agricultural machinery) and their
supply to the foreground system. All the necessary data related to the background system
were based on Ecoinvent 3 (Weidema et al., 2013), unless otherwise stated in the text below.
Data for the foreground system are elaborated in the following sections.
2.2.1. Crop production data
Table 1 shows the detailed LCI for the production of the selected biomasses. All the material
inputs (agro-chemicals, fuel, energy, etc.) were estimated on an annual basis. These inputs
were calculated from the total inputs estimated during the crop production life cycles and
were divided by their respective number of life cycle years.
The material inputs and finally the environmental burdens for straw were economically
allocated from the production of spring barley. The allocation factor was 19% to straw based
on sale prices for straw and cereals for the period 2011-2015 (SEGES, 2015). Y ield of straw
was based on average figures for spring barley cultivated on Danish sandy soil (Oksen, 2012;
Statistics Denmark, 2013), and was 55% of the grain yield (Taghizadeh-Toosi et al., 2014a).
The frequencies of farm operations (tillage, application of agro-chemicals and harvest) were
all based on Jørgensen et al. (2011), or otherwise stated in the text below. The application
rate of synthetic fertilizer (N, P, K) followed the Danish regulations (NaturErhvervstyrelsen,
2015). The amount and type of pesticides, i.e. the active ingredients (a.is), assumed for barley
were based on the actual practice of their application on Danish farms (Ørum and Samsøe-
Petersen, 2014). Details on the application of the selected pesticides over the crop production
life cycle years are given in the Supporting Information (SI) in Table S.4.
Production of willow was divided into two stages: (i) production of cuttings, (ii) production of
the main crop following field preparation (tillage and application of agro-chemicals), planting
136
of cuttings, harvesting and field restoration at the end of the life cycle of 22 years (i.e. also
including the cuttings production) (Figure 1.b). The planting density was set to 12,000
cuttings ha-1 (Sevel et al., 2012) and material inputs for the cutting production are shown in
SI3 (Table S.3). After the cutback process the cuttings were transported for the plantation for
a distance of 3 km (to the farm, single trip). The weight of the cuttings was 20 g cutting-1
(Rewald et al., 2016). The annual application rate of pesticides for the production of willow
was calculated from its total recommended life cycle dose (SEGES, 2010) (see SI, Table S.4).
The first fertilizer application was assumed to take place after field preparation, since it has a
tendency to lower the potential nitrate leaching (Heller et al., 2003). Fertilization after
planting was assumed to be carried out in every harvest-year and a year after each of the
harvest-years. This amounted to 13 applications per ha (1 + 2*6 harvests excluding the last
harvest) for the 21 years (Figure 1.b). Frequency of farm operations was in accordance with
Hamelin et al. (2012). The average annual fertilizer input estimated from the life cycle years
was comparable with Pugesgaard et al. (2015). Harvesting of willow was assumed to occur
every three years (i.e. a total of seven cuts), with the first harvest occurring after four years
(Heller et al., 2003; Pugesgaard et al., 2015). The annual average yield was adapted from the
studies reported by Hamelin et al. (2012) and Lærke et al. (2010) (Table 1). A single-stage
harvester (cut and chip) was assumed, with a fuel consumption of 14 lha-1 (representative of
Danish practice) (Djomo et al., 2015a), and consistent with the practice in Goglio and
Owende (2009) and Heller et al. (2003). The restoration process involved pressing back the
stools into the soil and application of herbicides during summer (Gonzalez-Garcia et al.,
2012). Fuel consumption related to the pressing of stools was estimated to 38.7 l/ha (Njakou
Djomo, 2016.pers. comm.).
Lastly, alfalfa was assumed to be a rotational crop with a three-year rotational cycle
(Jørgensen et al., 2011) and with three harvests per year. The yield (Table 1) was taken from
NaturErhvervstyrelsen (2015) and Møller et al. (2005b). The quantity of seeds was calculated
from Jørgensen et al. (2011). The annual application of fertilizers was based on SEGES
(2010). Frequency of farm operations was as reported in Jørgensen et al. (2011). Types of
herbicides and total doses over the crop production cycle were based on SEGES (2010) (see
SI, Table S4). After the land preparation and growing the crop, the harvesting process was
followed by mowing, swathing, baling and loading of the fresh biomass (Jørgensen et al.,
2011). The baled biomass was assumed to be transported a distance of 3 km to the farm
(Table 1). The transportation unit is expressed as tonne kilometre (t km) per ha.
Table 1: Crop production data. All data are per ha
137
2.2.2. Calculation of soil organic carbon change
The SOC turnover was calculated as the differences between carbon input available from the
reference crop and from the selected crops. Spring barley production (with 100% straw
incorporated into soil) was set as the reference land use (Table 2). The contribution from SOC
change was calculated in a 100-year perspective according to Petersen et al. (2013), assuming
a sequestration of 9.7% of C input. Results for 20 years are also shown in Table 2. In the case
of straw production, 1 t DM straw was removed from the total production of straw from 1 ha
of land and the rest was ploughed back into the soil. The method to calculate the net C
assimilation for spring barley (for straw) and alfalfa followed Taghizadeh-Toosi et al. (2014a)
and was based on the non-harvestable above- and below-ground residues. Non-harvestable
residues were estimated from the harvest index, the DM in the primary yield and the DM in
the secondary yield. The harvest index represents the portion of the primary yield of total
above-ground biomass at harvest, and both primary yield and total yield are expressed in
terms of DM (Hamelin, 2011). The necessary parameters to estimate the harvest index for
alfalfa were calculated based on Djurhuus and Hansen (2003) and Pietsch et al. (2007) (see
SI Table S.1), whereas for straw production it was based on Taghizadeh-Toosi et al. (2014a).
In the case of willow, the non-harvestable above-ground biomass was partitioned into the
DM yield from leaves and from woody material (branches, twigs) (Eq. (i)) in accordance with
Hamelin (2011). For willow the amount of below-ground residues was calculated from the
fraction of total biomass production going to roots (fR) using Eq. (ii).
×+×
−−+×= PYpyflwf
PYRfLf
LfPY
pyflwf
DMWNHAG )1( ………….Eq.(i)
………….Eq (ii)
where NHAGDMW = non-harvestable above-ground DM for willow; flw = woody biomass loss
during harvest = 7.5%; fpy = expected primary yield of the total potential primary yield (PY) =
92.5%; fL = proportion of total biomass production going to leaves = 20%; fR = proportion of
total biomass production going to roots = 25% (Hamelin et al., 2012); NHBGDMW = non-
harvestable below-ground residues for willow.
Table 2: Crop-specific assessment parameters used in the calculation of SOC change
2.2.3. Soil quality
With regard to soil quality, SOC stock change (∆ SOC, in t C hay) was used as an indicator
(Brandão et al., 2011; IPCC, 2000), but there are additional indicators that affect soil quality
×
−+×
−−= PYpyf
lwfPY
RfLfRf
DMWNHBG
)1()1(
138
such as compaction and soil nutrients (Arshad and Martin, 2002). The method used to
calculate ∆ SOC stock is presented in the form of Eq. (iii), in accordance with Brandão et al.
(2011) and Milà i Canals et al. (2007b). The first component of the numerator in Eq. (iii)
corresponds to the impact of the postponed relaxation of the land use system (i.e. during
transformation), and the second component refers to the impact from changes in soil quality
(i.e. during the occupation of the land) (Brandão et al., 2011). Relaxation was defined as the
tendency of the SOC stock of the current land use reverting to the prior level (Brandão et al.,
2011). This is basically guided by the annual rate of SOC change of the current land use
relative to the reference system. For this purpose, a reference system was defined as a
situation where the current crop management was not in practice, e.g. natural relaxation was
assumed in Milà i Canals et al. (2007b). In this study Danish forestry was assumed for the
natural relaxation situation, and the relaxation rate was adapted from Nielsen et al. (2010)
and Grüneberg et al. (2014) (Table 3). Relaxation time is another important parameter, since
it is the period taken for the soil quality to revert to the equilibrium condition (Brandão et al.,
2011) and in this study it was based on: final years (tf) = 20 years, initial (ti ni) = 1.
The ∆ SOC stock (in t C y ha-1 y-1) (Brandão et al., 2011) was thus calculated with respect to:
potential SOC (SOCpot) stock, i.e. if the reference land use was left undisturbed; initial SOC
(SOCi ni) stock, i.e. currently used arable land (Table 3); final SOC (SOCfi n) stock and the
natural relaxation SOCfin stock was calculated after accounting the SOC change estimated for
growing the selected crops (Table 2) and was contributing to the SOCi ni stock. Hence, the
difference between the stocks SOCi ni and SOCfin was the annual soil C sequestration from the
production of the selected biomasses (Table 2). The annualized ∆ SOC stock (t C ha-1 y-1) was
calculated for the accounting period of 20 years. The temporal scope of 20 years was chosen
to be consistent with IPCC (2000) for the assessment of soil quality. Uncertainties related to
the estimation of soil C change and its effects on soil quality, especially based on the net C
input to the current land use system, are also further discussed in section 4.
)(
)(*)(2/1)(*)(
initfintfinSOCiniSOCinitrelaxtinitrelaxtiniSOCpotSOC
SOC −
−−+−−=∆
………….Eq. (iii)
Table 3: Basic parameters used for calculating the SOC stock change
2.2.4. Calculation of emission related to fertilizer application
A field N-balance method (Brentrup et al., 2000; Hansen et al., 2000) was used to calculate
N-leaching, after accounting for all the N-related inputs and outputs (Table 4). Direct and
indirect nitrous-oxide emissions (N2O-N) were based on the emission factors reported in
IPCC (2006). The emission factors for NH3 emission were 2% of the N-fertilizer input (EEA,
2013; Nemecek and Kägi, 2007) and from the crops it was set to 0.5 kg N ha-1y-1 (Sommer et
139
al., 2004). Denitrification was calculated using the SimDen model (Vinther, 2005). These
methods represent the specific agro-climatic condition that the current study has considered.
The soil organic nitrogen (SON) change was calculated based on the SOC change (Table 2)
and applying the C/N ratio of 1:10. The method was in accordance with Mogensen et al.
(2014). The calculated SON changes for spring barley, willow and alfalfa were comparable
with the uptakes reported by Hamelin (2013), Pugesgaard et al. (2015) and Rasmussen et al.
(2012), respectively. Phosphorus losses were calculated based on factor (i.e. 5% of the P
surplus), as suggested in Nielsen and Wenzel (2007).
Table 4: Biomass-specific N balances and emissions related to their production per ha
2.2.5. Calculation of emission related to pesticide application and total freshwater ecotoxicity
Potential freshwater ecotoxicity specifically related to pesticide application at the farm level
was calculated from the emission distribution fractions of the respective active ingredients
(Birkved and Hauschild, 2006). Based on PestLCI 2.0.6 (Birkved and Hauschild, 2006) and
after applying different field scenarios (e.g. month of the pesticide application, development
stage of the crops and application technique), Parajuli et al. (2016) simulated emission
distribution fractions of the most commonly used pesticides for cereal crops and grasses
grown in Denmark. Hence, average emission distribution fractions calculated from their
study were used in this study. The emission distribution to air (first number in parentheses)
and freshwater (second number in parentheses) were in the order of: herbicides (8%,
0.003%); fungicides (14.83%, 0.0003%); insecticides (5.63%, 0.00021%); growth regulator
(36.92%, 0006%).
It should be noted that for the assessment of total PFWTox, both background and foreground
systems emissions were utilized. For the calculation of total PFWTox, the chemical class of
the pesticides was identified based on Footprint PPDB (2011) and ChemicalBook Inc. (2008),
and when pesticide classes could not be identified from the two data sources they were
classified as “unspecified class” (Weidema et al., 2013).
2.3. Sensitivity analysis
The major uncertainty analysis included the following assessments:
i. Temporal perspective on SOC change: This included the assessment of SOC change
after 20 years and was based on IPCC Tier 1 (IPCC (2006). Land use transformation
factors assumed for the calculation are presented in SI Table S.2.
ii. Variation in soil quality: This included the assessment of soil quality by varying (a) the
rates of SOC change, as calculated based on the above method and (b) the initial SOC
stock (Table 5).
iii. Calculation of GWP1 00 without SOC change.
140
iv. Calculation of GWP1 00 using Urea instead of Calcium Ammonium Nitrate (CAN) as a
source of synthetic N-fertilizer.
Table 5: Main parameters for the calculation of Δ SOC stock for the production of the
selected crops for the alternative scenarios
3. Results
3.1. Potential Environmental impacts
Global Warming Potential: The obtained GWP1 00 for producing straw was 264 kg CO2 eq
tDM-1. For alfalfa and willow it was only 31% and 38%, respectively, of the impact calculated
for straw (Table 6). The carbon debited during the production of straw was 18% of the
obtained GWP1 00, but for willow and alfalfa soil C sequestration was -66% and -44%,
respectively, of the obtained GWP1 00. The contribution from N2O to GWP1 00 during the
production of straw, willow and alfalfa was 32%, 37% and 16%, respectively (Figure 3a). The
variations in the contribution of N2O to total GHG emissions were mainly caused by the
fertilization rate and the related N-emissions (Table 4). The production of agrochemicals
contributed 29%, 71% and 41% to GWP100 for straw, willow and alfalfa, respectively. The field
operation processes (tillage, application of agrochemicals and harvest) contributed 17% for
straw, but 45% and 75% for willow and alfalfa, respectively. The frequency of harvesting and
loading was higher for alfalfa than the other biomasses; hence the contribution from farm
operations was higher for this biomass. The production of willow cuttings (cutback)
contributed 4.4% to total GHG emissions obtained for the biomass production
Transportation contributed 10 to 11% to total GHG emissions for willow and alfalfa, and was
2% for straw. When comparing the two perennial crops, GWP1 00 per t DM was higher for
willow than for alfalfa, but opposite was the case for heat content (Table 6).
Eutrophication Potential: The eutrophication potential was lowest for willow, followed by
alfalfa and straw (Table 6). The impact was primarily related to field emissions, e.g., nitrate
leaching and ammonia and phosphate emissions (see related emissions in Table 4). These
jointly contributed in the range of 40 to 68% to the total impact (Figure 3b).
Non-Renewable Energy use: The NRE use per t DM was highest for alfalfa, which was partly
because of its higher harvesting frequency and higher primary energy use for baling the fresh
biomass coupled with higher moisture content (Table 1). A major contributor to NRE use was
the production of agrochemicals (ranged from 20 to 47% of the respective NRE use calculated
for the selected biomasses), and for willow and straw the impact was mainly due to the
production of N-fertilizer (Figure 3c). In contrast to this, the impact in terms of energy
content was lower for willow than for the other biomasses. Production of willow cuttings
contributed 3% to total NRE use for willow, which was comparable to the range reported in
Djomo et al. (2011).
141
Agricultural Land Occupation and Potential freshwater ecotoxicity:
The ALO calculated per t DM of biomasses was lowest for straw, followed by alfalfa and
willow. With regard to freshwater ecotoxicity, particularly for the emissions at farm level, it
was highest for straw, followed by alfalfa and willow (Table 6).
Soil quality: A detrimental effect of land use change on soil quality was found for straw
(Table 6), which was partly because of larger differences between (i) the relaxation rate and
the SOC change during the production of spring barley (Table 2 and Table 3) and (ii) SOCpot
and SOCi ni , and likewise between SOCi ni and SOCfi n. The impact was mainly caused by the
postponed relaxation time during the production of the selected crops. For the spring barley
production, this was 20.96 years indicating that longer time is needed to return to the level of
natural relaxation. The situation was similar for alfalfa, but the difference between the
natural relaxation rate and the SOC change rate was not so high compared to spring barley
production. A similar effect was reported for an annual crop in Brandão et al. (2011), further
highlighting that a delay in relaxation would take place in such a situation and that land
occupation itself has little effect compared to the delayed relaxation. For willow there was an
increase in the SOC stock, as the relaxation time was shorter (i.e. 18.7 years), hence soil
quality was able to revert quickly to the reference level (Table 6).
Table 6: Environmental impact potentials per t DM biomass production
Figure 2: Environmental impact potentials per ha of the biomass production.
Figure 3: Environmental hotspots related to GWP1 00, EP and NRE use.
4. Sensitivity analysis
4.1 Variations in SOC change based on different methods
Table 7 lists the variations found for the SOC change when IPCC method (IPCC, 2000) was
used instead of the method used in the basic scenario. The annualized rate of SOC change
calculated for the 20-year timeframe ranged from -0.4 to -0.9 t C ha-1y-1 for willow. This was
comparable to the range reported for SRC (Brandão et al., 2011; Dawson and Smith, 2007;
Murphy et al., 2014). For alfalfa it ranged from-0.25 to -0.62 t C ha-1y-1, which was close to
the -0.5 to -0.62 t C ha-1y-1, as reported for perennial grasses and ley rotations in Dawson and
Smith (2007). The SOC change related to straw production based on spring barley ranged
from 0.15 to 0.32 t C ha-1y-1 (Table 7).
4.2. Variations in the Global Warming Potential-100
GWP100 without SOC Change: GWP1 00 not including the SOC change was 83% lower for
straw, whilst it was 60% and 70% higher for willow and alfalfa compared to the basic scenario
(Table 7).
142
GWP100 with Urea as the type of N-fertilizer: Compared to CAN, the inclusion of urea
produced a 75% lower GWP100, but NRE use was 94% higher. The reason for the higher GHG
emissions in the basic scenario was related to the nitric acid production − one of the
important formulating compounds in the production of CAN (Agri-footprint, 2014).
GWP100 for willow with two-stage harvesting: GWP1 00 and NRE use after assuming a two-
stage harvesting process was found to increase by 19% and 37%, respectively, compared to
the basic scenario. This was due to the higher diesel consumption in the two-stage harvesting
method (reported in the footnotes of Table 7).
Table 7: Sensitivity analysis of soil C sequestration, GHG emissions and NRE use for the
production of the selected biomasses compared to the basic scenario
4.3. Soil quality
With regard to soil quality, for scenario (i) the difference between the initial SOC stock and
the potential SOC stock was somewhat lower and was thus the main reason for the quick
recovery to the prior level, particularly for willow and alfalfa (Table 8). In addition, when ∆
SOC stock was calculated based on SOC change using the IPCC Tier 1 method, it resulted in
values of 0.3, -1.44 and -0.76 t C ha-1y-1 for straw, willow and alfalfa, respectively. Termansen
et al. (2015) reported that the effect on SOC stock during the shift from a cereal crop rotation
to grass was about -0.49 t C ha-1y-1in Danish soil, and further argued that it will take place
over a longer period until a new equilibrium in the soil is reached (estimated to be 20-40
years). This was comparable to the situation for alfalfa, as reported under scenario (i).
Table 8: Variations in calculated soil quality as a result of changes in SOC sequestration and
initial SOC stock (values are given per ha; negative value indicates an increase in SOC stock)
In general, the conversion of a natural ecosystem, such as forest land to managed agriculture,
resulted in a 10-59% decline in SOC stock, particularly when arable crops plantation are
replaced by woody plantation (Qin et al., 2016). In this study, based on the final SOC stock, a
decline of 54% in SOC stock was found while growing willow (Table 7). However, if the basic
scenario was analysed based on the difference between initial SOC stock and final SOC stock,
the decrease in the SOC stock for straw was 0.33% whilst it increased by 0.44% and 0.28%
for willow and alfalfa respectively relative to the initial SOC stock (Table 7). The results also
showed that the rate of SOC change in current land use plays an important role in the SOC
stock change and hence in soil quality, e.g. as calculated for alfalfa in scenario (i) and
scenario (iii), which differed from the other scenarios (Table 8). It is thus concluded that the
LCA practitioners should take into account the variations on the SOC stock due to variations
of SOC change, whenever interpretations are to be made for soil quality using the adopted
method, and also keeping in mind there are also other factors that determine the quality of
soil.
143
5. Discussions
5.1. Comparison with other studies
5.1.1. Straw production
Mogensen et al. (2014) reported a carbon footprint for the production of straw from barley,
excluding and including the SOC change, of 68 and 91 kg CO2 eq t DM-1, respectively. The
difference in the carbon footprint compared to our study was partly due to the use of different
allocation factors, fertilization rates and assumptions made on emission factors for fuel use
during farm operations and fertilizer production. In addition, there was also a difference in
the estimated SOC change. In contrast, Korsaeth et al. (2012) reported a carbon footprint of
straw from spring barley as 356 kg CO2 eq t DM-1 (with SOC changes), which differed from
this study and was mainly due to different assumptions on SOC change. Although there were
variations in the results compared to other studies, based on the contribution from biomass
production value chains the results were comparable with the stated other studies. For
instance, the contribution of N2O emissions to the GWP1 00 , as reported in this study (section
3.1), was found to be similar to the range reported in Roer et al. (2012) and Kramer et al.
(1999).
With regard to the freshwater ecotoxicity potential, a higher equivalent score was reported
for the production of spring barley, particularly in the studies of Niero et al. (2015), Roer et
al. (2012) and Korsaeth et al. (2012). The reason behind the differences was partly the
different types and application rates of pesticides, and apparently a dissimilar emission
distribution of applied pesticides to that we modelled in our study. Furthermore, in Niero et
al. (2015) emissions from the inorganic elements deriving from animal slurry was also
included, which was one of the main reasons for the difference.
5.1.2. Willow production
The carbon footprint of SRC, including willow, ranged from 0.6-12 kg CO2 eq GJ-1(Djomo et
al., 2011; Dubuisson and Sintzoff, 1998; Krzyzaniak et al., 2013; Matthews, 2001; Murphy et
al., 2014; Pacaldo et al., 2012). Heller et al. (2003) reported a value of 0.68 kg CO2 eq GJ-1, its
size explained by the higher carbon sequestration, which was based on below-ground
residues. There were also some variations in the methods used to estimate the residues and
carbon assimilation, e.g. the method to calculate the below-ground biomass. For instance, the
shoot-to-root ratio was used in Pacaldo et al. (2012) and Heller et al. (2003). Brandão et al.
(2011), on the other hand, reported farm-gate GHG emissions of -102 kg CO2eq GJ-1 (with -
497 kg CO2 eq ha-1 y-1avoided due to SOC change), but when excluding it the result was
comparable. Sartori et al. (2007) reported both declined and increased SOC for the different
methods of calculating the available residues in soil.
The direct primary energy input for willow was comparable to those found by Matthews
(2001) and Pugesgaard et al. (2015). Including the background processes, the impact
144
potential per ha (Figure 2) was also comparable to 21.3 GJ ha-1y-1 , as reported by Matthews
(2001). In contrast, Brandão et al. (2011) reported 6.4 GJ ha-1y-1 as the total energy input.
Minor differences compared to our study were related to the processes covered by the
background system, assumed life cycle span and the frequency of fertilization. Regarding the
freshwater ecotoxicity calculated for the foreground system it was comparable to that of Salix
(Nordborg et al., 2014).
In this study, an accumulation of SOC was found during the production of willow, which was
the result of a higher SOC change relative to relaxation rate (Table 5). The annualized SOC
stock change (in t C ha-1 y-1) for SRC is reported to range from -0.3 to -2.8 t Cha-1 y-1,
depending on the annualized period used for the calculation (e.g. 25 to 115 years) (Dawson
and Smith, 2007). The results obtained in our study also fell within that range, as did the
results of Falloon et al. (2004) and Murty et al. (2002). Furthermore, Tonini and Astrup
(2012) reported that the change in SOC stock during a land use change from spring barley to
willow yielded -15 t Cha-1, and -8 t C/ha with a conversion from cropland to grassland. This
was comparable to the basic scenario (i.e. -21 t C ha-1) and scenario (i) for willow (-29 t C ha-1)
and alfalfa (-15 t C ha-1). It can thus be concluded that the variation was primarily related to
differences in the soil C sequestration rate of the current production system and the initial
SOC stock (Table 8).
5.1.3. Alfalfa production
Alfalfa production, as undersown in rotation (corn-soybean-alfalfa, conventional), was
reported to have GHG emission and NRE use of 71 kg CO2 eq ha-1 y-1and 1.5 GJ ha-1,
respectively (Adler et al., 2007). In their study, the system boundary covered only the
processes starting from sowing and until harvest. The differences in the results were partly
due to different emission factors assumed for diesel use and the different system boundary
used for the assessment. In contrast, Gallego et al. (2011) reported a higher carbon footprint
and a total NRE use of 3.8 GJ t DM-1. The reason for the differences is that they included a
drying process to achieve a higher DM content (i.e. 89%), whereas if the drying process were
excluded from their results, the value for NRE use would be comparable. Likewise, Sooriya
Arachchilage (2011) and Vellinga et al. (2013) reported value of approximately 100 kg CO2 eq
t DM-1 for alfalfa production including transport to a biorefinery plant, which was very
similar to our result. The reported NRE use by Vadas et al. (2008) was 4 GJ ha-1, and this was
based on the mass allocation from the total normal yields of crops in a four-year rotation.
With regard to EP, values for alfalfa range from 0.4 to 1.14 kg PO43-eq t DM-1 (Gallego et al.,
2011; Sooriya Arachchilage, 2011). The major contributing processes and emissions were
from applied N fertilizer, and the main substances responsible for the impact are nitrate and
phosphate leaching, which is consistent with the results of the current study.
145
With regard to annualized Δ SOC stock (Table 6 and Table 8), it was found comparable to
leys in rotation and permanent grassland (-0.35 to -1.6 t Cha-1 y-1), as reported in Guo and
Gifford (2002), Murty et al. (2002) and Smith et al. (1997).
6. Overall synopsis of the results
An understanding of environmental issues is important to understand the implications of
different agricultural management practices, for example with regard to SOC changes,
maintenance of soil health and emissions from field operations. These concerns were also
reproduced in this study. For instance, willow and alfalfa contributed positively to soil
quality, but the result was depending on the rate of SOC change these crops induced during
their production. Perennial crops had a higher nutrient use efficiency and lower nutrient
leaching. In addition, this study also showed that N2O emission was one of the major
contributors to GWP1 00. Furthermore, for almost all impact categories the production of
agrochemicals had the largest impact, as also reported by (Heller et al., 2003; Parajuli et al.,
2016). This stresses the need of minimizing the use of synthetic fertilizer, e.g. by
recycling/reusing organic matter in waste streams of biomass conversion technologies such
as biorefineries. Some of the opportunities in this area, particularly in biorefining, could be to
recover the potassium chloride from the liquid fraction of the lignocellulosic biorefinery (Larsen et
al., 2008) and to recycle the digestate slurry from a biogas production system.
In the context of diversifying the biomass supply, it is also relevant to know if the biomass
production system is a net energy producer or a consumer. In the current study, the total
energy output-to-input ratio for 1 t DM of biomass was 7, 13 and 7 for straw, willow and
alfalfa, respectively. The value for willow was close to the ratio of SRC reported in Manzone et
al. (2009) and also corresponds to the lower range for SRC reported in Djomo et al. (2011).
Willow and alfalfa were found to increase SOC input to the carbon pool, provided that soil C
sequestration was higher than the relaxation rate, thus enabling a quick recovery of soil
quality.
7. Conclusions
The general conclusion of the study was that the advantages of perennial crops over annual
crops were their higher biomass and energy yields and their relatively low potential
environmental impacts. The impacts largely depended on the agriculture management
practices and the intensity of material inputs, e.g. fuel and agrochemicals entering into the
agriculture system. Finally, a comparison of biomass feedstocks as assessed at the farming
system level may not give a complete picture of the environmental sustainability, as it also
depends on how feedstocks are going to be utilized to satisfy societal demands. Feedstocks
are also dependent on their chemical constituents and hence their conversion efficiency in
bioenergy and biorefinery value chains. Hence, a future research perspective could be to
assess the environmental and economic impact of biomass conversions in relevant
biorefinery platforms and compare them with the impacts of producing conventional
146
products. This requires integration of an agricultural system LCA, e.g. assessed at the farm
gate level as in this study, with the LCA of the industrial processing of biomass to produce
biobased products, e.g., via a biorefinery.
Acknowledgement
The article is written as part of a PhD study at the Department of Agroecology, Aarhus
University (AU), Denmark. The study is co-funded by the Bio-Value Platform
(http://biovalue.dk/), funded under the SPIR initiative by The Danish Council for Strategic
Research and The Danish Council for Technology and Innovation, case no: 0603-00522B
and is moreover relevant to the Nitroportugal EU project. The first author would like to thank
the Graduate School of Science and Technology (GSST) of AU for the PhD scholarship.
Thanks to Margit Schacht (from Agro Business Park) for providing necessary support in
editing this article.
147
Reference List
Adhikari K, Hartemink AE, Minasny B, Bou Kheir R, Greve MB, Greve MH. Digital Mapping
of Soil Organic Carbon Contents and Stocks in Denmark. PLoS ONE 2014; 9: e105519.
Adler PR, Del Grosso SJ, Parton WJ. Life-Cycle Assessment of Net Greenhouse-Gas Flux for
Bioenergy Cropping Systems. Ecological Applications 2007; 17: 675-691.
Agri-footprint. Agri-footprint: Methodology and basic principles. Version 1.0. Blonk Agri-
footprint BV. The Netherlands, 2014, pp. 1-36.
Arshad MA, Martin S. Identifying critical limits for soil quality indicators in agro-ecosystems.
Agriculture, Ecosystems & Environment 2002; 88: 153-160.
Berhongaray G, El Kasmioui O, Ceulemans R. Comparative analysis of harvesting machines
on an operational high-density short rotation woody crop (SRWC) culture: One-
process versus two-process harvest operation. Biomass and Bioenergy 2013; 58: 333-
342.
Birkved M, Hauschild MZ. PestLCI - A model for estimating field emissions of pesticides in
agricultural LCA. Ecological Modelling 2006; 198: 433-451.
Brandão M, Milà i Canals L, Clift R. Soil organic carbon changes in the cultivation of energy
crops: Implications for GHG balances and soil quality for use in LCA. Biomass and
Bioenergy 2011; 35: 2323-2336.
Brentrup F, Küsters J, Lammel J, Kuhlmann H. Methods to estimate on-field nitrogen
emissions from crop production as an input to LCA studies in the agricultural sector.
The International Journal of Life Cycle Assessment 2000; 5: 349-357.
Caputo AC, Palumbo M, Pelagagge PM, Scacchia F. Economics of biomass energy utilization
in combustion and gasification plants: effects of logistic variables. Biomass &
Bioenergy 2005; 28: 35-51.
ChemicalBook Inc. CAS DataBase List. ChemicalBook Inc.
http://www.chemicalbook.com/CASDetailList_0_EN.htm (accessed Feb 22, 2015),
2008.
Cherubini F, Jungmeier G, Mandl M, Philips C, Wellisch M, Jørgensen H, et al. IEA
bioenergy Task 42 on biorefineries: co-production of fuels, chemicals, power and
materials from biomass. IEA Bioenergy Task 42 – Countries Report. IEA, 2007, pp. 1-
37.
148
Dalgaard T, Halberg N, Porter JR. A model for fossil energy use in Danish agriculture used to
compare organic and conventional farming. Agriculture Ecosystems & Environment
2001; 87: 51-65.
Dawson JJC, Smith P. Carbon losses from soil and its consequences for land-use
management. Science of The Total Environment 2007; 382: 165-190.
Dijkman TJ, Birkved M, Hauschild MZ. PestLCI 2.0: a second generation model for
estimating emissions of pesticides from arable land in LCA. International Journal of
Life Cycle Assessment 2012; 17: 973-986.
Dillen SY, Djomo SN, Al Afas N, Vanbeveren S, Ceulemans R. Biomass yield and energy
balance of a short-rotation poplar coppice with multiple clones on degraded land
during 16 years. Biomass & Bioenergy 2013; 56: 157-165.
Djomo SN, Ac A, Zenone T, De Groote T, Bergante S, Facciotto G, et al. Energy performances
of intensive and extensive short rotation cropping systems for woody biomass
production in the EU. Renewable and Sustainable Energy Reviews 2015a; 41: 845-
854.
Djomo SN, Kasmioui OE, Ceulemans R. Energy and greenhouse gas balance of bioenergy
production from poplar and willow: a review. GCB Bioenergy 2011; 3: 181-197.
Djomo SN, Witters N, Van Dael M, Gabrielle B, Ceulemans R. Impact of feedstock, land use
change, and soil organic carbon on energy and greenhouse gas performance of
biomass cogeneration technologies. Applied Energy 2015b; 154: 122-130.
Djurhuus J, Hansen EM. Dry Matter and Nitrogen in Crop Residues in Agriculture. Internal
note. Aarhus University, Denmark, 2003, pp. 8.
Dubuisson X, Sintzoff I. Energy and CO2 balances in different power generation routes using
wood fuel from short rotation coppice. Biomass and Bioenergy 1998; 15: 379-390.
EEA. EMEP/EEA air pollutant emission inventory guidebook 2013. European Environment
Agency, Copenhagen, Denmark, 2013, pp. 1-43.
Ellermann T, Andersen HV, Bossi R, Brandt J, Christensen JH, Frohn LM, et al. Atmosfærisk
deposition 2005: NOVANA. DMU Report no.595. Aarhus Universitet, DCE-Nationalt
Center for Miljø og Energi, 2005, pp. 1-69.
Environdec. EPD Method. Characterization factors for default impact assessment categories.
EPD International AB, Stockholm Sweden. http://www.environdec.com/en/The-
International-EPD-System/General-Programme-Instructions/Characterisation-
factors-for-default-impact-assessment-categories/ (accessed Feb 02, 2015). 2015,
2013.
149
European Commission. Joint Research Centre - Institute for Environment and Sustainability:
International Reference Life Cycle Data System (ILCD) Handbook - General guide for
Life Cycle Assessment - Detailed guidance. First edition March 2010. EUR 24708 EN.
Luxembourg. Publications Office of the European Union; 2010., 2010, pp. 1-417.
European Commission. Characterisation factors of the ILCD recommended life cycle impact
assessment methods. Database and supporting information. >RC. Luxembourg. . 10,
Luxembourg, 2012, pp. 85727.
European Commission. Product Environmental Footprint (PEF). News. European
Commission, Brussels, Belgium, 2015.
Falloon P, Powlson D, Smith P. Managing field margins for biodiversity and carbon
sequestration: A Great Britain case study. Soil Use and Management 2004; 20: 240-
247.
Finkbeiner M. Carbon footprinting—opportunities and threats. The International Journal of
Life Cycle Assessment 2009; 14: 91-94.
Footprint PPDB. The footprint pesticide properties database. Agriculture and Environmental
Research unit (AERU), University of Hertfordshire, page cited 28 April 2011, 2011.
Freibauer A, Mathijs E, Brunori G, Damianova Z, Faroult E, Gomis JG, et al. Sustainable food
consumption and production in a resource-constrained world. The 3rd SCAR
(European Commission–Standing Committee on Agricultural Research) Foresight
Exercise. European Commission, Office SDME 08/001, 2011.
Gallego A, Hospido A, Moreira MT, Feijoo G. Environmental assessment of dehydrated
alfalfa production in Spain. Resources, Conservation and Recycling 2011; 55: 1005-
1012.
Godard C, Boissy J, Gabrielle B. Life-cycle assessment of local feedstock supply scenarios to
compare candidate biomass sources. GCB Bioenergy 2013; 5: 16-29.
Goedkoop M, Heijungs R, Huijbregts M, De Schryver A, Struijs J, van Zelm R. ReCiPe 2008.
A life cycle impact assessment method which comprises harmonised category
indicators at the midpoint and the endpoint level. First edition, Report I:
Characterisation. http://www.pre-
sustainability.com/download/misc/ReCiPe_main_report_final_27-02-
2009_web.pdf (accessed Nov 5, 2014). 1, 2009, pp. 126.
Goglio P, Owende PMO. A screening LCA of short rotation coppice willow (Salix sp.)
feedstock production system for small-scale electricity generation. Biosystems
Engineering 2009; 103: 389-394.
150
Gonzalez-Garcia S, Mola-Yudego B, Dimitriou I, Aronsson P, Murphy R. Environmental
assessment of energy production based on long term commercial willow plantations
in Sweden. Sci Total Environ 2012; 421-422: 210-9.
Grüneberg E, Ziche D, Wellbrock N. Organic carbon stocks and sequestration rates of forest
soils in Germany. Global Change Biology 2014; 20: 2644-2662.
Guo LB, Gifford RM. Soil carbon stocks and land use change: a meta analysis. Global Change
Biology 2002; 8: 345-360.
Hamelin L. Inventory report for modelling direct land use changes of perennial and annual
crop in Denmark. Version 0. Presented for the CEESA WP5 report.University of
Southern Denmark, Denmark.
http://www.ceesa.plan.aau.dk/digitalAssets/114/114492_24178_lci-report---direct-
luc-data-for-selected-e-crops-v18-09-11-2010-ceesa.pdf (accessed Nov 17, 2014),
2011, pp. 1-137.
Hamelin L. Carbon management and environmental consequences of agricultural biomass in
a Danish renewable energy strategy. PhD thesis. Department of Chemical
Engineering, Biotechnology and Environmental Technology, University of Southern
Denmark.
http://www.ceesa.plan.aau.dk/digitalAssets/114/114494_71029_thesis_lh.pdf
(accessed Mar 15, 2016), 2013, pp. 1-104.
Hamelin L, Jørgensen U, Petersen BM, Olesen JE, Wenzel H. Modelling the carbon and
nitrogen balances of direct land use changes from energy crops in Denmark: a
consequential life cycle inventory. Global Change Biology Bioenergy 2012; 4: 889-
907.
Hansen B, Kristensen ES, Grant R, Høgh-Jensen H, Simmelsgaard SE, Olesen JE. Nitrogen
leaching from conventional versus organic farming systems — a systems modelling
approach. European Journal of Agronomy 2000; 13: 65-82.
Helby P, Börjesson P, Hansen AC, Roos A, Rosenqvist H, Takeuchi L. Market Development
Problems for Sustainable Bio-energy Systems in Sweden:(The BIOMARK Project).
Report no. 38. Citeseer, 2004, pp. 1-194.
Heller MC, Keoleian GA, Volk TA. Life cycle assessment of a willow bioenergy cropping
system. Biomass and Bioenergy 2003; 25: 147-165.
Høgh-Jensen H, Kristensen ES. Estimation of Biological N2 Fixation in a Clover-Grass
System by the 15N Dilution Method and the Total-N Difference Method. Biological
Agriculture & Horticulture 1995; 11: 203-219.
151
IPCC. Watson, R. T., Noble, IR., Bolin, B., Ravindranath, N. H., Verardo, DJ, Dokken, DJ
(Eds.). Land Use, Land-Use Change and Forestry. Intergovernmental Panel on
Climate Change. Available from Cambridge University Press, Cambridge, England,
2000, pp. 1-375.
IPCC. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the
National Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L., Miwa
K., Ngara T. and Tanabe K. (eds). Published: IGES, Japan. 4, 2006, pp. 11.1- 11.24.
ISO. ISO14040: Environmental Management–Life Cycle Assessment–Principles and
Framework. London: British Standards Institution 2006.
Jørgensen K, (Edts)., Hummelmose AB, Pedersen BK, Wøyen TT, Maegaard E, et al.
Budgetkalkuler 2010-pr. oktober 2010. SEGES, Aarhus, Denmark, Denmark, 2011.
Jørgensen M, Detlefsen N, Hutchings N. FarmN: A decision support tool for managing
Nitrogen flow at the farm level. EFITA/WCCA, EFITA konferencen, Vila Real,
Portugal, 2005, pp. 25-28.
Jørgensen U, Sørensen P, Adamsen AP, Kristensen IT. Energi fra biomasse-Ressourcer og
teknologier vurderet i et regionalt perspektiv. Aarhus Universitet, Det
Jordbrugsvidenskabelige Fakultet, Aarhus University, Aarhus, Denmark, 2008, pp.
78.
Kibblewhite MG, Ritz K, Swift MJ. Soil health in agricultural systems. Philosophical
Transactions of the Royal Society B: Biological Sciences 2008; 363: 685-701.
Kim S, Dale BE. Global potential bioethanol production from wasted crops and crop residues.
Biomass and Bioenergy 2004; 26: 361-375.
Korsaeth A, Jacobsen AZ, Roer AG, Henriksen TM, Sonesson U, Bonesmo H, et al.
Environmental life cycle assessment of cereal and bread production in Norway. Acta
Agriculturae Scandinavica, Section A — Animal Science 2012; 62: 242-253.
Kramer KJ, Moll HC, Nonhebel S. Total greenhouse gas emissions related to the Dutch crop
production system. Agriculture, Ecosystems & Environment 1999; 72: 9-16.
Krogh L, Noergaard A, Hermansen M, Greve MH, Balstroem T, Breuning-Madsen H.
Preliminary estimates of contemporary soil organic carbon stocks in Denmark using
multiple datasets and four scaling-up methods. Agriculture, Ecosystems &
Environment 2003; 96: 19-28.
Krzyzaniak M, Stolarski M, Szczukowski S, Tworkowski J. Life Cycle Assessment of Willow
Produced in Short Rotation Coppices for Energy Purposes. Journal of Biobased
Materials and Bioenergy 2013; 7: 566-578.
152
Lærke PE, Jørgensen U, Kjeldsen JB. Udbytte af pil fra 15 års forsøg. Plantekongress 2010 -
produktion, miljø og natur, 232-233. Aarhus Universitet, Det
Jordbrugsvidenskabelige Fakultet.
http://pure.au.dk/portal/files/3036645/udbytte_pil.pdf (accessed Jan 21, 2015),
2010, pp. 232-233.
Lal R. Restoring Soil Quality to Mitigate Soil Degradation. Sustainability 2015; 7: 5875.
Larsen J, Østergaard Petersen M, Thirup L, Wen Li H, Krogh Iversen F. The IBUS Process –
Lignocellulosic Bioethanol Close to a Commercial Reality. Chemical Engineering &
Technology 2008; 31: 765-772.
Mandl MG. Status of green biorefining in Europe. Biofuels, Bioproducts and Biorefining
2010; 4: 268-274.
Manzone M, Airoldi G, Balsari P. Energetic and economic evaluation of a poplar cultivation
for the biomass production in Italy. Biomass and Bioenergy 2009; 33: 1258-1264.
Matthews RW. Modelling of energy and carbon budgets of wood fuel coppice systems.
Biomass and Bioenergy 2001; 21: 1-19.
Milà I Canals L, Bauer C, Depestele J, Dubreuil A, Knuchel RF, Gaillard G, et al. Key
elements in a framework for land use impact assessment within LCA. International
Journal of Life Cycle Assessment 2007a; 12: 5-15.
Milà i Canals L, Romanyà J, Cowell SJ. Method for assessing impacts on life support
functions (LSF) related to the use of 'fertile land' in Life Cycle Assessment (LCA).
Journal of Cleaner Production 2007b; 15: 1426-1440.
Mogensen L, Kristensen T, Nguyen TLT, Knudsen MT, Hermansen JE. Method for
calculating carbon footprint of cattle feeds – including contribution from soil carbon
changes and use of cattle manure. Journal of Cleaner Production 2014; 73: 40-51.
Møller J, Thøgersen R, Helleshøj ME, Weisbjer M, Søegaard K, Hvelplund T.
Fodermiddltabel 2005. Sammensætning og foderværdi af fodermidler til kvæg.
Rapport nr. 112. SEGES, Aarhus, Denmark.
https://www.landbrugsinfo.dk/kvaeg/foder/sider/fodermiddeltabel_2005.aspx
(accessed July 22, 2015), 2005a.
Møller J, Thøgersen R, Kjeldsen A, Weisbjer M, Søegaard K, Hvelplund T, et al.
Fodermiddltabel. Sammensætning og foderværdi af fodermidler til kvæg.Rapport nr.
91. SEGES, Aarhus, Denmark. https://www.landbrugsinfo.dk (accessed July 22,
2015), 2000.
153
Møller J, Thøgersen R, Kjeldsen A, Weisbjer M, Søegaard K, Hvelplund T, et al.
Fodermiddltabel 2005. Sammensætning og foderværdi af fodermidler til kvæg.
Landbrugets Rådgivningscenter, 2005b.
Møller S, Christensen TB, Sloth N. Næringsindhold i korn fra høsten. Videncenter for
Svineproduktion. Notat nr. 1226, Denmark.
http://vsp.lf.dk/~/media/Files/PDF%20-
%20Publikationer/Notater%202012/Notat%20nr%201226.pdf (accessed Oct 7,
2015). Videncenter for svineproduktion, Denmark, 2012, pp. 1-16.
Møller S, Sloth N. Næringsindhold i korn fra høsten. Notat nr. 1334. Videncenter for
Svineproduktion, Denmark. http://vsp.lf.dk/~/media/Files/PDF%20-
%20Publikationer/Notater%202013/Notat_1334.ashx (accessed July 22, 2015),
Denmark, 2013, pp. 1-16.
Møller S, Sloth N. Næringsindhold i korn fra høsten.Notat nr. 1432. Videncenter for
Svineproduktion, Denmark. http://vsp.lf.dk/~/media/Files/PDF%20-
%20Publikationer/Notater%202014/Notat_1432.pdf (accessed Oct 7, 2015), 2014,
pp. 1-18.
Murphy F, Devlin G, McDonnell K. Energy requirements and environmental impacts
associated with the production of short rotation willow (Salix sp.) chip in Ireland.
GCB Bioenergy 2014; 6: 727-739.
Murty D, Kirschbaum MUF, McMurtrie RE, McGilvray H. Does conversion of forest to
agricultural land change soil carbon and nitrogen? A review of the literature. Global
Change Biology 2002; 8: 105-123.
Nanda S, Azargohar R, Dalai AK, Kozinski JA. An assessment on the sustainability of
lignocellulosic biomass for biorefining. Renewable and Sustainable Energy Reviews
2015; 50: 925-941.
NaturErhvervstyrelsen. Vejledning om gødsknings-og harmoniregler: Planperioden 1. august
2014 til 31. juli 2015. Document number 6. Agriculture and Fisheries (in Danish).
Ministeriet for Fødevarer, Landbrug og Fiskeri,Copenhagen, Denmark.
http://www.nordfynskommune.dk/~/media/Files/Dokumenter/Teknik%20og%20M
iljoe/Natur%20og%20Miljoe/Landbrug/Vejledning%20om%20g%C3%B8dnings-
%20og%20harmoniregler.pdf (accessed May 15, 2015), 2015, pp. 1-173.
Nemecek T, Kägi T. Life cycle inventories of agricultural production systems. Swiss Centre for
Life Cycle Inventories,, Duebendorf, Switzerland, 2007.
Nielsen O-K, Lyck E, Mikkelsen MH, Hoffmann L, Gyldenkærne S, Winther M, et al.
Denmark's National Inventory Report 2012. Emission Inventories 1990-2010 -
154
Submitted under the United Nations Framework Convention on Climate Change and
the Kyoto Protocol. Aarhus University, DCE – Danish Centre for Environment and
Energy, 1168 pp. Scientific Report from DCE – Danish Centre for Environment and
Energy No. 19. http://www.dmu.dk/Pub/SR19.pdf (accessed Jun 15, 2016), 2010, pp.
1-1171.
Nielsen P. Heat and power production from straw (Produktion af kraftvarme fra halm). The
Institute for Product Development, Denmark.
http://www.lcafood.dk/processes/energyconversion/heatandpowerfromstraw.htm
(accessed Oct 18, 2012), 2004.
Nielsen PH, Wenzel H. Environmental assessment of Ronozyme® P5000 CT phytase as an
alternative to inorganic phosphate supplementation to pig feed used in intensive pig
production. The International Journal of Life Cycle Assessment 2007; 12: 514-520.
Niero M, Ingvordsen CH, Peltonen-Sainio P, Jalli M, Lyngkjær MF, Hauschild MZ, et al. Eco-
efficient production of spring barley in a changed climate: A Life Cycle Assessment
including primary data from future climate scenarios. Agricultural Systems 2015; 136:
46-60.
Nordborg M, Cederberg C, Berndes G. Modeling Potential Freshwater Ecotoxicity Impacts
Due to Pesticide Use in Biofuel Feedstock Production: The Cases of Maize, Rapeseed,
Salix, Soybean, Sugar Cane, and Wheat. Environmental Science & Technology 2014;
48: 11379-11388.
Oksen A. Landbrugets driftsresultater 2011, Tabel 4. Malkekvægsbrug - inddelt efter
besætningsstørrelse. Landbrugets driftsresultater 2011,SEGES P/S, Agro Food Park
15, 8200 Aarhus N. https://www.landbrugsinfo.dk/Oekonomi/Oekonomiske-
analyser/Driftsresultater-priser-prognoser/Sider/Landbrugets-driftsresultater-
2011.aspx (accessed Sep 22, 2015), Denmark, 2012, pp. 1-10.
Ørum JE, Samsøe-Petersen L. Bekæmpelsesmiddelstatistik 2013: behandlingshyppighed og
belastning. Miljøstyrelsen, Copenhagen, Denmark.
http://www2.mst.dk/Udgiv/publikationer/2014/12/978-87-93283-33-6.pdf
(accessed Dec 15, 2015), 2014, pp. (6) 1-66.
Pacaldo RS, Volk TA, Briggs RD. Greenhouse Gas Potentials of Shrub Willow Biomass Crops
Based on Below- and Aboveground Biomass Inventory Along a 19-Year
Chronosequence. BioEnergy Research 2012; 6: 252-262.
Parajuli R, Knudsen MT, Dalgaard T. Multi-criteria assessment of yellow, green, and woody
biomasses: pre-screening of potential biomasses as feedstocks for biorefineries.
Biofuels Bioproducts & Biorefining-Biofpr 2015; 9: 545-566.
155
Parajuli R, Kristensen IS, Knudsen MT, Mogensen L, Corona A, Birkved M, et al. (in-press,
Accepted Manuscript). Environmental life cycle assessments of producing maize,
grass-clover, ryegrass and winter wheat straw for biorefinery.
http://dx.doi.org/10.1016/j.jclepro.2016.10.076. Journal of Cleaner Production 2016.
Parajuli R, Løkke S, Østergaard PA, Knudsen MT, Schmidt JH, Dalgaard T. Life Cycle
Assessment of district heat production in a straw fired CHP plant. Biomass and
Bioenergy 2014; 68: 115-134.
Petersen BM, Knudsen MT, Hermansen JE, Halberg N. An approach to include soil carbon
changes in life cycle assessments. Journal of Cleaner Production 2013; 52: 217-224.
Pietsch G, Friedel JK, Freyer B. Lucerne management in an organic farming system under
dry site conditions. Field Crops Research 2007; 102: 104-118.
Pugesgaard S, Olesen JE, Jørgensen U, Dalgaard T. Biogas in organic agriculture—effects on
productivity, energy self-sufficiency and greenhouse gas emissions. Renewable
Agriculture and Food Systems 2013; 29: 28-41.
Pugesgaard S, Schelde K, Larsen SU, Laerke PE, Jørgensen U. Comparing annual and
perennial crops for bioenergy production - influence on nitrate leaching and energy
balance. GCB Bioenergy 2015; 7: 1136-1149.
Qin Z, Dunn JB, Kwon H, Mueller S, Wander MM. Soil carbon sequestration and land use
change associated with biofuel production: empirical evidence. GCB Bioenergy 2016;
8: 66-80.
Rasmussen J, Søegaard K, Pirhofer-Walzl K, Eriksen J. N2-fixation and residual N effect of
four legume species and four companion grass species. European Journal of
Agronomy 2012; 36: 66-74.
Rewald B, Kunze ME, Godbold DL. NH4 : NO3 nutrition influence on biomass productivity
and root respiration of poplar and willow clones. GCB Bioenergy 2016; 8: 51-58.
Roer A-G, Korsaeth A, Henriksen TM, Michelsen O, Strømman AH. The influence of system
boundaries on life cycle assessment of grain production in central southeast Norway.
Agricultural Systems 2012; 111: 75-84.
Rosenbaum R, Bachmann T, Gold L, Huijbregts MJ, Jolliet O, Juraske R, et al. USEtox—the
UNEP-SETAC toxicity model: recommended characterisation factors for human
toxicity and freshwater ecotoxicity in life cycle impact assessment. The International
Journal of Life Cycle Assessment 2008; 13: 532-546.
156
Sabbatini S, Arriga N, Bertolini T, Castaldi S, Chiti T, Consalvo C, et al. Greenhouse gas
balance of cropland conversion to bioenergy poplar short rotation coppice.
Biogeosciences Discuss. 2015; 12: 8035-8084.
Sartori F, Lal R, Ebinger MH, Eaton JA. Changes in soil carbon and nutrient pools along a
chronosequence of poplar plantations in the Columbia Plateau, Oregon, USA.
Agriculture, Ecosystems & Environment 2007; 122: 325-339.
Schmidt JH, Dalgaard R. National and farm level carbon footprint of milk-Methodology and
results for Danish and Swedish milk 2005 at farm gate. Arla Foods, Aarhus, Denmark,
2012, pp. 1-119.
SEGES. Growing instructions-Crops. SEGES, Agro Food Park, Aarhus, Denmark.
https://dyrk-
plant.dlbr.dk/Web/(S(pgsviibw4c1053wjgai5ni1p))/forms/Afgroeder.aspx?kategori=1
(accessed Sep 12, 2015), 2010.
SEGES. Farmtal Online. Vinterhvede (1.års). SEGES, Agro Food Park, Aarhus, Denmark.
https://farmtalonline.dlbr.dk/Kalkuler/VisKalkule.aspx?Prodgren=K_1150&Forudsa
etninger=31-12-2015;K_1150;1;1;2;1;2;1;1;1;3;1;n;n;0;n (accessed Feb 04, 2016),
2015.
Sevel L, Nord-Larsen T, Raulund-Rasmussen K. Biomass production of four willow clones
grown as short rotation coppice on two soil types in Denmark. Biomass and Bioenergy
2012; 46: 664-672.
Smith P, Powlson D, Glendining M, Smith JO. Potential for carbon sequestration in
European soils: preliminary estimates for five scenarios using results from long-term
experiments. Global Change Biology 1997; 3: 67-79.
Sommer SG, Schjoerring JK, Denmead OT. Ammonia Emission from Mineral Fertilizers and
Fertilized Crops. Advances in Agronomy. Volume 82. Academic Press, 2004, pp. 557-
622.
Sooriya Arachchilage K. Life cycle analysis of alfalfa stem-based bioethanol production
system. PhD thesis. Department of Chemical and Biological Engineering,University of
Saskatchewan http://ecommons.usask.ca/bitstream/handle/10388/ETD-2011-08-
166/SOORIYA-ARACHCHILAGE-THESIS.pdf?sequence=4 (accessed Jan 10, 2016),
2011, pp. 1-158.
Statistics Denmark. HST77: Harvest by region, crop and unit. Statistik om landbrug, gartneri
og skovbrug. Statbank Denmark, Denmark.
http://www.statistikbanken.dk/statbank5a/SelectVarVal/Define.asp?Maintable=HS
T77&PLanguage=1 (accessed Jul 07, 2015), 2013.
157
Stephen JD, Mabee WE, Saddler JN. Will second-generation ethanol be able to compete with
first-generation ethanol? Opportunities for cost reduction. Biofuels, Bioproducts and
Biorefining 2012; 6: 159-176.
Taghizadeh-Toosi A, Christensen BT, Hutchings NJ, Vejlin J, Kätterer T, Glendining M, et al.
C-TOOL – A soil carbon model and its parameterisation. Ecological Modelling 2014a;
292: 11-25.
Taghizadeh-Toosi A, Olesen JE, Kristensen K, Elsgaard L, Østergaard HS, Lægdsmand M, et
al. Changes in carbon stocks of Danish agricultural mineral soils between 1986 and
2009. European Journal of Soil Science 2014b; 65: 730-740.
Termansen M, Gylling M, Jørgensen U, Hermansen J, Hansen LB, Knudsen MT, et al. GRØN
BIOMASSE. DCA RAPPORT NR. 068, Aarhus Universitet, Københavns Universitet.
http://pure.au.dk/portal/files/93114938/DCArapport068.pdf (accessed April 28,
2016), 2015, pp. 1-38.
Thøgersen R, Kjeldsen AM. Grovfoder 2014. Tal om Kvæg : Grovfoder, SEGES, Kvæg..
Aarhus, Denmark. https://www.landbrugsinfo.dk/Kvaeg/Tal-om-
kvaeg/Sider/fod2014.aspx (accessed Mar 23, 2015), 2014.
Tonini D, Astrup T. LCA of biomass-based energy systems: A case study for Denmark.
Applied Energy 2012; 99: 234-246.
Tonini D, Hamelin L, Wenzel H, Astrup T. Bioenergy Production from Perennial Energy
Crops: A Consequential LCA of 12 Bioenergy Scenarios including Land Use Changes.
Environmental Science & Technology 2012; 46: 13521-13530.
Vadas PA, Barnett KH, Undersander DJ. Economics and Energy of Ethanol Production from
Alfalfa, Corn, and Switchgrass in the Upper Midwest, USA. BioEnergy Research
2008; 1: 44-55.
Vellinga TV, Blonk H, Marinussen M, Van Zeist W, De Boer I. Methodology used in feedprint:
a tool quantifying greenhouse gas emissions of feed production and utilization.
Report 674. Wageningen UR Livestock Research. http://edepot.wur.nl/254098
(accessed Jun 12, 2015), 2013, pp. 1-121.
Vils E, Sloth N. Videncenter for Svineproduktion. Næringsindhold i korn fra høsten, Notat nr.
0345. Landsudvalget for svin, Dansk Landbrugsrådgivning og Landscentret | svin.
Videncenter for svineproduktion.
http://vsp.lf.dk/Publikationer/Kilder/Notater/2004/0345.aspx?full=1 (accessed Oct
7, 2015), Denmark, 2003, pp. 1-12.
Vinther F. SimDen–A simple empirical model for quantification of N2O emission and
denitrification, Tjele, Denmark., 2005, pp. 4.
158
von Blottnitz H, Curran MA. 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 2007; 15: 607-619.
Wagner M, Lewandowski I. Relevance of environmental impact categories for perennial
biomass production. GCB Bioenergy 2016: n/a-n/a.
Weidema BP, Bauer C, Hischier R, Mutel C, Nemecek T, Reinhard J, et al. Overview and
methodology. Data quality guideline for the ecoinvent database version 3. Ecoinvent
Report 1(v3). St. Gallen: The ecoinvent Centre. Swiss Centre for Life Cycle
Inventories. http://vbn.aau.dk/ws/files/176769045/Overview_and_methodology.pdf
(accessed Feb 12, 2015), 2013, pp. 1-159.
159
Figure captions
Figure 1: System boundaries for the selected biomasses and related elementary flows.
(Figure 1a represents the general system boundary and Figure 1b represents the production
cycle of willow.).
Figure 2: Environmental impact potentials per ha of the biomass production.
Figure 3: Environmental hotspots related to GWP1 00, EP and NRE use.
160
Table 1: Crop production data. All data are per ha
Materials Unit
Amount Remarks
Spring barley straw
Willow Alfalfa
Inputs
Land (ha) ha 1 1 1
Seed (kg) ha-1y-1 32 - 11 See footnotes
Cuttings numbers ha-1 - 12000 - See section 2.2.1
Synthetic fertilizera kg ha-1y-1
(NaturErhvervstyrelsen, 2015)
N
23 74b -
P
6 32 33
K
8 172 214
Lime kg ha-1y-1 31.7 8 56 after Hamelin et al. (2012)
Pesticides kg ha-1y-1 0.11 1.04 0.33 SI (Table S.5)
Lubrication oil l ha-1y-1 2 4 14 Dalgaard et al. (2001)
Direct primary energy input
MJ ha-1y-1 492 458 4189 diesel (a + b); cuttings included in the case of willow (SI Table S.3).
a. Field preparationb MJ ha-1y-1 325 214 688 Diesel input (Dalgaard et al., 2001)
b. Harvesting + loading -handlingc
MJ ha-1y-1 167 234 3501
c. Transport
- seedsd t km ha-1 6.1 - 2
Cuttings t km ha-1 - 48 - SI, Table S.3
- agrochemicalse t km ha-1 14.25 73 78
- biomass (field to farm)f
t km ha-1 4.18 64 105
Output at farm gate
Net biomass yield t DM ha-1 y-1 2.24 10.63 12.2 (Table 2)
161
Net biomass yieldg GJ ha-1 y-1 34 199 170
Assumptions: a N-fertilizer input: N-norms —N-fixation + N-seeds + N-deposition. (see Table 4) b Included tillage and application of agrochemicals. Heating value of diesel = 35.95 MJl-1, Density = 0.84 kg/l (Weidema et al., 2013).
c Calculation for the loading and handling: † Baling = DM/ha * bale/160 kgfw/% DM *1000 kg/t * 0.23 (Hamelin et al., 2012). Diesel input = 0.743 kg bale-1. ϼ Bale loading (straw and alfalfa) = (Number of bales/ha /0.23) * 0.0811 kg/bale (Hamelin et al., 2012). ↓ Loading for barley grain = 0.119 litre m-3 fodder (Møller et al., 2000). Fodder (m3) = DM/ha * kgfw/DM% * 0.004 m3 fodder loading/kgfw *1000 kg/t (Hamelin et al., 2012).
d Mass of seed * distance (= 200 km) (Parajuli et al., 2014). e Materials (fertilizer + lime + pesticides) * distance (200 km)
f Tonnes of fresh biomass (at farm) * 3 km (single trip). Distance assumed, as in Mogensen et al. (2014). DM content: straw (85%) and alfalfa (35%) (Møller et al., 2005b), willow (50%) (Heller et al., 2003). The emission stage for the truck used was EUR5 (Weidema et al., 2013), single trip. g Lower heating value (MJkg DM-1): *straw bales = 15 (Nielsen, 2004); alfalfa bales = 14 (Jørgensen et al., 2008); willow chips = 18.7 (Pugesgaard et al., 2015).
162
Table 2: Crop-specific assessment parameters used in the calculation of SOC change
Parameters/Crop types Unit Spring barleya Willow Alfalfa
Biomass yield t DM ha-1y-1 4.08 10.63 12.2
Straw yield t DM ha-1y-1 (2.24) - -
Plant growth, totala t DM ha-1y-1 10.44 13.27 22.7
Below-ground residuesa t DM ha-1y-1y 1.77 a 5.22b 5.92a
Above-ground residues t DM ha-1y-1 3.55c 5.46b 3.17 c
Total plant residuesd t DM ha-1y-1 5.32 10.69 9.09
Plant residues Ne kg N ha-1y-1 45 53 89
C input from residues from the reference cropland f
kg C ha-1y-1 2924 2924 2924
C input from DM from the crop residue kg C ha-1y-1 1417 4915 4182
Soil C change
- in 100 yearsg kg C ha-1y-1 146 -193 -122
- in 20 yearsg kg C ha-1y-1 298 -394 -249
Emissions from soil C change (100-years)h
kg CO2 ha-1y-1 536 -708 -447
Assumptions: a Harvest index (alpha) and root mass (beta) relative to above-ground residues for: barley (Taghizadeh-Toosi et al., 2014a); for alfalfa elaborated in SI, Table S.1. Barley, 1 t DM straw (i.e. 46% of the straw yield) was removed from the field, as the feedstock. b Non-harvestable residues of willow were calculated based on Eq.(i) and Eq. (ii). c Non-harvestable above-ground residues = Total plant residues – total root residues. d Total non-harvestable plant residues = above- + below-ground residues. e Calculated from the “Total plant residue”, see footnoted and norms of crude protein (CP) (% DM) in stubble/straw, root. CP = Barley (10.6, 3.3) (average of years 2000-2013, based on reports (Møller et al., 2005a; Møller et al., 2012; Møller and Sloth, 2013; Møller and Sloth, 2014; Vils and Sloth, 2003); willow (0.45) (Pugesgaard et al., 2015); and alfalfa (16.2, 14.7) (Djurhuus and Hansen, 2003; Thøgersen and Kjeldsen, 2014). f Calculated from the total C assimilation (Taghizadeh-Toosi et al., 2014a). g Negative values indicate soil C sequestration. h Emission from SOC change (in kg C ha-1y-1) multiplied by the ratio of the mol. weight of CO2 to C (44/12).
163
Table 3: Basic parameters used for calculating the SOC stock change
Parameters Basic Scenario
SOC change in the current land use (t C ha-1 y-1) See Table 2
Natural relaxation rate (t C ha-1 y-1)a 0.31
SOCi ni stock (t C ha-1)b 90
SOCpot stock (t C ha-1)c 168
Assumptions: a Danish forest land was used as the reference for the relaxation rate = 0.31 t C/ha/y (Grüneberg et al., 2014; Nielsen et al., 2010). b SOCi ni stock of agricultural land (Taghizadeh-Toosi et al., 2014b). c SOCpot stock based on forest land use (Krogh et al., 2003).
164
Table 4: Biomass-specific N balances and emissions related to their production per ha
Unit Amount Comments/Remarks
Barley-Straw† Willow Alfalfa
Total N-inputa kg N ha-1y-1 26 89 358
N-output kg N ha-1y-1 16 48 291 Table 1
Field balance kg N ha-1y-1 10 41 67 Ni nput-Noutput
N losses kg N ha-1y-1
NH3-N
0.83 3.49 0.5 (EEA, 2013; Nemecek and Kägi, 2007; Sommer et al., 2004)
NOx-N
0.11 0.48 0.07 NOx -N: NH3-N = 12:88 (Schmidt and Dalgaard, 2012)
Denitrification 0.17 9 13 (Vinther, 2005).
Soil change, N kg N ha-1y-1 -3.61 19 13 See section 2.2.4
Potential leaching kg N ha-1y-1 11 9 41 Field balance - losses
Total N2O-N losses
(direct +indirect)
kg N ha-1y-1 0.41 0.85 0.34 (IPCC, 2006)
P losses kg P ha-1y-1 0.15 1.6 1.65 Section 2.2.4
Assumptions: † N balance for straw was allocated from the spring barley production. a Total N-input = F(sy ntheti c fer ti l i zer -N ) + Nfi x ati on
ϼ + Ndeposi ti on† + Nseed±. ϼ Nfi xation for alfalfa = 353 kg N ha-1y-1(Høgh-Jensen and Kristensen, 1995) and (Rasmussen et al., 2012). †N deposition = 15 kg N ha-1 (Ellermann et al., 2005) ±Nseed calculated after the Farm-N model (Jørgensen et al., 2005).
165
Table 5: Main parameters for the calculation of Δ SOC stock for the production of the
selected crops for the alternative scenarios
Scenario
(i)
Scenario
(ii)
Scenario (iii)
SOC change for the selected crops (t C ha-1 y-1) IPCC Tier 1a Table 2b IPCC Tier 1a
Relaxation rate (t C ha-1 y-1)c 0.31 0.31 0.31
SOCi ni stock (t C ha-1) 153d 153d 140e
SOCpot stock (t C ha-1 a)e 168 168 168
Assumptions: a, Soil C sequestration (after 20 years) based on IPCC method. b Table 2 and using the (Petersen et al., 2013) method for 20 years. c Relaxation rate = 0.31 t C ha-1 y-1(Grüneberg et al., 2014; Nielsen et al., 2010). d Based on Adhikari et al. (2014). e Based on Krogh et al. (2003).
166
Table 6: Environmental impact potentials per t DM biomass production
Environmental impacts Unit
Spring barley-
straw Willow Alfalfa
GWP1 00
- with SOC change
kg CO2 eq t DM-1 264 100 84
kg CO2 eq GJ-1 18 5 6
EP kg PO4 eq t DM-1 1.35 0.8 1.26
kg PO4 eq GJ-1 0.09 0.04 0.09
NRE use MJ eq t DM-1 1225 1416 1991
MJ eq GJ-1 82 76 143
ALO m2 t DM-1 869 949 852
m2 GJ-1 58 51 61
PFWTox
- at field level only CTUe t DM-1 33 0.35 4.44
CTUe GJ-1 2.23 0.02 0.32
- total CTUe t DM-1 113 61 71
CTUe GJ-1 8 3 5
Soil quality (Δ SOC stock)a t C t DM-1 1.22 -0.1 0.06
t C GJ-1 0.08 -0.01 0.004
167
Table 7: Sensitivity analysis of soil C sequestration, GHG emissions and NRE use for the
production of the selected biomasses compared to the basic scenario
Impact potentials for the alternative scenarios Spring barley straw Willow Alfalfa
A. Basic Scenario
i. NRE use (MJ eq/t DM) 1225 1416 1991
ii. Soil C sequestration in 100 yearsa (kg CO2 eq t DM-1) 45 -67 -37
iii. Soil C sequestration in 20 yearsa (kg CO2 eq t DM-1) 93 -136 -75
B. Alternative scenarios
i. Soil C sequestration in 20 years (kg CO2 eq t DM-1) based on IPPC Tier 1 method (IPCC, 2006)b 99 -313 -186
ii. Net GWP1 00 (without SOC change) (kg CO2 eq t DM-1) 222 167 120
iii. Changed N-fertilizer use (Urea)c
- Net GWP1 00 (kg CO2 eq t DM-1) 212 63 -
- NRE use (MJ eq t DM-1) 1283 1486 -
iv. Use of two-stage harvesting method for willowd
- Net GWP1 00 (kg CO2 eq t DM-1) - 119 -
- NRE use (MJ eq t DM-1) - 194 -
Assumptions: a Emission reduction potential in 100 and 20 years = 9.7% and 19.8%, respectively, of the net C input to the soil (Petersen et al., 2013). Negative values indicate soil C sequestration and positive value indicates emissions from soil C change. b See SI, Table S.2 for the factors of the land use changes . c CFs (Urea) for GWP1 00 = 1.24 kg CO2 eq kg N-1 and NRE use = 53.51 MJ eq kg N-1(Agri-footprint, 2014)
d Diesel consumption = 22 kg ha-1 (for cutting) and 21 kg ha-1 (for chipping) (Berhongaray et al., 2013).
168
Table 8: Variations in calculated soil quality as a result of changes in SOC sequestration and
initial SOC stock (values are given per ha; negative value indicates an increase in SOC stock)
Scenarios
Spring barley
straw Willow Alfalfa
∆ SOC
stock
(t C ha-1y-1)
relaxation time
(years)
∆ SOC
stock
(t C ha-1y-1)
relaxation time
(years)
∆ SOC
stock
(t C ha-1y-1)
relaxation time
(years)
Basic scenario 1.47 20.96 -1.06 18.73 0.77 19.2
Scenario (i) 0.30 21.03 -1.44 17.08 -0.76 18
Scenario (ii) 0.29 20.96 -0.21 18.73 0.15 19.2
Scenario (iii) 0.55 21.03 -2.69 17.08 -1.42 18
169
Figure 1: System boundaries for the selected biomasses and related elementary flows.
(Figure 1a represents the general system boundary and Figure 1b represents the production
cycle of willow.)
170
Figure 2: Environmental impact potentials per ha of the biomass production.
171
Figure 3: Environmental hotspots related to GWP1 00, EP and NRE use.
172
Supporting Information (SI)
Environmental Life Cycle Assessment of willow, alfalfa and straw from spring
barley as feedstocks for bioenergy and biorefinery systems
Ranjan Parajulia,*, Marie Trydeman Knudsena, Sylvestre Njakou Djomoa, Andrea Coronab,
Morten Birkvedb, Tommy Dalgaarda
aDepartment of Agroecology, Aarhus University, Blichers Allé 20, DK-8830 Tjele, Denmark
bDepartment of Management Engineering, Technical University of Denmark, Building 424,
DK-2800 Lyngby, Denmark
*Corresponding author, email: [email protected], Phone: +4571606831
Contents
S1. Calculation of non-harvestable residues for alfalfa
S.2. Calculation of the soil C sequestration according to IPCC
S3. Inventory for willow-cuttings production
S4. Environmental impact potentials per hectare of the crops production
173
S1. Calculation of non-harvestable residues for alfalfa
The consistency of the required parameters (Table S.1) (Taghizadeh-Toosi et al., 2014a) to
calculate the SOC change for alfalfa was checked by taking an average of two studies
(Djurhuus and Hansen, 2003) and (Pietsch et al., 2007), which was further compared with
the dataset of C-tool (Petersen et al., 2013; Taghizadeh-Toosi et al., 2014a).
Table S.1: Required parameters for alfalfa to calculate the non-harvestable residues and
related C assimilation (values indicated as C= calculated, M = measured, A = assumed) after
the given references
Parameters/Sources Unit
Calculated after,
(Djurhuus
and
Hansen,
2003)
(Pietsch et al.,
2007)
Average
a. Above ground biomass
removed t DM/ha 12.2 (C) 8.68 (M) 10.44
b. Total root biomass1 t DM/ha 2.68 (C) 2.26 (C) 2.68
c. Root + stubble t DM/ha 4.85 (M) 5.75 (M) 5.3
d. Stubble only t DM/ha 2.17 (M) 3.49 (M) 2.83
e. Senescence t DM/ha 1 (M) 1 (A) 1
f. Stubble/(harvested yield)
0.18 (C) 0.4 (C) 0.29 (C)
Harvest index of main crop
relative to aboveground biomass
(alfa)2 ratio 0.79 (C) 0.66 (C) 0.53
Root and exudate C as proportion
of total C assimilation (beta), root
of total fixed C3
0.26 (C) 0.26† (C) 0.26
Stubble + root/net yield4
0.4 (C) 0.66 (8C) 0.53
Assumptions: 1 Calculated from the above data sets (c-d) 2Calculated from above data sets [a/(a+d+e)]. 3 Calculated based on the ratio of root DM (Djurhuus and Hansen, 2003) to the net yield. †
Calculated accordingly assuming the root DM as in Djurhuus and Hansen (2003). 4 Calculated from above data sets (c /a).
174
S.2. Calculation of the soil C sequestration according to IPCC
The SOC change in 20 years and 100 years in the basic scenario was calculated based on the
method suggested in Petersen et al. (2013). In the sensitivity analysis, the method was based
on the IPCC Tier 1 methodology (IPCC, 2006), modified in Grogan and Matthews (2002).
SOC available from the non-harvestable above ground and below ground residues were
calculated for the production of selected crops and the reference crop land (equation i).
Available SOC from the below and above ground residues for the main crop (CBGmi and CAGmi
respectively) and from the reference crop (CBGr and CAGr) are shown in Table 2 of the main
document.
66.3*)*(
)(***)()(
−+−+−=∆
TmiY
iLUmFLUrFIFmgFiniSOCAGmiCAGrCBGmiCBGrCSOCGHG
………………………..Eq. (i)
where, initial SOC stock (SOCini) = 90 t C/ha (Taghizadeh-Toosi et al., 2014b), FMG and FI are
the relative stock change factors associated to management and inputs. Relative stock change
factors for the reference land use and the main crop production indicated over 20 years were
adapted from IPCC (2006). The reference land use was assumed, as was continuously
managed by annual crops for greater than 20 years (IPCC, 2006). Y mi (t DM/ha/y) is the
annual yield of the biomasses (Table 2, in the main document). A negative value of ∆GHGSOC
implies a carbon sink where as a positive value represents a source for GHG emissions In the
case of barley the impact related to agricultural residues removal was also calculated from
equation (ii) (IPCC, 2006) and was added to the equation (i).
66.3*)*(
)(***
−=∆
TimYRIFINrFLUmiFmgFiniSOC
residuesGHG
………………………..Eq. (ii),
where, SOCini = initial SOC stock; FLU and FMG are the relative stock change factors related to
land use and management. FIN and FIR are the relative stock change factors related to inputs
under no residues removal and their removal respectively (IPCC, 2006). T=20 years is the
accounting period, and 3.66 is the ratio of the molecular weight of CO2 to C. Related
parameters are shown in Table S.2.
175
Table S.2. Relative stocks change factors related to the land use and management over 20
years, adapted from IPCC (2006)
Parameters/Crops Spring barley Willow Alfalfa
Accounting period (T) 20 20 20
Yield (Y m) 3.94 10.63 12.204
Factors assumed:
- Initial Soil C (Co) 90 90 90
- Reference land usea (FLU r) 0.69 0.69 0.69
- Main crop1 (FLU m) 0.69a 0.82b 0.82b
- Tillagec (Fmg) 1 1 1
- Input (FI) 0.92d 1.11 e 1 f
Factors assumed for residues:
- Straw residues removed (FR) 0.92 - -
- Straw residues incorporated F(i n) 1 - -
Assumptions: a Area continuously managed for >20 yrs, to predominantly annual crops.
b Relative change of SOC stock for temporary set aside of annually cropland. c Full tillage
d low residues return. e High residue return without manure.
f Medium residue return.
S3. Inventory for willow-cuttings production
The database for the willow-cuttings production was based on the annual average material
inputs assumed for the production of willow during the period of the plantation until the first
harvest. The inputs estimated per 1 ha of land (Table 1 in the main document) were assumed
for the production of about 300,000 cuttings (Sevel et al., 2012). The numbers of cuttings
assumed for the plantation in 1 ha of land was 12,000 (Hamelin et al., 2012; Sevel et al.,
2012).
176
Table S.3. Inventory for the willow cuttings production
Activities Units Input per ha1 Remarks
a. Direct primary energy input
(diesel)2
Ploughing MJ/ha/y 35 Diesel = 20.7 l/ha
Rotary harrowing MJ/ha/y 8 Diesel = 4.76 l/ha
Fertilizing MJ/ha/y 45 Diesel = 2 l/ha
weed control MJ/ha/y 10 Diesel = 1.5 l/ha
Whip harvester, diesel MJ/ha/y 156 Diesel= 90.85 l/ha
b. Agro-chemicals input
N kg/ha/y 74 Table 1
P kg/ha/y 73 Table 1
K kg/ha/y 207 Table 1
Glyphosate + other herbicides kg/ha/y 1.036 Table 1
c. Transport of cuttings3 t km 0.72
Assumptions: 1 Based on the annual input considered for the willow production until the first harvest (Table
1). 2 Norms of diesel input was based on Dalgaard et al. (2001). ± Diesel for the harvest of willow
cuttings was based on Caputo et al. (2013). 3 Transport of cuttings as the plantation stock. Weight of cuttings = 20 g/cuttings (Rewald et
al., 2016) at 3 km distance.
S4. Environmental impact potentials per hectare of the crops production
The active ingredients of the pesticides, as shown in Table S.4 were the total doze applied 1 ha
of land for the production of the selected crops (Ørum and Samsøe-Petersen, 2014; SEGES,
2010). The annual average doze, as presented in the main analysis (Table 1) was calculated
after dividing by the respective life cycle years of the selected crops production.
177
Table S.4. List of pesticides and total doze applied over the life cycle years of the selected
crops (Herbicides = H, Growth regulator= G, Fungicides = F and Insecticides = I)
Pesticides, a.is. Types CAS Application (kg/ha)1 Spring barley± Alfalfa±± Willow±±
2,4-d H 94-75-7 0.0052 - -
Bentazone H 25057-89-0 0.0202 0.479 -
Aminopyralid H 150114-71-9 0.0002 - -
Bromoxynil H 1689-84-5 0.0355 -
Clodinafop-propargyl H 105512-06-9 0.00002 - -
Clopyralid H 1702-17-6 - - 0.031
Cycloxydim H 101 205-02-1 - - 0.286
Dicamba H 83164-33-4 0.0007 - -
Diflufenican H 71283-80-2 0.0070 - 0.0476
Fenoxaprop-p-ethyle H 145701-23-1 0.0058 0.069 -
Florasulam H 144740-54-5 0.0003 - -
Foramsulfuron H 173159-57-4 - - 19.048
Fluroxypyr H 69377-81-7 0.0207 - -
Glyphosate H 1071-83-6 - - -
Iodosulfuron-methyl-natrium
H 144550-36-7 0.0006 - -
Ioxynil H 1689-83-4 0.0324 - -
MCPA H 94-74-6 0.2448 - -
Metsulfuron-methyl H 74223-64-6 0.0003 - -
Pendimethalin H 40487-42-1 0.0119 0.45 0.19
Propaquizafop H 111479-05-1 - - 0.095
Triasulfuron H 52888-80-9 0.00003 - -
Tribenuron-methyl H 400852-66-6 0.0021 - -
Sulfosulfuron H 141776-32-1 0.00002 - -
Thifensulfuron-methyl H 79277-27-3 0.0002 - -
Chlormequat-chlorid G 104206-82-8 0.0110 - -
178
Ethephon G 101-21-3 0.0257 - -
Mepiquat-chlorid G 1596-84-5 0.0037 - -
Prohexadion-calcium G 16672-87-0 0.00004 - -
Trinexapac-ethyle G 56425-91-3 0.0025 - -
Azoxystrobin F 131860-33-8 0.0016 - -
Boscalid F 188425-85-6 0.0109 - -
Cyprodinil F 121552-61-2 0.0016 - -
Epoxiconazole F 133855-98-8 0.0158 - -
Fenpropidine F 67306-00-7 0.0064 - -
Imazalil F 35554-44-0 0.0082 - -
Metrafenone F 220899-03-6 0.0011 - -
Picoxystrobin F 117428-22-5 0.0013 - -
Propiconazole F 60207-90-1 0.0044 - -
Prothioconazole F 178928-70-6 0.0292 - -
Pyraclostrobin F 175013-18-0 0.0179 - -
Tebuconazole F 107534-96-3 0.0423 - -
Thiabendazole F 148-79-8 0.0001 - -
Alpha-cypermethrin I 67375-30-8 0.003 - -
Cypermethrin I 52315-07-8 0.0054 - -
Dimethoate I 60-51-5 0.0065 - -
Gamma-cyhalothrin I 76703-62-3 0.00001 - -
Lambda-cyhalothrin I 91465-08-6 0.0004 - -
Pirimicarb I 23103-98-2 0.0065 - -
Tau-fluvalinate I 102851-06-9 0.0029 - -
Total (in a life cycle) 0.60 0.998 21.65 Assumptions: 1 Data source: ±(Ørum and Samsøe-Petersen, 2014); ±±after SEGES (2010) for the total life cycle years.
179
Reference List
Caputo J, Balogh SB, Volk TA, Johnson L, Puettmann M, Lippke B, et al. Incorporating
Uncertainty into a Life Cycle Assessment (LCA) Model of Short-Rotation Willow
Biomass (Salix spp.) Crops. BioEnergy Research 2013; 7: 48-59.
Dalgaard T, Halberg N, Porter JR. A model for fossil energy use in Danish agriculture used to
compare organic and conventional farming. Agriculture Ecosystems & Environment
2001; 87: 51-65.
Djurhuus J, Hansen EM. Dry Matter and Nitrogen in Crop Residues in Agriculture. Internal
note. Aarhus University, Denmark, 2003, pp. 8.
Grogan P, Matthews R. A modelling analysis of the potential for soil carbon sequestration
under short rotation coppice willow bioenergy plantations. Soil Use and Management
2002; 18: 175-183.
Hamelin L, Jørgensen U, Petersen BM, Olesen JE, Wenzel H. Modelling the carbon and
nitrogen balances of direct land use changes from energy crops in Denmark: a
consequential life cycle inventory. Global Change Biology Bioenergy 2012; 4: 889-
907.
IPCC. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the
National Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L., Miwa
K., Ngara T. and Tanabe K. (eds). Published: IGES, Japan. 4, 2006, pp. 11.1- 11.24.
Ørum JE, Samsøe-Petersen L. Bekæmpelsesmiddelstatistik 2013: behandlingshyppighed og
belastning. Miljøstyrelsen, Copenhagen, Denmark.
http://www2.mst.dk/Udgiv/publikationer/2014/12/978-87-93283-33-6.pdf
(accessed Dec 15, 2015), 2014, pp. (6) 1-66.
Petersen BM, Knudsen MT, Hermansen JE, Halberg N. An approach to include soil carbon
changes in life cycle assessments. Journal of Cleaner Production 2013; 52: 217-224.
Pietsch G, Friedel JK, Freyer B. Lucerne management in an organic farming system under
dry site conditions. Field Crops Research 2007; 102: 104-118.
Rewald B, Kunze ME, Godbold DL. NH4 : NO3 nutrition influence on biomass productivity
and root respiration of poplar and willow clones. GCB Bioenergy 2016; 8: 51-58.
SEGES. Growing instructions-Crops. SEGES, Agro Food Park, Aarhus, Denmark.
https://dyrk-
plant.dlbr.dk/Web/(S(pgsviibw4c1053wjgai5ni1p))/forms/Afgroeder.aspx?kategori=1
(accessed Sep 12, 2015), 2010.
180
Sevel L, Nord-Larsen T, Raulund-Rasmussen K. Biomass production of four willow clones
grown as short rotation coppice on two soil types in Denmark. Biomass and Bioenergy
2012; 46: 664-672.
Taghizadeh-Toosi A, Christensen BT, Hutchings NJ, Vejlin J, Kätterer T, Glendining M, et al.
C-TOOL – A soil carbon model and its parameterisation. Ecological Modelling 2014a;
292: 11-25.
Taghizadeh-Toosi A, Olesen JE, Kristensen K, Elsgaard L, Østergaard HS, Lægdsmand M, et
al. Changes in carbon stocks of Danish agricultural mineral soils between 1986 and
2009. European Journal of Soil Science 2014b; 65: 730-740.
181
9.5. Paper V
Status: resubmitting
Evaluating the environmental impacts of standalone and integrated biorefinery systems
using consequential and attributional approaches: cases of bioethanol and biobased lactic
acid production
Ranjan Parajuli, Marie Trydeman Knudsen, Morten Birkved, Sylvestre Njakou Djomo,
Andrea Corona, Tommy Dalgaard
Journal: Journal of Cleaner Production
182
183
Evaluating the environmental impacts of standalone and integrated biorefinery
systems using consequential and attributional approaches: cases of bioethanol
and biobased lactic acid production
Ranjan Parajulia,*1, Marie Trydeman Knudsena, Morten Birkvedb, Sylvestre Njakou Djomoa,
Andrea Coronab, Tommy Dalgaarda
aDepartment of Agroecology, Aarhus University, Blichers Allé 20, DK-8830 Tjele, Denmark
bDepartment of Management Engineering, Technical University of Denmark, Building 424,
DK-2800 Lyngby, Denmark
*Corresponding author, email: [email protected], Phone: +4571606831
Abstract:
This study assesses the environmental impacts of producing bioethanol and biobased lactic
acid in standalone and integrated biorefinery plants by using Life Cycle Assessment (LCA)
method. Two scenarios were developed and evaluated using both attributional (ALCA) and
consequential (CLCA) approaches. In the first scenario, bioethanol from straw and biobased
lactic acid from alfalfa are produced separately from the two standalone systems. In the
second scenario both bioethanol and biobased lactic acid are co-produced from an integrated
biorefinery plant. The results obtained relying on both approaches arrived at the same
conclusions with lower differences in the environmental impacts when they were compared
with petrol and conventional lactic acid. The system integration showed a clear advantage for
producing bioethanol compared to the standalone system. Bioethanol and biobased lactic
acid also had net savings in terms of GHG emissions and NRE use compared to petrol and
conventional lactic acid respectively.
Key words: biobased product, economic allocation, consequential LCA, biorefinery,
environmental footprints
1 Corresponding author: [email protected]
184
1. Introduction:
The regulatory mechanisms to promote biofuel in Europe primarily aimed at enhancing
sustainable use of biomass sources and to mitigate climate change challenges (COM, 2007).
The increasing demand of biomass for biofuels has spurred the food vs fuels debates and lead
to exploration of environmental impacts of devoting croplands for producing biofuels (Lange,
2007; Marris, 2006). Biofuels have been classified to distinguish between 1st generation
produced from food crops, 2nd generation produced from cellulosic crops, and 3r d produced
from algae biomass generation. Contention over the last decade to 1st generation biofuels is
mainly due to their poor environmental performance and also due to the fact that they
conflict with food security (Gressel, 2008; Mosier et al., 2005; Sims et al., 2010). Apart from
other environmental concerns land use change and indirect land use change are also widely
discussed issues in the context of occupying land for biofuel or biobased products conversion
(Khanna et al., 2011; Templer & van der Wielen, 2011).
Biorefinery technology (René & Bert, 2007) is now bringing entirely new types of biobased
products (Cherubini, 2010) compared to fossil based refinery value chains (Mickwitz et al.,
2011). The biorefinery concept is seen as an alternative to try and to avoid the food vs fuel
conflicts (Valentine et al., 2012). It aims at maximizing the value derived from biomass
feedstock by using all its components. Moreover, sustainability of the energy sector initiatives
is primarily stressing for the production of biofuel (Demirbas, 2008), and on this biorefinery
can add values by co-producing both fuel and non-fuel products (IEA, 2011). Among different
biobased products, sustainability assessment of bioethanol has been the most studied
production chain (Cherubini, 2010; Cherubini & Ulgiati, 2010; Kim & Dale, 2005).
Among the contemporary development, the concept of green biorefinery (GBR) is gaining
importance in Europe as it is seen as an alternative option of using grassland biomass
(Mandl, 2010; O’Keeffe et al., 2011). The GBR has primarily focused on producing protein in
order to reduce the import dependency of livestocks feed, e.g. soy cake and soy meal, and
secondly to generate chemicals such lactic acid, lysine for the chemical and food industries
(Kamm et al., 2009). Like to the green protein and other biobased chemicals, biobased lactic
acid (LA) from the GBR is also among the important products that are of key interests
(Ghaffar et al., 2014; Kamm et al., 2009; Kim & Moon, 2001; O’Keeffe et al., 2011; Panesar et
al., 2007; Sreenath et al., 2001; Thomsen, 2004; Wee et al., 2006). Lactic acid is a simple
hydroxy carboxylic acid and a platform-chemical with wide industrial applications, such as in
food, chemical, pharmaceuticals and health care industries
Biorefining of grass is often seen as environmentally benign (IEA, 2011), however
environmental impacts of GBR concepts, technologies, and value chains are limitedly
studied. Life cycle assessment (LCA) has been widely used as a tool for assessment of
185
environmental performance of different production systems (European Commission, 2015a).
Many LCA studies on biofuels reported a reduction in the global warming potential (GWP)
and savings in fossil fuel use (Gasol et al., 2007; Kim & Dale, 2005; Larson, 2006; Quintero
et al., 2008). In the evaluation of environmental claims for different biobased products,
emphasis were given on the need to develop and apply standardized LCA methodologies
(European Commission, 2015b). Furthermore, when making an environmental evaluation
and when applying LCA methodology, in most cases multi-functional processes are included,
and the handling of the co-products becomes paramount. Within the LCA community there
are methodological debates on the distinction and on appropriate application of attributional
(ALCA) and consequential (CLCA) approaches. Within ALCA approach, co-product allocation
is recommended and most frequently used (Crown & Carbon Trust, 2008). Avoiding
allocation by system expansion is the only acknowledged way to deal with co-products within
CLCA (Weidema, 2003).
The objective of this study is to evaluate the environmental burdens of producing bioethanol
and lactic acid in standalone plants and in an integrated biorefinery plant using an ALCA and
a CLCA approach. The standalone systems represent separately producing bioethanol from
straw (System A) and biobased lactic acid from alfalfa (System B). In the integrated system,
both bioethanol and biobased lactic acid are co-produced (System C). It should be noted that
even the standalone systems, as we presented here have some kind of resource integration
and are reported as an integrated system in the studies related to biorefineries (Kromus et al.,
2004; Sadhukhan et al., 2008). But in the current study we have defined in such a way that
production system can be shown separately in one hand and also the same can be shown
after combing the two systems. The integrated system was however termed in accordance to
the definitions for “process integration” and “feedstock and product integration”, as
suggested in Stuart and El-Halwagi (2012).
2. Materials and methods
2.1. System boundaries, functional units and environmental impact categories
The potential environmental impacts of the two standalone plants producing bioethanol
(System A) and biobased lactic acid (System B) were calculated. The two standalone plants
were then integrated based on the exchange of the useful energy and material streams
between them and the environmental impacts were quantified, assuming that bioethanol
represent the main product of the integrated system (System C). The functional unit (FU) for
the standalone bioethanol was 1 MJEtOH (MJEtOH) whereas the FU for System B producing
lactic acid was 1 kg pure lactic acid (kgLA). As bioethanol was the main product of the System
C, 1 MJEtOH was adopted as the FU for System C. The system boundaries of the standalone
plants are shown in Figure 1 and 2, respectively, while the process flow diagram of System C
is illustrated in Figure 3. The environmental impact categories covered were: Global
186
Warming Potential-100 years (GWP1 00), Eutrophication Potential (EP), Non-Renewable
Energy use (NRE use), and Agricultural Land Occupation (ALO). The first three impact
categories were assessed using the “EPD” method (Environdec, 2013), while the ReCiPe
method (Goedkoop et al., 2009) was used to evaluate the ALO, as it was not in the “EPD”
method. The modelling of the environmental impacts were facilitated by the use of the LCA
software “SimaPRO ver. 8.0.4” (PRé Consultants, 2015), which incorporates these
assessment methods.
Figure 1: Process flow diagram of the standalone production of straw-based bioethanol
(System A). Electricity produced represents net values of the system (i.e., plant’s own
consumptions have been subtracted). The dotted lines indicate the avoided products
considered in the CLCA approach.
Figure 2: Process flow diagram for the standalone production of lactic acid from alfalfa
(System B). Electricity produced represent net values of the individual system (i.e., plant’s
own consumptions have been subtracted). The dotted lines indicate the avoided products
considered in the CLCA approach. The index for material flow lines are as shown in Figure 1.
Figure 3: Process flow and energy balance of the integrated biorefinery (system C). The
index for material flow lines are as presented in Figure 1.
2.2. Methods of the assessment
2.2.1. CLCA approach
The decision to choose the main product and the co-products was based on the potential
revenue that can be drawn from each biorefinery system. The revenues were calculated based
on the unit prices of each product (Table 1) and the amount of biobased products produced
from each system (Figure 1-3).
With regard to the consequential effects related to the biomass production, for straw the
identified consequences were in the form impacts related to straw removal from the field
(Petersen & Knudsen, 2010). They were assessed in relative to the situation if straw was
ploughed back into the field. The approach included: (i) emissions from soil C change (ii)
compensation of displaced nutrients by synthetic fertilizer and (iii) related N emissions
because of the stated consequences. The consequences of straw removal thus amounted to
143 kg CO2 eq/ t straw (85% DM) (Parajuli et al., 2014).
Likewise, any utilization of a productive land is claimed for increasing overall pressure on the
frontier between “nature” and the considered current land management practices, which are
argued for inducing indirect land use change effects (iLUC) (Audsley et al., 2009; Schmidt &
Brandao, 2013; Schmidt & Muños, 2014). The iLUC effect was defined as the upstream
consequences due to land occupation, regardless of what is done to it (Schmidt. J. H. et al.,
187
2012). Hence, in the current study, iLUC factor for producing alfalfa, in terms of induced
GHG emissions was set to 1.4 t CO2eq/ha/y (Audsley et al., 2009). The results of
consequential effects of straw and including iLUC effect for alfalfa for the current study are
shown separately in section 3.3.
Table 1: Co-products and assumed substitutable products in the conventional market
2.2.2. ALCA approach
The total environmental impacts obtained for each biorefinery systems were economically
allocated to the respective biobased products. The allocation factors were based on potential
revenues, estimated from the prices and the generated quantity of co-products from each
system (Figure 1-3 and Table 2). When working with ALCA approach, environmental impacts
for producing straw was also economically allocated (Parajuli et al., 2016b). In the case of
System A, the estimated allocation factor for bioethanol was 73%, while in System C it was
39%. The reason behind the differences in the allocation factors was because of the economic
value of wide range of coproducts produced from System C, which proportionately change
the factors. In the case of System B, the allocation factor used for biobased lactic acid was
67%.
2.3. Data source and basic assumptions
The basic assumptions related to this study are summarized in Table 2, unless otherwise are
stated in the text below. The carbohydrate content was set to 56% for alfalfa and 76% for
straw (Møller et al., 2005). Likewise, the crude protein (CP) and lactic acid content in the
ensiled alfalfa were 15% and 6% of the total DM, respectively (Møller et al., 2005; O’Keeffe et
al., 2011). The mass and energy flows for System A included straw (1 t, 85% DM), enzyme,
chemicals, heat, energy, and water; and these were based on the studies of Bentsen et al.
(2006), Kaparaju et al. (2009) and Wang et al. (2013). The conversion of biomass and
material flows in the bioethanol plant is shown in Supporting Information (SI-2)-Figure S.1.
With regard to enzyme, the environmental impact potentials induced by producing Cellic
CTec3 EU were provided by Novozymes (Kløverpris, J.H, 2016, pers. comm.). It was also
communicated that the estimation was based on the method and principles described by
Nielsen et al. (2006).
In the case of System B, the mass transformation of carbohydrate and protein content of the
fresh biomass to the respective products (Figure 2) were based on O’Keeffe et al. (2011) and
Kamm et al. (2009) (see SI-3, Figure S.2). The mass and energy balance were however
adjusted in terms of the DM content of alfalfa assumed in the current study (i.e. 35% at
harvest) which was 20% in O’Keeffe et al. (2011) and Kamm et al. (2009). In general, ensiled
biomass is preferable for lactic acid production and suitable for the plant to rely on the
biomass without being affected by the harvesting seasons (Ambye-Jensen et al., 2013). The
188
lactic acid content in the ensiled biomass also favours biomass storage without burden of
drying the fresh grasses (Kromus et al., 2004). Furthermore, it is also important for the
biochemical conversion processes in GBR (Ambye-Jensen et al., 2013; Buxton & Muck, 2003;
Chen et al., 2007). Detailed descriptions on the relevant processes involved in System B are
described in section 2.5.1.
Table 2: Basic assumptions considered in the inventory analysis
2.4. Life Cycle Inventory for the biomass production
The CLCA approach included the removed straw after harvesting from winter wheat. Hence,
the consequences of removing it from the field were taken into account and the value was
adapted from Parajuli et al. (2014). The additional processes included were baling and
loading of straw and then transporting to a biorefinery plant. The diesel fuel consumed for
baling and handling of the biomass was based on Dalgaard et al. (2001). Regarding alfalfa
production, the crop production data (Table 3) was adapted from Parajuli et al. (2016a). A 3
years rotation with three harvests per year (Jørgensen et al., 2011) was assumed for alfalfa
production. The emission factors for the materials produced in the background system were
based on the default consequential unit process values, as reported in Ecoinvent v3
(Weidema et al., 2013).
In the case of ALCA approach, related material inputs and the environmental impacts of
producing straw from winter wheat was adapted from Parajuli et al. (2016b); and for alfalfa it
was adapted from Parajuli et al. (2016a). Transport distance for the biomasses was set to 200
km (Bentsen et al., 2009) and the distance covered up to the gate of the biorefinery plant.
The emission factors for the materials produced in the background system were based on the
default allocation unit process values, as reported in Ecoinvent v3 (Weidema et al., 2013).
Table 3: Input-output of the materials flow assumed for the alfalfa production, per 1 t DM,
summarised after Parajuli et al. (2016a)
2.5. Life Cycle Inventory for biomass conversion in biorefinery plants
2.5.1. Standalone system
System A: The material consumption for System A is summarized in Table 4. The conversion
of straw in the biorefinery followed the four major steps: (i) pretreatment of the straw, (ii)
hydrolysis, (iii) fermentation, and (iv) the recovery of the products. Hydrothermal
pretreatment was assumed for the breakdown of strong lignocellulosic structures (Zhang,
2008) into reactive cellulosic intermediates (Galbe et al., 2007). The pretreatment process
also necessitates the removal of potassium chloride (KCl) and the recovered mass can be
assumed applicable as fertilizer (Larsen et al., 2012) (Table 4). Ammonia treatment was also
considered (Bentsen et al., 2006). Ammonia treatment can selectively remove lignin from
189
biomass and it was reported that a high purity of hemicellulose and lignin can be recovered.
Furthermore, a high enzymatic digestibility and fermentability were found for the cellulose
fraction because of the stated pretreatment and hydrolysis processes (Yoo, 2012). A
simultaneous saccharification and fermentation process followed by a distillation process was
applied after the hydrolysis process (Galbe et al., 2007). The solid particles collected from the
distillation column can be pelletized into lignin pellets. Lignin pellets can be co-fired with
coal in a Combined Heat and Power (CHP). The liquid particles from the stillage and
hydrolysate were used as substrates to produce biogas (SI, Figure S.1)
System B: Table 5 summarizes the material inputs to System B. The primary mass and
energy flows for system B were calculated following O’Keeffe et al. (2011) and Kamm et al.
(2009). The handling of alfalfa is assumed to be initiated with mechanical processing
including the chopping of the biomass, followed by the extraction of press-juice (DM 5 %)
and press-cake from a mechanical screw-press (O’Keeffe et al., 2011). The fractions of press
juice and the press cake were set to 70% and 30% respectively of the fresh matter (O’Keeffe et
al., 2011). Losses of fiber during the washing steps were assumed to be about 5% of the total
fiber fraction, but were utilized as residues for biogas production. The press juice stream was
divided into two sub-streams, one for the protein extraction and another for the lactic acid
production (Kamm et al., 2009; O’Keeffe et al., 2011). After the mechanical separation of
press-juice and press cake, the DM content in the press cake (i.e. after the 2nd pressing) was
assumed to be hydrothermally pretreated and then followed by enzymatic hydrolysis. In the
current study, in contrast to the mass flow presented in O’Keeffe et al. (2011), enzymatic
hydrolysis was considered, as the process yield more glucose from the celluloses of the press
cake (Alvira et al., 2010). In this study, for the hydrolysis process, amount of enzyme added
was calculated from the enzyme loadings per cellulose content of the pretreated biomass
(Bentsen et al., 2006; Kaparaju et al., 2009). The calculated mass of enzyme was 17 kg. The
assumed mass of enzyme was also close to the value 20 kg per tonne dry biomass, as reported
in Wolfrum et al. (2013). The yield of glucose after hydrolysis process was estimated based on
Cybulska et al. (2010). The output in terms of glucose content in the liquid fraction and solid
fraction after the hydrolysis process were 5 and 125 kg DM per t DM of the biomass,
respectively (see SI-3, Table S.3). In the liquid and solid fractions, the conversion factors for
glucose were set to 1.3% and 32% respectively of the glucose content estimated for press cake.
Likewise, the press-juice fraction which was fractionated after the mechanical press is
assumed to contain 26 kg lactic acid per t DM. With an extraction efficiency of 70% (O’Keeffe
et al., 2011) the resulted yield of lactic acid from the stated stream was 18 kg DM/t DM. The
conversion factor of the glucose content in the hydrolysed biomass (total 125 kg DM, Table
S3) to lactic acid was set to 79% (Doran-Peterson et al., 2008), which resulted to produce 81
kg DM of lactic acid per t DM of the biomass. Silage fermentation of the fresh biomass can be
190
optimized by the application of lactic acid producing bacteria (Pahlow et al., 2003).
Additionally, commercially important fermenting media were also sugarcane molasses, corn-
steeping (Ghaffar et al., 2014) and whey (Panesar et al., 2007). In this study, in addition to
the lactic acid bacteria, whey from dairy industries was assumed as a fermenting media. The
mass flow analysis reported in Kamm et al. (2010) was used for the proximity to calculate the
amount of fermenter. However, the limitation on the use of whey is that the produced lactic
acid has an inhibitory effect. This however can be reduced to a certain extent by conducting
fermentation in a continuous dialysis process or in an electro-dialysis system (Kim & Moon,
2001). The recovery of lactic acid included the processes: ultrafiltration, reverse osmosis
(Patel et al., 2006), bipolar electro-dialysis (Kim & Moon, 2001) and distillation. Energy
consumption for such processes is summarized in SI-1, Table S.2. During the process, the
protein from the fermentation broth can be separated using an ultrafiltration membrane (Li
et al., 2006). Sodium hydroxide was used as a base material, which results into sodium
lactate. The total lactic acid yield from the conversion of glucose of the hydrolysed press cake
and the press juice was thus 90 kg DM per t DM of the biomass after subtracting 10%
impurities (Kamm et al., 2009; O’Keeffe et al., 2011). The obtained liquid residues available
for biogas digestion was 6% per t DM of the raw biomass, which was close to the amount
reported in Kamm et al. (2009).
Table 4: Primary materials input and output related to the conversion of 1 t straw (with 85%
DM) to bioethanol (System A)
Table 5: Input-output of materials for the conversion of 1 t DM alfalfa to lactic acid (System
B)
2.5.2. Integrated System
Figure 3 describes the process and energy flows involved to produce both bioethanol and
lactic acid from System C, which was designed by combining the two standalone systems. The
net surplus electricity produced from System C was 1.23 GJe, after fulfilling the energy
consumption to pursue the primary biomass conversion tasks in the biorefinery, and
exchanging from one system to another. The energy balance of the system was however
deficit in terms of heat energy, but had surplus electricity (see SI-1, Table S.1, Figure 3).
2.6. Secondary processing
2.6.1. Energy balancing for the biorefinery
The secondary processing included in System A was the conversion of lignin to heat and
power in a CHP plant. Lignin pellet was applied as suitable co-firing fuel with coal, as it
represents about 40% of the heat content in biomasses (Kim et al., 2003). Emissions from
the combustion were based on firing coal in a CHP plant (Danish Energy Agency, 2012).
Likewise, the conversion of C5 molasses and the liquid residues collected from System A and
191
the decanted liquid residues from the System B were considered to produce biogas. The mass
of the substrates for biogas conversion in System A was calculated after the studies reported
by Bentsen et al. (2009) and Kaparaju et al. (2009) (Table 1) (see SI-2, Figure S.1 ). The
volatile solids (VS) (%) in the stillage fractions of the total wet weight of the stillage were
based on Kaparaju et al. (2009) (Table 2). In the case of molasses, of the total wet-weight of
the cake the total solids (TS) and VS were assumed, as reported in Drosg et al. (2012) (Table
2). In the case of System B, the total mass of fermentable substrate for the production of
methane (CH4) was based on the VS (%) in the decanted press juice (O’Keeffe et al., 2011)
(Table 2). For both systems, the total methane yield was calculated utilizing Eq. (i)
(Pugesgaard et al., 2013).
67.0*0**)(4 BVSpotentialCH e=
….Eq. (i)
where, CH4 (potential) = methane production (kg); VS (in kg, see Table 2); Bo is the
maximum methane-producing capacity of the added material (m3kgVS−1) (Table 2); ε =
process efficiency = 0.8, based on the average efficiency of hydrolysate and stillage fractions,
as reported in Kaparaju et al. (2009) and 0.67 was the conversion factor from volume to kg
CH4 (Olesen et al., 2004). The energy input to biogas plant was based on Berglund and
Börjesson (2006). Methane loss during combustion was set to 1.8% of the total conversion
(Pugesgaard et al., 2013). Conversion of biogas to heat and electricity was 18.69 MJh and 26.7
MJe respectively per kg of CH4, with LHV of CH4 set as 35.8 MJm-3, and the heat and
electricity conversion efficiency were set to 35% and 50% (Jørgensen, 2009). Likewise, the
amount of substrate available from the GBR to the biogas digester was calculated based on
the studies of O’Keeffe et al. (2011), Kamm et al. (2009) and (Kamm et al., 2010) (Table 5). In
this case the Bo of the added material was assumed 39.5%, estimated based on Pugesgaard et
al. (2013) for crop residues and calculated after Levin et al. (2007).
2.6.2. Nutrient recovery
The nutrient recovery was calculated in the form of total N, P and K content available in the
digestate (Drosg et al., 2015) (Table 1). The total mass of digestate was estimated assuming
50% loss of the TS contents after the anaerobic digestion compared to the initial pre-digester
level (Drosg et al., 2015; Lebuf et al., 2013). About 40% of the recovered N and 100% each for
P and K was assumed to substitute the equivalent amount of N,P, K available from the
synthetic fertilizer (Hansen et al., 2006). Likewise, recovery of K from KCL was also
considered for System A and (Table 4) was also considered for System C.
192
2.7. Sensitivity analysis
2.7.1. Avoided products scenario
Marginal electricity: Natural gas was assumed as a fuel source for the alternative scenario of
marginal electricity (Mathiesen et al., 2009).
Marginal energy-feed: Grass silage was assumed as marginal energy feed as an alternative
scenario. It was assumed that the estimated fibers obtained from GBR can meet the
equivalent feed unit (in terms of energy-feed) that would have been conventionally available
from grasses to livestocks. Furthermore, in the current study grass is used as the principal
raw material to the biorefinery system and might be useful to argue that conventional way of
utilizing them is partially changed, but serving the similar purpose. In this scenario, the
supply was assumed to be from Danish farm.
2.7.2. Sensitivity related to variation on the yield of bioethanol and lactic acid.
Utilization of C5 sugar to boost bioethanol yield: In the basic scenario we assumed that the
bioethanol conversion was from the fermentation of C6 sugars, and the C5 sugar contained in
the molasses was used for the biogas conversion. Alternatively, it was assumed that with the
use of an advanced yeast technology the yield of bioethanol can be boosted up by
simultaneously consuming both C6 and C5 sugars. The reported increase in the yield of
bioethanol was from 20% to 40% per tonne of biomass (Inbicon, 2013; Losordo et al., 2016).
It means that after the recirculation of C5 sugars the molasses that was assumed to be
available for biogas conversion will accordingly decrease, and hence would result in the
decrease in the energy generation. It was found that when the yield of bioethanol was
increased from +20% to 31%, the decrease in the electricity production was from -12% to -
37% (Losordo et al., 2016). Hence, to set this alternative scenario, the decrease in the
electricity production was set to -24%, while the increase in the yield of bioethanol was set to
+23%, averaging from the above stated changes. These changes were also adjusted on the
energy input, as was estimated for the biogas production in the basic scenario (see SI-1, Table
S.1).
Varying the yield of biobased lactic acid: The yield of biobased lactic acid was varied from -
10% to +10% compared to the yield used in the basic scenario. The lower range was set
approximately matching the yield as reported in Kamm et al. (2009).
3. Results
3.1. General overview of CLCA and ALCA approaches
Global Warming Potential
The use of CLCA and ALCA approach resulted to yield a comparable range for net GWP1 00
in the bioethanol conversion in System A (Table 6). In the case of the conversion of
193
biobased lactic acid (System B), net GWP1 00 obtained using CLCA approach was
significantly lower compared to ALCA approach. A higher result on the impact with ALCA
approach was due to uneven sharing of the burden by the co-products and transferring the
most to lactic acid. This was in fact determined by the allocation factors estimated for the
biobased products produced from System B. The major allocation share in the case of
System B was for lactic acid (67%) and electricity (22%), and the remaining was for the
other co-products (e.g. feed protein, fodder silage and recovered nutrients). These features
revealed that in the case of ALCA approach, smaller burdens were attributed to other co-
products compared to the main product. Contrary to this, in CLCA approach, the stated co-
products were substantially avoiding GHG emissions, which were then credited to the main
product. For instance, co-products, such as fodder silage and feed protein were assumed to
displace marginal source of energy-feed and protein respectively. The displacement was
thus occurring for avoiding the production of Ukrainian barley and soymeal, including their
transportation, which jointly avoided 79% of the gross GWP1 00 obtained for System B.
Moreover, in System C the co-products, such as lactic acid, electricity and bioethanol were
more or less equally sharing the burden, in the order of 32%, 24% and 38% respectively.
This resulted to yield the result with a marginal difference, as was obtained using ALCA and
CLCA approaches (Table 6).
Eutrophication Potential
The use of CLCA approach yielded with 52% lower result on EP compared to ALCA
approach for bioethanol (System A); however the results were not so modest in absolute
values (Table 6). Likewise, for System C the impact calculated for bioethanol using CLCA
approach was 39% lower than ALCA approach. The difference in the obtained result was
substantial for lactic acid production (System B), which was 111% lower when CLCA
approach was used compared to ALCA approach. The reason behind this was also due to
more avoidance of eutrophying emissions when barley and soymeal were displaced, e.g.
32% of the gross EP was avoided by displacing soymeal and more than 100% was avoided
by displacing the barely.
Non Renewable Energy use and Agricultural Land Occupation
Like in the case of GHG emission, the results obtained on NRE use for the production of
bioethanol (both in System A and System C) and biobased lactic acid (System C) was lower
for CLCA approach compared to ALCA approach (Table 6).
In the case of System B, the feed products jointly avoided 49% of the gross NRE use, and
additionally co-produced electricity avoided 28% of the impact. Contribution from the
avoided impact was substantial in the case of System C, which yielded with 122% less NRE
use for bioethanol conversion when compared with ALCA approach.
194
Finally, the result obtained on ALO for producing bioethanol (System A) was 82% lower
when CLCA approach. Contrary to this, for System C, bioethanol yielded with more ALO,
when CLCA approach was used compared to ALCA approach (Table 6). In the case of System
C, particularly for ALO, the impact was induced mainly by the production of alfalfa, and in
the meantime, the co-products related to the conversion of alfalfa were merely avoiding 33%
of the gross ALO. The contribution from the production of alfalfa to the gross ALO on the
hand was almost 100%.
Table 6: Potential environmental impacts of producing bioethanol and lactic acid from
standalone plants and from System C (values in the parenthesis are the gross impacts, i.e.
without avoided impacts) (FU is 1 MJEtOH for System A and System C and 1 kgLA for System B)
From the above comparison, it can be concluded that the environmental footprints of
bioethanol production was lower when produced from the integrated system (System C) than
the standalone system (System A). Furthermore, based on the results obtained using CLCA
approach it is further concluded that all the biorefinery systems were benefited by co-
products, mainly by crediting the environmental burdens. A clear picture was that the
system constituted with co-products, such as feed protein and fodder silage were
substantially crediting the environmental burdens. This also attracted to make a sensitivity
analysis taking an alternative scenario of marginal feed (see section 4).
3.2. Environmental hotspot analysis
The contribution from respective value chains to the assessed environmental impacts are
shown in Figure 4. The results obtained for most of the impact categories followed almost
similar pattern on the contribution, regardless of the approach used. For example, in System
A it was the production of straw contributing the most to the gross GWP1 0o. Here the
contribution amounted to cover 27% and 34% of the gross impact obtained using,
respectively ALCA and CLCA approaches. In the case of CLCA approach, however the impact
of biomass production was represented by the negative effects related to the straw removal
process. It was in the form of contributing 18% to the aforementioned contribution. On the
contrary, in the case of ALCA approach straw was credited from the soil C sequestration
obtained for producing winter wheat (Parajuli et al., 2016b), which was then reducing the
GHG emissions in the bioethanol production system. This amounted to -0.02 kg CO2
eq/MJEtOH, and resulted at mitigating 26% and 11% of the obtained gross GWP1 00 for System
A and System C respectively. Meanwhile, the added burden from N2O emissions from the
biomass production was 5% and 10%, based on the impact calculated using CLCA and ALCA
approach respectively. In the case of System B, the contribution from the biomass production
was estimated at 75% and 93% of the gross GWP1 00 obtained using ALCA and CLCA
approaches respectively (Figure 4). Similarly, in System C, 46% of the gross GWP1 00 was as a
195
result of emissions from the biomass production system (for ALCA approach), which on the
other hand was 41% for CLCA approach. Hence a comparable trend was found for the results
obtained using CLCA and ALCA approach.
After the biomass production system the biorefining value chain covering the primary task of
processing the biomass was the major contributor to most of the impact categories. The
biorefining processes here included the emissions related to the production and the
consumption of the assumed materials entering into the biorefinery plant. In the case of
bioethanol conversion (System A) the biorefining processes contributed 62% of the gross
GWP1 00 obtained using ALCA approach, which was 66% in the case of CLCA approach.
Furthermore, of the stated range of 62%-66%, the stake of primary energy input ranged from
17-20%. The contribution from the enzyme production ranged from 25% to 28% of the gross
impact obtained using the ALCA and CLCA approach. In System C the contribution from the
biorefining processes ranged from 54% to 58% for the results obtained using ALCA and CLCA
approaches. On the stated range, the contribution from the enzyme production ranged from
60% to 73%.
Like in the case of GWP, the contribution to NRE use was mainly related to the biomass
production system. For example, it was 49% in System A, 82 % and 67% in System B and
System C respectively, based on the results obtained using ALCA approach. The contribution
from biomass production was with similar pattern when the results obtained using CLCA and
ALCA approach were analyzed (Figure 4), e.g. ranging from 37% in System A to 98% in
System B. The contribution to NRE use by the biorefining proceses ranged from 20% to 49%
of the gross impacts obtained using the CLCA approaches.
With regard to the EP, a significant difference was found for the contributions from different
value chains. For example, in System A, the biomass production stage contributed 40% of the
gross EP obtained using ALCA approach. In contrast it was 11% contributing from the same
value chain for the result obtained using CLCA approach. It was however logical to get a
lower EP in CLCA approach, as the contribution was mainly related to emissions induced
from the equivalent compensated N and P fertilizer, as calculated in the context of assessing
the consequences of removing the straw from the field. But, in the case of ALCA approach, it
was the contribution from the production of winter wheat crop (Parajuli et al., 2016b) (see
section 3.3). Like the contribution from the biomass production system, the contributions to
the impact from other value chain were also on the comparable range for both approaches
(Figure 4).
Figure 4: Contribution of processes involved in the entire biobased products chains.
Products for: System A and System C = bioethanol and System B = lactic acid. (ALCA =
Attributional LCA and CLCA = Consequential LCA).
196
3.3. Consequences of straw and alfalfa production
In the current study, consequences of straw removal were accounted on the basis of negative
effect induced due to SOC change and from the compensation of nutrients in relative to the
situation straw ploughed back into soil. Consequences of straw removal thus eventually
ended with emitting 0.03 kg CO2eq per 1 MJEtOH. Moreover, avoided N2O emission
(equivalent to the avoided emissions that would have occurred due to the decomposition of
the residues) was also accounted on the calculation, and it was -0.003 kg CO2eq/MJEtOH.
In the case of alfalfa, the effect of iLUC, in terms of GHG emissions was equivalent to 117 kg
CO2 eq per t DM alfalfa. Hence, the obtained carbon footprint of the biomass was 29% higher
compared to the case excluding the iLUC. As a result of such, net GWP1 00 obtained for lactic
acid producing from System B turned out to be 0.06 kg CO2 eq per kgLA. The net impact
excluding iLUC was -1.24 kg CO2 eq per kgLA (Table 6). With the similar effect, net GWP1 00
obtained for bioethanol producing from System C was 0.05 kg CO2 eq per MJEtOH; which was
0.03 kg CO2 eq per MJEtOH excluding iLUC.
4. Results on the sensitivity analysis
4.1. Avoided products scenarios
Table 7 lists the environmental impacts obtained after considering the alternative scenarios
of the avoided products (see section 2.7). When natural gas was assumed as marginal fuel for
electricity generation, net GWP1 00 obtained for the conversion of bioethanol from System A
and System C was higher by 19% and 103% respectively compared to the basic scenario.
Likewise, for biobased lactic acid it was higher by 68% compared to the basic scenario. The
NRE use obtained per FU of the respective biobased products was higher in the alternative
marginal electricity scenario compared to the basic scenario (Table 7).
Similarly, when grass silage was selected as the marginal supply of energy-feed, both net
GWP1 00 and NRE use obtained for lactic acid (System B) and bioethanol (System C) were
higher compared to the basic scenario. The difference was due to less credit offered by the
displacement of this alternative livestock feed compared to barley (Table 7).
4.2. Variation in the biobased products yield
When the yield of bioethanol was increased by 23%, net GWP1 00 obtained for bioethanol
(System A) was 6% lower compared to the basic scenario. In contrast, NRE use was higher by
11% compared to the basis scenario. On contrary, for System C both net GWP100 and NRE use
obtained for bioethanol was higher compared to the basic scenarios (Table 7). System C in
most of the cases were benefited by the co-produced electricity and also from the internal
useful energy demand maintained by sharing energy from one system to another, which was
in the gross reduced by 24% after the utilization of C5 sugar for the fermentation process.
197
Hence, the environmental credit that was offered in basic scenario from avoided marginal
electricity was lowered in this scenario.
A 10% increase in the yield of lactic acid resulted to decrease net GWP1 00 by 8%. In the same
manner the impact was increased by 10% when yield was lowered by 10% compared to the
basic scenario. Likewise, similar pattern was found for other impact categories (Table 7).
In the same manner, for System C if the yield of lactic acid was lowered by 10% without
varying the yield of bioethanol, the obtained carbon footprint and NRE use for bioethanol
were higher 49% and 106% respectively compared to the basic scenario (Table 7).
Table 7: Results obtained from the sensitivity analysis. Units are net GWP100 = kg CO2 eq per
FU and NRE use = MJ eq per FU of each systems
5. Discussions
5.1. System designing and overall synopsis
The estimated yield of bioethanol in the present study was 0.22 kg DM per kg DM biomass
(Table 4), which was close to the range of 0.15-0.29 kg DMEtOH per kg biomass DM (Larsen et
al., 2012; Larsen & Henriksen, 2014; Wang et al., 2012). Similarly, the yield of lactic acid
from 1 t DM of alfalfa calculated in the current study was close to 83 kg per t DM of the grass
silage, as calculated from Kamm et al. (2009). In contrast, the value was higher than O’Keeffe
et al. (2011), and the differences were due to the enzymatic treatment that was considered in
the current study which enhanced the availability of glucose for the fermentation process.
The overall conversion efficiency of System A, obtained based on the total energy input
(energy content of the straw + net primary energy supplied to the biorefinery plant) and the
produced energy (bioethanol + electricity) was 51% (Figure 1). Larsen and Henriksen (2014)
reported 69% as the overall efficiency for the Inbicon technology; however the net output
seems not the final energy. In the current study, if heat content of lignin was considered then
the energetic efficiency for System A was 66%. The direct primary energy input to the
biorefinery plant (excluding the energy input to the biogas and lignin fired CHP plants) was
calculated as 26 MJ per kg ethanol production, which was comparable with the range
reported for cellulosic ethanol plant (approximately 5-25 MJ per kg), as reported in the
various studies (Kim & Dale, 2005; Luo et al., 2009; Pimentel & Patzek, 2005; Sheehan et al.,
2003). The energy recovery potential both from the biogas and lignin fuelled CHP plants was
able to fulfil 45% and 181% of the thermal and electric energy demand of the bioethanol
system, however heat energy required was on deficit, see SI, Table S1). The energy recovery
was close to the range reported for bioethanol plant in Drosg et al. (2012). The main reason
for a lower energetic efficiency for System C (21%) was that despite energy input from
biomass valued 13 GJ/ t DM, the net energetic output was 1.4 GJ/t DM, as other co-products
were with non-energetic value (e.g. lactic acid, feed protein and fodder silage).
198
5.2. Comparison with other studies
5.2.1. Bioethanol production
In this study, the net GWP1 00 obtained for System C and System A ranged from 0.03 to 0.1 kg
CO2 eq/MJEtOH (i.e. 0.9 to 2.8 kg CO2 eq per kg bioethanol), obtained relying on CLCA
approach. Typically for the conversion of straw, and in general for lignocellulosic biomasses,
the reported GWP1 00 in various studies ranged from -0.007 to 3.9 kg CO2/kgEtOH (Borrion et
al., 2012b; Degussa et al., 2006; González-García et al., 2012; Morales et al., 2015; Muñoz et
al., 2013; Wang et al., 2013). Besides straw conversion to bioethanol, sugar beet and maize
based production system were reported with lower carbon footprint (Muñoz et al., 2013). The
variations for the impacts in these studies were mainly due to use of different biomass
feedstocks and the used methodological approaches and assumptions.
In the current study, the contribution from the production of wheat straw to net GWP1 00 was
27-34% and was comparable to the range 30-60% reported in Wang et al. (2013). The
contribution from the enzyme production was 34% of net GWP, which fits within the range of
40%-60%, (Wang et al., 2013). Likewise, NRE use for the production of bioethanol based on
different agricultural wastes and assuming with and without cogeneration system was
reported ranging from 0.1 to 0.8 MJ/MJEtOH (García et al., 2011; Morales et al., 2015).
The energy saving potential compared to petrol was differently reported in various studies
(Borrion et al., 2012a). In the current study the savings from the bioethanol production
compared to petrol in terms of GHG emissions was 67% and 90%, respectively from System
A and System C. The reported savings on GHG emissions for the system identical to the
current study (i.e. System A) ranged from 11% to 56% (Cherubini & Ulgiati, 2010; Michael et
al., 2012; Wang et al., 2013), and was varying for different biomasses along with the
assumptions on the system boundaries. Likewise, savings in terms of NRE use compared to
petrol was 88-96% in the current study. For corn based bioethanol production system the
reported savings on NRE use was 95% (Sheehan et al., 2003).
5.2.2. Biobased lactic acid
The reported GWP1 00 for producing 1 kg biobased lactic acid using the approach of economic
allocation was 4.34 kg CO2 eq (Daful et al., 2016), which was comparable with our results
obtained relying on the ALCA approach (i.e. 3.3 kg CO2 eq/kgLA, Table 6). The case study, as
reported in Daful et al. (2016) was about the conversion of lignocellulose biomass in a
biorefinery plant, which was integrated with sugar mill. Likewise, GWP1 00 for producing
biobased lactic acid was ranging from -0.6 to 2.7 kg CO2 eq (Degussa et al., 2006; European
Commission, 2016). NRE use was ranging from 3.5 to 20 MJ/kgLA, considering digestion with
energy recovery, but at the factory gate it was 32-43 MJ/kgLA (Degussa et al., 2006). The
range of NRE use, as reported in European Commission (2016) was 9-37 MJ per kgLA. In the
199
current study, it was 12 and 53 MJ/kg LA, obtained relying on CLCA and ALCA approaches
respectively (Table 6). The minor differences in the results with the European Commission
(2016) were however partly because of different feedstocks (corn, sugarcane and corn stover)
and the use of different approaches (system expansion and economic allocation, indicating
the lower and the higher values respectively of the reported impact). Furthermore, compared
to other mentioned studies, minor differences could be partly due to different assumptions
on the system boundaries in the biomass production system. In the current study, alfalfa
since is nitrogen fixing plant, additional synthetic N-fertilizer was not required (SEGES,
2010) and the crop was grown for 3 years production cycle (Parajuli et al., 2016a). The
additional advantage offered by alfalfa was thus on emitting lower N2O, as the production of
N-fertilizer and N2O emissions from the field are among the main environmental hotspot of
an agricultural system which are in nexus with the rate of fertigation and soil microbial
processes (Brentrup et al., 2004; Cederberg & Mattsson, 2000; Parajuli et al., 2016a; Parajuli
et al., 2016b). Upon the comparison with the conventional lactic acid, the net savings in
terms of GHG emissions for producing biobased lactic acid was 127% and 7% based on the
results obtained relying on CLCA and ALCA approach respectively (Table 6). Likewise, the
net savings in the EP compared to the conventional lactic acid was the obtained difference on
the impact, which was 0.004 kg PO4 eq/kgLA (based on both CLCA and ALCA approaches).
This was reported 0.001 kg PO3- per kgLA in Degussa et al. (2006). Savings in terms of NRE
use was 93% and 32% compared to conventional lactic acid, based on the results obtained
relying on CLCA and ALCA approach respectively.
6. Overall synopsis of the study
With regard to the environmental impacts obtained for the biorefinery systems, it was found
that beside the contribution from the biomass production system contribution was largely
from the production of enzyme and energy input to the biorefinery systems. Weiss et al.
(2013) reported that enzyme dosage can be reduced by 30-50% by recovering/recycling the
insoluble biomass fraction (containing the enzymatic activity) (Ramos et al., 1993) to achieve
the same glucose yields under the most favourable conditions. This might be additional way
of optimizing the biorefinery system and thus may provide different environmental footprints
for the biobased products. Likewise, based on the personal communication with Novozymes,
it was reported that current development in the enzymes production showed possibilities to
further lower the GHG profile, around 50-70% compared to Cellic CTec3 (Kløverpris, J.H.
2016.pers.comm.). This can be regarded as another potential opportunity to further lower the
environmental footprints of the biobased products.
Upon the comparison with petrol, the production of bioethanol had net savings in terms of
GHG emissions and fossil fuel use, regardless of the biorefinery systems scenarios. Likewise,
200
the production of biobased lactic acid also had net savings in terms of GHG emission and
NRE use compared to the conventional lactic acid
Based on the comparison of the results obtained relying on ALCA and CLCA approaches, it
can be concluded that to support in the decision making process the recommendations on the
biobased products would be more or less the same. The two approaches differed in terms of
the absolute magnitude, in the cases where assumptions on the marginal product scenarios,
particularly for System B and System C were avoiding substantial environmental impacts.
Despite these, they yielded comparable impact pattern ratios and would hence provide the
same decision support.
The iLUC effects resulted with additional environmental burdens to produce biobased lactic
acid from System B and also to bioethanol for System C. However, both bioethanol and
biobased lactic acid still yielded net savings in terms of GHG emission compared to petrol
and conventional lactic acid respectively. However, the net savings reduced by 29% in the
case of biobased lactic acid and 8% in the case of bioethanol.
Last, but not the least, variations on the results may further occur along with the changes in
the yield of biobased products and also with respect to the scenarios of marginal products.
The results might also vary along with the changes in the biorefinery process configurations,
especially in a commercial production system
7. Main conclusions
The current study highlights that GHG emissions in agriculture stage are largely determined
by the emission of nitrous oxide and SOC credits, whereas in biobased-production stage it
was determined by energy input to the biorefinery system and emissions from the enzyme
production. Finally, the comparison between the standalone system and the integrated
system, mainly producing bioethanol showed that the recirculation of resources generated
from biorefinery was beneficial to reduce the environmental footprints. The benefits from the
integrated system resulted in the form of higher net savings in terms of GHG emissions, NRE
use and EP compared to the standalone system.
Acknowledgement
The article is written as part of a PhD study at the Department of Agroecology, Aarhus
University (AU), Denmark. The study is co-funded by the Bio-Value Platform
(http://biovalue.dk/), funded under the SPIR initiative by The Danish Council for Strategic
Research and The Danish Council for Technology and Innovation, case no: 0603-00522B.
The first author would like to thank to the Graduate School of Science and Technology
(GSST) of AU for the PhD scholarship. Sincere gratitude also goes to Jesper Hedal Kløverpris
201
and the team from the Novozymes for providing the information on environmental burden of
producing enzyme.
202
Reference List
Alvira, P., Tomás-Pejó, E., Ballesteros, M., Negro, M.J. 2010. Pretreatment technologies for
an efficient bioethanol production process based on enzymatic hydrolysis: A review.
Bioresource Technology, 101(13), 4851-4861.
Ambye-Jensen, M., Johansen, K.S., Didion, T., Kádár, Z., Schmidt, J.E., Meyer, A.S. 2013.
Ensiling as biological pretreatment of grass (Festulolium Hykor): The effect of
composition, dry matter, and inocula on cellulose convertibility. Biomass and
Bioenergy, 58, 303-312.
Audsley, E., Brander, M., Chatterton, J.C., Murphy-Bokern, D., Webster, C., Williams, A.G.
2009. How low can we go? An assessment of greenhouse gas emissions from the UK
food system and the scope reduction by 2050. Report for the WWF and Food Climate
Research Network. WF-UK and Food Climate Research Network.
80.http://dspace.lib.cranfield.ac.uk/handle/1826/6503 (accessed Oct 28, 2014).
Bentsen, N.S., Felby, C., Ipsen, K.H. 2006. Energy balance of 2 nd generation bioethanol
production in Denmark. DONG
Energy.http://www.tekno.dk/pdf/projekter/p09_2gbio/ClausFelby/p09_2gbio%20B
entsen%20et%20al%20(2006).pdf (accessed May 05, 2014).
Bentsen, N.S., Thorsen, B.J., Felby, C. 2009. Energy, feed and land-use balances of refining
winter wheat to ethanol. Biofuels Bioproducts & Biorefining-Biofpr, 3(5), 521-533.
Berglund, M., Börjesson, P. 2006. Assessment of energy performance in the life-cycle of
biogas production. Biomass and Bioenergy, 30(3), 254-266.
Borrion, A.L., McManus, M.C., Hammond, G.P. 2012a. Environmental life cycle assessment
of bioethanol production from wheat straw. Biomass and Bioenergy, 47, 9-19.
Borrion, A.L., McManus, M.C., Hammond, G.P. 2012b. Environmental life cycle assessment
of lignocellulosic conversion to ethanol: A review. Renewable & Sustainable Energy
Reviews, 16(7), 4638-4650.
Brentrup, F., Kusters, J., Kuhlmann, H., Lammel, J. 2004. Environmental impact assessment
of agricultural production systems using the life cycle assessment methodology - I.
Theoretical concept of a LCA method tailored to crop production. European Journal
of Agronomy, 20(3), 247-264.
Buxton, D.R., Muck, R.E. 2003. Silage science and technology. American Society of
Agronomy [etc.], Madison, WI.
Cederberg, C., Mattsson, B. 2000. Life cycle assessment of milk production — a comparison
of conventional and organic farming. Journal of Cleaner Production, 8(1), 49-60.
Chen, Y ., Sharma-Shivappa, R.R., Chen, C. 2007. Ensiling Agricultural Residues for
Bioethanol Production. Applied Biochemistry and Biotechnology, 143(1), 80-92.
203
Cherubini, F. 2010. The biorefinery concept: Using biomass instead of oil for producing
energy and chemicals. Energy Conversion and Management, 51(7), 1412-1421.
Cherubini, F., Ulgiati, S. 2010. Crop residues as raw materials for biorefinery systems – A
LCA case study. Applied Energy, 87(1), 47-57.
COM. 2007. An Energy Policy for Europe, Communication from the Commission to the
European Council and the European Parliament. SEC(2007) 12}. Commisssion of the
European Communities: Brussels. Commission of the European Communities,
Brussel. 1-27.http://eur-lex.europa.eu/legal-
content/EN/TXT/PDF/?uri=CELEX:52007DC0001&from=EN (accessed Dec 15,
2012).
Crown, Carbon Trust. 2008. Guide to PAS 2050: How to assess the carbon footprint of goods
and services. BSI British Standards, London, UK.http://aggie-
horticulture.tamu.edu/faculty/hall/publications/PAS2050_Guide.pdf.
Cybulska, I., Lei, H., Julson, J. 2010. Hydrothermal Pretreatment and Enzymatic Hydrolysis
of Prairie Cord Grass. Energy & Fuels, 24(1), 718-727.
Daful, A.G., Haigh, K., Vaskan, P., Görgens, J.F. 2016. Environmental impact assessment of
lignocellulosic lactic acid production: Integrated with existing sugar mills. Food and
Bioproducts Processing, 99, 58-70.
Dalgaard, R., Schmidt, J., Halberg, N., Christensen, P., Thrane, M., Pengue, W.A. 2008. LCA
of soybean meal. International Journal of Life Cycle Assessment, 13(3), 240-254.
Dalgaard, T., Halberg, N., Porter, J.R. 2001. A model for fossil energy use in Danish
agriculture used to compare organic and conventional farming. Agriculture
Ecosystems & Environment, 87(1), 51-65.
Danish Energy Agency. 2012. Technology data for energy plants, Copenhagen, Denmark. . 5-
212.http://www.energinet.dk/SiteCollectionDocuments/Danske%20dokumenter/For
skning/Technology_data_for_energy_plants.pdf (Marc 22, 2014).
Degussa, A., DSM NV, H., DuPont, B.H., Frères, R., Lestrem, F. 2006. Medium and long-
term opportunities and risks of the biotechnological production of bulk chemicals
from renewable resources-the potential of white biotechnology. Final report prepared
under the European Commission’s GROWTH Programme (DG Research) ,
Department of Science, Technology and Society (STS) / Copernicus Institute, Utrecht,
The Netherlands. 1-
474.http://brew.geo.uu.nl/BREW_Final_Report_September_2006.pdf (accessed Jul
14, 2016).
Demirbas, A. 2008. Biofuels sources, biofuel policy, biofuel economy and global biofuel
projections. Energy Conversion and Management, 49(8), 2106-2116.
Doran-Peterson, J., Cook, D.M., Brandon, S.K. 2008. Microbial conversion of sugars from
plant biomass to lactic acid or ethanol. The Plant Journal, 54(4), 582-592.
204
Drosg, B., Fuchs, W., Al Seadi, T., Madsen, M., Linke, B. 2015. Baxter, D. (Eds.). Nutrient
Recovery by Biogas Digestate Processing. IEA Bioenergy, Task 37. Ireland 1-
24.http://www.iea-biogas.net/ (accessed on Jul 04, 2016).
Drosg, B., Fuchs, W., Meixner, K., Waltenberger, R., Kirchmayr, R., Braun, R., Bochmann, G.
2012. Anaerobic digestion of stillage fractions – estimation of the potential for energy
recovery in bioethanol plants. Water Science and Technology, 67(3), 494-505.
Energitilsynet. 2012. Results and Challanges 2012. Danish Energy Regularity Authority,
Valby, Denmark. 1-
74.http://energitilsynet.dk/fileadmin/Filer/publikationer/R_U_2012_UK_pdfa.pdf
(accessed June 10, 2012).
Environdec, 2013. EPD Method. Characterization factors for default impact assessment
categories. EPD International AB, Stockholm Sweden.
http://www.environdec.com/en/The-International-EPD-System/General-
Programme-Instructions/Characterisation-factors-for-default-impact-assessment-
categories/ (accessed Feb 02, 2015). 2015.
EUBIA, 2016. Bioethanol: Bioethanol in the world. European Biomass Industry Association
(EUBIA), Brussels, Belgium. .http://www.eubia.org/index.php/about-
biomass/biofuels-for-transport/bioethanol (accessed June 19, 2016).
European Commission. 2016. Environmental fact sheet:lactic acid. European
Commission,FP7 Project REFERENCES in CORDIS. 1-
4.https://biobs.jrc.ec.europa.eu/analysis/environmental-factsheet-lactic-acid
(accessed Jul 14, 2016).
European Commission, 2012. Eurostat: Energy price statistics, Brussels,
Belgium.http://ec.europa.eu/eurostat/statistics-
explained/index.php/Energy_price_statistics#Electricity_prices_for_industrial_con
sumers (accessed Jun 06, 2016).
European Commission, 2015a. Product Environmental Footprint (PEF). News. European
Commission, Brussels,
Belgium.http://ec.europa.eu/environment/eussd/smgp/ef_news.htm (accessed Feb
4, 2016).
European Commission. 2015b. Workshop “Integrated biorefineries and innovations in the
optimal use of biomass” European Commission, Directorate F - Bioeconomy F.2 –
Bio-based products and processing 1-
7.https://ec.europa.eu/research/bioeconomy/pdf/workshop_on_optimal_use_of_bi
omass-integrated_biorefineries_10Dec2015.pdf (accessed Jul 11, 2016).
FAOSTAT, 2013. Agri-environmental statistics.Food and Agriculture Organization of the
United Nations, Statistics Division. http://faostat.fao.org/ (accessed Dec 11, 2013).
205
Galbe, M., Sassner, P., Wingren, A., Zacchi, G. 2007. Process Engineering Economics of
Bioethanol Production. in: Biofuels, (Ed.) L. Olsson, Vol. 108, Springer Berlin
Heidelberg, pp. 303-327.
García, C.A., Fuentes, A., Hennecke, A., Riegelhaupt, E., Manzini, F., Masera, O. 2011. Life-
cycle greenhouse gas emissions and energy balances of sugarcane ethanol production
in Mexico. Applied Energy, 88(6), 2088-2097.
Gasol, C.M., Gabarrell, X., Anton, A., Rigola, M., Carrasco, J., Ciria, P., Solano, M.L.,
Rieradevall, J. 2007. Life cycle assessment of a Brassica carinata bioenergy cropping
system in southern Europe. Biomass and Bioenergy, 31(8), 543-555.
Ghaffar, T., Irshad, M., Anwar, Z., Aqil, T., Zulifqar, Z., Tariq, A., Kamran, M., Ehsan, N.,
Mehmood, S. 2014. Recent trends in lactic acid biotechnology: A brief review on
production to purification. Journal of Radiation Research and Applied Sciences,
7(2), 222-229.
Goedkoop, M., Heijungs, R., Huijbregts, M., De Schryver, A., Struijs, J., van Zelm, R. 2009.
ReCiPe 2008. 126.
González-García, S., Iribarren, D., Susmozas, A., Dufour, J., Murphy, R.J. 2012. Life cycle
assessment of two alternative bioenergy systems involving Salix spp. biomass:
Bioethanol production and power generation. Applied Energy, 95, 111-122.
Gressel, J. 2008. Transgenics are imperative for biofuel crops. Plant Science, 174(3), 246-
263.
Hamelin, L., Jørgensen, U., Petersen, B.M., Olesen, J.E., Wenzel, H. 2012. Modelling the
carbon and nitrogen balances of direct land use changes from energy crops in
Denmark: a consequential life cycle inventory. Global Change Biology Bioenergy,
4(6), 889-907.
Hamelin, L., Wesnæs, M., Wenzel, H., Petersen, B.M. 2011. Environmental Consequences of
Future Biogas Technologies Based on Separated Slurry. Environmental Science &
Technology, 45(13), 5869-5877.
Hansen, T.L., Bhander, G.S., Christensen, T.H., Bruun, S., Jensen, L.S. 2006. Life cycle
modelling of environmental impacts of application of processed organic municipal
solid waste on agricultural land (Easewaste). Waste Management & Research, 24(2),
153-166.
IEA. 2011. Bio-based Chemicals Value Added Products from Biorefineries. IEA Bioenergy
Task42,Wageningen UR - Food and Bio-based Research, The Netherlands. 1-
36.http://www.qibebt.ac.cn/xwzx/kydt/201202/P020120223409482956847.pdf
(accessed Feb 25, 2015).
Inbicon, 2013. DONG Energy and DSM prove cellulosic bio-ethanol fermentation on
industrial scale with 40% higher yield. Inbicon, Denmark.
http://www.inbicon.com/About_inbicon/News/Data/Pages/DONGEnergyandDSMp
206
rovecellulosicbio-ethanolfermentationonindustrialscalewith40higheryield.aspx
(accessed Aug 01, 2013).
index mundi, 2016. Commodity Price http://www.indexmundi.com/commodities/ (accessed
Jun 06, 2016).
Jørgensen, K., (Edts)., Hummelmose, A.B., Pedersen, B.K., Wøyen, T.T., Maegaard, E.,
Jørgensen, K., Bruun, L.K. 2011. Budgetkalkuler 2010-pr. oktober 2010. SEGES,
Aarhus, Denmark,
Denmark.https://www.landbrugsinfo.dk/Oekonomi/Budgetkalkuler/Sider/Budgetka
lkuler_2010-2011_okt10.aspx (accessed Feb 5, 2015).
Jørgensen, P.J. 2009. Biogas-green energy. 1-
34.http://www.lemvigbiogas.com/BiogasPJJuk.pdf (accesed April 14, 2016).
Kamm, B., Hille, C., Schönicke, P., Dautzenberg, G. 2010. Green biorefinery demonstration
plant in Havelland (Germany). Biofuels, Bioproducts and Biorefining, 4(3), 253-262.
Kamm, B., Schönicke, P., Kamm, M. 2009. Biorefining of Green Biomass – Technical and
Energetic Considerations. CLEAN – Soil, Air, Water, 37(1), 27-30.
Kaparaju, P., Serrano, M., Thomsen, A.B., Kongjan, P., Angelidaki, I. 2009. Bioethanol,
biohydrogen and biogas production from wheat straw in a biorefinery concept.
Bioresource Technology, 100(9), 2562-2568.
Khanna, M., Crago, C.L., Black, M. 2011. Can biofuels be a solution to climate change? The
implications of land use change-related emissions for policy. Interface Focus, 1(2),
233-247.
Kim, S., Dale, B.E. 2005. Life cycle assessment of various cropping systems utilized for
producing biofuels: Bioethanol and biodiesel. Biomass and Bioenergy, 29(6), 426-
439.
Kim, T.H., Kim, J.S., Sunwoo, C., Lee, Y .Y . 2003. Pretreatment of corn stover by aqueous
ammonia. Bioresource Technology, 90(1), 39-47.
Kim, Y .H., Moon, S.-H. 2001. Lactic acid recovery from fermentation broth using one-stage
electrodialysis. Journal of Chemical Technology & Biotechnology, 76(2), 169-178.
Kromus, S., Wachter, B., Koschuh, W., Mandl, M., Krotscheck, C., Narodoslawsky, M. 2004.
The green biorefinery Austria-development of an integrated system for green biomass
utilization. Chemical and biochemical engineering quarterly, 18(1), 8-12.
Lange, J.P. 2007. Lignocellulose conversion: An introduction to chemistry, process and
economics. Biofuels, Bioproducts and Biorefining, 1(1), 39-48.
Larsen, J., Haven, M.Ø., Thirup, L. 2012. Inbicon makes lignocellulosic ethanol a commercial
reality. Biomass and Bioenergy, 46(0), 36-45.
Larsen, J., Henriksen, N. 2014. Status for the Inbicon technology by end of 2014. The Inbicon
process is ready for industrial scale. DONG Energy, Denmark. 1-
8.https://assets.dongenergy.com/DONGEnergyDocuments/Inbic/Status%20for%20t
207
he%20Inbicon%20technology%20by%20end%20of%202014.pdf (accessed May 15,
2016).
Larsen, J., Østergaard Petersen, M., Thirup, L., Wen Li, H., Krogh Iversen, F. 2008. The
IBUS Process – Lignocellulosic Bioethanol Close to a Commercial Reality. Chemical
Engineering & Technology, 31(5), 765-772.
Larson, E.D. 2006. A review of life-cycle analysis studies on liquid biofuel systems for the
transport sector. Energy for Sustainable Development, 10(2), 109-126.
Lebuf, V., Accoe, F., Van Elsacker, S., Vaneeckhaute, C., Michels, E., Meers, E., Ghekiere, G.,
Ryckaert, B. 2013. Inventory: techniques for nutrient recovery from digestate.
Interreg IV. B NWE Arbor, Belgium. http://hdl.handle.net/1854/LU-7010573
(accessed Jul 05,2016). 1-28.
Levin, D.B., Zhu, H., Beland, M., Cicek, N., Holbein, B.E. 2007. Potential for hydrogen and
methane production from biomass residues in Canada. Bioresource Technology,
98(3), 654-660.
Li, Y ., Shahbazi, A., Kadzere, C.T. 2006. Separation of cells and proteins from fermentation
broth using ultrafiltration. Journal of Food Engineering, 75(4), 574-580.
Losordo, Z., McBride, J., Rooyen, J.V., Wenger, K., Willies, D., Froehlich, A., Macedo, I.,
Lynd, L. 2016. Cost competitive second-generation ethanol production from
hemicellulose in a Brazilian sugarcane biorefinery. Biofuels, Bioproducts and
Biorefining, 10(5), 589-602.
Lund, H., Mathiesen, B., Christensen, P., Schmidt, J. 2010. Energy system analysis of
marginal electricity supply in consequential LCA. International Journal of Life Cycle
Assessment, 15(3), 260-271.
Luo, L., van der Voet, E., Huppes, G., Udo de Haes, H. 2009. Allocation issues in LCA
methodology: a case study of corn stover-based fuel ethanol. The International
Journal of Life Cycle Assessment, 14(6), 529-539.
Lynd, L., Wayman, C., Laser, M., Johnson, D., Landucci, R. 2005. Strategic Biorefinery
Analysis: Analysis of biorefineries. 2002. Prepared under Subcontract No. ADZ-2-
31086-01. Contract No. DE-AC36-99-GO10337. National Renewable Energy
Laboratory. Colarado. 1-40.http://www.nrel.gov/docs/fy06osti/35578.pdf (accessed
June 5, 2016).
Mandl, M.G. 2010. Status of green biorefining in Europe. Biofuels, Bioproducts and
Biorefining, 4(3), 268-274.
Marris, E. 2006. Sugar cane and ethanol: Drink the best and drive the rest. Nature,
444(7120), 670-672.
Mathiesen, B.V., Münster, M., Fruergaard, T. 2009. Uncertainties related to the
identification of the marginal energy technology in consequential life cycle
assessments. Journal of Cleaner Production, 17(15), 1331-1338.
208
Michael, W., Jeongwoo, H., Jennifer, B.D., Hao, C., Amgad, E. 2012. Well-to-wheels energy
use and greenhouse gas emissions of ethanol from corn, sugarcane and cellulosic
biomass for US use. Environmental Research Letters, 7(4), 045905.
Mickwitz, P., Hildén, M., Seppälä, J., Melanen, M. 2011. Sustainability through system
transformation: lessons from Finnish efforts. Journal of Cleaner Production, 19(16),
1779-1787.
Møller, J., Thøgersen, R., Helleshøj, M.E., Weisbjer, M., Søegaard, K., Hvelplund, T. 2005.
Fodermiddltabel 2005. Sammensætning og foderværdi af fodermidler til kvæg.
Rapport nr. 112. SEGES, Aarhus, Denmark.
https://www.landbrugsinfo.dk/kvaeg/foder/sider/fodermiddeltabel_2005.aspx
(accessed July 22, 2015).
Morales, M., Quintero, J., Conejeros, R., Aroca, G. 2015. Life cycle assessment of
lignocellulosic bioethanol: Environmental impacts and energy balance. Renewable
and Sustainable Energy Reviews, 42, 1349-1361.
Mosier, N., Wyman, C., Dale, B., Elander, R., Lee, Y .Y ., Holtzapple, M., Ladisch, M. 2005.
Features of promising technologies for pretreatment of lignocellulosic biomass.
Bioresour Technol, 96(6), 673-86.
Muñoz, I., Flury, K., Jungbluth, N., Rigarlsford, G., i Canals, L.M., King, H. 2013. Life cycle
assessment of bio-based ethanol produced from different agricultural feedstocks. The
International Journal of Life Cycle Assessment, 19(1), 109-119.
Muñoz, I., Schmidt, J.H., Dalgaard, R. 2014. Comparative life cycle assessment of five
different vegetable oils. Proceedings of the 9th International Conference on Life Cycle
Assessment in the Agri-Food Sector (LCA Food 2014), San Francisco, California,
USA, 8-10 October, 2014. American Center for Life Cycle Assessment. pp. 886-894.
NaturErhvervstyrelsen. 2015. Vejledning om gødsknings-og harmoniregler: Planperioden 1.
august 2014 til 31. juli 2015. Document number 6. Agriculture and Fisheries (in
Danish). Ministeriet for Fødevarer, Landbrug og Fiskeri,Copenhagen, Denmark.
http://www.nordfynskommune.dk/~/media/Files/Dokumenter/Teknik%20og%20M
iljoe/Natur%20og%20Miljoe/Landbrug/Vejledning%20om%20g%C3%B8dnings-
%20og%20harmoniregler.pdf (accessed May 15, 2015). ISBN: 978-87-7120-635-7. 1-
173.
Nielsen, P., 2004. Heat and power production from straw (Produktion af kraftvarme fra
halm). The Institute for Product Development, Denmark.
http://www.lcafood.dk/processes/energyconversion/heatandpowerfromstraw.htm
(accessed Oct 18, 2012).
Nielsen, P.H., Oxenbøll, K.M., Wenzel, H. 2006. Cradle-to-gate environmental assessment of
enzyme products produced industrially in denmark by novozymes A/S. The
International Journal of Life Cycle Assessment, 12(6), 432-438.
209
O’Keeffe, S., Schulte, R.P.O., Sanders, J.P.M., Struik, P.C. 2011. I. Technical assessment for
first generation green biorefinery (GBR) using mass and energy balances: Scenarios
for an Irish GBR blueprint. Biomass and Bioenergy, 35(11), 4712-4723.
Olesen, J.E., Weiske, A., Asman, W.A., Weisbjerg, M.R., Djurhuus, J., Schelde, K. 2004.
FarmGHG. A model for estimating greenhouse gas emissions from livestock farms.
Internal Report No. 202. Danish Institute of Agricultural
Sciences.http://agrsci.au.dk/fileadmin/DJF/JPM/Klima/JEO/FarmGHG.zip
(accessed Jan 04, 2013).
Pahlow, G., Muck, R.E., Driehuis, F., Elferink, S.J.W.H.O., Spoelstra, S.F. 2003. Microbiology
of Ensiling. in: Silage Science and Technology, (Eds.) D.R. Buxton, R.E. Muck, J.H.
Harrison, American Society of Agronomy, Crop Science Society of America, Soil
Science Society of America. Madison, WI, pp. 31-93.
Panesar, P.S., Kennedy, J.F., Gandhi, D.N., Bunko, K. 2007. Bioutilisation of whey for lactic
acid production. Food Chemistry, 105(1), 1-14.
Parajuli, R., Knudsen, M.T., Djomo, S.N., Corona, A., Birkved, M., Dalgaard, T. 2016a.
Environmental Life Cycle Assessment of producing straw based on spring barley,
willow and alfalfa for bioenergy or biorefinery purposes. Submitted to Science of the
Total Environment.
Parajuli, R., Kristensen, I.S., Knudsen, M.T., Mogensen, L., Corona, A., Birkved, M., Peña, N.,
Graversgaard, M., Dalgaard, T. 2016b. (in-press, Accepted Manuscript).
Environmental life cycle assessments of producing maize, grass-clover, ryegrass and
winter wheat straw for biorefinery. http://dx.doi.org/10.1016/j.jclepro.2016.10.076.
Journal of Cleaner Production.
Parajuli, R., Løkke, S., Østergaard, P.A., Knudsen, M.T., Schmidt , J.H., Dalgaard, T. 2014.
Life Cycle Assessment of district heat production in a straw fired CHP plant. Biomass
and Bioenergy, 68(0), 115-134.
Patel, M., Crank, M., Dornburg, V., Hermann, B., Roes, L., Hüsing, B., Overbeek, L.,
Terragni, F., Recchia, E. 2006. Medium and Long-term Opportunities and Risks of
the Biotechnological Production of Bulk Chemicals from Renewable Resources-The
Potential of White Biotechnology. Final report. Prepared under the European
Commission’s GROWTH Programme (DG Research). Utrecht University, Department
of Science, Technology and Society (STS) / Copernicus Institute, Heidelberglaan 2,
NL-3584 CS Utrecht, Netherlands. 1-444.http://www.bio-
economy.net/applications/files/Brew_project_report.pdf /accessed April 23, 2016).
Petersen, B.M., Knudsen, M.T. 2010. Consequences of straw removal for soil carbon
sequestration of agricultural fields, Using soil carbon in a time frame perspective.
Aarhus University, Faculty of Agricultural Sciences, Aarhus University, Aarhus,
Denmark. 1-49.http://pure.au.dk/portal/en/publications/consequences-of-straw-
210
removal-for-soil-carbon-sequestration-of-agricultural-fields(ab049e95-9471-463d-
97b7-69635ec81518).html (accessed Nov 15,, 2015).
Pimentel, D., Patzek, T.W. 2005. Ethanol Production Using Corn, Switchgrass, and Wood;
Biodiesel Production Using Soybean and Sunflower. Natural Resources Research,
14(1), 65-76.
PRé Consultants, 2015. SimaPro 8.0.4. Pre Consultants. Amersfort. The Netherlands.
2013.http://www.pre-sustainability.com/simapro-lca-software (acccessed Nov 25,
2015).
Pugesgaard, S., Olesen, J.E., Jørgensen, U., Dalgaard, T. 2013. Biogas in organic agriculture—
effects on productivity, energy self-sufficiency and greenhouse gas emissions.
Renewable Agriculture and Food Systems, 29(01), 28-41.
PURE. European Renewable Ethanol. Enabling Innovation and Sustainable Development.
State of the Industry 2015. Brussels, Belgium. 1-
16.http://www.bdbe.de/application/files/8314/3575/7908/ePURE-State-of-the-
Industry-2015.pdf (accessed June 12, 2016).
Quintero, J.A., Montoya, M.I., Sanchez, O.J., Giraldo, O.H., Cardona, C.A. 2008. Fuel ethanol
production from sugarcane and corn: Comparative analysis for a Colombian case.
Energy, 33(3), 385-399.
Ramos, L.P., Breuil, C., Saddler, J.N. 1993. The use of enzyme recycling and the influence of
sugar accumulation on cellulose hydrolysis by Trichoderma cellulases. Enzyme and
Microbial Technology, 15(1), 19-25.
René, v.R., Bert, A. 2007. Status Report Biorefinery 2007.Report 847. Agrotechnology and
Food Sciences Group. NL-6700 AA Wageningen
http://www.biorefinery.nl/uploads/media/StatusDocumentBiorefinery2007final2111
07.pdf (accessed Mar 11, 2013). 1-110.
Sadhukhan, J., Mustafa, M.A., Misailidis, N., Mateos-Salvador, F., Du, C., Campbell, G.M.
2008. Value analysis tool for feasibility studies of biorefineries integrated with value
added production. Chemical Engineering Science, 63(2), 503-519.
Schmidt, J.H., Brandao, M. 2013. LCA screening of biofuels-iLUC, biomass manipulation and
soil carbon. Concito, Copenhagen, Denmark. . 3-
97.http://concito.dk/files/dokumenter/artikler/biomasse_bilag1_lcascreening.pdf
(accessed May 12, 2013).
Schmidt, J.H., Muños, I. 2014. The carbon footprint of Danish production and consumption:
Literature review and model calculations. Energistyrelsen. Copenhagen, Denmark. 1-
119.http://vbn.aau.dk/files/196725552/_dk_carbon_footprint_20140305final.pdf
(accessed Feb 02, 2016).
Schmidt. J. H., Reinhard, J., Weidema, B.P. 2012. A Model of Indirect Land Use Change. 8th
International Conference on LCA in the Agri-Food Sector, 2-4 October 2012, Rennes,
211
France. pp. 1-6http://www.google.dk/#hl=en&tbo=d&sclient=psy-
ab&q=A+Model+of+Indirect+Land+Use+Change%2C+Schmidt&oq=A+Model+of+I
ndirect+Land+Use+Change%2C+Schmidt&gs_l=hp.3...5438.7450.1.7711.9.9.0.0.0.0.
120.755.8j1.9.0.les%3B..0.0...1c.1.3.psy-
ab.gIwRbwvMp0I&pbx=1&bav=on.2,or.r_gc.r_pw.r_qf.&bvm=bv.42452523,d.d2k&f
p=2b304979f2f46b00&biw=1280&bih=904.
SEGES, 2010. Growing instructions-Crops. SEGES, Agro Food Park, Aarhus, Denmark.
https://dyrk-
plant.dlbr.dk/Web/(S(pgsviibw4c1053wjgai5ni1p))/forms/Afgroeder.aspx?kategori=1
(accessed Sep 12, 2015).
Sheehan, J., Aden, A., Paustian, K., Killian, K., Brenner, J., Walsh, M., Nelson, R. 2003.
Energy and Environmental Aspects of Using Corn Stover for Fuel Ethanol. Journal of
Industrial Ecology, 7(3-4), 117-146.
Sims, R.E.H., Mabee, W., Saddler, J.N., Taylor, M. 2010. An overview of second generation
biofuel technologies. Bioresource Technology, 101(6), 1570-1580.
Sreenath, H.K., Moldes, A.B., Koegel, R.G., Straub, R.J. 2001. Lactic acid production by
simultaneous saccharification and fermentation of alfalfa fiber. Journal of Bioscience
and Bioengineering, 92(6), 518-523.
Statistics Denmark, 2016. FODER6: Value of feeding stuffs by quantity, average price and
value. Statbank Denmark.
http://www.statistikbanken.dk/statbank5a/SelectVarVal/Define.asp?Maintable=FO
DER6&PLanguage=1 (accessed Jun 12, 2016).
Stuart, P.R., El-Halwagi, M.M. 2012. Integrated biorefineries: design, analysis, and
optimization. CRC Press.
Templer, R., van der Wielen, L. 2011. Biorenewables, the bio-based economy and
sustainability. Interface Focus, 1(2), 187-188.
Thomsen, M. 2004. Lactic acid fermentation of brown Juice in the Green Crop Drying Plant.
PhD Thesis.
Tonini, D., Hamelin, L., Wenzel, H., Astrup, T. 2012. Bioenergy Production from Perennial
Energy Crops: A Consequential LCA of 12 Bioenergy Scenarios including Land Use
Changes. Environmental Science & Technology, 46(24), 13521-13530.
Valentine, J., Clifton-Brown, J., Hastings, A., Robson, P., Allison, G., Smith, P. 2012. Food vs.
fuel: the use of land for lignocellulosic ‘next generation’ energy crops that minimize
competition with primary food production. GCB Bioenergy, 4(1), 1-19.
Wang, L., Littlewood, J., Murphy, R.J. 2013. Environmental sustainability of bioethanol
production from wheat straw in the UK. Renewable & Sustainable Energy Reviews,
28(0), 715-725.
212
Wang, M., Han, J., Dunn, J.B., Cai, H., Elgowainy, A. 2012. Well-to-wheels energy use and
greenhouse gas emissions of ethanol from corn, sugarcane and cellulosic biomass for
US use. Environmental Research Letters, 7(4), 045905.
Wee, Y .-J., Kim, J.-N., Ryu, H.-W. 2006. Biotechnological production of lactic acid and its
recent applications. Food Technology and Biotechnology, 44(2), 163-172.
Weidema, B.P. 2003. Market information in life cycle assessment. The Danish
Environmental Protection Agency. Environmental Project No. 863 2003
Miljøprojekt. Miljøstyrelsen.
Weidema, B.P., Bauer, C., Hischier, R., Mutel, C., Nemecek, T., Reinhard, J., Vadenbo, C.,
Wernet, G. 2013. Overview and methodology. Data quality guideline for the ecoinvent
database version 3. Ecoinvent Report 1(v3). St. Gallen: The ecoinvent Centre. Swiss
Centre for Life Cycle Inventories.
http://vbn.aau.dk/ws/files/176769045/Overview_and_methodology.pdf (accessed
Feb 12, 2015). 1-159.
Weiss, N., Börjesson, J., Pedersen, L.S., Meyer, A.S. 2013. Enzymatic lignocellulose
hydrolysis: Improved cellulase productivity by insoluble solids recycling.
Biotechnology for Biofuels, 6(1), 1-14.
Wolfrum, E.J., Ness, R.M., Nagle, N.J., Peterson, D.J., Scarlata, C.J. 2013. A laboratory-scale
pretreatment and hydrolysis assay for determination of reactivity in cellulosic
biomass feedstocks. Biotechnol Biofuels, 6, 162.
Yoo, C.G. 2012. Pretreatment and fractionation of lignocellulosic biomass for production of
biofuel and value-added products. Graduate Theses and Dissertations. Paper 12700.
Iowa State University., pp. 1-159.
Zhang, Y .H.P. 2008. Reviving the carbohydrate economy via multi-product lignocellulose
biorefineries. Journal of Industrial Microbiology & Biotechnology, 35(5), 367-375.
213
Figure captions
Figure 1: Process flow diagram of the standalone production of straw-based bioethanol
(System A). Electricity produced represents net values of the system (i.e., plant’s own
consumptions have been subtracted). The dotted lines indicate the avoided products
considered in the CLCA approach.
Figure 2: Process flow diagram for the standalone production of lactic acid from alfalfa
(System B). Electricity produced represent net values of the individual system (i.e., plant’s
own consumptions have been subtracted). The dotted lines indicate the avoided products
considered in the CLCA approach. The index for material flow lines are as shown in Figure 1.
Figure 3: Process flow and energy balance of the integrated biorefinery (system C). The
index for material flow lines are as presented in Figure 1.
Figure 4: Contribution of processes involved in the entire biobased products chains.
Products for: System A and System C = bioethanol.
214
Table 1: Co-products and assumed substitutable products in the conventional market
Biobased products and unit (kg) Substitutable products and data sources
Bioethanol Petrol
Lactic acid (kg) Conventional Lactic acid: (GLO) marketa
Feed protein Soybean meal: (GLO) marketa,b
Fodder silage
(mainly fiber-residues) (kg)
Ukrainian barley (Ukraine), as energy feed c
((data as: Gross (GLO) barley grain to generic
marketa))
Electricity (kWh) Coal fired electricity production, DKa, d.
Digestate (kg)e Recovered from the designed systems (Figure 1-3)
Assumptions: a Consequential and allocation unit process database were adapted from Ecoinvenet v3
(Weidema et al., 2013). b Crude Protein (CP) for feed protein = 65% CP (O’Keeffe et al., 2011). Soybean meal with
50% CP per t DM (FAOSTAT, 2013) was proportionately calculated for the substitutable
amount in CLCA approach. c Ukrainian barley as marginal feed (Muñoz et al., 2014; Schmidt & Brandao, 2013). Feed
energy value and the equivalent mass were calculated as 15.2 and 11.9 MJ per kg DM for
barley and alfalfa respectively (Møller et al., 2005). d Marginal electricity = Coal as fuel type (Lund et al., 2010; Mathiesen et al., 2009). e Substituting marginal synthetic fertilizers: Calcium Ammonium Nitrate (CAN, Triple super
phosphate (P2O5), Potassium Chloride (K2O) (Hamelin et al., 2012; Hamelin et al., 2011;
Tonini et al., 2012).
215
Table 2: Basic assumptions considered in the inventory analysis
Parameters Values References
A. Lower heating value
- Bioethanol (MJ/kg) 28.09 (Cherubini & Ulgiati,
2010)
- Lignin (MJ/kg) 22.9 (Cherubini & Ulgiati,
2010)
- Methane (CH4) (MJ/m3) 35.8 (Jørgensen, 2009).
B. Parameters for biogas
production::
i. C5 molasses (System A) (Drosg et al., 2012)
- Total solids (TS)a 31.1%
- Volatile solids (VS) a 30.1 %
ii. Stillage fractions (System A)
- TSb 12% See footnote
- VS b 10.2%
iii. Residues from decanted press
juice (System B) VS c
82% of DM c
C. Emission factors
(g per MJ bioethanol production) d
NOx = 38, CH4 = 1.5, N2O
= 0.8
(Danish Energy
Agency, 2012).
D. Heat and electricity inpute
i. Biogas digester Heat (H) = 1110 MJh and
electricity (E) = 660 MJe
(Berglund & Börjesson,
2006; Pugesgaard et
al., 2013)
ii. Combustion of lignin H = 40 MJh and
E =660 MJe
Based on straw fired in
CHP (Nielsen, 2004).
E. Nutrient content in the
digestate, in g/kg digestate
(System A and B)f (N, P, K)
5, 0.9, 2.8 respectively (Drosg et al., 2015;
O’Keeffe et al., 2011).
Prices for computing allocation
factors
- Bioethanol (Euro/MJ)g 0.03
- Electricity (Euro /kWh)h 0.25
- Heat (Euro/MJ)i 0.03
- KCl (Euro/kg)j 0.28
- Lactic acid (Euro/kg)k 1.36
- Feed protein (Euro/kg)l 0.33
216
- Fodder silage (EUR/kg)m 0.02
Assumptions: a TS and VS of the C5 molasses are based on the total weight of molasses. b TS and VS of the stillage fractions are based on the total weight of stillage. c DM represents the substrate available for biogas after the decanted press juice (O’Keeffe et
al., 2011) (SI-3, Figure S.2) d Assumed similar to coal. e Energy input per t of the DM fuel. f NPK content in the digestate are per t fresh substrates (SI-3, Figure S.2).
g Average price of denaturated fuel ethanol was for the period May 2006-Apr 2016 (index
mundi, 2016; PURE) and also checked with EUBIA (2016).
h Price of electricity applied representative for the average Danish electricity price, including
VAT and other recoverable taxes and levies the period of 2011-2015 (European Commission,
2012). i Based on annual heat price of Denmark (Energitilsynet, 2012). j Average price of KCl (May 2006-Apr 2016) (index mundi, 2016). k Price taken after Refs. (Lynd et al., 2005; Wee et al., 2006). Lactic acid considered a purity
level of 90% (Kamm et al., 2009). l Price of protein based on soybean meal (May 2006-Apr 2016) (index mundi, 2016; Statistics
Denmark, 2016). Danish database represents feed compound for cattle (except calves, with
high protein content). Price calculated for the crude protein content of soymeal (50% of the
DM) (Dalgaard et al., 2008) and price proportionately calculated for the protein extracted
from the grass (with 65% CP) (O’Keeffe et al., 2011) m Silage fodder traded in Denmark from 2005-2011 (Statistics Denmark, 2016).
217
Table 3: Input-output of the materials flow assumed for the alfalfa production, per 1 t DM,
summarised after Parajuli et al. (2016a)
Units Alfalfa Comments/Remarks
Biomass output t DM/ha 12.02 (Møller et al., 2005;
NaturErhvervstyrelsen, 2015)
Farm inputs
Synthetic fertilizera kg/ t DM
SEGES (2010)
N
-
P
3
K
18
Lime kg/ t DM 4.57 Based on Hamelin et al. (2012)
Pesticides kg/ t DM 0.02 Based on SEGES (2010)
Direct primary
energy input MJ/ t DM 343 Field preparation and harvesting
Transport materials t km/ t DM 6 (seed +agri-chemicals)
Emissions Parajuli et al. (2016a)
N2O kg CO2 eq/ t DM 16
SOC change kg CO2 eq/ t DM -37
Leaching kg N/t DM 3.4
P losses kg P/t DM 0.14
Transport biomass t km/t DM 200 field to the biorefinery plant
218
Table 4: Primary materials input and output related to the conversion of 1 t straw (with 85%
DM) to bioethanol (System A)
Materials Units Amount
A. Input
Straw t (85% DM) 1
Watera kg 2747
Enzymea kg 40
Energyb
- Heat MJh 4071
- Electricity (kWh elec/t straw) MJe 850
Additives kg
- Diaamonium Phosphate (DAP)c 1.87
- Corn steep liquor c 14.2
- NaOH (49%)b 0.53
- Ammonia water (25%)b 1.76
B. Output (Primary)
- Bioethanola kg 186
- C5 molasses + residues from stillaged kg 392
- Lignind kg 152
- KCle 12
Emissionsd
- CO2 kg 162
- Ethanol kg 12
Assumptions: a Average of the studies from Bentsen et al. (2006), Kaparaju et al. (2009) and Wang et al.
(2013). b Based on Bentsen et al. (2006). c Based on Wang et al. (2013). d Based on Bentsen et al. (2006) and Kaparaju et al. (2009). e Total mass from the hydrolysate and stillage fractions. Recovery rate 90% (Larsen et al.,
2008).
219
Table 5: Input-output of materials for the conversion of 1 t DM alfalfa to lactic acid (System
B)
Materials Unit Amount Remarks
Input
Alfalfa t DM 1
Energy
- Heat, steama MJ/ t DM 126
- Electricitya MJe/ t DM 211
Fermentation mediab kg/t DM 5.94
Enzyme kg/t DM 18
Waterc kg/t DM 450
Output d
Lactic acid kg DM/t DM 90 DM 90%
Feed protein kg DM/t DM
26 DM 40%, fodder protein the pure form of
CP (65%)
Fodder silage kg DM/t DM 371 DM 40%
VS, residues for
biogas
kg DM/t DM 152 Fresh weight 2.95 t (6% DM)
Assumptions: a Calculated based on O’Keeffe et al. (2011) and Kamm et al. (2009). b Calculated based on Kamm et al. (2010) (see section 2.5.1). c Calculated also considering the re-circulated water (O’Keeffe et al., 2011). d Products output calculated based on O’Keeffe et al. (2011) and Kamm et al. (2009) for 1 t
DM of alfalfa (with 35% DM at harvest).
220
Table 6: Potential environmental impacts of producing bioethanol and lactic acid from
standalone plants and from System C (values in the parenthesis are the gross impacts, i.e.
without avoided impacts) (FU is 1 MJEtOH for System A and System C and 1 kgLA for System B)
Impact
categories Units CLCA ALCA
System
A
System
B
System
Ca
System
A
System
B
System
C
GWP1 00
kg CO2
eq
0.1
(0.14)
-1.24
(3.4)
0.03
(0.25) 0.106 3.3 0.08
EP
kg PO4
eq
1.3*10-4
(1.8*10-4)
-9.4 *10-4
(0.01)
1.1*10-4
(5.4*10-4) 2.7*10-4 8.4*10-3 1.8*10-4
NRE use MJ eq
0.5
(0.85)
12
(60)
-0.2
(2.62) 0.83 53 0.9
ALO m2a
0.02
(0.023)
6
(11)
0.16
(0.24) 0.11 8 0.12 a The gross impact potentials in the case of System C calculated after accounting the useful
energy utilization in the system, as shown in SI-1, Table S.1.
221
Table 7: Results obtained from the sensitivity analysis. Units are net GWP100 = kg CO2 eq per
FU and NRE use = MJ eq per FU of each systems.
Basic
Scenario
Marginal products Variations in the yields
Electricitya Energy feed b
+23 %
bioethanol
+10%
LA
-10%
LA
System A (FU = per MJEtOH)
GWP1 00 0.1 0.12 - 0.09 - -
NRE use 0.48 0.47 - 0.54 - -
System B (FU = per kgLA)
GWP1 00 -1.24 -0.4 0.43 - -1.14 -1.37
NRE use 12.4 11.8 34. - 11.23 13.91
System C (FU = per MJEtOH)
GWP100 0.032 0.066 0.05 0.05 0.03 0.05
NRE use -0.2 -0.23 0.11 0.11 -0.31 0.01 a Natural gas as source for marginal electricity production b Grass-silage as a source of energy-feed.
222
Figure 1: Process flow diagram of the standalone production of straw-based bioethanol
(System A). Electricity produced represents net values of the system (i.e., plant’s own
consumptions have been subtracted). The dotted lines indicate the avoided products
considered in the CLCA approach.
223
Figure 2: Process flow diagram for the standalone production of lactic acid from alfalfa
(System B). Electricity produced represent net values of the individual system (i.e., plant’s
own consumptions have been subtracted). The dotted lines indicate the avoided products
considered in the CLCA approach. The index for material flow lines are as shown in Figure 1.
224
Figure 3: Process flow and energy balance of the integrated biorefinery (system C). The
index for material flow lines are as presented in Figure 1.
225
Figure 4: Contribution of processes involved in the entire biobased products chains.
Products for: System A and System C = bioethanol and System B = lactic acid. (ALCA =
Attributional LCA and CLCA = Consequential LCA).
226
Supporting information (SI):
Evaluating the environmental impacts of standalone and integrated biorefinery
systems using consequential and attributional approaches: cases of bioethanol
and biobased lactic acid production
Ranjan Parajulia,*, Marie Trydeman Knudsena, Morten Birkvedb, Sylvestre Njakou Djomoa
Andrea Coronab, Tommy Dalgaarda
aDepartment of Agroecology, Aarhus University, Blichers Allé 20, DK-8830 Tjele, Denmark
bDepartment of Management Engineering, Technical University of Denmark, Building 424,
DK-2800 Lyngby, Denmark
*Corresponding author, email: [email protected], Phone: +4571606831
227
S-1. Supporting parameters and energetic inputs considered in the basic
scenario:
Table S.1: Energy balance calculated for the integrated biorefinery plant (System C). The
balance accounted all useful energy consumption within the biorefinery. The process flow is
shown in Figure 3.
Descriptions Units Amount
A. System A
Total energy input (per 1 t, 85% DM
straw)
- Heat GJh 4.1
- Electricity Gje 0.8
Total energy output
- Heat GJh 2.1
- Electricity Gje 1.7
Deficit/surplus
- Heat GJh -2.01
- Electricity Gje 0.83
B. System B
Total Energy input (per 1 t DM, alfalfa)
- Heat GJh 0.86
- Electricity Gje 0.31
Total energy output
- Heat GJh 0.59
- Electricity Gje 0.84
Deficit/surplus
- Heat GJh -0.99
- Electricity Gje 0.53
C. Net balance (System C)
- Heat GJh -2.78
- Electricity Gje 1.3
228
Table S.2: Energy balance of System B, calculated based on (O’Keeffe et al., 2011) and
(Kamm et al., 2009)
Biomass Processing and
stages in GBR Units per t DM of alfalfa
Pumping kWh/t 0.69
Fiber processing to silage
fodder
Pressing kWh/t 4.9
Protein extraction
Steam coagulation MJ/t 126
Skimming kWh/t 1.31
Centrifuging kWh/t 3.41
Decanting kWh/t 1.03
Lactic acid production
Stirring kWh/t 3.75
Ultrafiltration kWh/t 4.85
Bipolar electrodialysis kWh/t 33
Reverse osmosis kWh/t 4.28
Distillation kWh/t 1.32
229
SI-2. Transformation of biomass in System A 1
2 Figure S.1: Mass flow for the conversion of straw to bioethanol, mass balance averaged from studies (Bentsen et al., 2006, Kaparaju et al., 2009, 3
Wang et al., 2013). 4
5
6
230
SI-3. Transformation of biomass in System B 1
2 Figure S.2: Mass flow for the conversion of alfalfa to lactic acid and other biobased products, data adapted from (O’Keeffe et al., 2011) and 3
adjusted for DM content of fresh alfalfa to 35%. The chemical constituents of the various fractionation steps are presented as the % DM of the 4
associated fraction, i.e. press cake or press juice, unless otherwise stated in the main document. Glucose content were based on Cybulska et al. 5
(2010) (see Table S.3), and the conversion factor of the glucose in the associated fraction was based on Doran-Peterson et al. (2008). The 6
production of LA as presented in the fraction “press juice with washing from PC” indicates the yield after enzymatic hydrolysis plus the conversion 7
of crude LA contained in the press juice. Mass of LA, as presented is with 10% impurity (color shaded represents the main raw chemical 8
constituents, raw material for secondary processing and the biobased products). 9
231
Table S.3: Material balance after the conversion of dry matter of the press cake undergoing
enzymatic hydrolysis process, calculation based on Cybulska et al. (2010)
Components Inputa
Output
Liquid Solid
% kg % Kg % kg
Glucose 33% 133 1.3% 5 31% 125
Hemicellulose 15.60% 63 5.90% 24 9.70% 39
Lignin 21% 85 18.50% 75 2.50% 10
Ash 5.65% 23 0.00% 0.000 5.65% 23 a Raw material for the lactic acid production is considered as the DM fractions of the press
cake after the 2nd pressing, as shown in Figure S2).
Reference List
Bentsen NS, Felby C, Ipsen KH, 2006. Energy balance of 2 nd generation bioethanol
production in
Denmark.http://www.tekno.dk/pdf/projekter/p09_2gbio/ClausFelby/p09_2gbio%2
0Bentsen%20et%20al%20(2006).pdf (accessed May 05, 2014).
Cybulska I, Lei H, Julson J (2010) Hydrothermal Pretreatment and Enzymatic Hydrolysis of
Prairie Cord Grass. Energy & Fuels, 24, 718-727.
Doran-Peterson J, Cook DM, Brandon SK (2008) Microbial conversion of sugars from plant
biomass to lactic acid or ethanol. The Plant Journal, 54, 582-592.
Kamm B, Schönicke P, Kamm M (2009) Biorefining of Green Biomass – Technical and
Energetic Considerations. CLEAN – Soil, Air, Water, 37, 27-30.
Kaparaju P, Serrano M, Thomsen AB, Kongjan P, Angelidaki I (2009) Bioethanol,
biohydrogen and biogas production from wheat straw in a biorefinery concept.
Bioresource Technology, 100, 2562-2568.
O’Keeffe S, Schulte RPO, Sanders JPM, Struik PC (2011) I. Technical assessment for first
generation green biorefinery (GBR) using mass and energy balances: Scenarios for an
Irish GBR blueprint. Biomass and Bioenergy, 35, 4712-4723.
Wang L, Littlewood J, Murphy RJ (2013) Environmental sustainability of bioethanol
production from wheat straw in the UK. Renewable & Sustainable Energy Reviews,
28, 715-725.
232
10. Additional Paper
Status: Published.
Life Cycle Assessment of district heat production in a straw fired CHP plant.
Ranjan Parajuli, Søren Løkke, Poul Alberg Østergaard, Marie Trydeman Knudsen, Jannick
H. Schmidt, Tommy Dalgaard
Biomass and Bioenergy. 68 (2014), 115-34. DOI: 10.1016/j.biombioe.2014.06.005