Environmental Sustainability ssessment of iomass and B A … · 2017. 2. 2. · Lublin District,...

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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

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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

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Main Supervisor

Professor Tommy Dalgaard


Aarhus University, Denmark

Co-supervisor

Marie Trydeman Knudsen

Researcher



Assessment Committee

Lars Elsgaard (Associate Professor)



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

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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

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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.

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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.

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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.

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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

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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

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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

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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.

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Appended Papers

1: Parajuli R, Dalgaard T, Jørgensen U, Adamsen APS, Knudsen MT, Birkved M, et al.


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.

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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).

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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

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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

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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

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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

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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.

http://www.journals.elsevier.com/journal-of-cleaner-production/

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


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

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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

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http://www.ecoinvent.org/files/dataqualityguideline_ecoinvent_3_20130506.pdf

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9. Supporting Papers

9.1. Paper I

Status: Published.


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

http://www.sciencedirect.com/science/article/pii/S0959652616316869

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

mailto:[email protected]

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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.

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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.

http://biovalue.dk/)

147

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Figure captions



cycle of willow.).



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



cycle of willow.)

170


171


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





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.


(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-


(accessed Sep 12, 2015), 2010.

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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

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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





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


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

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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

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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

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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.

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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

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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.

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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

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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).

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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.

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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).

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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

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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

http://biovalue.dk/)

201

and the team from the Novozymes for providing the information on environmental burden of

producing enzyme.

202

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213

Figure captions












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





223





224



225


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






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


Deficit/surplus

- Heat GJh -2.01


B. System B

Total Energy input (per 1 t DM, alfalfa)

- Heat GJh 0.86


Total energy output

- Heat GJh 0.59


Deficit/surplus

- Heat GJh -0.99


C. Net balance (System C)

- Heat GJh -2.78


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,


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


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

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