The Integration of Environmental and Social Sustainability Impacts of the Biojet Fuel Product System Within the Life Cycle Assessment Framework

The integration of environmental and social sustainability impacts of the biojet fuel product system within the life cycle assessment framework

June 20th,2016

Stephen Ivan aan den Toorn MSc Industrial Ecology Leiden University and Delft University of Technology

Supervisors Drs. L.F.C.M. van Oers Dr. S.I. Mussatto Dr. J.A. Posada Duque

The integration of environmental and social sustainability impacts of the biojet fuel product system within the life cycle assessment framework

Author: Stephen Ivan aan den Toorn Institute of Environmental Sciences, Leiden University Department of Biotechnology, Delft University of Technology

Date of completion: June 20th, 2016

Supervisors: Drs. L.F.C.M. van Oers Dr. S.I. Mussatto Dr. J.A. Posada Duque

Abstract

Biojet fuel is developed as a potentially more sustainable alternative to existing petroleum based jet fuels. To establish the environmental and social sustainability of the biojet fuel product system, the full life cycle of the production should be analyzed. The environmental LCA (ELCA)and social LCA (SLCA) have been developed as two independent tools, but they cannot be used to analyze the same product system as their system boundaries differ. This research studies the possibility of harmonizing the two tools into a single social and environmental LCA (SELCA) methodology in light of its use in the biojet fuel product system. SELCA was developed through an extensive literature review, followed by catering it to the biojet fuel product system based on a survey and a dummy case study. The ELCA is based on unit processes while the SLCA uses organizations as its base. To reconcile these two system elements, the organizations were treated as multifunctional processes that can be allocated to the product system. The DPSIR model was used to ensure that data on interventions and economic flows were equivalent to each other and capable of being causally connected to the product system. The resulting methodology has consistent system boundaries and data for both social and environmental impacts. The majority of existing SLCA characterization methods and indicators are not compatible with SELCA. Indicators must be based on DPSIR pressure level data and be capable of being aggregated for the product system in order to be comparable to ELCA indicators. Many SLCA methodologies are based on statistical data which conceptually cannot be attributed to a particular organization or unit process. The only operational methodology is that of Hunkeler (2006) which uses quantitative labor hour data. As a methodology for assessing the biojet fuel, SELCA has several limits. The existing indicators do not address all issues considered important by academia and the industry. Also, despite that SELCA can in principle include the full biojet product system, in practice it will be limited by the lack of background databases. This results in more cut-off points and smaller system boundaries as shown in the dummy case study. However, with further research and development of characterization methods and databases, SELCA has the potential to fully integrate the environmental and social impact assessments of the biojet fuel product system.

Acknowledgements

These past few months were an intense learning experience for me. I had to wrap my mind around theoretical challenges and put my thoughts into paper. All of this would not have been possible without the help and support of others.

Foremost, I would like to thank my supervisors Lauran van Oers, Solange Mussatto, and John Posada for their guidance and patience. Through your comments and the long-lasting meetings, I was able to reach this point and finish my thesis.

I would also like to express my gratitude to the survey respondents for their time and for giving valuable information and insights into various aspects of biomass production and the biojet fuel industry.

Finally, I thank my parents for their love and care in this hectic period, my friends for keeping me sane and well-supplied with alcohol, and my wonderful girlfriend Linh, my first and only love, for being with me throughout this time. Your support has kept me going through the good and tough times.

Table of Contents Abstract ...... 2

Acknowledgements ...... 3

List of Tables ...... 6

List of Figures...... 7

List of Abbreviations ...... 8

List of Definitions ...... 8

1 Problem statement ...... 11

2 Background ...... 13

2.1 Biojet fuel production ...... 13 2.2 Sustainability considerations in the biojet fuel supply chain ...... 15 3 Social and environmental life cycle assessment integration ...... 17

3.1 Goal and scope ...... 17 3.2 Inventory analysis ...... 19 3.3 Impact assessment ...... 39 3.4 Interpretation ...... 50 3.5 Conclusions of social and environmental life cycle assessment integration ...... 51 4 SELCA and assessing biojet fuel social and environmental issues ...... 53

4.1 Biojet fuel product system in SELCA ...... 53 4.2 Biojet fuel social and environmental sustainability issues in SELCA ...... 53 4.3 Conclusions of SELCA and biojet fuel ...... 55

5 SELCA dummy case study: Biojet fuel in the Netherlands ...... 56

5.1 Goal and scope ...... 56 5.2 Inventory analysis ...... 57 5.3 Impact assessment ...... 61 5.4 Interpretation ...... 63 5.5 Dummy case study conclusions ...... 65 6 Discussions ...... 66

6.1 Interpretation of results ...... 66 6.2 Limitations ...... 68 7 Conclusions ...... 69

8 Recommendations ...... 70

References ...... 71

Appendix 1: Lists of Biojet Fuel Environmental and Social issues ...... 76

Appendix 2: Survey ...... 81

Appendix 3: Social unit processes ...... 95

Appendix 4: Dummy case study - quantitative inventory tables ...... 106

Appendix 5: Dummy case study - comparative analysis ...... 108

Appendix 6: Dummy case study – flow charts ...... 115

List of Tables Table 1: T-shirt example function (UNEP 2009) Table 2: Example biojet fuel SLCA function based on UNEP (2009) Table 3: General unit process ELCA. Adapted from: Guinée et al (2002) Table 4: General unit process SLCA Table 5: Simplified mass balance of farm at landscape Table 6: Simplified mass balance of farm at plant Table 7: Simplified mass balance of farm at harvest Table 8: Allocation factors mass and economic value Table 9: Example allocation of retail emissions and labor-hours Table 10: General social unit process flows Table 11: Environmental and social compartments of impact sinks Table 12: Quantitative inventory table Table 13: Semi-quantitative inventory table Table 14: Basic environmental midpoint categories and category indicators (Guinée 2002) Table 15: Climate change infrared radiative forcing characterization factors Table 16: UNEP (2009, 45) SLCA endpoint impact categories Table 17: UNEP (2009, 49) Stakeholder subcategory/midpoint impact categories Table 18: Grouped indicators Table 19: Causal indicators Table 20: SELCA biojet fuel function Table 21: HEFA producer Table 22: Dummy allocation factors Table 23: Cost values for Hunkeler’s (2006) impact categories in Netherlands Table 24: Labor hours per unit for agricultural and industrial labor in the Netherlands Table 25: Characterization factors for social damages of labor units per hour for agricultural and industrial labor in the Netherlands Table 26: Contribution analysis for each impact category

List of Figures Figure 1: Product system integrated in the socioeconomic system within the environmental system Figure 2: Simplified biojet fuel ELCA product system with economic flows and environmental interventions Figure 3: Simplified SLCA biojet fuel product system with social interventions Figure 4: Example ELCA flow chart. Source: Guinée 2002. Figure 5: Visualization system boundary of farm at landscape level Figure 6: Visualization system boundary of farms at plant level Figure 7: Visualization system boundary of farms at harvest level Figure 8: Functional flows Figure 9: Firm with processes producing biojet fuel and regular jet fuel Figure 10: Firm producing biojet and regular jet fuel traded through the retail process Figure 11: Quantitative allocation of Labor-hours and CO2 emissions Figure 12: SELCA social unit process Figure 13: The DPSIR framework for Reporting on Environmental Issues. Source: EEA 1999 Figure 14: Example SUP pressures and state of education and employment Figure 15: Direct and indirect indicators example hiring local workers Figure 16: Flowchart with economic flows of HEFA producer as organizational allocation example

List of Abbreviations AtJ Alcohol-to-Jet DSHC Direct Sugar to Hydrocarbons ELCA Environmental lifecycle assessment FHS Fermented Hydroprocessed Sugar FTS Fischer-Tropsch synthesis FU Functional unit GHG Greenhouse gas HDCJ Hydrotreated Depolymerized Cellulosic Jet HEFA Hydroprocessed Esters and Fatty Acids ISCC International Sustainability and Carbon Certification LCA Life cycle assessment LCAA Life cycle attribute assessment LCC Life cycle costing LCSA Life cycle sustainability assessment RSB STD Roundtable on Sustainable Biomaterials Sustainability Standard SELCA Social and environmental life cycle assessment SIP Synthesized iso-paraffins SKA Synthetic paraffinic kerosene with aromatics SLCA Social life cycle assessment SPK Synthetic paraffinic kerosene SUP Social unit process

List of Definitions Term Definition Source of adaptation Aggregation The action of summing or bringing together UNEP 2009 information from smaller units into a larger unit. Allocation The assigning of physical flows and social Guineé 2002 interactions to a reference flow or functional flow Alternatives One set of product systems a particular Guineé 2002 ELCA, SLCA or SELCA study Area of protection A cluster of categories of recognizable Guineé 2002 value to society. Attributes Properties or characteristics of a process or UNEP 2009 social interaction, which are of interest to stakeholders. Background process A process for which secondary data is sued Guineé 2002

Driver-pressure- A causal framework useful in describing the EEA 1999 state-impact- relationships between the origins and response framework consequences of environmental problems. Environmental Element of organization’s activities, UNEP 2009 aspect products or services that can interact with the environment Environmental life An assessment technique that aims at UNEP 2009 cycle assessment addressing the environmental aspects and their potential environmental impacts throughout a product’s life cycle. Foreground process A process for which primary, site-specific Guineé 2002 data is used Function A service provided by a product system or Guineé 2002 unit process Functional unit The quantified function provided by the Guineé 2002 product system under study, for use as a reference basis Impact category A class representing a sustainability issue of Guineé 2002 concern to which environmental flows and social interactions are assigned. Internal function A function within a SUP New Life cycle attribute A method that enables to express the UNEP 2009 assessment percentage of a supply chain that possesses (or lacks) an attribute of interest Life cycle costing Life cycle costing, or LCC, is a compilation UNEP 2009 and assessment of all costs related to a product, over its entire life cycle Life cycle impact The third phase of an ELCA, SLCA or SELCA, Guineé 2002 assessment concerned with understanding and evaluating the magnitude and significance of the potential environmental impacts of the product systems under study Life cycle inventory The second phase of an ELCA, SLCA or Guineé 2002 analysis SELCA, in which the relevant inputs and outputs of the product systems under study throughout the life cycle compiled Life cycle The overarching framework combining the UNEP 2011 sustainability ELCA, SLCA, and LCC. assessment Multifunctional A (social) unit process with more than one Guineé 2002 process functional flow.

Product system A set of interlinked (social) unit processes Guineé 2002 and performing one or more defined functions Qualitative indicator Qualitative indicators are nominative: they UNEP 2009 provide information on a particular issue using words. Quantitative A quantitative indicator is a description of UNEP 2009 indicator the issue assessed using numbers. Reference flow Quantified flow generally connected to the Guineé 2002 use phase of a product system and representing one way of obtaining the functional unit Semi-quantitative Semi-quantitative indicators are indicators UNEP 2009 indicator that have results expressed into a yes/ no form or a scale (scoring system) Social and An assessment technique that aims to New environmental life assess the social, socio-economic and cycle assessment environmental aspects of products and their potential impacts throughout their life cycle Social aspect Element of organization’s activities, New products or services that can interact with social actors Social life cycle A social impact assessment technique that UNEP 2009 assessment aims to assess the social and socioeconomic aspects of products and their positive and negative impacts along their life cycle. Social unit process The smallest portion of product system for New which data is gathered in a SELCA Unit process The smallest portion of a product system Guineé 2002 for which data is gathered in an ELCA

1 Problem statement The global connectivity that air travel provides for people across the world is undeniable. The demand for flights remains on the rise with the International Civil Aviation Organization (ICAO 2013) expecting an annual growth rate of 4% to 5% for passenger flights. The increase in air travel comes at the cost of a higher sustainability impact. the aviation industry also contributes to climate change through the use of its jet fuels. The use of jet fuel is responsible for 2% of annual anthropogenic greenhouse gas (GHG) emissions and it is projected to grow to 5% by 2050, but the direct emission into the upper atmosphere increases the effect that these emissions have compared to GHG emissions at ground level (Ecofys1 2014; Lang & Elhaj 2014). To reduce their impact on climate change, almost all the major airlines are doing research and conducting trials to create feasible biojet fuels (IEA Bioenergy 2012). However, the use of biomass for energy comes with other sustainability issues that need to be addressed. Currently biomass is mostly produced from conventional monoculture agriculture with research focusing on how to utilize more of these plants and how to improve growth efficiency (Richardson 2012; Langeveld et al 2010). This large scale production of biomass is not without consequence, resulting in impacts such as climate change, eutrophication, water usage, salinization and loss of biodiversity. On a more social level, the utilization of biomass could compete with local food production and impoverish farmers (Milieudefensie 2012). These issues show that for biojet fuels to be a more sustainable alternative for existing jet fuels, the aviation industry and producers of biojet fuels have to consider the consequences within the whole supply chain. To assess the sustainability of bioenergy production such as biojet fuels, McBride et al. (2011) proposed a set of environmental sustainability indicators and Dale et al. (2013) made a similar list for social and socio-economic indicators. The aim of their lists is to create a minimum set of indicators that can be applied to the whole bioenergy supply chain and are useful for decision making. Life cycle thinking may provide guidelines in reaching these goals through the different life cycle assessment (LCA) methods. The environmental life cycle assessment (ELCA) has a standardized minimum set of impact categories with their respective indicators to assess environmental sustainability along the full supply chain (Guinée 2002). The social life cycle assessment (SLCA) is still in a relatively early developmental stage, but aims to assess social and socio-economic impacts by following the same ISO 14040 LCA standards as the ELCA (UNEP 2009). There are some important methodological differences between ELCA and SLCA, but efforts have been made to combine them through the life cycle sustainability assessment (LCSA) framework. That framework combines the ELCA and SLCA, together with life cycle costing (LCC) which quantifies economic sustainability (Sala et al. 2013). Since only the environmental and social aspects of sustainability of biojet fuel are considered in this research, the LCC will be left outside of the research scope. The SLCA concept is still in an early stage, so there are several knowledge gaps to be addressed. One of the knowledge gaps is how to set boundaries so the ELCA and SLCA can be combined into a cohesive system. UNEP (2011) in its recommendations for LCSA recommends setting separate system boundaries for each of the tools and determining the LCSA boundaries by adding them together. The underlying model for this takes the unit process and the organizations as two separate but related system elements. However, this does not address the fundamental problem that the system elements of unit processes and organizations are not

11 equivalent to each other. The consequence of this is that the SLCA and ELCA systems cannot be made equivalent since their basic building blocks differ. Another existing knowledge gap is the lack of standard social indicators within the SLCA. Assessing existing proposed social indicators based on a combined SLCA and ELCA could guide the formation of a standard indicator list. The goal of this study is to create a harmonized social and environmental LCA (SELCA) and to select suitable social indicators alongside existing environmental indicators in order to assess the sustainability of the biojet fuel supply chain. So the main research question is:

Can the analysis of environmental and social impacts of the biojet fuel be integrated within a combined social and environmental life cycle assessment framework?

To answer the main question, this research will first have to answer sub questions that are more general about combining the SLCA and ELCA. First the system boundaries and elements have to be made equivalent for the two tools to be integrated. Following this is the determination which social data can be used that is comparable to the more mature use of environmental data which will lead to certain requirements for potential indicators. These results can then be used to answer the third sub question by choosing indicators for the specific assessment of the biojet fuel supply chain, especially promising social indicators that can be brought in line with the existing ELCA environmental indicators. The resulting sub-questions are:

1. How can the social and environmental life cycle assessment system boundary and elements be adapted to include both unit processes and social actors?

2. What are the requirements to ensure that potential social and environmental indicators are comparable within the social and environmental life cycle assessment?

3. Which social indicators are promising to be used alongside existing environmental indicators within the social and environmental life cycle assessment?

4. What are the possibilities and limitations of applying the social and environmental life cycle assessment to the biojet fuel product system?

To answer these questions this study will first give a short background in biojet fuel production and current efforts to measure its sustainability (chapter 2). Following this, ELCA and SLCA will be harmonized into a single SELCA methodology answering sub-questions 1, 2, and 3 (chapter 3). The SELCA methodology is then assessed on its compatibility with the biojet fuel product system which will answer sub-question 4 (chapter 4). To set up the case study, a survey is conducted among biojet fuel production stakeholders (appendix 2) which leads to application of the proposed SELCA methodology and indicators on the biojet fuel product system in the Netherlands dummy case (chapter 5). Finally, the results are discussed (chapter 6), final conclusions are made (chapter 7), and recommendations are given (chapter 8).

2 Background

2.1 Biojet fuel production The aviation industry is looking at alternative energy carriers that can replace petroleum based jet fuel (petrojet fuel). More attention is paid to biojet fuels compared to hydrogen and electrochemical storage, as biojet fuels have to potential to become drop-in fuels (IEA Bioenergy 2012). Drop-in fuels are fuels that can be used in existing engines and infrastructure at airports which simplifies the implementation as the existing infrastructure and aircrafts can be kept in use. However, the use of biomass for energy comes with other sustainability issues that need to be addressed. Currently biomass is mostly produced from conventional monoculture agriculture with research focusing on how to utilize more of these plants and how to improve growth efficiency (Richardson 2012; Langeveld et al 2010). This large scale production is not without consequence, resulting in large impacts such as climate change, eutrophication, water usage, salinization and loss of biodiversity. On a more social level, the utilization of biomass could compete with local food production and impoverish farmers (Milieudefensie 2012). These issues show that for biojet fuels to be a more sustainable alternative for existing jet fuels, the aviation industry and producers of biojet fuels have to consider the consequences within the whole supply chain. The commercial aviation industry consumes around 200 million tonnes of jet fuel a year (Lang & Elhaj 2014). There are two types of jet fuels: the kerosene-type with a carbon number distribution between 8 and 16, and naphtha-type with a carbon number distribution between 5 and 15 (Davidson et al. 2014). Naphtha-type fuels such as Jet B are mainly used in cold climate regions, but for most of the world the kerosene-types Jet A in the United States and Jet A-1 globally are the predominant fuels for commercial aviation (Davidson et al. 2014; Neuling & Kaltschmitt 2014). Jet A-1 has three main sets of properties that have resulted in it becoming the main fuel for aviation. The first is its functionality as an energy storage which is restricted by a minimum energy content of 42.8 MJ/kg and a density range between 775 kg/m3 and 840 kg/m3 (Novelli 2011). Also, Jet A-1 has a freezing point of -47°C or lower with a viscosity at -20°C below 8 mm2 /s. The second set of requirements is a result of the operation requirement of the jet engines. Besides being an energy storage, fuel is also used for cooling and as a lubricant which results in standards for thermal stability and lubricity (Novelli 2011). Aromatics and Sulphur are especially important for the latter and need to be of a sufficient concentration. The third set is about safety considerations through standards on volatility, flash point, electric conductivity, and material compatibility (Novelli 2011). Drop-in biojet fuels are fuels that can meet these standards set by the ASTM D1655 standards. However, at present none of the approved biojet fuels fulfill all the requirements and so mixing with petroleum derived jet fuel is needed in order for the biojet fuels to be used (Novelli 2011; Ecofys 2013). Biojet fuel has been classified into several main categories (Neuling & Kaltschmitt 2014): Synthetic paraffinic kerosene (SPK), Synthetic paraffinic kerosene with aromatics (SKA), Synthesized iso-paraffins (SIP). SPK is the first type of biojet fuel that has been approved for use by the ASTM. It has a carbon number distribution between 9 and 16 with an aromatic content of less than 0.5% of its

13 volume (ICAO 2014; Neuling & Kaltschmitt 2014). This means that most characteristics of SPK are similar to Jet A-1, except for the aromatics content. Since aromatics are so important for the lubrication of jet engines, SPK cannot be used as a drop-in fuel. Instead it has been approved by the ASTM in 50% mixture with regular Jet A-1 fuel (Neuling & Kaltschmitt 2014). SKA is a similar to SPK in that it has a carbon number distribution between 9 and 16. However, as its name implies, the aromatics content is larger than 0.5%. The inclusion of aromatics may lead to a biojet fuel that could fully function as a drop-in fuel (Neuling & Kaltschmitt 2014). SPK and SKA can be produced through the same main biojet fuel production pathways: Hydroprocessed Esters and Fatty Acids (HEFA), Fischer-Tropsch synthesis (FTS), and Alcohol-to-Jet (AtJ). At present SPK through HEFA and FTS has been approved by the ASTM with AtJ being reviewed and possibly approved in the first quarter of 2016, while there are no SKA pathways that have been approved. (Biofuels digest 2015). SIP is the most recent biojet fuel approved by ASTM in 2014 (ICAO 2014). Similar to SPK, it has an aromatic content of less than 0.5% of its volume (Neuling & Kaltschmitt 2014). However, it differs in that it only has a carbon number distribution of 15 (ICAO 2014). The consequence of this limitation is that a mixture with Jet A-1 fuel is only allowed to contain a maximum of 10% SIP, though there are developments in creating engineered yeast that could produce a wider carbon number distribution more closely to Jet A-1 (ICAO 2014). There is only one main biojet fuel production pathway for SIP and that is Fermented Hydroprocessed Sugar (FHS), also known as Direct Sugar to Hydrocarbons (DSHC). Various feedstocks for biojet fuel exist that can be used in the different production pathways. Neuling and Kaltschmitt (2014) categorized biomass into different fractions: fats and oils, starch and sugars, and lignocellulose. Another classification would be to divide feedstocks as main products, by-products, and waste streams. The conversion of fats and oils is the furthest developed method with HEFA as its pathway. There is a shift away from vegetable fats towards non-food vegetable oils and waste streams such as cooking oil and waste animal fats from slaughterhouses and residues from the production of palm oil to avoid conflicts with the food production system and other sustainability impacts (SkyNRG 2016). The starch and sugars content can be converted into biojet fuel through the FHS/DSHC and AtJ pathways (Neuling & Kaltschmitt 2014). Bioethanol is already produced on a large scale for biofuels with 1.47 million barrels per day globally in 2012 (EIA 2016), and the same feedstocks like sugarcane and sugar beet can be used for biojet fuel. Lignocellulose can be processed by turning it into syngas or methane, and bio-crude which can then be turned into biojet fuels by respectively FT and Hydrotreated Depolymerized Cellulosic Jet (HDCJ), one of the production pathways in development (ICAO 2016). There are also developments and test-plants for the production bioethanol from lignocellulose (Menon & Rao 2012) which can be further processed along the starch and sugar pathways. Lignocellulosic material can be sourced from wood and Miscanthus or from by-products such as wood residues and straw which does not compete with food, but does compete with other uses of the biomass. The different biomass sources and production pathways that are in development for them could ensure a widespread adoption of biojet fuels, but each pathway and feedstock will have its own associated sustainability issues.

2.2 Sustainability considerations in the biojet fuel supply chain The switch from petrochemical-derived jet fuel (petrojet fuel) to biojet fuel is mainly based on reducing GHG emissions to combat climate change. Though this is an important aspect of environmental sustainability, there are several other impacts to consider such as eutrophication, acidification, land use, and water use. In addition, sustainability consists of more than environmental issues, it also deals with socioeconomic aspects. The popular triple bottom line acknowledges this, and newer sustainability concepts such as the circles of sustainability place an even larger emphasis on the social, socio-economic and economic aspects (Circles of Sustainability 2015), though the economic aspect will not be in the focus of this research. The impacts of biojet fuels are not limited to one part of the supply chain, but are the consequence of all. The first step is biomass production with the agricultural production methods used and the type of biomass grown. Though non-agricultural biomass sources are possible, the production of biojet fuel is likely to make use of agricultural outputs. However, the use of biomass has competed with food production, and could encourage more destruction of forests, and impoverish farmers (Milieudefensie 2012). Current biomass is largely produced from conventional monoculture agriculture with research focusing on how to utilize more of these plants and how to improve growth efficiency (Richardson 2012; Langeveld et al 2010). This large scale production is not without consequence, resulting in large environmental impacts. Agriculture, without including land use change, emits about 13% of total GHG emissions, including N2O and CH4 for which agriculture contributes 58% and 47% of the emissions each respectively (UNEP 2011). The GHG emissions of land use change from forests to agricultural lands adds an extra 18% of the global emissions. The practice of monoculture agriculture with high pesticide and herbicide use exposes the soil to water and wind which erodes it and results in soil fertility loss (Miller & Spoolman 2013). Eutrophication is another problem caused by excess nutrients in run-off water from farms where fertilizers are over abundantly used (UNEP 2011). The use of pesticides also affects the health of agriculturalists and mono-cropping practice has reduced agrobiodiversity and wildlife biodiversity (Miller & Spoolman 2013; UNEP 2011). Another major issue with 70% of the water used in human society is used for irrigation of only 20% of all farms, though they produce 45% of all food (Miller & Spoolman 2013). The result of mismanaged water use is salinization and waterlogging, which both may result in yield losses and eventually barren land. For the conversion of biomass to biojet fuel, the different pathways require varying quantities of inputs such as hydrogen, energy, and capital goods. The sourcing and production of these resources contribute to the sustainability of the supply chain as well. Also the efficiency and emissions during production are part of the environmental impacts of biojet fuels. The transportation of fuels requires the use of energy and infrastructure which contributes to environmental impacts. The development of drop-in fuels greatly contributes to using existing infrastructure, so major impacts related to construction are avoided. However, the transport of biomass and biojet fuels will remain important as the sources and demand will not be equally distributed. The use of less polluting sources and wastes is an important aspect of bio jet fuels, but there is more to sustainability than environmental impacts. Another important aspect is social or socioeconomic sustainability. Valance and colleagues (2011) reviewed previous attempts to

15 conceptualize social sustainability and concluded that there are three main divisions viewpoints: bridge, maintenance, and developmental social sustainability. Bridge sustainability researches how sustainability and the environment are perceived by people and how desired behavioral changes can be stimulated to reach sustainability. A distinction is made between transformative and non-transformative approaches where the former tries to challenge or reshape views on sustainability, while the latter gives information about sustainability and more specific smaller changes such as adopting new recycling schemes. In maintenance social sustainability the focus lies on the reaction of people to changes, for example during the implementation of the European ban on vacuum cleaners over 1600 watts a consumer group reacted by encouraging the purchase of those to be banned before they were gone (The Telegraph 2014). So research in maintenance social sustainability tries to describe and explain the reactions and underlying mechanisms to perceived changes. Finally, the aim of developmental social sustainability is to meet developmental goals such as ensuring basic needs which is similar to the way Littig and Griesler (2005) view social sustainability. These needs can be tangible like having enough food or sufficient income, and intangible such as education and labor rights. Developmental social sustainability is the aspect that is of importance for this study. Where bridge social sustainability leads to normative and maintenance social sustainability to descriptive statements, the developmental social sustainability view results in topics of interest that could be potentially be measured or valued. It is related to relation between actors such as employer and employee, or the treatment between firms and other stakeholders. The ability to influence and improve these relations makes it possible to perceive changes in developmental social sustainability. This viewpoint is also taken by voluntary biojet fuel sustainability schemes that include social sustainability such as: the International Sustainability and Carbon Certification (ISCC) consisting of six principles including legislative compliance, the safety of employees, and the protection of human, labor, and land rights; the Roundtable on Sustainable Biomaterials Sustainability Standard (RSB STD) including legislation, labor conditions, and land rights, as well as rural or social development programs and local food security. The voluntary sustainability schemes were created to limit negative environmental and socioeconomic effects and encourage the development of sustainable biojet fuel. The aviation industry has not been required to lower their GHG emissions by governing bodies, though public and political pressure to reduce emissions is an important factor for joining the voluntary schemes (IEA 2012). There are also efforts to quantify the sustainability impacts of biojet fuels through their life cycle. Fan et al. (2013), Fortier et al. (2014), Bailis and Baka (2010), and Li and Mupondwa (2014) have researched the environmental impacts of a particular biomass source, respectively pennycress, microalgae, jatropha curcas, and camelina oil. There have also been comparative studies such as Agusdinata et al. (2011) where different biomass sources are compared in the USA, and Wong (2008) who compares various jet fuel sources derived from both petroleum and biomass. All these studies have focused on environmental impacts, and a majority only compare GHG emissions. The lack of social aspects in these studies on biojet fuel is a result of the more general difference in maturity between ELCA and SLCA. To make this possible, this study will further develop and harmonize both methodologies in order to identify a possible set of indicators for both environmental and social sustainability within the biojet fuel production system.

3 Social and environmental life cycle assessment integration The LCA is a framework to comprehensively assesses the total impacts of a product or service throughout its lifecycle (Rebitzer et al. 2004). Though the term LCA is normally used for the matured assessment of environmental impacts, in this study it will be called ELCA. This is to better differentiate between the ELCA and the SLCA which assesses the social sustainability of products (UNEP 2009). Though the economic aspect is important as part of the triple bottom line of sustainability and is addressed by the LCC, the economic sustainability of biojet fuel will be considered outside the scope of this study. The ELCA and SLCA are based on the ISO 14040 standards and consist of four phases: the goal and scope, inventory analysis, impact assessment, and the interpretation (Guinée 2002). This chapter compares the ELCA and SLCA based on a literature review to analyze the conceptual similarities and differences which will be adapted to harmonize the two into SELCA.

3.1 Goal and scope The goal and scope is the first part of both ELCA and SLCA where the purpose, research questions, and the object of study are defined. It consists of three parts: the goal definition; the scope definition; the function, functional units, alternatives, and reference flows. The goal definition should state the reason and justification for the research, the usage of the results, who commissioned and are involved in the research, and for whom the results are intended (UNEP 2009; Guinée 2002). There are three general types of questions that can be answered through LCA methodologies, though the third has not been applied yet for the SLCA:

1. What are the main environmental/social sustainability impacts in a product system and where do they occur?

2. How do the environmental/social sustainability impacts of alternative product system compare to each other?

3. Do the environmental/social sustainability impacts of a specific product system comply with external standards?

To answer the first type of questions, a hotspot analysis is performed. The aim is to identify the parts in a supply chain that contribute significantly to the sustainability impacts of a product, so it can be determined where most of the attention for improvement is necessary. For example, the SLCA research goal of Revéret et al. (2015) was ‘assessing the socioeconomic performance of the Canadian milk production sector and identifying potential social hotspots’ and an ELCA illustration is the tomato ketchup case study of Andersson et al. (1998) where one of the main goals was to identify which steps of the life cycle give the most significant rise to environmental in- and output flows. For the second type of question, a comparative analysis is needed which compares the sustainability impacts of alternative products. The ELCA of Cherubini and Jungmeier (2010) is a comparative analysis where products from biorefineries are compared to fossil-derived products. Another example is the SLCA conducted by Weldegiorgis and Franks (2014) comparing three

17 different alternative for the energy supply for iron reduction in steelmaking. There are stricter guidelines if the results of a comparative analysis are published. Statements that show one product’s superiority over another can be misleading as the outcomes of an LCA depend on the data availability and assumptions made by the researchers (Guinée 2002). The third question leads to a compliance evaluation to evaluate whether a product complies with externally set standards. An example is the CO2 reduction standards of the European Commission (2016) for biofuels. This type of research is important for showing compliance with policy and could be used for voluntary biojet fuel sustainability schemes such as the RSB STD and ISCC EU mentioned in before. However, this questions can only be addressed by the ELCA, as the SLCA as a methodology is lacks standardization and maturity. The choice for any of these questions will depend on the purpose, the usage and the people who are responsible for the research which will be different for each study. Ideally the SELCA could answer all three types of questions, but only maturity and acceptance of the methodology ensures usage for compliance evaluation. So in this study SELCA will focus on the first two types of questions. The scope definition describes the time period, geography, technology, basic economic processes, and interactions with the environment that are taken into account which is similar in ELCA and SLCA (Guinée 2002; UNEP 2009). The first three will depend on the questions set by the researchers, while the latter two will in part be determined by the way system elements and boundaries of the SELCA system are defined. Another distinction is made between studies that follow supply chains from the start till end use (cradle-to-grave), and from the start till some point before the final use(cradle-to-gate). All of these topics have to be justified in relation to the stated goal of the study (UNEP 2009). The final part is defining the function, the functional units (FU) and the reference flows. The only difference between the ELCA and SLCA is in the function where the definition of a product or service usage is given. In an ELCA ‘using drop-in biojet fuel for aviation’ is an example of a function, but the SLCA expands on this by adding social functions to a product. UNEP (2009) mentions the use of functionality, technical specification, additional services, aesthetics, image, costs, and specific social or environmental properties as further specifications to the ELCA function. UNEP (2009) uses the example of a t-shirt to illustrate the extra functions which results in the function ‘T-shirt from a popular sport brand to cover the body keeping a person comfortable and dry, made from certified organic cotton with short sleeves without buttons and is durable and washable, embroidered and printed with a design distinguishable from last year’s design which can be used as a cloth after discarding, and costs below a certain amount’ (see table 1). The details in a function have to balance the tension between the comparability of products and the diversity of alternatives. The UNEP (2009) example would exclude t-shirts of nylon or other materials that may have all the other functions, but comparison between two such cotton shirts will likely show very specific differences and hotspots of interest to the stakeholders. A more general function for biojet fuel could be the function ‘to fuel an aircraft with a drop-in biojet fuel that is produced from waste biomass sources’ (see table 2). This could accommodate various methods of turning waste biomass into biojet fuel regardless of costs and other constraints, but in other cases it may be interesting to compare different sources or limiting the function to a specific biojet fuel pathway.

Table 1: T-shirt example function (UNEP 2009) Property Description Functionality To cover the body, to keep a person comfortable and dry Technical specification Cotton, short sleeves, no buttons, durable and washable Additional services To be used as a cloth after discarding Aesthetics To be embroidered and printed and to have a design that is distinguishable from last year’s design Image To be of a popular sport brand Costs To be below a certain cost Specific environmental and To be made of certified organic material social properties

Table 2: Example biojet fuel SLCA function based on UNEP (2009) Property Description Functionality To fuel an aircraft Technical specification Drop-in biojet fuel Specific environmental property Waste biomass as source

The purpose of the function is to lead to the FU which is a quantified version of the function that forms the basis of comparison. For the biojet fuel example this could be quantified in terms of mass or energy: ‘1 ton of drop-in biojet fuel made from waste biomass sources consumed in an aircraft’, or ‘1 GJ of drop-in biojet fuel made from waste biomass sources consumed in an aircraft’. The FU can be fulfilled by several different alternatives and each will have its own reference flow. For example, ‘1 ton of drop-in HEFA produced biojet fuel made from waste animal fats consumed in an aircraft’ can be compared to ‘1 ton of drop-in FT produced biojet fuel made from wood residues consumed in an aircraft’. So a more general function can still lead to alternatives that are more specified both in ELCA and SLCA. To harmonize the ELCA and SLCA, SELCA should take the more expanded SLCA function. For each research it has to be decided to what extent a function is specified to better reflect the products or to have a less detailed function that broadens the possible alternatives for comparison. The choice of function will determine the FU and the details of alternatives, since alternatives will be more specified than the FU itself.

3.2 Inventory analysis The inventory analysis is the second phase of an ELCA and SLCA where the product system is further defined, inputs and outputs are allocated, and data is gathered. To focus of this section is to answer the first sub-question: How can the social and environmental life cycle assessment system boundary and elements be adapted to include both physical unit processes and social actors? This section compares the ELCA and SLCA on their respective product systems, allocation methods, inventory data, system boundaries, flow charts, and inventory tables. The result is a consistent SELCA product system that includes environmental and social interventions.

3.2.1 Product system At the heart of the life cycle methodologies is the conceptualization that there are three systems: the environmental system, the socioeconomic system, and the product system (see fig. 1). The socioeconomic system is part of the environmental system and intervenes in the other parts of the environmental system through extracted resources and emissions, together called environmental interventions. Interactions within the socioeconomic system consist of product and service exchanges called economic flows and other relations between various people and organizations termed social interventions. Each particular product and service has a product system which is the portion of the socioeconomic system related to establishing that product or service. Essentially, the ELCA attempts to assign part of the environmental interventions to the product system, while the SLCA assigns part of the social interventions to it. Product systems can be analyzed through two different perspectives: the engineering system perspective and the actor perspective (de Bruin & Herder 2009) and they are composed of system elements interacting with one another and with the surrounding outside of the considered system (Dijkema & Basson 2009; Graedel & Allenby 2010).

Figure 1: Product system integrated in the socioeconomic system within the environmental system.

The engineering system perspective focuses on the technical network and analyzes the interactions between physical elements and the flows between them (de Bruin & Herder 2009). The ELCA is based on the engineering system perspective in which the system elements are production processes. These elements are called unit processes and it is their interaction with the environmental system that is related to environmental sustainability. To illustrate, the biojet fuel production system has been simplified into six unit processes with only CO2 emissions and resource extractions as environmental interventions (see fig. 2): biomass production, biomass transport, biojet fuel production, biojet fuel distribution and transport, and biojet fuel use. Environmental interventions of the product system are extracted resources and emissions, while physical flows within the product system are economic flows (see table 3). In ELCA the product

20 system is the part of the socioeconomic system that contributes through physical flows of materials, energy and services to a product. This means in theory that there is no exchange between the product system and the rest of the socioeconomic system, so the latter is left out of ELCA. In the example, biomass production takes up CO2, water and nutrients from the environment and produces biomass. Biomass transport, biojet fuel production and biojet fuel distribution and transport are both assumed to require energy, without having emissions of their own. The production of energy from petrol is simplified to extract oil from the environment and release CO2 emissions. Finally, the biojet fuel is used and releases CO2 into the atmosphere. All the CO2 emissions and resource extractions in this simple example together are the product system’s environmental sustainability impacts, which is why the engineering perspective is ideal for the ELCA.

Figure 2: Simplified biojet fuel ELCA product system with economic flows and environmental interventions

Table 3: General unit process ELCA. Adapted from: Guinée et al 2002, 117 Unit Process Inputs Outputs Economic flows Goods Goods Services Services Waste (for treatment) Waste (for treatment) Environmental interventions Abiotic resources Emissions to air Land transformation Emissions to water Biotic resources Emissions to soil Waste heat

The actor perspective looks into the social network of a product system and analyzes social interactions rather than physical flows (de Bruin & Herder 2009). The system elements are the actors such as the various supply chain members (see fig. 3). Their interactions influence decision- making and in turn those decisions can also influence the actors and their interactions, so the actor perspective maps out relevant actors for a system. The SLCA combines the engineering and actor perspectives. UNEP (2009) guidelines suggest following the ELCA unit processes to create the system, followed by replacing the unit processes with the responsible organizations which are then analyzed. The study of Revéret and colleagues (2015) is a clear example of this where the ELCA unit processes were used as the basis for the system, but where it was acknowledged that for further steps the organizations were used instead of unit processes. Other studies such as those by Arcese et al. (2015) and Vavra et al. (2015) appears to use a single unit process as their basis for analysis, but I would argue that this appears more similar to social impact assessments and should be avoided as it does not include the life cycle of the product. Following the approach supported by UNEP (2009) applied by Revéret et al. (2015), a simplified SLCA product system for biojet fuel would start with a simplified ELCA product system (see fig. 2) and then overlay the social actors to create the SLCA product system (see. Fig. 3). As the SLCA deals with social rather than environmental interventions, the environmental system is replaced by the part of the socioeconomic system outside of the product system. Social actors and groups such as the local community, government, and society at large fall into this grouping which could all be affected by social interventions of the product system, so the socioeconomic system remains important. The general SLCA unit process consists of the economic flows together with the social interventions such as labor inputs and company conduct to the different social actors (see table 4).

Figure 3: Simplified SLCA biojet fuel product system with social interventions

Table 4: General unit process SLCA Unit Process Inputs Outputs Economic flows Goods Goods Services Services Waste (for treatment) Waste (for treatment) Social interventions Labor Conduct to workers Conduct to value-chain members Conduct to other social stakeholders

As social sustainability results from social interactions within the socioeconomic system, the efforts of SLCA to overlay social actors as perceived in the actor perspective unto the unit processes is going in the right direction. UNEP (2011) also recommends in the LCSA guidelines to combine the unit processes and organizations within the system boundaries of the ELCA, SLCA, and LCC. However, this combination poses the problem that organizations may consist of multiple unit processes. For example, an ELCA unit process ‘biojet fuel production’ in an SLCA may belong to an organization that refines crude oil. In the ELCA, ‘crude oil refining’ is outside of the ‘biojet fuel production’ unit process, but in SLCA it is within the organization that produces biojet fuel. If the full firm is systematically taken as the element in a life cycle approach, all corresponding supply chains of the different products besides the product that is being evaluated have to be included. If this is the basis for assessing the sustainability of a reference flow, it is also dependent on the sustainability of firms and processes that are not themselves involved in the production of the reference flow. So in a generalized sense, the inclusion of the whole firm would involve assessing the sustainability of firms unrelated to the reference flow. Since all the firms have their own suppliers, this might in the extreme case mean that the whole socioeconomic system is included which counteracts the purpose of an LCA to assign only a part of the environmental flows and social interactions of the socioeconomic system to a product.

3.2.2 Flow chart The product systems in ELCA and SLCA are represented by flow charts which maps the various unit processes and their interrelations. In ELCA, the physical flows between unit processes are central to the chart while environmental flows are not visualized (see fig. 4) as the purpose is to show the interrelations and structure of the product system (Guinée 2002). The interconnectedness of the socioeconomic system makes it difficult to show all unit processes involved in the product system. To solve this, a flow chart based on boxes of aggregated unit processes can be shown with additional partial flow charts to give detailed information for each aggregated box (Guinée 2002).

Figure 4: Example ELCA flow chart. Source: Guinée 2002.

UNEP (2009) recommends for the SLCA to build the product system upon the ELCA and explains the construction of a flow chart, but it does not mention the necessity of flow charts specifically. This has led to a wider variety of flow charts with those that include the product system such as Revéret et al. (2015), those that adapted the product system like Ciroth and Franze (2011) who included a differentiation of processes based on geography, and others who did not include flow charts such as Nemarumane and Mbohwa (2015). The SLCA studies that include flow charts only show the economic flows between the processes, leaving out social interventions similarly to ELCA flow charts excluding environmental interventions. Flow charts are used in both ELCA and most SLCA studies, so they should be adapted for use in SELCA as well to more clearly represent the product systems under study. Similar to ELCA and SLCA, flow charts in SELCA only include economic flows and exclude environmental and social interventions.

3.2.3 System boundaries To start harmonizing ELCA and SLCA, the system boundaries have to be consistent. The product system of ELCA and SLCA is a system within the socioeconomic which in turn is part of the environmental systems (see chapter 3.2.1). Separating the product system requires setting three system boundaries: the boundary between the product system and the socioeconomic system, the boundary between the product system and the environment, and the boundary between product systems.

3.2.3.1 Boundary socioeconomic system The boundary between the product system and the socioeconomic system is ideally set by including all the unit processes involved in the life cycle of a product system (Guinée 202). In reality this is not possible, since there is a lack of existing data. In ELCA, databases are used to provide background data for the product system (see chapter 3.2.3). However, they do not include all unit processes and at times it will be necessary to cut-off a product system. This means that certain flows are ignored which due to time and resource constraints can be neither gathered or estimated (Guinée 2002). When applied, cut-offs need to be well reported and justified with cut-off criteria, the criteria assumptions, and if possible an estimation how the cut- off effects the results. The SLCA follows the product system of the ELCA and adapts them for organizations (UNEP 2009). The boundary constraints for SLCA are even greater, as gathering primary data for each organization is resource intensive and background databases are less developed (see chapter 3.2.3). Often priorities within the product systems are set based on the influence a particular stakeholder has and the impact an organization or life cycle has on the product system (UNEP 2009). Using this as criteria may pose the problem that the influence does not overlap with the life cycle with the greatest impact. Similar to the ELCA, the criteria and choices have to be reported in a transparent manner. Another important aspect of the boundary with the socioeconomic system in SLCA is the extent of inclusion of actors in unit processes. An implicit assumption in SLCA is that workers are not included as part of the unit process, as collecting data on the impacts on workers is common and encouraged by UNEP (2009). If the workers were considered part of the unit process, the conduct to them would be internal and not shown, similarly to the way that only in- and outflows 25 of water in a power plant are included in the ELCA unit process rather than the internal water flows between different parts. Having labor-hours as an input for the SLCA unit process requires the workers to be considered external to the unit process and if the labor-hours are not the product of a unit process, then they have to come from the socioeconomic system in an analogous way to the extraction of resources from the environmental system. Both ELCA and SLCA are limited in available data, so the ideal product system which includes all contributing unit processes cannot be constructed and using cut-off is necessary. SELCA alters the unit processes used in ELCA and SLCA, so their respective databases are in theory not usable. Thus the SELCA relies to a greater extent on cut-off than either the ELCA and SLCA, which in turn limits the total product system that can be analyzed and impacts the final results.

3.2.3.2 Boundary environmental system The boundary between the product system and the environmental system is important for the ELCA, but is not considered in the SLCA as it does not include environmental interventions. The boundary with the environment in ELCA is at the point of extraction of material and energy from sources that have not been transformed by humans, and at the point where emissions and wastes are released in areas without being transformed again by people (Guinée 2002). However, the production of biomass for biojet fuel poses a problem. In agriculture and forestry, the question arises at which point biological assets like plants and animals are part of the socioeconomic system or of the environmental system. There are three potential ways of dealing with this issue that have a major influence on how mass balances function. The first is to include the whole landscape including the soil as part of the agricultural or forestry business which means that the physical exchanges measured are those that come into or leave from the whole farm (see fig. 5 and table 5). This method has the advantage that it can show in great detail what effects the farm has on the landscape around it, such as tracking build up and erosion of soil. However, it cannot differentiate between the actions of the farmer and of natural processes on the farm. In addition, conceptually there is no clear way to determine till which depth the soil should be regarded as the socioeconomic system, and in the practical sense it will be very hard to keep track all this data.

Figure 5: Visualization system boundary of farm at landscape level

Table 5: Simplified mass balance of farm at landscape Simplified mass balance farm (at landscape) Input Output Socioeconomic Seed Produce Plant residues (transported to other Pesticide actor) Fertilizer Water (irrigation) Environmental CO2 (atmosphere) O2 (biogenic) Water (rain, ground, surface) Water (surface, ground, evaporation) Soil (formation/entering farm) Soil (erosion) Pesticide (losses from farmland) Fertilizer (losses from farmland)

The second option is to only include the biological assets as part of the agricultural and forestry businesses, so the soil and natural water reservoirs on the site are excluded. The International Accounting Standards (IAS) on agriculture includes biological assets as a requirement for financial balance sheets, with some exceptions such as plants that produce crops for multiple seasons (IASPlus 2016). The land and soil itself is considered separately in the IAS as there are agricultural and forestry businesses that rent the land rather than own it. This is an important consideration for social aspects of land ownership that may impact social sustainability. If in similar line the biological assets are considered as part of the element, the exchanges between the environment and the element are those that enter the plant or leave the plant such as excess fertilizer and pesticides (see fig. 6 and table 6).

Figure 6: Visualization system boundary of farms at plant level

Table 6: Simplified mass balance of farm at plant Simplified mass balance farm (at plant) Input Output Socioeconomic Seed Produce Plant residues (transported to other Pesticide actor) Fertilizer Water (irrigation) Environmental CO2 (atmosphere) O2 (biogenic) Water (plant uptake) Water (irrigation, evaporation) Soil nutrients (plant uptake) Pesticide (not remaining on plant) Fertilizer (not taken up by plant)

The third option is to only include the produce and wastes after harvest that are traded or treated. Guinée (2002) recommends this option which considers the non-harvested parts of the biological assets as being part of the environment. However, there is a contradiction between this standpoint and the view that CO2 uptake of plants should be recorded as an environmental inflow of biogenic carbon. The uptake is an exchange between the plant and the atmosphere which should not be part of agriculture and forestry in this third view, since only the harvested produce is considered part of the socioeconomic system. This means that all socioeconomic inputs that are put on the land and plants are flows into the environment and that the produce and plant parts harvested are flows from the environment (see fig 7 and table 7). The clear disadvantage from such a view is that all inputs become environmental interventions, while likely only a part of for example pesticides and fertilizer cause impacts of interest while the rest is taken up by or remaining on the plant.

Figure 7: Visualization system boundary of farms at harvest level

Table 7: Simplified mass balance of farm at harvest Simplified mass balance farm (at harvest) Input Output Socioeconomic Seed Produce Plant residues (transported to other Pesticide actor) Fertilizer Water (irrigation) Environmental Produce Seed (all) Plant residues (for transport to other actor) Pesticide (all) Fertilizer (all) Water (irrigation)

The choice of method will depend on the desired level of detail and effort for data gathering. However, the first and third method both have major drawbacks with the former being data heavy and the latter inadequately representing flows that cause impact. Only including the plant has the advantage of including climate change related impacts and for showing which emissions are sent into the soil and water. For SELCA, the eventual choice of environmental system boundaries will depend in part on the data availability and on the desired indicators in the impact assessment step, but it is likely going to be the methodology of including the farm at the plant level.

3.2.3.3 Boundary between product systems The boundary between product systems is where the ELCA and SLCA are not compatible. In a simple unit process, goods are turned into a single good and potentially some wastes streams. However, there are processes that produce more than one good or can use waste as an input. In ELCA each produced good or treated waste is considered a function of a process. Multifunctional processes face the problem that each function is linked to different product systems. The boundary between the product systems is set through allocation which is defined as ‘partitioning the input or output flows of a process or a product system between the product system under study and one or more other product systems’ (UNEP 2009, 64). Essentially it is a method to divide emissions of unit processes between different functions in ELCA. The underlying problem in combining the ELCA and SLCA product systems is the multifunctionality of organizations. So for the harmonization of ELCA and SLCA, SELCA requires the allocation of organizations to processes.

3.2.4 Allocation To properly define the product system and the system elements in the SELCA, allocation in the socioeconomic system has to be systematically performed. In ELCA, the first step is to gather more detailed data to model processes, so only the internal function of interest to the assessed product system remains (Guinée 2002). Functions can be determined by quantitative functional flows which can either be products as outputs or waste as input, the latter being a waste treatment function (see fig. 8).

Figure 8: Functional flows

If one process has multiple products outputs or waste inputs, the process is multifunctional. For example, if a farmer grows tomato plants and wheat the farms would be a multifunctional unit process producing tomatoes, grains, and stalks as products. However, if the wheat is grown on a specific plot of land with inputs and outputs unrelated to the tomato plants, then the farm can be modelled as two separate unit processes, one for tomatoes and one for wheat. Allocation is needed when separate modelling is not possible, so dividing emissions is done within a unit process where different functions cannot be separated from one another. Following the earlier example, wheat production results in grains and stalks as goods which cannot be grown separately from each other. This multifunctionality is resolved by allocating the emissions resulting from the biomass production and its upstream processes have to be divided among these two functions. The allocation in ELCA can be based on a variety of characteristics of the two functions such as the economic value or physical traits like mass and energy content of the products (Guinée 2002). As allocation is essentially an accounting trick, there is no strictly correct way to allocate. Researchers should therefore explicitly mention all allocation choices to ensure transparency and reproducibility of an assessment. SLCA generally follows the ELCA product system which includes allocation of unit processes (UNEP 2009). However, this cannot be applied to non-quantitative data which will be discussed later (see chapter 3.2.3), and at present allocation is not applied to organizations. This latter case is the cause of the mismatch between ELCA and SLCA product system. To harmonize them in SELCA, system elements have to be defined that include both the unit process of the ELCA and at least part of the organization of the SLCA. To illustrate this, an example will be given of a company that owns a biojet fuel production facility and regular jet fuel production facility (see fig. 9).

Figure 9: Firm with processes producing biojet fuel and regular jet fuel

In this case the firm was modeled as having two separate processes with each their own environmental and social interventions and separate supplier and customer. The multifunctional firm with two production processes can be modeled as two separate unit processes with each one product, so no allocation is required. However, it is possible that some parts of a firm are not exclusively related to either of the product systems. An example is Neste, one of the major biojet fuel producers, where the retail of all its products is done through the ‘Oil retail’ department (Neste 2015). If in our example we would have a similar set up, another ‘retail’ process is added (see fig. 10).

Figure 10: Firm producing biojet and regular jet fuel traded through the retail process

Now the retail has taken over the social interventions with suppliers and customers of the other processes while also having its own environmental flows. In this case, the interventions of ‘retail’ has to be divided among the two other processes. Following the first allocation step, it might be possible to model the retail process more detailed if the amount of labor hours of the department is known for both the biojet fuel retail and the regular jet fuel retail. However, this will not always be possible in which case the retail has is multifunctional with two functions which is similar to the problem multifunctionality problem of grain and stalks in wheat biomass production. Both the retail and the biomass production cannot be modeled as separate processes, so allocation is required to divide the environmental and social interventions between functions.

Figure 11: Quantitative allocation of Labor-hours and CO2 emissions

Table 8: Allocation factors mass and economic value Allocation factor Allocation method Biojet fuel production Regular jet fuel production Mass 0.8 0.2 Economic value 0.6 0.4

Table 9: Example allocation of retail emissions and labor-hours CO2 emissions and labor-hours per FU Allocation Biojet fuel Regular jet fuel method CO2 Labor-hours CO2 Labor-hours No allocation 175kg 27.5h 175kg 17.5h (labor-hours known) Mass 190kg 29h 160kg 16h Economic 180kg 28h 170kg 17h value 32

To illustrate this, quantitative values have been given to the biojet fuel producer example (see fig. 11). If the labor-hours are known as is the case in the example, then no allocation is required and the emissions of retail are divided equally. However, if the labor-hours are not known allocation is required using either product mass or economic value by giving allocation factors to the two production facilities (see table 8). All three scenarios would lead to different results which could potentially have a big impact (see table 9). For firms with industrial processes, retail, marketing, and other office departments may not appear to contribute much to the environmental impact, but those functions may potentially have more social interventions, such as labor-hours. So departments of firms that are not directly responsible for production should not be ignored in SELCA as they are in the ELCA. Unlike quantitative interventions, non- quantitative social interventions are not affected by allocation. However, they are affected by modelling a firm as separate unit processes which could result in certain social interventions not being associated with the product system in SELCA, while they would have in an SLCA. For example, if a firm has a production facility where workers are paid below minimum wage and other facilities where they are paid above it, the first group of workers could be excluded from the product system if their production facility is not related to the output of interest. This ensures that the part of a firm that is included in the product system is not affected by social interventions of other parts of the firm. So specifying unit processes is important for all environmental and social interventions.

Figure 12: SELCA social unit process

Table 10: General social unit process flows Unit Process Inputs Outputs Economic flows Goods Goods Services Services Waste (for treatment) Waste (for treatment) Social interventions Labor Conduct to workers Conduct to value-chain members Conduct to other social stakeholders Environmental interventions Abiotic resources Emissions to air Land transformation Emissions to water Biotic resources Waste heat

The result of the allocation steps in the SELCA is the creation of a consistent system element with environmental and social interventions (see fig. 12 and table 10) which will be called the social unit process (SUP) to distinguish it from the regular unit process. The SUP has both environmental and social interventions, but it is not the simple addition of ELCA and SLCA unit processes. Through modeling and allocation, a firm may be split between different SUPs which are all treated as separate cohesive units, though an individual SUP may consist of several departments within a firm. The SUP enables SELCA to assess a coherent product system, instead of being two assessments with different product systems as ELCA and SLCA are now.

3.2.5 Inventory data The ELCA and SLCA require unit process data for the analysis of the environmental and social sustainability of each system element and the product system as a whole. There are three different forms of data: quantitative, qualitative, and semi-quantitative. The difference can be illustrated by comparing two forests which can be quantitatively done by counting trees, qualitatively by describing the forests in words, and semi-quantitatively by scoring the quality of the forests on a scale or by looking at qualities that are present or absent. ELCA is based on quantitative data related to the extraction of resources and the release of emissions to the environmental system by a unit process (Guinée 2002). Quantitative data can be expressed per unit of process output and added together to show the total for a product system in accordance with the ISO standards (UNEP 2009). Guinée (2002) recommends the compartmentalization of environmental flows into air, fresh water, seawater, agricultural soil, and industrial soil (see table 8). SLCA can also use quantitative data like the amount of labor hours per unit of process output, but many forms of data are qualitative and semi-quantitative (UNEP 2009). The data is related to the interactions of firms with other actors in the socioeconomic system. Qualitative data is transformed into semi-quantitative data to make comparisons between alternatives easier. This is done by expressing the description in a yes/no form, e.g. ‘does an employee

34 education program exist?’, or through a scoring system, e.g. ‘rating of employee education program on a scale from 1-5’. Though UNEP (2009) does not explicitly state the use of compartments similar to those in ELCA, the stakeholder categories of workers, consumers, local community, society and value chain actors, could fulfill a similar role though they are not exclusive compartments, as someone in the local community can be a worker and is part of the larger society. The compartments in ELCA are the sink of the physical flows and as social impacts are the result of social interactions between the element and others either in the supply chain or the socioeconomic system, the stakeholder categories are similarly a sink for these interactions (see table 11)

Table 11: Environmental and social compartments of impact sinks Compartments Environmental Social Air Workers Fresh water Consumers Seawater Local community Agricultural soil Society Industrial soil Value chain actors

SELCA will have to combine the ELCA and SLCA unit process data in order to include both environmental and social sustainability issues which requires that the data in the SUP are equivalent to each other. The ‘Driving forces-Pressure-State-Impact-Response’ (DPSIR) framework could be used to illustrate how the data relates to an SUP. The DPSIR framework (see fig. 9) was adopted by the European Environmental Agency as a typology for the increasing number of indicators based on the way society and the environment interact (EEA 1999). ‘Drivers’ are the socioeconomic activities and developments with ‘pressures’ being the resulting material and energy flows into the environment. These flows change the ‘state’ of the environment such as air, water and soil quality. This in turn has an ‘impact’ on the socioeconomic system and other aspects of the environment, which in turn creates a ‘response’ from society. The indicators used to assess environmental sustainability can be subdivided depending on what part of the DPSIR framework is attempted to be described. The key observation of DPSIR is that other elements in the framework can be used as proxies for the impacts affecting us.

Figure 13: The DPSIR framework for Reporting on Environmental Issues. Source: EEA 1999

If the DPSIR framework is applied to ELCA, the unit process becomes the driver. Inventory data such as CO2 emissions are an output of the unit process, which means that data is on the pressure level. The CO2 emissions change the state of the CO2 concentration in the atmosphere which in turn impacts society through climate change. That data is on the pressure level is not a coincidence as pressures can be attributed to specific drivers. State and impact indicators can be influenced by different systems such as other unit processes emitting CO2, and through drivers and pressures, states, and impact in the past, for example the lag between the emissions CO2 in the past and the climate impacts at present. Though intended for categorizing the interaction between the socioeconomic and environmental system, the DPSIR framework could potentially be used to analyze interactions within the socioeconomic system too. If the SUP is the driver, the social inputs and outputs are the pressures. As shown before (see 3.2.1) the social inputs are labor hours and social outputs are the conduct of a firm, however in context of the DPSIR framework the conduct should be further clarified. For example, if a SUP provides education to the local community the act itself is the pressure, but education level of the community is at the state level (see fig. 10). Another example is employing workers; the act of employment is the pressure of the SUP with the total employees of an organization being a state and the labor hour input can be seen as the amount of labor employed per unit of process output (see fig. 10). A possible example of semi- quantitative data related to conduct is the presence of discrimination based on gender during recruitment (Foolmaun & Ramjeeawon 2013).

Figure 14: Example SUP pressures and state of education and employment

There are two general sources of inventory data: primary data which is gathered personally and secondary data which is gathered from literature and databases (UNEP 2009). The processes for which primary data is gathered are foreground processes, while secondary data is gathered for background processes (Guinée 2002). In ELCA primary data is gathered through interviews or by collaborating with firms, but crucial for the total product system is the existence of several large databases containing unit processes, such as Ecoinvent (2016), GaBi databases (2016), and NREL (2016). SLCA practitioners focus largely on primary data, since established databases similar to ELCA are lacking. National statistics are a source used for background data, but the creation of the Social Hotspot Database (2016) is a start towards a dedicated SLCA database. This background data is on the industry sector level rather than unit process, so it cannot be used to the same extent as ELCA databases. Without well-developed databases for background data, the scope of SLCA is usually limited to the foreground processes (Revéret et al. 2015). The inventory data of SELCA is a potential combination of quantitative and semi- quantitative data used in ELCA and SLCA. To ensure that the inventory of SELCA is harmonized, all data should be on the DPSIR pressure level. However, using databases will not be feasible as long as SLCA databases do not correspond to the unit processes of ELCA databases. Ideally a combined database would be formed, but in its absence SELCA is dependent on primary data which sets limits on the total size of the product system that can be included in an assessment.

3.2.6 Inventory tables The result of the inventory analysis is the inventory table of the product system. This consists of two steps in ELCA, the first is the creation of a matrix containing every unit process which can then be used to calculate the environmental interventions in relation to the FU (Guinée 2002). This results in a single table containing all the emissions throughout the life cycle of a product system. In SELCA the quantitative social interventions are also added to the quantitative inventory table as shown in table 12, though in practice this list would be much longer.

Table 12: Quantitative inventory table Quantitative intervention Compartment Quantity Dinitrogen monoxide Air 1kg Nitrate Water 4kg Phosphate Water 0.1kg Methane Air 1kg Labor Workers 10h

The inventory tables have not been defined systematically for the SLCA by UNEP (2009), as the scope of an SLCA generally only includes organizations that are considered significant. The same procedure as the ELCA is possible when using quantitative data, but semi-quantitative data cannot be aggregated. One method used in SLCA is to use tables for each unit process that records the data as applied by Ciroth and Franze (2011) which is only practical in relatively small product systems. Another method used by Martínez-Blanco et al. (2014) and Ekener-Petersen and Finnveden (2013) is to list all the semi-quantitative indicators in the first column and have all the other columns stand for a particular unit process score (see table 13).

Table 13: Semi-quantitative inventory table Social unit Compartment processes Workers Local community Semi-quantitative interventions Child labor Discrimination Local education program (yes/no) (yes/no) (score 1-5) Biojet fuel No No 2 producer Biomass Yes No 4 producer Transporter No Yes 3 Electricity Yes No 1 producer Hydrogen No Yes 2 producer

SELCA could potentially hold both quantitative and semi-quantitative data, so both table 12 and 13 can be used in the inventory tables. The quantitative inventory table would hold the environmental interventions and quantitative social interventions, while the semi-quantitative inventory table would mainly consist of social interventions, though it is in principle possible for semi-quantitative environmental data to be included too. Together, the quantitative and semi- quantitative inventory tables will be the main outcomes of the inventory analysis which will be the input into the impact assessment.

3.3 Impact assessment The life cycle impact assessment is the third part of both the ELCA and SLCA methodologies where the results of the inventory analysis are analyzed, aggregated, and assessed to determine the environmental and social sustainability impacts of the product system. Choices will have to be made regarding the impact categories that are assessed and the methods of linking the inventory analysis data to them. This study will only consider the mandatory parts of the impact assessment which are impact category choice, classification, characterization and normalization.

3.3.1 Selection of impact categories and characterization methods Impact categories are groupings of the inventory analysis data to organize and differentiate sustainability impacts. The impacts relate to certain aspects of sustainability called areas of protection within the ELCA and SLCA terminology. The common ELCA areas of protection are: human health, natural environment, natural resources, and man-made environment (Guinée 2002). For SLCA, ‘human well-being’ has been proposed as another area of protection (Dreyer et al 2006). The aim of ELCA and SLCA is to connect the inventory data to the areas of protection through impact categories.

3.3.1.1 Selection of environmental impact categories and characterization methods In ELCA impact categories can be on different levels along a causal-chain between the inventory data and the areas of protection. The first is the midpoint category which aims to capture a particular problem and is close to the environmental interventions and flows, for example climate change, while endpoint categories aim to capture a particular damage on society or the environment, such as damage to ecosystem diversity through loss of species (Goedkoop et al. 2008). The advantage of midpoint categories is less uncertainty as the causal link to the data is shorter, while endpoint categories may correspond better with problems faced by decision makers despite having more uncertainty through extra assumptions in the modelling (Bare et al. 2000). Guinée (2002) recommend the use of the basic set of midpoint impact categories all with quantitative category indicators, as there is a greater consensus on their use (see table 14). Each of the basic impact categories has a characterization method that defines a category indicator and gives characterization factors for specific environmental interventions. Using DPSIR, the characterization methods turn pressure level data into state or impact level indicators. For example, carbon dioxide, methane and nitrous oxide emissions are pressure level data that contribute to climate change which is on the state level. The pressure data is changed into infrared radiative forcing through the ‘global warming potential for a 100-year time horizon’ (GWP100) characterization method developed by IPCC which is expressed as CO2 equivalent emissions (see table 15). As the basic impact categories are standardized in ELCA, they should be incorporated into SELCA for the environmental impacts.

Table 14: Basic environmental midpoint categories and category indicators (Guinée 2002) Impact category Category indicator Unit Depletion of abiotic Depletion of the ultimate reserve in relation Kg (antimony- resources to annual use equivalent) Impacts of land Land occupation m2*year competition Climate change Infrared radiative forcing Kg (CO2 equivalent) Stratospheric ozone Stratospheric ozone breakdown Kg (CFC-11 depletion equivalent) Human toxicity Acceptable daily intake/predicted daily Kg (1,4- intake dichlorobenzene equivalent) Freshwater aquatic Predicted environmental Kg (1,4- ecotoxicity concentration/predicted no-effect dichlorobenzene concentration equivalent) Marine aquatic Predicted environmental Kg (1,4- ecotoxicity concentration/predicted no-effect dichlorobenzene concentration equivalent) Terrestrial ecotoxicity Predicted environmental Kg (1,4- concentration/predicted no-effect dichlorobenzene concentration equivalent) Photo-oxidant Tropospheric ozone formation Kg (ethylene formation equivalent) Acidification Deposition/acidification critical load Kg (SO2 equivalent) Eutrophication Deposition/N/P equivalents in biomass Kg (PO4 equivalent)

Table 15: Climate change infrared radiative forcing characterization factors Environmental Inventory result Characterization Category indicator result: Kg (CO2 emission factor: GWP100 equivalent) Carbon dioxide 1000 kg 1 1000 kg (CO2 equivalent) Fossil methane 50 kg 30 1500 kg (CO2 equivalent) Nitrous oxide 10 kg 265 2650 kg (CO2 equivalent) Total 5150 kg (CO2 equivalent)

3.3.1.2 Selection of social impact categories and characterization methods For SLCA, ‘human well-being’ has been proposed as the area of protection (Dreyer et al 2006). Unlike ELCA, there are no currently proposed SLCA impact categories and characterization methods with corresponding indicators that have been generally accepted and standardized. As the ELCA is further developed and the SELCA aims to harmonize the ELCA and SLCA, the proposed social impact and indicators will be critically assessed based on two selection criteria derived from ELCA indicators: 1. Indicators should be based on inventory data in the SUP which are at the DPSIR pressure level. 2. Category indicators should theoretically be capable of being aggregated for the full product system

In this study a review has been conducted that analyzes existing SLCA impact assessment frameworks proposed and applied by various researchers with varying impact categories, characterization methods and indicators. The impact assessment frameworks can be divided between methods that group different indicators together based on a specific topic and methods that causally link the category indicators to data (Wu et al. 2014). For each framework, the impact categories, number of indicators, the type of indicators, whether the data is on the DPSIR pressure level, and whether the indicators could be aggregated for a full product system are all stated. These latter two determine the compatibility with the existing ELCA indicators.

3.3.1.3 Grouping based impact categories and characterization methods Table 16: UNEP (2009, 45) SLCA endpoint impact categories Social endpoint impact categories Stakeholder categories Impacts categories Worker Human rights Consumer Working conditions Local community Health and safety Society Cultural heritage Value chain actors Governance Socioeconomic repercussions

Table 17: UNEP (2009, 49) Stakeholder subcategory/midpoint impact categories Stakeholder category Subcategory Worker Freedom of Association and Collective Bargaining Child Labor Fair Salary Working Hours Forced Labor Equal opportunities/Discrimination Health and Safety Social Benefits/Social Security Consumer Health & Safety Feedback Mechanism Consumer Privacy Transparency End of life responsibility Local community Access to material resources Access to immaterial resources Delocalization and Migration Cultural Heritage Safe & healthy living conditions Respect of indigenous rights Community engagement Local employment Secure living conditions Society Public commitments to sustainability issues Contribution to economic development Prevention & mitigation of armed conflicts Technology development Corruption Value chain actors Fair competition Promoting social responsibility Supplier relationships Respect of intellectual property right

Table 18: Grouped indicators SLCA impact assessment Endpoint Impact categories Midpoint Indicator DPSIR Product system framework impacts type pressure aggregation Ciroth and Franze 2011 - Workers 25/18 Score 1-6 Partially No (stakeholders) - Society 15/8 - Local community 25/21 - Value chain actors 8 - Consumers 15 Franze and Ciroth 2011 - Workers 8 Existent/non- No No (stakeholders) - Society 4 existent - Local community 3 - Value chain actors 2 Ekener-Petersen and - Workers Ordinal No No Finnveden 2013 - Society categories (stakeholders) - Local community - Value chain actors - Consumers 54 Foolmaun and - Workers 9 Score 0-4 Partially No Ramjeeawon 2013 - Society 1 (stakeholders) - Local community 1 Hosseinijou et al. 2014 - Workers 6 Score 1-9 Partially No (stakeholders) - Society 5 - Local community 12 - Value chain actors 2 - Consumers 2 Martínez-Blanco et al. - Workers 3 Ordinal No No 2014 - Society 2 categories (stakeholders) - Local community 3 - Consumers 4 Aparcana and Salhofer - Human rights 4 Score 0-1 No No 2013 - Working conditions 19 (UNEP impact) - Socio-economic repercussions 3 Manik et al. 2013 - Human rights 3 Score 1-7 No No (UNEP impact) - Working conditions 5 - Cultural heritage 8 - Socio-economic repercussions 5 - Governance 3 Hutchins and - Labor equality 1 4 semi- Partially No Sutherland 2008 - Healthcare 1 quantitative - Safety 1 ratios - Philanthropy 1 Dreyer et al. 2010 - Child labor 1 Semi- No No - Forced labor 1 quantitative (driver) - Discrimination 1 risk based - Restriction of freedom and 1 score association Vinyes et al. 2013 Single aggregated social impact 11 Score 1-5 No No 43

Grouping based characterization methods have been promoted by UNEP (2009) which mentions two different sets of impact categories. Interestingly, the impact categories include human health which is also addressed in ELCA through human toxicity. The difference being that in ELCA health is only affected by toxic substances while in SLCA it is the result of practices such as overworking and safety regulations. The two sets of impact categories start by grouping data together into subcategories, but are differentiated by aggregating either into stakeholder categories or impact categories (see table 16). UNEP has proposed a list of subcategories for the stakeholders (see table 17) but lacks characterization models and a similar list for the impact categories within the first type. Much of recent research has focused on proposing and applying characterization models and indicators for the UNEP stakeholder and impact category lists. From the reviewed methodologies based on grouping, six have followed the UNEP stakeholder approach, two following the UNEP impact categories, and the final three having different indicators (see table 18). Generally, scores are given for a set of indicators that based on primary data and secondary in the form of national statistics too, most notably Ekener-Petersen and Finveden (2013) who solely depend on secondary data. The large majority of the social impact assessment frameworks are not based on pressure level data and Ciroth and Franze (2011) even have two sets of indicators for foreground and background processes. The lack of indicators based on pressure data can be attributed to the use of so-called direct indicators measuring a social issue and indirect indicators measuring the policy of a firm regarding the social issue. For example, if a company hires local workers this could be measured directly through the amount of local laborers, or indirectly through the management and policy regarding the employment of local workers (see fig. 15). The DPSIR model shows that the direct indicators are based on data at the state level and indirect indicators on data at the driver level. Any indicator based on either of these will not use data on the pressure level and is excluded on the basis of the criteria based on ELCA.

Figure 15: Direct and indirect indicators example hiring local workers

There are four exceptions that are partially based on pressure level data. Ciroth and Franze (2011) and Hosseinijou et al. (2014) included indicators for the impact on the local community based on resource extraction and water usage which are environmental and economic flows on the pressure level. In contrast, Foolmaun and Ramjeeawon (2013) have indicators that are based on the conduct of the company rather than physical flows, such as whether workers are forced to work or if there is a discrimination based on gender during recruitment. There is also an indicator based on how much funds are spent on community projects and this flow of money from the firm to the local community is at the pressure level. Hutchins and Sutherland (2008) have a quite different set of impact categories and indicators from the rest. The impact categories are labor equity, healthcare, safety, and philanthropy with indicators based on ratios. Some of these ratios are based on monetary flows like labor cost, healthcare expenses, and charitable contributions which are also on the pressure level. However, monetary flows are beyond the scope of this research so they will not be further considered. Dreyer et al. (2010) have taken quite a different approach with the impact category of child labor, forced labor, discrimination, and restriction of freedom and association. This is an indirect method, so it measures the management policies and efforts in place. The methodology starts by assigning three scores to individual management measures based on ‘guidelines and practices’, ‘communication and delegation of responsibility’, and ‘systematic active control’. The scores are then multiplied for individual management measures creating a single score for each. These management measure scores are then added up and are termed the ‘company performance’ (CP). The CP is then transformed into the ‘company free rein’ (CFR) by subtracting the CP from the maximum CP of the ideal firm and then dividing it by the maximum CP:

The CFR is then turned into the ‘company risk’ (CR) by multiplying the CFR with a ‘contextual adjustment factor’ (CAF). The CAF is a factor based on the particular context such as industry sector and geographical location for a particular company which is differentiated into five categories each with a corresponding factor value between 0.4 and 1.

The final step is to relate the CR with the particular product of interest in the product system by multiplying with the ‘product relation factor’ (PRF) to create the ‘product risk score’ (PRS). Though the PRS relates to products, it is not based on pressure data as the PRF divides the driver among its products analogous to allocating organizations to product systems. This results in a more limited driver, but lacks the link to pressure data. Though driver data is used as the basis for the framework, it has an interesting method that starts with a scoring system similar to other grouping frameworks, but aggregates and transforms the data systematically into an indicator representing the risk of something happening. Despite the existence of some indicators based on pressure level data, none can be aggregated over a supply chain. The social impact frameworks assess the impacts of a single company or particular part of a supply chain and give them a score, but there is no proposed mechanism to aggregate various scores into a score for the supply chain. Ciroth and Franze (2011) have attempted to include a larger portion of the product system in several distinct unit processes. Rather than aggregating the category indicator scores for the whole product system,

45 the scores of all the indicators were aggregated into a single score for each individual unit process. Dreyer et al. (2010) attempt to relate their impact score to a particular company product, but also lack a method to aggregate the scores of multiple companies within a product system. The lack of aggregated values for the product system make all of the grouping based social impact assessment frameworks incompatible with the ELCA indicators.

3.3.1.4 Causal based impact categories and characterization methods

Table 19: Causal indicators SLCA impact Impact Number of Indicator type Data on Product assessment categories indicators DPSIR system framework pressure level aggregation Brent and - Internal human 3 Quantitative Partially No Labuschagne resources 2006 - External 12 population - Macro social 2 performance - Stakeholder 1 participation Feschet et al. - Human health 1 Quantitative life expectancy Partially No 2013 over reference time frame in specific geographical location Hunkeler - Housing 1 Quantitative, provision of Yes Yes 2006 - Healthcare 1 housing/healthcare/education - Education 1 - Necessities 1 Weidema - Well-being 1 Quantitative QALY Potentially Yes 2006 yes

The second type of impact categories and characterization methods differ from the grouping based methods in that they are causally connected pathways from the data to the impact categories rather than grouped based on a common topic (Wu et al. 2014). Four such methods have been reviewed, most of which predates the UNEP (2009) guidelines (see table 21). Though each method differs, all have quantitative indicators. Brent and Labuschagne (2006) have made causal chains from possible inventory data through 18 midpoints to four endpoint indicators. Data on the DPSIR state level such as total employees and the SO2 concentration are measured and normalized through the use of local or regional social footprints. The normalized data is then multiplied with a significance value that is based on the current state and target of the social footprint. Following this the values can be added together, but theoretically this requires characterization factors which in the example has been assumed as equal. No actual characterization factors have been proposed, but Brent and Lubaschagne (2006) have partially quantified the inventory data for their case study. In its current state neither of the two criteria are fulfilled and, as concluded by themselves, there is a lack of social footprints to normalize data. Interestingly, the social impact scores could in principle

46 be aggregated if the inventory data was on the pressure level and related to the FU. However, the lack of characterization factors and social footprints make this social impact assessment framework unusable as it is. Weidema (2006) in his methodology aims to quantify human well-being as the social area of protection. His starting point is to establish how various forms of social impacts can be quantified into the quality adjusted life years (QALY) metrics. His examples are preliminary estimates on the global scale made on various issues such as impacts of anxiety and autonomy infringement. Though acknowledging that comprehensive pathways from social inventory data is lacking, some straight forward links were mentioned and quantified such as the QALY for child labor hours. In principle this method could be based on pressure level data and it can be aggregated, but without proper pathways from the inventory data it is not yet an operational method. In the research of Feschet et al. (2013), the life expectancy at birth is used as a proxy for human health with life expectancy being correlated to national GDP through the Preston curve. To link a firm to a particular national GDP, the firms local value added is calculated as a midpoint which is divided in direct primary value added based on primary data, indirect primary value added based on national statistics, and indirect secondary value added also based on national statistics. This means that only the direct primary value added data is on the pressure level, while the others are not. By assuming that the local value added contributes linearly to the GDP of a country, it can through characterization be transformed into the contribution to the life expectancy at birth. At present the value is calculated for a whole firm, so the value cannot be aggregated along a product system. However, if the local value added were only on the pressure level and scaled to a product output, it would be possible to aggregate. Since this methodology is based on monetary flows, it will not be further considered in this study. The final methodology is of Hunkeler (2006) where labor hours for each geographic region are tracked as inventory data. Ideally the labor hours are an input into the unit processes, but at present large inventory databases do not have this data. Instead he proposed deriving labor hours from economic flows and either way the data is based on pressure level inventory data. Hunkeler then made his impact categories about the costs related to housing, healthcare, education, and necessities. To link the labor-hour data to the impact categories, he first determined the cost of one unit for each impact category, for example one house. He then divides the costs by the average wage per hour in a country to determine the amount of labor-hours needed for one unit. This is then inverted into the amount of units per labor-hour which can be multiplied with the labor-hours from the product system. He also included a weighing step within the characterization method by assigning the labor-hours to the different impact categories, because he reasoned that the wage earned to pay for one impact cannot be used to pay another at the same time. He arbitrarily chose to equally divide his labor-hours among the four impact categories, resulting in one labor-hour contributing 0.25 labor-hours for each impact. The weighed labor hours are then multiplied by characterization factors for the housing, healthcare, education, and necessities impact categories specified for each country or geographic location. The category indicator values of labor hours in different countries can be added together for a specific topic such as housing to give the total housing impact.

The most interesting aspect of Hunkeler’s methodology is that the impacts relate to the FU in the same way as ELCA indicators. The differentiation of labor hours between countries can be further refined by distinguishing between specific sectors within a country as the average wage not only differs between countries but also between sectors. However, there are also some problems with his original case study. It is not stating how the values for each impact category were determined and it appears that it was done on different time scales. For example, housing costs could be for a period of 30 or more years, while necessities may be for a week, month, or year. Dividing the labor hours among them does not make sense until the impacts have been normalized. This also sets the weighing step after normalization which is more in line with ELCA, though this is discussed later (see chapter 3.3.3). Explicitly stating the units and time scales for each impact category value and applying normalization before weighing would make Hunkeler’s (2006) social impact assessment methodology fully operational in SELCA.

3.3.1.5 Life cycle attribution assessment The LCAA methodology is a separate development that combines certification-based indicators with the LCA methodology (Wu et al. 2014). It has as research question as ‘what percentage of my supply chain has attribute X?’ instead of researching what the impact score is. The attribute must be a specific certification scheme such as the Forest Stewardship Council certification (Wu et al. 2014). It is possible to research the contribution of different unit processes to the attribute percentage and to study alternatives by comparing the percentages with each other. Andrew et al. (2009) followed the methodology of Hunkeler (2006) by using labor hours as the pressure level inventory data. However, instead of transforming the inventory data into equivalent labor hours, Andrew et al. (2009) directly use labor hours in the inventory. The unit processes are assigned a value of 0 or 1 to signify the lack or compliance to a specific certification, though the method also works for other values between 0 and 1. These are the characterization factors given to the labor hours which are related to the FU. So after aggregation it is possible to calculate the percentage of labor hours per FU that have a particular attribute. As a critical condition for the LCAA is the relation between an attribute and a unit process flow. An attribute should only be linked to a specific kind of flow if the attribute is about that flow, so in the case of labor hours only attributes related to the workers can be used. If non-worker related attributes, such as land rights, are assigned to labor hours, then the importance and weighing of the land rights attribute would depend on the amount of labor needed to produce a certain product rather than the amount of land. So one of the drawbacks is the lack of flows such as labor hours in existing ELCA databases and the high burden of gathering data for the compliance of each company with a certification or attribute (Wu et al. 2014).

3.3.1.6 Conclusions of the selection of impact categories and characterization methods The ELCA and SLCA impact categories and characterization methods vary largely from each other. To create a coherent set of indicators for SELCA, two criteria were derived from ELCA which has a well-established basic set of indicators. All characterization methods have to use pressure level data and theoretically be capable of being aggregated for the full product system. The ELCA characterization methods are automatically included as the criteria are based on them, but most SLCA methods do not conform as they either do not use pressure level data or are able to be aggregated. Hunkeler (2006) and the LCAA methodology of Andrew et al. (2009) are the SLCA

48 methods that could potentially be used, though the LCAA methodology burden for gathering company specific attribute data is too large. The methodology of Hunkeler is operational since the input data is based solely on labor-hours, though the impact categories do not match those promoted by UNEP (2009). Interestingly, some characterization methods are based on monetary flows rather than physical flows or social conducts. This shows that it may be fruitful to integrate the economic aspects with monetary flows through LCC or other methods into SELCA as some social aspects could be linked to them. At present, the SELCA can combine the basic set of ELCA characterization methods together with the Hunkeler (2006) SLCA method based on unit process flows.

3.3.2 Classification and characterization The classification and characterization steps are two mandatory parts of the impact assessment. In both ELCA and SLCA, classification involves qualitatively assigning inventory data to a particular impact assessment. During characterization the inventory data is transformed into the indicator result which depends on the characterization methods for the impact categories mentioned before (see chapter 3.3.1). As there is no difference between ELCA and SLCA in classification and characterization steps, the inventory data in SELCA will also undergo these steps according to the characterization methods chosen for the impact assessment.

3.3.3 Normalization, grouping and weighing In ELCA the results after characterizations can still be further transformed through normalization, grouping and weighing. By normalizing, quantitative results are transformed relative to outside reference information into values between 0 and 1, such as relating the CO2-equivalent emissions for the impact on climate change to the total global or national emissions (Guinée 2002). The importance of the product systems impact is then better illustrated. For grouping, the different category indicators are aggregated into sets for sorting or ranking (Guinée 2002). This is not mandatory and also not methodologically clear how it should be performed. In weighing the normalized impact category results are multiplied by factors based on relative importance which could then be aggregated as well (Guinée 2002). In SLCA the different characterization methods have incorporated various of these steps. Grouping and weighing is often already included in the grouping based methods where different indicators are grouped into one impact category which requires weighing. Normalization is only possible with quantitative data so it is not generally applied in SLCA impact assessment frameworks as most are based on semi-quantitative data. However, Brent and Labuschagne (2006) have a similar step when relating their inventory data to social footprints. For ELCA only normalization is recommended while grouping and weighing, which lack standardized methodological guidelines, are not recommended. In SLCA normalization, grouping, and weighing are not recommended, because the impact categories and characterization methods are still in development. Because of the difference between ELCA and SLCA, SELCA will include normalization if possible, and exclude weighing and grouping until the social indicators are further developed.

3.4 Interpretation The final phase of ELCA and SLCA is the interpretation where the study and its results are evaluated and conclusions are drawn (UNEP 2009). The ELCA is the most complete with the consists consistency and completion checks, contribution and perturbation analysis, sensitivity and uncertainty analysis, and the conclusion and recommendation (Guinée 2002).

3.4.1 Consistency and completion checks The consistency check is equivalent for the ELCA and SLCA. In both, the study is checked for its consistency in assumptions, methods, data, and models with the goal and scope (Guinée 2002; UNEP 2009). This is followed by the completeness check which in ELCA is evaluation of the study by internal or external ELCA or technical experts, ideally based on comparisons with previous studies (Guinée 2002). Completeness is also checked in SLCA to find out if all the relevant data was available and used and to check if the indicators that have been used were correct, though no specific method is proposed (UNEP 2009). The SELCA follows the consistency check which is similar in ELCA and SLCA, and the completeness check of ELCA if possible.

3.4.2 Contribution and perturbation analyses The contribution analysis looks at the extent certain flows or processes contribute to a certain impact which is a standard approach in ELCA (Guinée 2002). In SLCA this is only possible for quantitative methods and LCAA methods based on quantitative interventions (UNEP 2009). None of the semi-quantitative characterization methods is capable of being aggregated for the product system, so calculating contributions is not possible. The perturbation analysis checks the effect of small changes and the sensitivity and uncertainty analysis checks the effect that different assumption could have on the results, though it is not mandatory in ELCA and not mentioned for SLCA (Guinée 2002). The SELCA includes quantitative indicators, so contribution analyses should be conducted and if possible the perturbation analysis can be included too, though it should not be mandatory as it is not standard practice in either ELCA or SLCA.

3.4.3 Sensitivity and uncertainty analysis The sensitivity and uncertainty analysis is used in ELCA to determine the robustness of the results by adding ranges for data inputs or using different variables (Guinée 2002). This will not be included in the SELCA, as it is not required for ELCA because suitable data often is lacking and it is not performed in SLCA.

3.4.3 Conclusions and recommendations The final part of the interpretation phase are the conclusions and recommendations. In both ELCA and SLCA the conclusions are drawn from the results summarizing the main sustainability issues and giving explanations and justifications for the assumptions and methodological choices made in relation to the goal and scope (Guinée 2002; UNEP 2009). After this the recommendations on possible actions are reported. As this step is the same in ELCA and SLCA, the SELCA will follow it as well.

3.5 Conclusions of social and environmental life cycle assessment integration The harmonization of ELCA and SLCA into SELCA requires adjustments in all the four phases. The first three sub-questions (see chapter 1) have been answered in chapter three of this study. In the goal and scope the choice of research questions and function are important. SELCA can accommodate hotspot and comparative analyses, but it cannot be used for compliance evaluation. For the latter maturity of the methodology is needed as is the case in ELCA. SELCA should use the expanded SLCA function, but the trade-off between the comparability of products and the diversity of alternatives must be kept in mind. The inventory analysis forms the basis of the of SELCA. The underlying problem in combining the ELCA and SLCA product systems is the multifunctionality of organizations. To solve this, allocation of organizations is needed to create consistent unit processes. I have termed the resulting unit processes as SUP which has both environmental and social interventions. Whereas in ELCA only includes flows and interventions directly related to the unit process and the SLCA includes the whole organization responsible for the unit process, the SUP includes the ELCA unit process and other parts of an organization allocated to the process. This alteration of the basic system element limits the use of existing background databases, since the unit processes on which they are based differ from the SUP. As a result, SELCA requires cut-offs to a greater extent than ELCA and SLCA which limits the total product system. However, the inventory data can in principle be quantitative and semi-quantitative, though all data should be on the DPSIR pressure level. The flow charts of SELCA product systems only include economic flows and exclude environmental and social interventions, similarly to ELCA and SLCA flow charts. The potential outputs of the inventory analysis are the quantitative and semi-quantitative inventory tables. The SELCA impact assessment determines the social and environmental indicators that can be applied. Two criteria were derived from ELCA to make a set of social indicators that is compatible with existing ELCA indicators. The first criterion is that all characterization methods must use pressure level data, and the second is that the impact scores must be capable of aggregation for the product system. All the ELCA indicators fulfill these criteria, but only the methodologies of Hunkeler (2006) and LCAA of Andrew et al. (2009) can be used which are linked to labor-hours. The former is operational though the impact categories do not match those of UNEP (2009), but for the latter, data gathering is impractical with larger product systems. Only Hunkeler’s methodology will be used alongside the environmental indicators in SELCA with the corresponding classification and characterization steps, though normalization, grouping, and weighing must be excluded until the social indicators are further developed. The interpretation phase of SELCA includes several of the checks and analyses of ELCA and SLCA. The consistency and completeness check are included as they are standard practice in ELCA. The contribution analysis is also a mandatory inclusion for the quantitative indicators. Perturbation, sensitivity and uncertainty analyses are left out of SELCA as they are not included in SLCA and not standard practice in ELCA. Finally, the conclusions and recommendations remain the same for SELCA as they are in SLCA and ELCA.

The answers for the sub-research questions are within the inventory analysis and impact assessment phases of SELCA. The first sub-question, “How can the social and environmental life cycle assessment system boundary and elements be adapted to include both physical unit processes and social actors?”, is answered through the creation of the SUP and the use of cut- offs that together form a coherent product system, though smaller than that of ELCA, for both environmental and social aspects. The second sub-question, “what are the requirements to ensure that potential social and environmental indicators are comparable within the social and environmental life cycle assessment?”, has been dealt with through the DPSIR pressure level data and the aggregation criteria for impact assessment characterization methods. Finally, the answer to the third sub-question, “Which social indicators are promising to be used alongside existing environmental indicators within the social and environmental life cycle assessment?”, is that only a few social indicators match the criteria set. The SELCA methodology has been established by answering these three sub-questions, so the next step is to put it in the context of the biojet fuel product system.

4 SELCA and assessing biojet fuel social and environmental issues The SELCA was created to in an attempt to combine the social and environmental sustainability assessments for biojet fuel. The previous chapter showed how the SELCA could work in principle. To understand if it is a suitable method for analyzing the biojet fuel product system, the methodology will be analyzed on three aspects: the representation of actual biojet fuel production system in SELCA, the social and environmental issues of biojet fuel addressed in the SELCA impact assessment compared to the impact categories considered important by biofuel academia and biojet fuel industry, and the setup for a dummy case study. The outcomes of this chapter will determine the exemplary case study in the next chapter.

4.1 Biojet fuel product system in SELCA To better understand the biojet fuel production system, a questionnaire has been conducted among several stakeholders (see appendix 2). The existence of trading as a separate process responsible for the exchange of goods between different producers is an important conclusion. One of the questionnaire respondents produced Miscanthus, but also functioned as the trader for themselves and for other agriculturalists. Though in this particular firm the producing and trading processes were combined, for the other Miscanthus producers this was a function outside their scope. Another respondent was a firm downstream of the biojet fuel production and facilitates the purchase of biojet fuel for customers, in essence being another firm functioning as a trader. It is important to note that trading is a separate process from transport providers. The latter is only responsible for providing transport which can be hired by firms with a trading process. Though combinations of trading and transport are possible similarly to the combined production and trading of Miscanthus, on a more general level these processes can be separated. Modelling these processes is possible in ELCA and SLCA too, but in this study these processes will be accommodated standardly in their own SUP within SELCA. The main limitation of SELCA the lack of data availability. As mentioned before, the existing background databases for ELCA and SLCA would not suffice for SELCA. The data in those databases do not allocate organizations in a consistent way, which is done in SELCA. Also the SLCA background databases have not been developed far enough and their structure is sector and country based rather than on the process level (see chapter 3.2.3). As long as databases are not modified to include SUPs, the SELCA relies on gathering primary data which is limits the extent of the product system, however it can in theory include all the relevant biojet fuel firms and unit processes coherently into one framework.

4.2 Biojet fuel social and environmental sustainability issues in SELCA Whether SELCA can be a successful tool for analyzing the biojet fuel product system depends on the capability of measuring environmental and social sustainability impacts considered of importance for biojet fuel. The list of impact indicators compatible with SELCA mentioned in the previous chapter (see chapter 3.3.1) will be compared to topics considered important by researchers in the field of bioenergy and by the aspects included in the voluntary biojet fuel standards.

4.2.1 Environmental sustainability impacts For environmental issues McBride et al. (2011) have compiled a set of quantitative indicators and propose six categories: soil quality, water quality and quantity, greenhouse gases, biodiversity, air quality, and productivity (see appendix 1.1). Also the RSB STD EU (2013) and ISCC (2015) standards include environmental aspects (see appendices 1.2 and 1.3). These indicators are not compatible with the SELCA methodologies, but they can show which impact categories are considered important by the biojet fuel industry and academic field. Some of the basic impact categories of ELCA overlap with the other three lists, while others do not. Greenhouse gas emissions are already covered by the ELCA climate change category. Other air quality impacts covered by management practices and restricted open-air combustion in the voluntary schemes and the tropospheric ozone and carbon monoxide in the air quality impact category of McBride et al. (2011) are also included in the ELCA impact categories of respectfully photo-oxidant formation and human toxicity impact. Also the water quality and quantity category the nitrate and phosphorous emissions into water are part of the ELCA eutrophication category, while herbicide emissions are part of the ecotoxicity impact category. However, ELCA does not have standardized characterization models for land use, water consumption, soil quality, and biodiversity. There are models in development for each of these topics which have been reviewed in the International Reference Life Cycle Data System handbook (EU 2010), though soil quality is more controversial as a separate impact category which is sometimes considered part of land use, and biodiversity is an endpoint indicator affected by many different existing midpoint impact categories. As long as topics such as those mentioned in the topic lists from McBride et al. (2011), RSB STD EU (2013) and ISCC (2015) are not fully addressed by environmental characterization methods, SELCA is not able to fully integrate all environmental impacts of interest to the biojet fuel product system.

4.2.2 Social sustainability impacts Dale et al (2013) have further built upon the work of McBride by creating a list of quantitative and semi-quantitative indicators for the socioeconomic sustainability of bioenergy production. A total of 16 indicators had been grouped into six categories: social well-being, energy security, external trade, profitability, resource conservation, and social acceptability (see appendix 1.4). These impact categories do not match up with those proposed by UNEP (2009) and most of the proposed indicators are either on the state level and cannot be aggregated in SELCA. The exception to this is the employment indicator which could be applied as the labor-hours/full time equivalent jobs relative to the reference flows. The resource conservation is also already covered by the abiotic resource depletion environmental impact category. The social acceptability indicators are on the pressure level too, but they require gathering a large amount of documents for each company which makes it impractical for larger product systems in a similar way as LCAA. The RSB STD EU (2013) and ISCC EU (2015) voluntary schemes also include social impacts of the biojet fuel product system. RSB STD EU divides the social certification criteria into legality, human and labor rights, rural and social development, food security, and land rights (see appendix 1.5). The ISCC EU social sustainability criteria are within four principles: employee safety and education; human, labor, and land rights; compliance with laws and regulations; management of production process (see appendix 1.6). These indicators also do not comply with

54 the criteria set for SELCA, but they show the social sustainability impacts of interest to biojet fuel industry and academia. The social impact categories with functioning characterization models are limited (see chapter 3.3.1). The only functioning methodology is from Hunkeler (2006) which addresses the housing, healthcare, education, and necessities of laborers. Hunkeler’s impact categories could be considered to address several impact categories of Dale et al. (2013). Tracking labor-hours covers the employment indicator and the four impact categories could be seen as addressing the issues of household income and food security by showing the contribution of the income to the basic needs of the employees. Other impact categories of Dale et al. (2013) and the ISCC (2015) and RSB STD EU (2013) voluntary schemes, such as local food security, compliance with laws related to land use, and providing benefits for the local community, cannot be addressed by SELCA at present.

4.3 Conclusions of SELCA and biojet fuel The application of SELCA to the biojet fuel product system has important limitations. The lack of existing databases complicates the data gathering for a comprehensive product system. Though it is in theory possible to include all relevant biojet fuel SUPs, the necessity of cut-offs may obscure important changes in background processes that are not included in a more limited product system. In addition, the characterization methods compatible with SELCA do not cover all the sustainability impacts of interest for the biojet fuel product system. Water consumption, soil quality, biodiversity, and land use are some of the environmental aspects that have not been fully incorporated in ELCA and are therefore not yet included in SELCA. However, indicators for these are in development and could in the future be integrated in the environmental impact categories of SELCA. The social impacts are even more limited, since only one social characterization method can be used. These impact categories address some of the labor issues, but neglect others such as local food security and compliance with laws. Interestingly, the proposed social impact categories are different from those proposed by UNEP (2009). This may indicate that the SELCA social impacts should not be limited to the UNEP list, as is the case with the methodology of Hunkeler (2006). Though these limitations are important, SELCA still is a potential tool for biojet fuel assessment. SELCA contains a decent portion of social and environmental impacts in a coherent way, so it can be used to measure those. For other indicators, different methodologies and indicators should be used together until SELCA develops further. The existing limitations are a sign of the underdeveloped state of SELCA and can in principle be solved with further research.

5 SELCA dummy case study: Biojet fuel in the Netherlands The previous chapter looked into the theoretical possibilities and limitations of SELCA in the assessment of the biojet fuel product system. In this chapter a dummy case study is conducted to look at practical capabilities and restrictions. The dummy case study is performed using the CMLCA software and uses data input based on ecoinvent, the CPM LCA database, and rough estimates. This dummy case study is only to be used to understand the SELCA methodology, so neither the data inputs nor the results can be used for SELCA studies or biojet fuel assessments.

5.1 Goal and scope 5.1.1 Goal definition The goal of this dummy case study is to demonstrate the workings of the SELCA framework. By comparing two fictional alternative pathways for the biojet fuel production in the Netherlands, each step of SELCA can be illustrated. The dummy case study will be conducted in such a manner to answer the following questions based on the first two general research questions of SELCA (see chapter 3.1): 1. Where in the product system of biojet fuel are the main social and environmental impacts? 2. How do the social and environmental impacts of the two alternative biojet fuel product system compare to each other?

5.1.2 Scope definition The scope of the case study is the production of biojet fuel in the Netherlands from biomass source till the point before mixing with jet A-1 fuel for commercial aircrafts. The dummy data is assumed to be from primary sources representing technology from the period 2010-2015 in the Netherlands. The SELCA methodology is used to study both the environmental and social impacts in a coherent product system.

5.1.3 Function, functional unit, alternatives, and reference flow The function of the SELCA is the use of SKA for mixing with jet A-1 fuel (see table 20). The FU is defined as 1 ton SKA biojet fuel to be used for mixing with jet A-1 fuel.

Table 20: SELCA biojet fuel function Property Description Functionality to be used for mixing with jet A-1 fuel Technical quality SPK type of biojet fuel

The alternative reference flows are:  1 ton SKA biojet fuel made from Miscanthus through FT to be used for mixing with jet A- 1 fuel for commercial aircrafts.  1 ton SKA biojet fuel made from waste animal fats through HEFA to be used for mixing with jet A-1 fuel for commercial aircrafts.

These two alternatives were chosen as the questionnaire (see appendix 2) shows that both Miscanthus and waste animal fats are available in the Netherlands, though Miscanthus is at present not used for biojet fuels. Each of the alternatives corresponds to one of the main biojet fuel production methods with Miscanthus being processed in FT and waste animal fats in HEFA.

5.2 Inventory analysis 5.2.1 Product systems The two product systems within this case study are cradle-to-gate systems. The Miscanthus based biojet starts at the biomass production through biomass processing and FT biojet fuel production. The animal fat based biojet starts with waste animal fats from slaughterhouses which is turned into goods through trading the waste fats as inputs for the HEFA biojet fuel production. Both product systems interact with the environmental system and the social system through environmental and social interventions. The product systems are composed of producers and traders. Viewing the trader as separate from producers and transport is based on the questionnaire result (see appendix 2). One of the respondents is a firm handling all downstream processes between the biojet fuel producer and the eventual customer, so they organize the trade and hire transport rather than being a transporting firm. The trading function does not have to be separate as one of the upstream firms combines Miscanthus production with the trading of Miscanthus from their own field and from other agriculture firms. The separation of the trader is thus seen as a general SUP that can be included within a different SUP in specific firms and supply chains.

5.2.2 Flow charts All the flow charts can be found in appendix 6. The alternatives are represented by two individual flow charts (see appendices 6.1 and 6.2) that include transportation and utilities as aggregated SUPs which have their own flow charts to give more detail (see appendices 6.3 and 6.4). The environmental and social interventions have not been included to increase readability.

5.2.3 System boundaries In this SELCA, the boundary with the environmental and social system have to be set. The environmental system boundary is at the point where flows are not further processed in waste treatment. For agriculture, the boundary for farms is set at the plant level, as the climate change impact requires the carbon uptake of the grown biomass to be included (see chapter 3.2.4.2). Important to note is the inclusion of biogenic carbon uptake in Miscanthus production compared to the exclusion for the HEFA biojet fuel product system, as animal fat is considered a waste product. Neither alternative product systems include biojet fuel combustion, so biogenic carbon is not released to the environment. For the boundary with the social system, labor is divided between labor within the system boundaries when delivered by an SUP and labor as a social intervention through employment of people in a SUP. Other social groups are not considered in this dummy case study, since the social indicators relate to workers alone. As there is no background database suitable for SELCA, cut-off has to be applied to the product systems. Besides the production and trading of biomass and biojet fuel, the production of tap water, electricity, natural gas, crude oil, diesel, transport, nitrogen-fertilizer, and hydrogen

57 is included in the product systems. Equipment and buildings have been cut-off as well as other inputs besides those provided by the above mentioned production processes. This means that in HEFA and FT only biomass and hydrogen are considered economic input, and that nitrogen fertilizer and transport are the only economic input for Miscanthus production.

5.2.4 Allocation Relating the data to the SUP requires allocation of organizations. If organizations have multiple processes creating goods, the flows from departments that do not provide goods to other stakeholders have to be assigned to the process of interest for the product system. The HEFA biojet fuel producer will be used to illustrate how this is to be done. In this example case, the HEFA producer has three economic outputs: HEFA biojet fuel, HEFA by-products, and biochemicals. This multifunctional organization is modelled having four departments: the company headquarters, the marketing department, the HEFA production facility, and the biochemical production facility. Each department has its own economic in- and outflows together with labor hour inputs as social interventions. All departments have electricity, natural gas, and tap water as economic inputs and labor from industry in the Netherlands as a social intervention. The HEFA production facility with the HEFA SKA biojet fuel and HEFA by-products as economic outputs also has animal fats, hydrogen, and marketing labor for HEFA as inputs. The biochemical production facility with biochemicals as an output, includes marketing labor specific to it as well. The marketing department thus has specific labor outputs to each of the production facilities. In contrast, the headquarters has labor for management as an economic output without specification towards which other departments it goes. To turn the organization into an SUP, the economic flows and social interventions have to be allocated to the SUP output of 140,000 tons of HEFA SKA biojet fuel (see fig. 16). The detailed economic flows and interventions can be found in appendix 3.2.

Figure 16: Flowchart with economic flows of HEFA producer as organizational allocation example

The biochemical production facility is unrelated to the production of HEFA biojet fuel, so it should be excluded from product system. However, marketing and headquarters cater to both HEFA related products and biochemicals, so their economic flows and social interventions have to be divided. In the example, the economic output of labor hours of marketing specifically to the HEFA production facility and the biochemical facility are known. So for marketing no allocation is necessary as the interventions are linked to the economic output. This is in contrast to the headquarter where it is not known how much of the economic labor hour output goes to respectively the HEFA production facility and the biochemical facility. To assign flows to each production facility, an allocation method is required. This allocation could be based on the economic value, mass, or energy of the HEFA production and biochemical facilities. Since this case study does not include economic values and energy contents, mass is taken as the basis for allocation. The result of using mass for the allocation factor is: Output of HEFA facility / (Output of biochemicals + output of HEFA facility) 200,000/ (300,000+200,000) = 0.4 The final SUP of the HEFA producer consists of adding the HEFA production facility, the related marketing department, and the allocated headquarters together (see table 21). In this way each multifunctional organization should be transformed into a SUP.

Table 21: HEFA producer Economic input Traded animal fat 192,400 ton Traded hydrogen 7,600 ton Traded electricity 10,040,710 kWh Traded natural gas 11,800 m3 Traded tap water 1,000,284 ton Economic output HEFA SKA biojet fuel 140,000 ton HEFA by-products 60,000 ton Social input Labor in the Netherlands 187,200 h

For the HEFA biojet fuel producer SUP, there are two economic outputs: HEFA SKA biojet fuel, and HEFA by-products. The mass allocation results in an allocation factor of 0.7 for HEFA SKA biojet fuel and 0.3 for HEFA by-products which are put into the CMLCA program. In total there are three SUP that need this kind of allocation and their factors are all based on mass, so the allocation is done consistently (see table 22).

Table 22: Dummy allocation factors HEFA biojet fuel producer[NL] Allocation Function Amount Unit factor HEFA SKA biojet fuel (good) 700 kg 0.7 HEFA by-products (good) 300 kg 0.3 Total 1000 kg 1

FT biojet fuel producer[NL] Allocation Function Amount Unit factor FT SKA biojet fuel (good) 800 kg 0.8 FT by-products (good) 200 kg 0.2 Total 1000 kg 1

Waste animal fat trader[NL] Allocation Function Amount Unit factor Waste animal fat (waste) 1 kg 0.5 Traded animal fat (good) 1 kg 0.5 Total 2 kg 1

5.2.5 Inventory data To assess the alternatives in this dummy case study, quantitative data is used. The recorded data are the various economic, social, and environmental in- and outflows. The data would in reality have to come from primary data as there are no available databases for SELCA, though the dummy data is mainly based on ecoinvent (Ecoinvent 2016), the CPM LCA database (Swedish Life Cycle Center 2016), and rough estimates. The SUPs are included in the appendices (see appendix 3.1).

5.2.6 Inventory tables The resulting inventory tables of this inventory analysis only includes quantitative data, so there is no semi-quantitative inventory table. The tables of the two alternatives can be found in appendix 4.

5.3 Impact assessment 5.3.1 Selection of impact categories and characterization methods For this dummy case study, environmental and social impact characterization methods are chosen that can be used in SELCA. The environmental impacts are covered by the basic ELCA impact categories (see chapter 3.3.1). CML 2001 is the set of characterization models used that covers each of the ten impact categories:  CML 2001, Eutrophication potential, generic  CML 2001, Resources, depletion of abiotic resources  CML 2001, Acidification potential, average European  CML 2001, Photochemical oxidation (summer smog), high NOx POCP  CML 2001, Climate change, GWP 100a  CML 2001, Terrestrial ecotoxicity, TAETP infinite  CML 2001, Marine aquatic ecotoxicity, MAETP infinite  CML 2001, Freshwater aquatic ecotoxicity, FAETP infinite  CML 2001, Stratospheric ozone depletion, ODP steady state  CML 2001, Human toxicity, HTP infinite

For the climate change impact category, biogenic carbon uptake is considered carbon neutral and given a characterization factor of 0 as the product system does not include the combustion of biojet fuel. If biogenic carbon uptake was given a value, this would benefit Miscanthus compared to animal fat, since the latter consists of biogenic carbon but does not include the uptake in the product system. The only social impact characterization method in SELCA is that of Hunkeler (2006), so his impact categories are used. In Hunkeler’s case study, he made estimates for several countries on the amount of hours needed to afford housing, health care, education, and necessities. For the Netherlands, estimated values have been made for the costs of housing for 30 years, a year of healthcare, a lifetime of education estimated at 80 years, and a month of necessities which are then transformed into a unit consisting of the yearly costs of each impact category (see table 23).

Table 23: Cost values for Hunkeler’s (2006) impact categories in Netherlands Category Euros per various time units Euros per year Housing 240,000 euro/30yr housing 8,000 euro/unit housing Health care 5,400 euro/1yr health care 5,400 euro/unit health care Education 20,000 euro/80yr education 250 euro/unit education Necessities 750 euro/month necessities 9,000 euro/unit necessities

To illustrate that Hunkeler’s methodology is not limited to geographic differences but can also be used for different economy sectors, the wages of agricultural labor and industrial labor in the Netherlands are assumed to be respectively 10 euros/h and 12 euros/h. Dividing the values in table 30 by the wages results in the following labor hours per unit (see table 24). By inverting these values, the characterization factor of units per hour can be calculated (see table 25).

Table 24: Labor hours per unit for agricultural and industrial labor in the Netherlands Category Agricultural labor h/unit Industrial labor h/unit Housing 800 h/unit housing 666.7 h/unit housing Health care 540 h/unit health care 450 h/unit health care Education 25 h/unit education 20.8 h/unit education Necessities 900 h/unit necessities 750 h/unit necessities

Table 25: Characterization factors for social damages of labor units per hour for agricultural and industrial labor in the Netherlands Category Characterization factors Agricultural labor Industrial labor unit/h unit/h Housing 0.00125 0.00150 unit housing/h unit housing/h Health care 0.00185 0.00222 unit health care/h unit health care/h Education 0.04000 0.04808 unit education/h unit education/h Necessities 0.00111 0.00133 unit necessities/h unit necessities/h

Housing, health care, education, and necessities are added to the ELCA indicators in the CMLCA program with the calculated characterization factors, resulting in four social midpoint impact categories:  Hunkeler 2006, Housing  Hunkeler 2006, Health care  Hunkeler 2006, Education  Hunkeler 2006, Necessities

5.3.2 Classification and characterization The environmental interventions are classification and characterization according to the CML 2001 ELCA methods and the social intervention flows of ‘labor agriculture in the Netherlands’ and ‘labor industry in the Netherlands’ are classified to the Hunkeler 2006 methodology with the characterization factors calculated in chapter 5.3.2. The results of impact assessment after the characterization step can be found in as the output tables and graphs in appendix 5. In this dummy case study, the HEFA SKA biojet fuel performed better on all environmental impacts. The exceptions to this are stratospheric ozone depletion, and terrestrial, marine aquatic, and freshwater aquatic ecotoxicity where neither HEFA nor FT SKA biojet fuel had an impact on these categories. FT has particularly high impact values for eutrophication and acidification, though that is to be expected with the inclusion agricultural production. Also the higher photochemical oxidation is due to more transport for the agricultural goods. For the social sustainability impact categories, FT SKA biojet fuel has a better performance. As environmental impacts are considered better the lower the value is, the social impacts have been turned into social damages by turning the impact values into negative values. This means that low values represent a better alternative than higher values, so a better score has a more negative value.

3.3.3 Normalization Normalization is performed for both the environmental and social impacts (see appendixes 5.1.2 and 5.2.11-5.2.12). The environmental interventions have been normalized based on the World 2000 totals list and the normalization of the social interventions is done by taking the total amount of jobs for agriculture and industry respectively from CBS (2016) and converting them to total amount of labor hours. For the normalized environmental impact, the biojet fuel product system contributes greatest to abiotic resources and climate change compared to the other impact categories, with FT performing the worst in all cases. For the normalized social impacts HEFA performs worst, though the values of the impact categories in a single alternative are equal to each other. The reason for this is the use of the same normalized labor input for all four impact categories

5.4 Interpretation The consistency check and contribution analysis are performed in this case study. As no internal or external experts were consulted, the completion check was not performed. No perturbation analysis was conducted either, as a result of time limitations.

5.4.1 Consistency check The purpose of this case study is to illustrate how SELCA would be applied in practice. The simplification of the SUPs and related economic flows and environmental, and social interventions are therefore justified. Also the use of rough estimations and the ecoinvent data does not interfere with the goal of the case study.

5.4.2 Contribution analysis The contribution analysis of specific SUPs to each impact category shows several patterns that influence the outcome of the impact assessment (see table 26). In the environmental impact categories there are three major contributing SUPs: the transporter, the natural gas producer, and the Miscanthus producer. The former two are important for both alternatives and the latter is specific for the FT SKA biojet fuel alternative. The prominence of the Miscanthus producer shows the significance of including the biomass production stage to the product system. In the HEFA SKA biojet fuel, the biomass source is considered a waste flow which ignores further upstream production. Including the production of animal fat would likely shift the impacts in a similar as it does to Miscanthus production in FT SKA biojet fuel. For the social impact categories, the inclusion of biomass production benefits FT SKA biojet fuel as the majority of labor comes from the Miscanthus producer. In HEFA SKA biojet fuel, the labor is mainly distributed among traders and the HEFA biojet fuel producer. These traders together with the transporter, natural gas producer, and Miscanthus producer are the SUPs of most concern in this dummy case study.

Table 26: Contribution analysis for each impact category Contribution analysis of impact categories Impact category HEFA SKA biojet fuel FT SKA biojet fuel Eutrophication 100% transporter 81% Miscanthus producer 9% transporter Abiotic resource depletion 99% natural gas producer 98% natural gas producer Acidification 99% transporter 80% Miscanthus producer 19% transporter Photochemical oxidation 57% transporter 73% transporter 40% natural gas producer 23% natural gas producer Climate change 96% hydrogen producer 54% hydrogen producer 35% Miscanthus producer 3% transporter 10% transporter Human toxicity 100% transporter 76% transporter 24% Miscanthus producer Hunkeler (2006) impact 26% HEFA SKA biojet fuel trader 74% Miscanthus producer categories 25% waste animal fat trader 6% natural gas trader 24% HEFA biojet fuel producer 5% FT SKA biojet fuel trader 13% tap water trader 7% gas trader

5.5 Dummy case study conclusions The results of this SELCA case study shows that it is possible to answer research questions as posed in the dummy goal definition (see chapter 5.1.1). For the first question on the hotspots within the product systems, the contribution analysis results show that specific processes in the alternative biojet fuel product systems are responsible for the majority of the impacts. For the environmental impacts, transport and natural gas producers are important in both product systems, while for FT SKA biojet fuel Miscanthus is another important contributor. For social impacts, Miscanthus remains important for FT SKA biojet fuel, with the traders and HEFA biojet producer being important for the HEFA SKA biojet fuel product system. This is likely the result of considering animal fat from the slaughter as a waste stream which excludes the biomass production stage in contrast with the FT SKA biojet fuel where Miscanthus is included in the product system. The second question regarding the comparison of the two alternatives reveals the difficulty of determining the superior product using multiple midpoint indicators. The results show that HEFA SKA biojet fuel performs better on environmental issues, while FT SKA biojet fuel is better in the social impacts. There is no objective way in SELCA to judge the importance of impacts relative to each other, so an external set of criteria would be needed to decide which alternative is most suitable by first separately weighing the normalized environmental impacts and the social impacts and then followed by weighing the environmental and social score together. The dummy case study has shown that SELCA can be applied using existing ELCA software such as CMLCA. This is limited to the condition that only quantitative data is used as social quantitative interventions can be added in a similar manner as environmental interventions. The case study was not able to determine how semi-quantitative data would be treated in SELCA, as it was not required for the impact assessment. The ability to include quantitative aspects of the SUP is confirmed as it is done in the same manner as a regular UP, though it requires allocation steps outside of the program. Implementing Hunkeler (2006) for social impacts was also possible within the CMLCA software. It required the addition of labor inputs, creating the characterization method, and adding the characterization factors for each impact category. The SELCA methodology with compatible impact categories can be applied practically with existing ELCA software. The methodology of Hunkeler (2006) shows that social characterization methods relying on quantitative data already work. Since no semi-quantitative data is used in this dummy case study, it is not known how such information can be included in existing software.

6 Discussions

6.1 Interpretation of results The aim of this study was the integration of environmental and social sustainability impacts of the biojet fuel product system within the life cycle assessment framework. ELCA and SLCA have developed as two independent tools whose system boundaries are not compatible. The first sub- question was how the SLCA and ELCA could be adapted to include unit processes with a specific function and social organizations with potentially several functions in a single framework named SELCA in this study. The key to harmonizing the system boundaries is the realization that organizations are similar to multifunctional unit processes that can be allocated to a specific product system. By doing this, the fundamental difference between organizations in SLCA and unit processes in ELCA can be resolved through the creation the SUP. This is in contrast to the LCSA which functions as an overarching framework that groups the ELCA, SLCA, and LCC together without harmonizing the differences. Integrating the ELCA and SLCA also means that the social and environmental indicators have to be comparable to each other. The second sub-question looks into which requirements are needed to ensure that potential social and environmental indicators are comparable within SELCA. From the better established ELCA indicators, two criteria were derived: the indicators should be based on DPSIR pressure level data, and the indicators should be capable of being aggregated for the product system. These criteria were applied to SLCA indicators for answering the third sub-question which researches the possible social indicators to be used alongside existing environmental indicators in SELCA. The only methodology passing the criteria while being operational was the methodology of Hunkeler (2006) which assesses impact categories that differ from those promoted by UNEP (2009). Most SLCA characterization methods used statistical data of a country and applied that to firms. This is problematic, as data such as education level and average lifespan cannot be directly linked to a company. Though the conduct of organizations towards their employees and other stakeholders have an influence on these issues, it is not possible to determine from this data whether there is a causal link with the company. A firm may promote education and health within a country with a low average and vice versa an organization can have a much worse effect than average national statistics suggests. Where Hunkeler differs is the strict use of pressure level data which is not limited to quantitative indicators. Though LCAA does not show impacts, it does link quantitative labor hours to semi-quantitative values. This ensures that a value can be given for the product system and in principle it should be possible to develop semi-quantitative indicators in this manner. To make the Hunkeler (2006) methodology more interesting for SELCA, adjustments were made. Originally, labor hours were only differentiated between countries by taking the average wage and impact costs for each country. This can be expanded by also differentiating between each industry sector as average wages will likely differ between them. A balance must be found between increasing the accuracy of labor impacts and the number of different labor hour interventions to be recorded. As an example, in the dummy case study labor hours in the Netherlands were divided between agriculture and industry. Another change to Hunkeler’s original case study is to explicitly show how the hours per unit for each impact category is constructed. It seemed as though the different impact categories were on different timescales,

66 so the implicit weighing is not valid until the values have been normalized. Using a single timespan for each impact may simplify the methodology, such as the choice for one year in the dummy case study. The added labor differentiation and explicit showing of the impact units make the Hunkeler (2006) methodology both more useful and more transparent. The fourth sub-question studied the possibilities and limitation of applying SELCA to the biojet fuel product system. The SELCA is in principle able to include the full biojet fuel product system. The set of impact categories from SELCA were compared to impact categories highlighted in academic literature for biofuel and those included in voluntary sustainability assessment schemes for the biojet fuel industry. Several of the environmental impacts overlapped, but the environmental characterization methods in SELCA do not fully deal with land use, water consumption, biodiversity and soil quality which are considered important in academic literature. The lack of indicators is even greater in social impacts where the four impact categories of Hunkeler (2006) do not overlap with the voluntary schemes. Though labor and wage is addressed, the labor rights of employees and impacts on other social compartments are not included. The question remains whether the analysis of environmental and social impacts of the biojet fuel product system can be integrated in a combined social and environmental life cycle assessment framework. The potential of SELCA can be seen as an alternative with a different outlook to that of the LCSA. The LCSA functions as a meta-framework in which guidelines are given to the use of ELCA and SLCA alongside each other without altering the individual methodologies, the most problematic guideline being the addition of product system boundaries. This essentially sacrifices compatibility in order to include the current most mature methodologies. In contrast, SELCA takes as its starting point the need for a unified product system. Compatibility is prioritized at the expense of being less mature on a theoretical level with possible indicators and on a practical level with the lack of databases and background data. SELCA can assess both the environmental and social impacts of a single biojet fuel product system, while LCSA can assess a wider variety of impacts over differently defined product systems. As the dummy case study shows, SELCA can already be applied with existing software. The main challenges are the availability of data and need for more social characterization methods. SELCA at present is not able to capture all the social and environmental impacts of concern to biojet fuel, however it has the potential to do so with further research and development.

6.2 Limitations The use of SELCA is limited in practical use at present. As the ELCA and SLCA are not based on the SUP, their current databases do not suffice for studying product systems in SELCA. The lack of readily available background data requires gathering more primary data. To make it more manageable, the inventory will likely include more cut-off points compared to ELCA which limits the size of the product system under study. For the study as a whole, two limitations were identified. The first is the limited response to the survey conducted among biojet fuel and biomass production stakeholders. The responses were too few to be statically interesting and none of the respondents gave information regarding quantitative data of their production or services. A bigger participation of firms could have influenced the capability of SELCA in light of the biojet fuel product system. The second limitation is the use of a dummy case study rather than an actual case. Though the dummy case study showcases the workings of SELCA, it does not give an indication if valuable results could already be gained from such a study. Though difficulties in data gathering are assumed, this is not tested in practice. An actual application of SELCA with real data would have shown the practical potential of the tool.

7 Conclusions The biojet fuel product system is regarded as a potentially alternative to regular petroleum jet fuels, but a methodology has to be developed to coherently assess environmental and social sustainable. ELCA and SLCA assess respectively the environmental and social impacts of a product over its life cycle, but an integration of the two is needed to better assess and compare the biojet fuel product system. This study looked into the possibility of creating a single LCA methodology that includes the assessment of both the social and environmental impacts for. This was done through a literature review that compared the conceptual framework of the ELCA and SLCA resulting in the creation of SELCA, a survey and literature review to evaluate and compare existing indicators with topics of interest in biojet fuel academia, and a dummy case study to illustrate the practical application of SELA. The underlying problem in combining ELCA and SLCA are the incompatible product systems. ELCA is based on unit processes producing a functional flow, while SLCA takes organizations as the basis which are conceptually placed on top of unit processes. This can cause conceptual problems if an organization includes more than a single unit process, as any impacts of the organization cannot be all attributed to unit process within the product system. To solve this problem, organizations can be treated as multifunctional unit processes which like any unit process can be allocated to a product flow. Through the allocation of organizations, SELCA creates the SUP and forms a coherent product system for the assessment of social and environmental impacts. The incompatibility between the SLCA and ELCA has resulted in the development of diverging indicators too. To ensure that interventions can be attributed to a particular SUP, a requirement was made that all flows have to be on the DPSIR model pressure level. As the ELCA midpoint indicators are the most well established, they were used to derive two criteria: indicators must use pressure level data, and indicators must in principle be capable of being aggregated for the full product system. Only the methodology of Hunkeler (2006) is applicable in SELCA with some adjustments to the characterization factors and having sectoral differentiation of labor hours. SELCA was applied to the dummy case study of the biojet fuel product system in the Netherlands. ELCA software such as CMLCA can be used, though existing background databases are not compatible with SELCA. Hunkeler’s (2006) methodology can be used within the program as it only uses quantitative data. Semi-quantitative data and indicators could not be tested, because there are none that comply with the two indicator criteria. Within these limits SELCA can be used to assess biojet fuel. With further development, SELCA can become a viable alternative to the LCSA approach of simply conducting ELCA and SLCA alongside each other. Not all sustainability concerns of the biojet fuel product system are addressed yet and it is still limited in background databases and social indicators. However, this study shows that the analysis of environmental and social impacts of the biojet fuel product system can conceptually be integrated, and with further research SELCA could become a practical tool for to be used in the sustainability assessments of the biojet fuel product system.

8 Recommendations To successfully integrate the social and environmental aspects of the biojet fuel industry in an LCA methodology, further work is needed. The first is the development of more social characterization methodologies that can are compatible with SELCA. A promising route might be to combine the LCAA calculation method and the impact assessment of Dreyer et al. (2010). In LCAA, a physical flow is given a semi-quantitative value which can be assessed along the product system. If this value is replaced by a CR of Dreyer et al. (2010), it may be possible to create comprehensive semi-quantitative indicators that are based on pressure level quantitative flows which would allow the indicators to be aggregated. By applying such a method to labor hours, the effects on employee labor rights can be included which is one of the major impacts in UNEP (2009) and the voluntary sustainability assessment schemes for biojet fuel. It will be necessary to define the exact multi-criteria on which management policies and efforts are assessed in such a way that it covers the different aspects of labor rights. To use such a methodology alongside that of Hunkeler (2006), the assessment would need to be done for labor differentiated by geography and economic sector rather than by company. It may also be possible to assess social impacts based on economic flows and interventions other than labor hours. If land use change is added as an intervention to SELCA, this flow could potentially be linked to land rights through a Dreyer et al. (2010) method. Another possibility is the inclusion of monetary flows to charity, community programs, and other expenses which could be considered economic interventions and be linked to social impacts on even more stakeholders. This requires SELCA to be expanded with the integration of economic aspects comparable to the LCC in LCSA. A successful inclusion would create a single methodology with the capability of assessing all three pillars of sustainability for a product system. To make SELCA more comprehensive, a suitable database should be created. This database should attempt to allocate a generic organization to each SUP. As a start, environmental interventions similar to those in Ecoinvent should be included together with labor hour data. This can be expanded by adding other flows necessary for social interventions such as the land use change and monetary flows mentioned before. The database will reduce the need for cut-offs and increase the effectiveness of SELCA to capture the environmental and social impacts of the full life cycle of a product system. Finally, SELCA must be applied in real case studies to gain practical experience for further development. This could show other shortcomings and research topics that need to be addressed to further flesh out the methodology. If new characterization methods with semi-quantitative indicators are used, the limitations of existing LCA software can be tested. SELCA will only become a viable methodology for analyzing environmental and social impacts if it is used by researchers and industry.

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Appendix 1: Lists of Biojet Fuel Environmental and Social issues

Appendix 1.1: Environmental sustainability indicators with DPSIR category. Adapted from: McBride et al 2011. Environmental sustainability indicator suite Category Indicator Unit Soil quality Total organic carbon Mg/ha Total nitrogen Mg/ha Extractable phosphorous Mg/ha Bulk density g/cm3 Water quality Nitrate concentration (C) in streams (C): mg/L and quantity and export (E) (E): kg/ha/year Total phosphorus concentration (C) in (C): mg/L streams and export (E) (E): kg/ha/year Suspended sediment concentration (C): mg/L (C) in streams and export (E) (E): kg/ha/year Herbicide concentration (C) in (C): mg/L streams and export (E) (E): kg/ha/year Peak storm flow L/s Minimum base flow L/s Consumptive water use m3/(ha)/day Greenhouse CO2-equivalent emissions kg Ceq/GJ gases Biodiversity Presence of taxa of special concern Presence Habitat area of taxa of special concern ha Air quality Tropospheric ozone ppb Carbon monoxide ppm Total particles less than 2.5 µm µg/m3 diameter Total particles less than 10 µm µg/m3 diameter Productivity Aboveground net primary productivity g C/m2/year

Appendix 1.2: RSB STD EU (2013) environmental standards Impact category Indicator Greenhouse gas Lifecycle greenhouse gas 50% lower than emissions fossil

Conservation (land Avoidance of land use labelled as no-go use) Soil Evaluation soil management practices Water Water availability assessment and evaluation water management practices Air Prevent open-air burning and evaluation emission control plan Appendix 1.3: ISCC EU (2015) environmental standards Impact category Indicator Principle 1: Avoidance of land with specific criteria Conservation (land use) Principle 2: Soil Evaluation management practices and six-yearly soil organic matter analysis Principle 2: Water Evaluation management of water and natural watershed Principle 2: Air Evaluation of management practices for fossil fuels and restrictions burning biomass Principle 1 + 2: Evaluation management practices and Biodiversity restrictions of genetically modified organisms

Appendix 1.4: Socioeconomic sustainability indicators with DPSIR category. Adapted from: Dale et al 2013. Socioeconomic sustainability indicator suite Category Indicator Unit Social well- Employment Number of full time equivalent jobs being Household income Dollars per day Work days lost due to Average number of work days lost per worker per injury year Food security Percent change in food price volatility Energy Energy security Dollars per gallon of biofuel security premium Fuel price volatility Standard deviation of monthly percent price changes over one year External Terms of trade Price exports/price imports trade Trade volume Net export or balance of payments Profitability Return on Net investment/initial investment investment Net present value Present value of benefits minus present value of costs

Resource Depletion of non- Amount of petroleum extracted per year (MT) conservation renewable energy sources Fossil energy return Ratio of amount of fossil energy inputs to amount of on investment useful energy output (MJ) Social Public opinion Percent favorable opinion acceptability Transparency Percent of indicators for which timely and relevant performance data are reported Effective stakeholder Percent of documented responses addressing participation stakeholder concerns and suggestions, reported on annual basis Risk of catastrophe Annual probability of catastrophic event

Appendix 1.5: RSB STD EU (2013) social indicators Impact category Indicator Legality  Compliance with applicable laws and regulations Human and labor rights  Compliance with right for association, organization, and collective bargaining  Absence of slavery or forced labor  Absence of child labor  Absence of discrimination, evaluation of equal treatment of women, and evaluation of career development encouragement  Compliance maximum work hours and at least minimum wage payment or equivalent, evaluation of worker facilities  Compliance national and international occupational health and safety standards  Implementation mechanism ensuring compliance of third party contractors Rural and social  Evaluation of socioeconomic baseline of development unemployed and underemployed local labor  Evaluation of local employee management  Implementation of a measure significantly benefitting locals Food security  Evaluation of local and regional food security  Implementation of enhancing local food security measures Land rights  Compliance with land rights and land purchase criteria

Appendix 1.6: ISCC EU (2015) social indicators Impact category Indicator Principle 3 - Safe  Evaluation health, safety and hygiene policy working conditions  Availability of suitable protective clothing through training and  Records of training and attendees education, use of  Evaluation of adequate worker competence through protective clothing and training proper and timely  Evaluation of employee facilities assistance in the event  Evaluation policy and procedures product and plant safety of accidents Principle 4 - Biomass  Compliance with self-declaration of good social on human production shall not rights violate human rights  Absence of slavery or forced labor labor rights or land  Absence of discrimination, evaluation of equal treatment rights. It shall promote  Evaluation of worker treatment responsible labor  Compliance to the right of establishing or joining labor conditions and workers' unions and perform health, safety  Compliance with at least minimum wage standards and welfare and shall be  Protection of local historical, cultural and spiritual sites based on responsible  Evaluation communication management and workers community relations  Assurance of primary education of employee children  Absence or precisely restricted use of child labor  Evaluation of employee management and legality  Documentation of daily working time  Evaluation food security impacts and measures

Principle 5 - Biomass  Legal land use and purchase production shall take  Compliance with national and international laws and place in compliance regulations with all applicable regional and national laws and shall follow relevant international treaties Principle 6 - Good  Evaluation of subcontractor compliance management practices shall be implemented

Appendix 2: Survey

Appendix 2.1: Enquête Duurzaamheid Biomassa Producenten

Introductie Deze enquête is deel van mijn master scriptie over de mogelijkheid om de ecologische en sociale duurzaamheidaspecten van biokerosene, inclusief de productie van biomassa, te analyseren doormiddel van een levenscyclusanalyse methodologie.

Het doel van de enquête is om een beter inzicht te krijgen in de ecologische en sociale aspecten die geïmplementeerd en van belang zijn in de productie van biomassa voor de bio-energie. Daarnaast is de enquête bedoeld om een idee te krijgen van de materiële stromen in biomassa productie en de bijbehorende besluitvorming over in- en verkoop.

De enquête bestaat uit vier onderdelen: Ecologische duurzaamheid (vraag 1-5), Sociale duurzaamheid (vraag 6-10), Materiële productie stromen (vraag 11-16), en Besluitvorming en Materiële Stromen (vraag 17-21). Sommige vragen worden overgeslagen afhankelijk van de gegeven antwoorden.

Hartelijke bedankt voor de deelname. Stuur alstublieft uw ingevulde enquête naar: [email protected] Als er vragen of opmerkingen zijn, hoor ik het graag.

Ecologische duurzaamheid

1 Zijn er interne ecologische duurzaamheid standaarden binnen het bedrijf?  Ja  Nee (Ga direct naar vraag 5) 2 Welke aspecten van ecologische duurzaamheid vallen onder de interne standaarden en zijn ze gekwantificeerd? (Bijv. klimaatsverandering, mest en pesticide gebruik, etc.)  Kwantitatief (Bijv. De hoeveelheid uitstoot van CO2):

 Kwalitatief (Bijv. Verbod op gebruik van specifieke pesticide):

3 Zijn er ook ecologische duurzaamheid standaarden waaraan leveranciers en klanten moeten voldoen?  Ja – Leveranciers  Ja – Klanten  Ja – Leveranciers en klanten  Nee  Wordt niet vrijgegeven 4 Op welk managementniveau worden de ecologische duurzaamheid standaarden bepaald?  Topmanagement niveau  Afdeling/locatie niveau  Wordt niet vrijgegeven  Anders:

5 Welke ecologische aspecten moeten volgens uw bedrijf toegevoegd worden aan de interne standaarden om de duurzaamheid van het bedrijf en de productieketen te verbeteren?

Sociale duurzaamheid 6 Zijn er interne sociale duurzaamheid standaarden binnen het bedrijf?  Ja  Nee (Ga direct naar vraag 10) 7 Welke aspecten van sociale duurzaamheid vallen onder de interne standaarden en zijn ze gekwantificeerd? (Bijv. arbeidsrechten, onderwijs voor werknemers, etc.)  Kwantitatief (Bijv. hoeveelheid uren aan sociale werkplekken)

 Kwalitatief (Bijv. Verbod op kinderarbeid)

8 Zijn er ook sociale duurzaamheid standaarden waaraan leveranciers en klanten moeten voldoen?  Ja – Leveranciers  Ja – Klanten  Ja – Leveranciers en Klanten  Nee  Wordt niet vrijgegeven 9 Op welk managementniveau worden de sociale duurzaamheid standaarden bepaald?  Topmanagement niveau  Afdeling/locatie niveau  Varieert  Wordt niet vrijgegeven  Anders:

10 Welke sociale aspecten moeten volgens uw bedrijf toegevoegd worden aan de interne standaarden om de duurzaamheid van het bedrijf en de productieketen te verbeteren?

Materiële productie stromen

11 Welke soort biomassa voor energieproductie (bijv. miscanthus, hout) wordt er geoogst?

12 Ontstaat afval(water) door de biomassa productie en is het noodzakelijk dit te verwerken of te zuiveren op het bedrijfsterrein of elders?  Nee, er is geen afvalwater of afval  Ja, maar er is geen verdere verwerking nodig  Ja, de verwerking van afval(water) wordt op eigen terrein gedaan  Ja, de verwerking van afval(water) wordt elders gedaan  Wordt niet vrijgegeven  Anders:

13 Wat zijn de bijproducten van biomassa voor de bio-energieproductie (bijv. hooi en bosafval)?

14 Is het mogelijk om kwantitatieve informatie vrij te geven over de materiele en energie input die nodig zijn voor de productie?  Ja  Nee (Ga direct naar vraag 17)

15 Wat zijn de belangrijkste grondstoffen die nodig zijn en hoeveel is er ongeveer nodig (in kg) per hectare (vul in wat van toepassing is)?  Zaden/Zaailingen

 Water

 Mest

 Manuren

 Energie (hitte, elektriciteit, diesel etc.):

 Andere grote input:

16 Hoeveel biomassa voor energieproductie en bijproducten worden geoogst per hectare (vul in wat van toepassing is)?  Biomassa energieproductie

 Bijproducten

Besluitvorming en Materiële Stromen

17 Wie is verantwoordelijk voor het inkopen van grondstoffen?  Management/werknemers op locatie  Management/werknemers op een andere locatie/afdeling  Wordt niet vrijgegeven  Anders:

18 Wie is er verantwoordelijk voor de verkoop van de geproduceerde biomassa?  Management/werknemers op locatie  Management/werknemers op een andere locatie/afdeling  Wordt niet vrijgegeven  Anders:

19 Wie betaalt en regelt het transport van ingekochte grondstoffen?  Het bedrijf dat inkoopt  Het bedrijf dat verkoopt  Varieert  Wordt niet vrijgegeven  Anders:

20 Wie betaalt en regelt het transport van verkochte biomassa?  Het bedrijf dat inkoopt  Het bedrijf dat verkoopt  Varieert  Wordt niet vrijgegeven  Anders:

21 Wie is er verantwoordelijk voor andere transactiekosten (bijv. Contracten en vergunningen)?  Management/werknemers op locatie  Management/werknemers op een andere locatie/afdeling

 Wordt niet vrijgegeven  Anders:

Appendix 2.2: Survey Sustainability Biojet Fuel Producers

Introduction This survey is part of a master research on how environmental and social aspects of the biojet fuel supply chain can be researched through life cycle assessment methodology.

The purpose of this survey is twofold. The first is to gain a better understanding of the environmental and social sustainability aspects and standards that the biojet fuel producing industry has implemented and believes are important for further improvement. The second is to get a better view of the physical flows and their relations to decision making.

The survey consists of 4 parts: Environmental sustainability (questions 1-5), Social sustainability (questions 6-10), Physical flows (questions 11-14), and Decision making and Physical flows (questions 15-19). Some questions may be skipped based on the given answers.

Thank you very much for your participation. Please send your response to: [email protected] Feel free to contact me for any other questions or comments.

Environmental sustainability

1 Are there internal environmental sustainability standards in the firm?  Yes  No (skip to question 5)

2 What aspects of environmental sustainability are addressed in the internal standards and are they quantified? (e.g. climate change, eutrophication, resource depletion, etc.)  Quantified (e.g. standards on amount of CO2 emissions):

 Non-quantified (e.g. only biomass from land already in production after 2007):

3 Do the environmental sustainability standards also include aspects that the suppliers and customers have to comply with?  Yes – Suppliers  Yes – Customers  Yes – Both suppliers and customers  No  Not disclosed

4 On which management level are the environmental sustainability standards determined?  Corporate level  Department level  Varies  Not disclosed  Other:

5 What aspects does the firm believe should be further developed or added to the standards to increase the environmental sustainability of the firm and the supply chain?

Social sustainability

6 Are there internal social sustainability standards in the firm?  Yes  No (skip to question 10)

7 What aspects of social sustainability are addressed in the internal standards and are they quantified? (e.g. legal conduct, labor rights, employee education/development, etc.)  Quantified (e.g. working hours at a production facility)

 Non-quantified (e.g. prohibition of child labor)

8 Do the social sustainability standards also include aspects that the suppliers and customers have to comply with?  Yes – Suppliers  Yes – Customers  Yes – Both suppliers and customers  No  Not disclosed

9 On which management level are the aspects of social sustainability standards determined?  Corporate level  Department level  Varies  Not disclosed  Other:

10 What aspects does the firm believe should be further developed or added to the standards to increase the social sustainability of the firm and the supply chain?

Physical flows

11 From which type of conversion technology does the firm procure biojet fuel (tick as many as apply)?  HEFA - hydroprocessed esters and fatty acids  DSHC - direct sugar to hydrocarbons  AtJ - alcohol-to-jet  Bio-GtL - gas-to-liquid  BtL - biomass-to-liquid  HDCJ - hydrotreated depolymerized cellulosic jet  Not disclosed  Other:

12 From where is the biomass sourced that is used in biojet fuel production (tick as many as apply)?  Same country as production site  Different country, but same continent as production site  Different continent then production site  Not disclosed

13 Which type of biomass is used in biojet fuel production (tick as many as apply)?  Vegetable oils  Waste oils or fats  Lignocellulose  Starch or sugar  Other:

14 If possible, please specify the source of biomass (e.g. sugar cane, forest residues, elephant grass)?

Decision making and Physical flows

15 Who is responsible for the purchase of inputs for a production facility?  Employees/department at the production facility  Employees/department at a different location  Not disclosed  Other:

16 Who is responsible for the sale of outputs from a production facility?  Employees/department at the production facility  Employees/department at a different location  Not disclosed  Other:

17 Who organizes and pays for the transport of the purchased inputs?  The purchasing firm  The selling firm  Varies  Not disclosed  Other:

18 Who organizes and pays for the transport of the produced outputs?  The purchasing firm  The selling firm  Varies  Not disclosed  Other:

19 Who is responsible for other transaction costs for biojet fuel production (e.g. permits, contracts, etc.)  Employees/department at the production facility  Employees/department at a different location  Not disclosed  Other:

Appendix 2.3: Survey results The survey had two main objectives and was targeted to upstream stakeholder such as biomass producers and biojet fuel producers, and stakeholders downstream. The first objective was to gather information of existing social and environmental issues that are considered important by the product system and which issues are part of internal standards. The second objective was to gain a deeper understanding of the physical exchanges and their relation to the monetary flows and organization between the biojet fuel product system members. Three out of the 12 surveys were completed, two firms related to biomass production and one downstream firm that organizes the supply of biojet fuel to aviation firms and airports, but the survey participants will not be disclosed. The low amount of completed surveys limits the results and in particular information of the biojet fuel producers is lacking. The data cannot be used to make generalized and statistically significant claims. However, the results do shed light on the complexity along the product system which was of great value. When it comes to sustainability standards within companies, two situations were given. The upstream respondents have no formalized internal standards and rely on the sustainability depends on the actual practices of each biomass producer. In contrast, the downstream respondent implemented the RSB STD voluntary scheme. This standard includes environmental and social aspects of sustainable business (see appendices 1.2 and 1.5 for more detail). There was one aspect mentioned that is missing from the sustainability standards and that is the indirect land use change. Another interesting insight is the role of trading and the distinction with transport. One upstream business produces Miscanthus, but also facilitates the trading of Miscanthus from other farmers. This shows that the function of trading biomass with downstream firms can be separate from the production, as the respondent takes on the trading function for other producers. The downstream firm is an example of a trading firm without production that is responsible for delivering biojet fuel to customers. For the trader to transport goods, a separate transporter can be hired to provide the service. For the trading of biomass, the responsibility of organizing and paying transport can differ per transaction. The transport of biomass from the upstream firms is sometimes paid by the purchasing and other times by the selling firm. However,

93 it is clear that firms that provide the trading function can be different from firms that provide the transport, as the former can hire the latter for their services.

Appendix 3: Social unit processes These social unit processes were made using dummy data based on ecoinvent, the CPM LCA database, and rough estimates. The purpose of these processes is to illustrate the SELCA methodology. None of this data should be used or applied for the assessment of the biojet fuel product system.

Appendix 3.1: SUP in CMLCA

Natural gas producer Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Traded electricity 0.012 kWh Traded tap water 0.0015 kg Economic outputs Natural gas [NL] 1 Nm3 Environmental inputs Water, river[resource_in water] 1.47E-06 m3 Water, salt, sole[resource_in water] 1.47E-06 m3 Gas, natural, in ground[resource_in ground] 1 Nm3 Water, salt, ocean[resource_in water] 1.47E-06 m3 Environmental outputs Heat, waste[air_low population density] 0.042 MJ Carbon dioxide, fossil[air_low population density] 0.00178 kg Methane, fossil[air_low population density] 0.00019 kg Social inputs Labor industry in the Netherlands 0.0001 hour(s)

Electricity producer Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Traded natural gas 0.03 Nm3 Traded tap water 2 kg Economic outputs Electricity[NL] 1 kWh Environmental outputs Heat, waste[air_high population density] 0.735 MJ Carbon dioxide, fossil[air_high population density] 0.056 kg

Social inputs Labor industry in the Netherlands 0.0001 hour(s)

Tap water producer Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Traded electricity 0.0004 kWh Economic outputs Tap water[RER] 1 kg Environmental inputs Water, river[resource_in water] 0.000513 m3 Water, well, in ground[resource_in water] 0.00041 m3 Water, lake[resource_in water] 0.000205 m3 Environmental outputs Heat, waste[air_high population density] 0.0014 MJ Social inputs Labor industry in the Netherlands 1.00E-05 hour(s)

Crude oil producer Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Traded electricity 0.014 kWh Economic outputs Crude oil[NL] 1 kg Environmental inputs Water, salt, sole[resource_in water] 0.000355 m3 Oil, crude, in ground[resource_in ground] 1 kg Gas, natural, in ground[resource_in ground] 0.00131 Nm3 Water, salt, ocean[resource_in water] 0.000137 m3 Environmental outputs Heat, waste[air_low population density] 0.05 MJ Carbon dioxide, fossil[air_low population density] 0.00211 kg Methane, fossil[air_low population density] 0.000226 kg BOD5, Biological Oxygen Demand[water_ocean] 0.000873 kg

DOC, Dissolved Organic Carbon[water_ocean] 0.000248 kg COD, Chemical Oxygen Demand[water_ocean] 0.000873 kg TOC, Total Organic Carbon[water_ocean] 0.000248 kg Social inputs Labor industry in the Netherlands 0.001 hour(s)

Transporter Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Traded diesel 0.246 kg Economic outputs Truck transport[NL] 5.8 tkm Environmental outputs Heat, waste[air_unspecified] 11.1 MJ Particulates, < 2.5 um[air_unspecified] 0.000255 kg Carbon dioxide, fossil[air_unspecified] 0.777 kg Carbon monoxide, fossil[air_unspecified] 0.00162 kg NMVOC, non-methane volatile organic compounds, unspecified origin[air_unspecified] 0.000363 kg Nitrogen oxides[air_unspecified] 0.00812 kg Social inputs Labor industry in the Netherlands 0.004 hour(s)

Diesel producer Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Traded electricity 0.0245 kWh Traded tap water 0.015 kg Traded crude oil 1 kg Economic outputs Diesel[NL] 1 kg Environmental inputs Water, cooling, unspecified natural origin[resource_in water] 0.00384 m3 Water, river[resource_in water] 0.000672 m3 Environmental outputs Heat, waste[air_high population density] 0.0518 MJ 97

Sulfur dioxide[air_high population density] 0.000168 kg Sodium, ion[water_ocean] 0.000128 kg Social inputs Labor industry in the Netherlands 0.001 hour(s)

Natural gas trader Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Natural gas[NL] 1 Nm3 Truck transport[NL] 1.01E-05 tkm Economic outputs Traded natural gas 1 Nm3 Social inputs Labor industry in the Netherlands 0.001 hour(s)

Electricity trader Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Electricity[NL] 1 kWh Economic outputs Traded electricity 1 kWh Social inputs Labor industry in the Netherlands 1.00E-05 hour(s)

Tap water trader Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Tap water[NL] 1 kg Truck transport[NL] 1.04E-05 tkm Economic outputs Traded tap water 1 kg Social inputs

Labor industry in the Netherlands 0.0001 hour(s)

Crude oil trader Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Crude oil[NL] 1 kg Truck transport[NL] 1.20E-05 tkm Economic outputs Traded crude oil 1 kg Social inputs Labor industry in the Netherlands 0.001 hour(s)

Diesel trader Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Truck transport[NL] 0.00475 tkm Diesel[NL] 1 kg Economic outputs Traded diesel 1 kg Social inputs Labor industry in the Netherlands 0.001 hour(s)

Hydrogen producer Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Traded natural gas 0.0406 Nm3 Economic outputs Hydrogen 0.0077 kg Environmental outputs Carbon dioxide, fossil[air_unspecified] 0.0856 kg Social inputs Labor industry in the Netherlands 7.70E-06 hour(s)

Hydrogen trader

Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Hydrogen 1 kg Economic outputs Traded hydrogen 1 kg Social inputs Labor industry in the Netherlands 0.001 hour(s)

HEFA biojet fuel producer Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Traded natural gas 1.18E+04 Nm3 Traded electricity 1.00E+07 kWh Traded tap water 1.00E+09 kg Traded hydrogen 7.60E+06 kg Traded animal fat 1.92E+08 kg Economic outputs HEFA SKA biojet fuel 1.40E+08 kg HEFA by-products 6.00E+07 kg Social inputs Labor industry in the Netherlands 1.87E+05 hour(s)

HEFA SKA biojet fuel trader Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Truck transport[NL] 60 tkm HEFA SKA biojet fuel 1.00E+03 kg Economic outputs HEFA SKA biojet fuel for mixing 1.00E+03 kg Social inputs

100

Labor industry in the Netherlands 1 hour(s)

Animal fat trader Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Animal fat (W) 1 kg Truck transport[NL] 0.1 tkm Economic outputs Traded animal fat 1 kg Social inputs Labor industry in the Netherlands 0.002 hour(s)

Slaughterhouse Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic outputs Animal fat (W) 1 kg

Miscanthus trader Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Miscanthus 1 kg Truck transport[NL] 0.618 tkm Economic outputs Traded Miscanthus 1 kg Social inputs Labor industry in the Netherlands 0.001 hour(s)

Miscanthus producer

101

Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Traded fertilizer 0.255 kg Economic outputs Miscanthus 1 kg Environmental inputs Transformation, from pasture and meadow[resource_land] 1.45 m2 Occupation, arable, non-irrigated[resource_land] 5 m2a Transformation, from arable, non-irrigated[resource_land] 3.55 m2 Transformation, to arable, non-irrigated[resource_land] 5 m2 Carbon dioxide, in air[resource_in air] 1.37 kg Energy, gross calorific value, in biomass[resource_biotic] 16 MJ Environmental outputs Phosphate[water_river] 0.000117 kg Dinitrogen monoxide[air_low population density] 0.00101 kg Nitrate[water_ground-] 0.00374 kg Phosphate[water_ground-] 0.000117 kg Nitrogen oxides[air_low population density] 0.000212 kg Ethofumesate[soil_agricultural] 0.002 kg Ammonia[air_low population density] 0.00121 kg Social inputs Labor agriculture in the Netherlands 0.017 hour(s)

FT SKA biojet fuel trader Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Truck transport[NL] 60 tkm FT SKA biojet fuel 1.00E+03 kg Economic outputs FT SKA biojet fuel for mixing 1.00E+03 kg Social inputs Labor industry in the Netherlands 1 hour(s)

FT biojet fuel producer Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Traded hydrogen 40 kg Traded Miscanthus 960 kg

102

Traded natural gas 59 Nm3 Traded electricity 50 kWh Traded tap water 3.00E+03 Kg Economic outputs FT SKA biojet fuel 800 kg FT by-products 200 kg Social inputs Labor industry in the Netherlands 0.8 hour(s)

Fertilizer trader Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Fertilizer 1 kg Economic outputs Trader fertilizer 1 kg Social inputs Labor industry in the Netherlands 0.001 hour(s)

Fertilizer producer Temporal representativeness 2010-2015 Geographical representativeness Netherlands Economic inputs Traded natural gas 0.876 Nm3 Traded electricity 0.036 kWh Economic outputs Fertilizer 1 kg Environmental outputs Carbon dioxide, fossil[air_low population density] 2.4 kg Dinitrogen monoxide[air_low population density] 0.0175 kg Nitrogen oxides[air_low population density] 0.0155 kg Ammonia[air_low population density] 0.011 kg Carbon monoxide, fossil[air_low population density] 0.00215 kg

103

Sulfur dioxide[air_low population density] 0.0033 kg Methane, fossil[air_low population density] 0.00045 kg Hydrogen chloride[air_low population density, long-term] 0.00011 kg Social inputs Labor industry in the Netherlands 0.001 hour(s)

Appendix 3.2: Allocation example unit processes of HEFA producer

Marketing department Economic input Traded electricity 67,850 kWh Traded natural gas 11,800 m3 Traded tap water 710 ton Economic output Labor for HEFA 41,600 h Labor for biochemicals 166,400 h Social input Labor industry in the Netherlands 208,000 h

Headquarters Economic input Traded electricity 67,850 kWh Traded natural gas 11,800 m3 Traded tap water 355 ton Economic output Labor for management 104,000 h Social input Labor industry in the Netherlands 104,000 h

HEFA production facility Economic input Labor for HEFA 41,600 h Traded animal fat 192,400 ton Traded hydrogen 7,600 ton Traded electricity 10,000,000 kWh Traded natural gas 11,800 m3 Traded tap water 1,000,000 ton Economic output HEFA SKA biojet fuel 140,000 ton HEFA by-products 60,000 ton

104

Social input Labor industry in the Netherlands 104,000 h

Biochemical production facility Economic input Labor for biochemicals 166,400 h Traded electricity 20,000,000 kWh Traded natural gas 10,000 m3 Traded tap water 750,000 ton Economic output Biochemicals 300,000 ton Social input Labor industry in the Netherlands 104,000 h

Marketing department allocated to HEFA Economic input Traded electricity 13,570 kWh Traded natural gas 2,360 m3 Traded tap water 142 ton Social input Labor in the Netherlands 41,600 h

Headquarters flows assigned to HEFA Economic input Traded electricity 27,140 kWh Traded natural gas 4,720 m3 Traded tap water 142 ton Social input Labor in the Netherlands 41,600 h

105

Appendix 4: Dummy case study - quantitative inventory tables Appendix 4.1: Quantitative inventory table HEFA SKA biojet fuel Alternative HEFA SKA biojet fuel for mixing Elementary flows Value Unit Heat, waste[air_low population density] 9.33 MJ Heat, waste[air_high population density] 47.9 MJ Carbon dioxide, fossil[air_low population density] 0.395 kg Water, cooling, unspecified natural origin[resource_in water] -0.0176 m3 Water, river[resource_in water] -2.63 m3 Carbon dioxide, fossil[air_high population density] 3.08 kg Sulfur dioxide[air_high population density] 0.00077 kg Water, salt, sole[resource_in water] -0.00195 m3 Methane, fossil[air_low population density] 0.0423 kg Heat, waste[air_unspecified] 207 MJ Water, well, in ground[resource_in water] -2.1 m3 Particulates, < 2.5 um[air_unspecified] 0.00476 kg Oil, crude, in ground[resource_in ground] -4.59 kg Gas, natural, in ground[resource_in ground] -217 Nm3 Water, salt, ocean[resource_in water] -0.00095 m3 Sodium, ion[water_ocean] 0.000586 kg BOD5, Biological Oxygen Demand[water_ocean] 0.00401 kg DOC, Dissolved Organic Carbon[water_ocean] 0.00114 kg COD, Chemical Oxygen Demand[water_ocean] 0.00401 kg Carbon dioxide, fossil[air_unspecified] 437 kg Carbon monoxide, fossil[air_unspecified] 0.0302 kg NMVOC, non-methane volatile organic compounds, unspecified 0.00677 origin[air_unspecified] kg Nitrogen oxides[air_unspecified] 0.151 kg Water, lake[resource_in water] -1.05 m3 TOC, Total Organic Carbon[water_ocean] 0.00114 kg Labor industry in the Netherlands -3.87 h

Appendix 4.2: Quantitative inventory table FT SKA biojet fuel Alternative FT SKA biojet fuel for mixing Elementary flows Value Unit Transformation, from pasture and meadow[resource_land] -1.39E+03 m2 Heat, waste[air_low population density] 13.4 MJ Heat, waste[air_high population density] 46.8 MJ Carbon dioxide, fossil[air_low population density] 0.568 kg

106

Phosphate[water_river] 0.112 kg Water, cooling, unspecified natural origin[resource_in water] -0.106 m3 Water, river[resource_in water] -1.62 m3 Carbon dioxide, fossil[air_high population density] 3.12 kg Sulfur dioxide[air_high population density] 0.00465 kg Water, salt, sole[resource_in water] -0.0103 m3 Occupation, arable, non-irrigated[resource_land] -4.80E+03 m2a Transformation, from arable, non-irrigated[resource_land] -3.41E+03 m2 Transformation, to arable, non-irrigated[resource_land] -4.80E+03 m2 Dinitrogen monoxide[air_low population density] 0.971 kg Nitrate[water_ground-] 3.59 kg Phosphate[water_ground-] 0.112 kg Nitrogen oxides[air_low population density] 0.204 kg Carbon dioxide, in air[resource_in air] -1.54E+04 kg Energy, gross calorific value, in biomass[resource_biotic] 1.92 MJ Ethofumesate[soil_agricultural] 1.17 kg Ammonia[air_low population density] 0.0607 kg Methane, fossil[air_low population density] 1.25E+03 kg Heat, waste[air_unspecified] -1.28 MJ Water, well, in ground[resource_in water] 0.0287 m3 Particulates, < 2.5 um[air_unspecified] -27.7 kg Oil, crude, in ground[resource_in ground] -287 kg Gas, natural, in ground[resource_in ground] -0.00423 Nm3 Water, salt, ocean[resource_in water] 0.00354 m3 Sodium, ion[water_ocean] 0.0242 kg BOD5, Biological Oxygen Demand[water_ocean] 0.00687 kg DOC, Dissolved Organic Carbon[water_ocean] 0.0242 kg COD, Chemical Oxygen Demand[water_ocean] -1.32E+03 kg Carbon dioxide, fossil[air_unspecified] 532 kg Carbon monoxide, fossil[air_unspecified] 0.182 kg NMVOC, non-methane volatile organic compounds, unspecified origin[air_unspecified] 0.0409 kg Nitrogen oxides[air_unspecified] 0.915 kg Water, lake[resource_in water] -0.639 m3 TOC, Total Organic Carbon[water_ocean] 0.00687 kg Labor industry in the Netherlands -4.07 h Labor agriculture in the Netherlands -16.3 h

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Appendix 5: Dummy case study - comparative analysis Appendix 5.1: Tables Appendix 5.1.1: Comparative analysis table Comparative analysis Impact assessment scores HEFA SKA biojet FT SKA biojet Characterization method fuel for mixing fuel for mixing Unit CML 2001, eutrophication potential, 0.0198 1.4 kg PO4- generic[GLO] Eq CML 2001, resources, depletion of abiotic 4.15 5.92 kg resources[GLO] antimo ny-Eq CML 2001, acidification potential, average 0.0767 2.43 kg SO2- European[RER] Eq CML 2001, photochemical oxidation 0.00111 0.00551 kg (summer smog), high NOx POCP[RER] ethylen e-Eq CML 2001, climate change, GWP 442 827 kg CO2- 100a[GLO] Eq CML 2001, terrestrial ecotoxicity, TAETP 0 0 kg 1,4- infinite[GLO] DCB-Eq CML 2001, marine aquatic ecotoxicity, 0 0 kg 1,4- MAETP infinite[GLO] DCB-Eq CML 2001, freshwater aquatic ecotoxicity, 0 0 kg 1,4- FAETP infinite[GLO] DCB-Eq CML 2001, stratospheric ozone depletion, 0 0 kg CFC- ODP steady state[GLO] 11-Eq CML 2001, human toxicity, HTP 0.186 1.48 kg 1,4- infinite[GLO] DCB-Eq Hunkeler 2006, housing -0.00581 -0.0265 units Hunkeler 2006, health care -0.0086 -0.0392 units Hunkeler 2006, education -0.186 -0.848 units Hunkeler 2006, necessities -0.00515 -0.0235 units

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Appendix 5.1.2: Normalized comparative table Normalized Comparative analysis Impact assessment scores HEFA SKA biojet FT SKA biojet Characterization method fuel for mixing fuel for mixing Unit CML 2001, eutrophication potential, generic[GLO] 1.34E-13 9.45E-12 year CML 2001, resources, depletion of abiotic resources[GLO] 2.27E-11 3.24E-11 year CML 2001, acidification potential, average European[RER] 3.21E-13 1.02E-11 year CML 2001, photochemical oxidation (summer smog), high NOx POCP[RER] 3.01E-14 1.50E-13 year CML 2001, climate change, GWP 100a[GLO] 1.04E-11 1.95E-11 year CML 2001, terrestrial ecotoxicity, TAETP infinite[GLO] 0 0 year CML 2001, marine aquatic ecotoxicity, MAETP infinite[GLO] 0 0 year CML 2001, freshwater aquatic ecotoxicity, FAETP infinite[GLO] 0 0 year CML 2001, stratospheric ozone depletion, ODP steady state[GLO] 0 0 year CML 2001, human toxicity, HTP infinite[GLO] 7.91E-14 6.32E-13 year Hunkeler 2006, housing -1.83E-09 -8.36E-09 year Hunkeler 2006, health care -1.83E-09 -8.36E-09 year Hunkeler 2006, education -1.83E-09 -8.35E-09 year Hunkeler 2006, necessities -1.83E-09 -8.36E-09 year

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Appendix 5.2: Comparative analysis graphs Appendix 5.2.1: Eutrophication Eutrophication 1.6 1.4 1.4 1.2

Eq 1 - 0.8

kg kg PO4 0.6 0.4 0.2 0.0198 0 HEFA SKA biojet fuel FT SKA biojet fuel for for mixing mixing

Appendix 5.2.2: Abiotic resource depletion Abiotic resource depletion 8

5.92 Eq - 6 4.15 4

2 kg antimony kg 0 HEFA SKA biojet fuel FT SKA biojet fuel for for mixing mixing

Appendix 5.2.3: Acidification Acidification 3 2.43 2.5

Eq 2 - 1.5

1 kg SO2 kg 0.5 0.0767 0 HEFA SKA biojet fuel FT SKA biojet fuel for for mixing mixing

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Appendix 5.2.4: Photochemical oxidation Photochemical oxidation 0.006 0.00551

0.005 Eq - 0.004 0.003 0.002 0.00111

kg ethylene kg 0.001 0 HEFA SKA biojet fuel FT SKA biojet fuel for for mixing mixing

Appendix 5.2.5: Climate change Climate change

1000 827

800 Eq - 600 442 400

kg CO2 kg 200 0 HEFA SKA biojet fuel FT SKA biojet fuel for for mixing mixing

Appendix 5.2.6: Human toxicity Human toxicity 2

Eq 1.48

- 1.5 DCB

- 1 0.5 0.186

kg 1,4 kg 0 HEFA SKA biojet fuel FT SKA biojet fuel for for mixing mixing

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Appendix 5.2.7: Housing Housing 0 HEFA SKA biojet fuel for mixing FT SKA biojet fuel for mixing -0.005 -0.00581 -0.01

-0.015

Housing unitsHousing -0.02

-0.025 -0.0265 -0.03

Appendix 5.2.8: Healthcare Healthcare 0 -0.005 HEFA SKA biojet fuel for mixing FT SKA biojet fuel for mixing -0.01 -0.0086 -0.015 -0.02 -0.025

-0.03 Healthcare units Healthcare -0.035 -0.04 -0.0392 -0.045

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Appendix 5.2.9: Education Education 0 -0.1 HEFA SKA biojet fuel for mixing FT SKA biojet fuel for mixing -0.2 -0.186 -0.3 -0.4 -0.5

-0.6 Education units Education -0.7 -0.8 -0.9 -0.848

Appendix 5.2.10: Necessities Necessities 0 HEFA SKA biojet fuel for mixing FT SKA biojet fuel for mixing -0.005 -0.00515 -0.01

-0.015 Necessities unitsNecessities -0.02

-0.025 -0.0235

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Appendix 5.2.11: Normalized comparative analysis environmental impacts Normalized environmental impacts 1.40E-10 1.19E-10 1.20E-10 1.00E-10 8.00E-11 6.01E-11 6.00E-11 7.91E-14 4.00E-11 2.27E-11 8.51E-12 1.55E-13 7.88E-12 1.04E-11 2.00E-11 1.34E-13 3.21E-13 3.01E-14 5.34E-13

0.00E+00 Normalized damage (yrs) damage Normalized

Impact categories

HEFA SKA biojet fuel for mixing FT SKA biojet fuel for mixing

Appendix 5.2.12: Normalized comparative analysis social impacts Normalized social damages

0.00E+00 -1.00E-09 Housing Health care Education Necessities -2.00E-09 -3.00E-09 -1.83E-09 -4.00E-09 -1.83E-09 -1.83E-09 -1.83E-09 -5.00E-09 -6.00E-09 -7.00E-09

Normalized damage (yrs) damage Normalized -8.00E-09 -7.76E-09 -7.76E-09 -7.75E-09 -7.77E-09 -9.00E-09 Impact categories

HEFA SKA biojet fuel for mixing FT SKA biojet fuel for mixing

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Appendix 6: Dummy case study – flow charts Appendix 6.1: Flow chart – FT SKA biojet fuel product system

115

Appendix 6.2: Flow chart – HEFA SKA biojet fuel product system

116

Appendix 6.3: Flow chart – Detail of aggregated utilities

117

Appendix 6.4: Flow chart – Detail of aggregated transportation

118