Author Biography Michael Z. Hauschild Involved in development of LCIA methodology since the early 1990s. Has led several SETAC and UNEP/SETAC working groups and participated in the development of the ISO standards and the ILCD methodological guidelines. Main LCA interests are chemical impacts, spatial differentiation and science-based boundaries in LCIA. M.Z. Hauschild


Chapter 7 Goal Definition Anders Bjørn, Alexis Laurent, Mikołaj Owsianiak and Stig Irving Olsen Abstract The goal definitionis the first phase of an LCA and determines the purpose of a study in detail. This chapter teaches how to perform the six aspects of a goal definition: (1) Intended applications of the results, (2) Limitations due to methodological choices, (3) Decision context and reasons for carrying out the study, (4) Target audience, (5) Comparative studies to be disclosed to the public and (6) Commissioner of the study and other influential actors. The instructions address both the conduct and reporting of a goal definition and are largely based on the ILCD guidance document (EC-JRC in European Commission—Joint Research Centre—Institute for Environment and Sustainability: International Reference Life Cycle Data System (ILCD) Handbook—General Guide for Life Cycle Assessment —Detailed Guidance. Publications Office of the European Union, Luxembourg 2010)

Hauschild et al., LCA: Theory and Practice


Publications Office of the European Union, Luxembourg 2010). Learning Objectives After studying this chapter, the reader should be able to: • Define the goal of any LCA study. • Explain the six goal aspects and their relevance for the subsequent LCA phases. A. Bjørn (&)  A. Laurent  M. Owsianiak  S.I. Olsen Division for Quantitative Sustainability Assessment, Department of Management Engineering, Technical University of Denmark, Kongens Lyngby, Denmark e-mail: anders.bjoern@polymtl.ca A. Bjørn CIRAIG, Polytechnique Montréal, 3333 Chemin Queen-Mary, Montreal, QC, Canada © Springer International Publishing AG 2018 M.Z. Hauschild et al. (eds.), Life Cycle Assessment, DOI 10.1007/978-3-319-56475-3_7


7.1 The goal definitionis the first phase of any LCA. Here, the purpose of the study is elaborately defined and described. This greatly influences the LCA because decisions made in later LCA phases (Chaps. 8–12) must be consistent with the goal definition. The influence may also go the other way, for example, if unforeseen data limitations in the inventory analysis (Chap. 9) necessitate a revision of the goal definition. Such a revision is an example of the iterative nature of LCA (see Chap. 6). The goal definition based on the ISO standard requirements generally contains six aspects: 1. Intended applications of the results 2. Limitations due to methodological choices 3. Decision context and reasons for carrying out the study 4. Target audience 5. Comparative studies to be disclosed to the public 6. Commissioner of the study and other influential actors. Each aspect must be considered when performing an LCA


Target audience 5. Comparative studies to be disclosed to the public 6. Commissioner of the study and other influential actors. Each aspect must be considered when performing an LCA. Aspects 1 and 3 are central for doing an LCA because they have pervasive influence on decisions made in later LCA phases. On the other hand, aspects 2, 4, 5 and 6 mainly relate to communicating the results of an LCA. For these aspects, we further refer to Chaps. 13, 37–39, which provide specific guidance on and examples of the reporting and reviewing of LCA results. 7.2 Intended Applications of the Results All LCAs involve studying one or more product systems and this can be used in several applications, such as • Comparing environmental impacts of specific goods or services. • Identifying the parts of a product system that contribute most to its environmental impact (i.e. “hot spot identification”, focusing in product development)


• Identifying the parts of a product system that contribute most to its environmental impact (i.e. “hot spot identification”, focusing in product development). • Evaluating improvement potentials from changes in product designs(analysis and ‘what-if’ scenarios in eco-design). • Documenting the environmental performance of products (e.g. in marketing using environmental product declarations or other types of product environmental footprints). • Developing criteria for an eco-label. • Developing policies that consider environmental aspects. It is important to determine the intended application(s) of the LCA results at the onset, because it influences later phases of an LCA, such as the drawing of system boundaries (Chap. 8), sourcing of inventory data (Chap. 9) and interpretation of


results (Chap. 12). Often, several separate applications are intended in a study. For example, the intended applications of the results of the illustrative case on window frames in Chap. 39 were both to benchmark a new window design against three windows already on the market and to identify hot spots in the life cycle of the compared windows with the aim of guiding future impact reduction efforts. 7.3 Limitations Due to Methodological Choices This aspect can be seen as a critical reflection of what the LCA results can and cannot be used for. If a study only covers climate change (often referred to as a “carbon footprint” study) it is, for example, important to stress that results cannot be used to claim a general environmental superiority of a studied product or conclude anything about its overall “environmental friendliness”. Also, if a comparative study disregards one or more life cycle stages, it is important to stress how that limits the interpretation of results


Also, if a comparative study disregards one or more life cycle stages, it is important to stress how that limits the interpretation of results. For example, a study comparing the production of 1 tonne aluminum to the production of 1 tonne steel from mining to ingot cannot be used to identify the environmentally soundest material for use in a car, because the density difference of the two metals leads to differences in the amount of metal used for the car body and differences in the car mileage (fuel consumption per kilometre), causing different environmental impacts in the use stage and finally also in the disposal stage. In the illustrative window frame case study (Chap. 39) a stated limitation of the study was that a site-generic LCIA approach was taken in spite of impacts being concentrated around Scandinavia, where the natural environments, for some impact categories, do not correspond to the global average (e.g


Scandinavian soils show a higher sensitivity to depositions of acidifying compounds). Note that the limitations stated here should only relate to the choices made in the goal and scope phases of an LCA (this chapter and Chap. 8). These choices all relate to the planning and use of an LCA. On the contrary, choices made during the inventory and impact assessment phases of an LCA (Chaps. 9 and 10) relate to unforeseen constraints and assumptions (for example with respect to data availability) and must be documented at a later point in an LCA report, for example, in the inventory analysis part (Chap. 9) or in the interpretation part of a report (see Chap. 12). 7.4 Decision Context and Reasons for Carrying Out the Study This is an important aspect of the goal definition because it strongly influences the appropriate elaboration of a life cycle inventory (Chap. 9). First, the reasons for carrying out a study must be understood. The reasons should be clearly connected Goal Definition


to the intended application of results (Sect. 7.2) and specifically address drivers and motivations with respect to decision-making. Figure 7.1 provides an example of reasons for carrying out a study in continuation of the intended applications. Note that there is some ambiguity about the differences between “Intended application” and “Reasons for carrying out the study” in the ILCD guideline. As a rule of thumb the former should describe what a study does, while the latter should address why a study is made. The reasons for carrying out at study help understanding its decision context. In the example shown in Fig. 7.1 the study is motivated by a need for decision supporton governmental recommendations of paper waste handling. This means that the results and recommendations of the study can be expected to lead to changes in the analysed system. These changes may, in turn, lead to so-called “structural changes” in other systems that the studied product system interacts with


These changes may, in turn, lead to so-called “structural changes” in other systems that the studied product system interacts with. A structural change occurs when a change in one product system has such a large influence on the demand for a good or a service that it leads to new equipment being installed (increase in production capacity) or existing equipment being prematurely taken out of use (decrease in production capacity). As a rule of thumb, structural changes can be assumed to take place if the analysed decision leads to an additional demand or supply of a product that exceed the average percentage of annual replacement of total capacity (100% divided by the average equipment lifetime in years, e.g. 20). Structural changes result in qualitative and quantitative differences of industries and this must be considered in the inventory modelling (Chap. 9)


20). Structural changes result in qualitative and quantitative differences of industries and this must be considered in the inventory modelling (Chap. 9). In combination the above considerations help identify three different decision context situations and any LCA should be classified into one of these as part of the goal definition. Box 7.1 presents these three decision contexts and Fig. 7.2 presents a decision tree for how to determine the correct decision context of an LCA study. Box 7.1 The Three Types of Decision Contexts Situation A (Micro-level decision support): The study results are intended used to support a decision, but the small scale of the studied product system means that regardless the decision made, it will not cause structural changes in the systems that the studied product system interacts with. Many studies that intend to compare individual product systems, identify hotspots within these (see Sect


Many studies that intend to compare individual product systems, identify hotspots within these (see Sect. 7.2) or document the environmental performance of a product Support decision on governmental recommendations for environmentally preferred future handling of paper waste from commercial and governmental offices in Australia Comparative assertion of the overall environmental impacts associated with nation-wide recycling (Option I) or incineration (Option II) of all used office paper in Australia Intended application Reasons for carrying out the study Fig. 7.1 Example of reasons for carrying out a study in continuation of the intended application


in the form of an environmental product declaration fall into this decision context. The decision support of the LCA study may lead to limited changes in other systems, e.g. a reduced demand for electricity, but the changes are not of a structural nature, e.g. no electricity production equipment will be prematurely taken out of use. Situation B (Meso/macro-level decision support): The study results are intended used to support a decision, and the scale of the studied product system is such that the decisions that are made are expected to cause structural changes in one or more processes of the systems that the studied product system interacts with. An example of a study that would be classified as belonging to this type of decision context is a study intended as decision support for policy development on potential nationwide substitution of diesel derived from oil with biodiesel for private cars


Such a decision will lead to structural changes in the biodiesel industry in the form of new equipment being installed to respond to the substantially increased demand for biofuels. Situation C (Accounting): The study is not to be used to support decisions and is of a purely descriptive nature. It is documenting what has already happened, or what will happen due to a decision that has already been taken. Therefore, the presence of the LCA study will not lead to changes (small or structural) on other systems. Interactions with other systems (whether taking place in the past or in the future), e.g. through energy generated from waste incineration, can either be included in the product system model (Situation C1) or considered partially in the LCA through allocation (see Chap. 8) (Situation C2). C1 is used unless C2 is specifically prescribed by the commissioner’s goal of the study


8) (Situation C2). C1 is used unless C2 is specifically prescribed by the commissioner’s goal of the study. Any decision to be taken from the LCA results? Are interactions with other systems included in the model? Are there any largescale consequences on some processes of the background system? Yes No Situation C2 Situation C1 No Yes No Yes Situation B Situation A Fig. 7.2 Decision tree for how to identify the correct decision context Goal Definition


Figure 7.2 shows that the identification of the decision context depends on: • Whether the study is intended as decision support • Whether structural changes in interacting systems are expected from a decision supported by the study. • Whether it is chosen to model interactions with other systems as part of the product system model or to handle them partially through allocation (see Chap. 8). In the illustrative case of the window frames, the reason to carry out the study was to attract environmentally conscious consumers, through the use of an eco-label that the LCA results would help obtain. The study is thus to be used for decision support, but since it is concerned with a single product, this decision support is not expected to lead to structural changes in other systems. The decision context of the study is therefore Situation A (Micro-level decision support). 7.5 Target Audience The goal definition must state the target audience of the study, i.e


The decision context of the study is therefore Situation A (Micro-level decision support). 7.5 Target Audience The goal definition must state the target audience of the study, i.e. to whom the results of the study are intended to be communicated. The target audience may be consumers, consumer organisations, companies (managers, product developers, etc.), government, NGOs and others. The target audience greatly influences the extent to which details of the study should be documented, the technical level of reporting (Chap. 8) and the interpretation of results (Chap. 12). In the illustrative window frame case study, the employees of the window producer NorWin’s environmental and design departments are the target audience. Since this audience is unfamiliar with LCA, the content of the report was presented pedagogically by explaining technical terms that the readers could not be expected to be familiar with


Since this audience is unfamiliar with LCA, the content of the report was presented pedagogically by explaining technical terms that the readers could not be expected to be familiar with. When the readers are unfamiliar with LCA it may also be appropriate to provide brief background information about LCA of the type given in Chap. 2 of this book. 7.6 Comparative Studies to Be Disclosed to the Public The goal definition should explicitly state whether the LCA study is of a comparative nature (see Sect. 7.2) and if it is intended to be disclosed to the public. If this is the case, the ISO standard specifies a number of requirements on the conduct and documentation of the study and an external review process, due to the potential consequences that the communication of the results of the study may have for external companies, institutions, consumers and other stakeholders. The ISO requirements are detailed in Chap


The ISO requirements are detailed in Chap. 8 and are basically meant to ensure transparency and good quality of a study.


7.7 Commissioner of the Study and Other Influential Actors The goal definition should also explicitly state who commissioned the study, who financed it (usually the commissioning organisation) and other organisations that have influence on the study, including those of the LCA experts conducting the study. This step of the goal definition is meant to highlight potential conflicts of interest to readers of the study. Such conflict of interest may occur if a key provider of data has an economic interest in particular LCA results and interpretations. In comparative studies, it may also lead to an unintentional bias of the data collection. The commissioner of the study will normally provide data that is up to date and reflects the current performance of the technology for the commissioner’s own product


The commissioner of the study will normally provide data that is up to date and reflects the current performance of the technology for the commissioner’s own product. In contrast, the data collection for the other product(s) in the comparison will typically have to be based on literature and databases and hence, due to the delay involved in publishing the data, represent the state of the art several years ago. This chapter is to a large extent based on the ILCD handbook and the ISO standards 14040 and 14044. Due to the scope of this chapter, some details have been omitted, and some procedures have been rephrased to make the text more relevant to students. For more details, the reader may refer to these texts: EC-JRC.: European Commission—Joint Research Centre—Institute for Environment and Sustainability: International Reference Life Cycle Data System (ILCD) Handbook—General Guide for Life Cycle Assessment—Detailed Guidance. First edition March 2010. EUR 24708 EN


First edition March 2010. EUR 24708 EN. Publications Office of the European Union, Luxembourg (2010) ISO.: Environmental Management—Life Cycle Assessment—Principles and Framework (ISO 14040). ISO, the International Organization for Standardization, Geneva (2006a) ISO.: Environmental Management—Life Cycle Assessment—Requirements and Guidelines (ISO 14044). ISO, the International Organization for Standardization, Geneva (2006b) Author Biographies Anders Bjørn part of the LCA community since the early 2010s. Main focus is interpretations of sustainability and integration of sustainability targets in LCA to enable absolute sustainability assessments. Alexis Laurent working with LCA since 2010 with a strong LCIA focus, particularly on normalisation aspects. Main LCA interests include development of LCIA methods, LCA applications and footprinting of large-scale systems for policy-making (e.g. nations, sectors), and LCA applied to various technology domains, including energy systems


nations, sectors), and LCA applied to various technology domains, including energy systems. Goal Definition


Mikołaj Owsianiak involved in development and application of life cycle impact assessment methods in sustainability assessment of technologies. Has worked on issues associated with: soils (remediation), metals (toxic impact assessment), biodiesel (fate in the environment), and carbonaceous materials (biochar and hydrochar). Stig Irving Olsen LCA expert both as researcher and as consultant. Involved in the development of LCA methodologies since mid 1990’s. Contributed to UNEP/SETAC working groups on LCIA methodology. Main LCA interest is human toxicity impacts, emerging technologies, and decision making.


Chapter 8 Scope Definition Anders Bjørn, Mikołaj Owsianiak, Alexis Laurent, Stig Irving Olsen, Andrea Corona and Michael Z. Hauschild Abstract The scope definition is the second phase of an LCA. It determines what product systems are to be assessed and how this assessment should take place. This chapter teaches howtoperform a scopedefinition.First, important terminologyand key concepts of LCA are introduced. Then, the nine items making up a scope definition are elaborately explained: (1) Deliverables. (2) Object of assessment, (3) LCI modelling framework and handling of multifunctional processes, (4) System boundaries and completeness requirements, (5) Representativeness of LCI data, (6) Preparing the basis for theimpactassessment,(7) Specialrequirementsforsystemcomparisons,(8) Critical review needs and (9) Planning reporting of results. The instructions relate both to the performance and reporting of a scope definition and are largely based on ILCD


The instructions relate both to the performance and reporting of a scope definition and are largely based on ILCD. Learning Objectives After studying this chapter, the reader should be able to: • Define the scope of any LCA study. • Explain each of the nine scope items and their relevance for the subsequent LCA phases. • Define a functional unit for any kind of LCA study. • Explain the fundamental characteristics of an attributional and a consequential modellingapproach and how the decision context determines the choice between them. • Explain how the iterative approach to LCA helps getting the system boundaries and completeness right. A. Bjørn (&)  M. Owsianiak  A. Laurent  S.I. Olsen  A. Corona  M.Z. Hauschild Division for Quantitative Sustainability Assessment, Department of Management Engineering, Technical University of Denmark, Kgs Lyngby, Denmark e-mail: anders.bjoern@polymtl.ca A


Hauschild Division for Quantitative Sustainability Assessment, Department of Management Engineering, Technical University of Denmark, Kgs Lyngby, Denmark e-mail: anders.bjoern@polymtl.ca A. Bjørn CIRAIG, Polytechnique Montréal, 3333 Chemin Queen-Mary, Montreal, QC, Canada © Springer International Publishing AG 2018 M.Z. Hauschild et al. (eds.), Life Cycle Assessment, DOI 10.1007/978-3-319-56475-3_8


8.1 The scope definition determines what product systems are to be assessed and how this assessment should take place. Together with the goal definition (Chap. 7) the scope definition serves as a firm guide for how the ensuing LCA phases should be performed (Inventory analysis, Impact assessment and Interpretation, including uncertainty and sensitivity analysis) and for how the LCA should be reported. An overarching aim of the scope definition is to ensure and document the consistency of methods, assumptions and data and strengthen the reproducibility of the study. A scope definition consists of the following nine scope items: 1. Deliverables 2. Object of the assessment 3. LCI modelling framework and handling of multifunctional processes 4. System boundaries and completeness requirements 5. Representativeness of LCI data 6. Preparation of the basis for the impact assessment 7. Special requirements for system comparisons 8. Needs for critical review 9. Planning reporting of results


Representativeness of LCI data 6. Preparation of the basis for the impact assessment 7. Special requirements for system comparisons 8. Needs for critical review 9. Planning reporting of results. Each item must be considered when performing an LCA. Items 2–6 are central for doing an LCA because these have a pervasive influence on decisions made in later LCA phases. Aspects 1, 7, 8 and 9 mainly relate to reporting and communicating an LCA study. For these items, we further refer to Chaps. 13, 37–39, which provide specific guidance on the reviewing and reporting of LCAs. Note that the aspect of data quality requirements, which ILCD proposes as a separate scope item, is here considered under scope items 4 and 5. 8.2 Terminology and Key Concepts Before explaining the nine scope items, we present the terminology and key concepts that are used in this chapter


8.2 Terminology and Key Concepts Before explaining the nine scope items, we present the terminology and key concepts that are used in this chapter. 8.2.1 Unit Process and Flows A unit process is the smallest element considered in a life cycle inventory model (see below) for which input and output data are quantified. Unit processes can therefore be considered the building blocks of a life cycle inventory model that are “glued together” by input and output data, which can be organised into six categories of physical flows:


Input flows: 1. Materials 2. Energy 3. Resources Output flows: 4. Products 5. Waste to treatment 6. Emissions. Figure 8.1 shows a unit process of steel sheet rolling with an example of flows for each of the six categories. In practice, a unit process can represent a single process, e.g. the rolling of steel, but it can also represent an entire facility that contains many different processes, e.g. a slaughterhouse, if this offers the sufficient level of detail for the inventory modelling. The latter type of unit process may be physically subdivided into two or more new unit processes in a life cycle inventory model, see Sect. 8.5.4. Generally, unit processes do not gain or lose mass over time and the sum of all input flowsshould therefore be equal to the sum of all output flows at the level of elements (e.g. copper) and in aggregation


copper) and in aggregation. Output flows belonging to the product or waste to treatment categories from one unit process can act as input flows belonging to the categories materials and energy for other unit processes and this is how unit processes are linked in a life cycle inventory model. By comparison, resources and emission flows are not exchanged between unit processes. They are referred to as elementary flows, and defined by ILCD (using a slight modification of the ISO definition) as “single substance or energy entering the system being studied that has been drawn from the ecosphere without previous human transformation, or single substance or energy leaving the system being studied that is released into the ecosphere without subsequent human transformation”. The ecosphere can be understood as “the environment” and is elaborated below


The ecosphere can be understood as “the environment” and is elaborated below. Note that a single substance should be seen as an ideal and that some elementary flows in existing LCA practice are heterogeneous materials (such Unit process: Steel sheet rolling Energy Ex: Electricity Resources Ex: Water Materials Ex: Steel, unalloyed Product Steel sheet Waste to treatment Ex: Mineral oil Emissions Ex: Particulates to air Elementary flows Fig. 8.1 The unit process of steel sheet rolling and examples of flows. The actual unit process contains 86 flows [inspired by: ecoinvent v3 (Weidema et al. 2013)] Scope Definition


as the elementary flow bauxite which contains different minerals, some of which, e.g. Al(OH)3, are sources of aluminium) or cover a group of individual substances (such as the elementary flow VOCs, volatile organic compounds). What makes resource flows differently from material and energy flows is that they have been “drawn from the ecosphere without previous human transformation”. This means that resource flows are not outputs from other unit processes. In the steel sheet example of Fig. 8.1, the resource flow “water” may be sourced directly from a river close to the location of the steel sheet rolling process (i.e. no previous human transformation), whereas unalloyed steel (a material flow) is the product flow of another unit process and acts as a material flow to the steel sheet rolling unit process


no previous human transformation), whereas unalloyed steel (a material flow) is the product flow of another unit process and acts as a material flow to the steel sheet rolling unit process. Also, in the example of a unit process composed of an entire slaughterhouse, solar influx may be harvested directly in photovoltaic panels on the roof of the slaughterhouse to produce electricity and the solar influx is then a resource flow to the unit process because it has not undergone a previous human transformation. If the slaughterhouse instead was purchasing electricity from the grid, this electricity would be an energy flow to the slaughterhouse unit process because it has undergone previous human transformation, meaning that it is a product flow of another unit process (e.g. a coal-fired power plant). Similarly, what makes emission flows differently from waste flows is that they are “released into the ecosphere without subsequent human transformation”


a coal-fired power plant). Similarly, what makes emission flows differently from waste flows is that they are “released into the ecosphere without subsequent human transformation”. This means that emissions are not inputs to other unit processes. In the steel sheet example shown in Fig. 8.1, particulates (emission flow) are emitted directly into the air, whereas mineral oil will go through treatment, i.e. be a material input for another unit process. Chapter 9 will further explain how these concepts are used to model an LCI. 8.2.2 The Technosphere and the Ecosphere LCA divides the world into a technosphere and an ecosphere, see Fig. 8.2. The technosphere can be understood as everything that is intentionally “manmade” and also includes processes that are natural in origin, but manipulated by humans, such as photosynthesis when part of an agricultural system. All unit processes of an LCI model belong to the technosphere


All unit processes of an LCI model belong to the technosphere. The ecosphere is sometimes referred to as “the environment” or “nature” in layman’s terms and can be understood as everything which is not intentionally “man-made”. In the ecosphere reside those qualities that LCA has been designed to protect, i.e. ecosystems, human health and resource availability. These qualities are called Areas of Protection or damage categories in the field of LCA (see Chap. 10). Changes to the ecosphere can be considered unintentional “man-made” consequences of activities in the technosphere. Note that the ecosphere also undergoes natural changes, for example, via ice age cycles or natural ecological successions, which means that it can be difficult to choose an appropriate natural reference state against which human impacts should be measured, see Chap. 10.


Elementary flows are per definition the only flows that go across the boundary between the technosphere and the ecosphere (see Sect. 8.2.1) and it is because of these flows that the Areas of Protections are potentially impacted by the product systems assessed in LCA. Note that there is no clear-cut large-scale spatial separation between the technosphere and the ecosphere. The two spheres are in fact largely intermingled and therefore quite abstract. Surely, natural reserves and undeveloped land largely belong to the ecosphere, but the transportation and tourism infrastructure (roads, trash bins, etc.) going through them belong to the technosphere. In addition, though cities may appear like they belong 100% to the technosphere, the outdoor or indoor air that the population inhales belongs to the ecosphere, because human health can be impacted through air pollution


Note also that the exact location of the boundary between the technosphere and the ecosphere is often debated in the LCA community, for example, with regards to agricultural systems (see Chaps. 29 and 30). 8.2.3 Foreground and Background System Often hundreds of unit processes are required to deliver the product studied in an LCA. It is useful to distinguish between unit processes belonging to the foreground Ecosphere Technosphere Legend Process Product or waste flow Elementary flow Fig. 8.2 Division between ecosphere and technosphere for a generic product system. Elementary flows are represented by blue arrows, while flows within the technosphere are in black Scope Definition


and background system. The foreground and background systems are indicated in Fig. 8.3 for a generic product system. The foreground system is commonly defined as comprising those processes of a product system that are specific to it. These processes are in the study of a product typically some of the tier-one suppliers, but may also be suppliers further up the supply chain (e.g. tier-two or tier-three) if these are known by the producer, e.g. through a system of material certification. The foreground system is largely modelled using primary data, i.e. data collected first-hand by the LCA practitioner, e.g. obtained through the commissioner of the study. From a management perspective, processes in the foreground system can often be changed by the decision-maker commissioning a study (e.g. a company), either because they are directly operated by the decision-marker (e.g. at the production site) or because the decision-maker has the power to change or influence the processes, e.g


a company), either because they are directly operated by the decision-marker (e.g. at the production site) or because the decision-maker has the power to change or influence the processes, e.g. via purchase decisions or consumer information. In this context, a change can be choosing another supplier (introducing a different unit process in the model) or influencing the way a unit process is operated, thereby changing all or some of its six types of flows qualitatively and quantitatively. Ecosphere Technosphere Foreground system Background system Upstream Downstream System boundaries Legend Process Product or waste flow Elementary flow Fig. 8.3 LCI model for the generic product system from Fig. 8.2. The green box represents the boundaries of the product system with the division between foreground and background systems indicated. Unit processes with grey shading belong to the foreground processes, while unit processes without shading belong to the background system


Unit processes with grey shading belong to the foreground processes, while unit processes without shading belong to the background system. Part of the background system lies upstream in the value chain and feeds into the foreground system. Another part lies downstream and receives input from the foreground system. Black arrows between unit processes indicate material, energy, product or waste flows. Blue arrows to and from each unit process represent elementary flows (resources and emissions)


The background system, in contrast, is commonly defined as those processes of a system that are not specific to it. Such processes take part in numerous product systems besides the one studied. Examples are society’s electricity supply, the production of metallic copper, or the waste management systems. Neither of these is specific to the product under study, but typically purchased in a market without possibility to choose between specified individual suppliers. The background system is typically modelled using LCI databases, which contain average industry data representing the process in specific nations or regions. From a management perspective, processes in the background system can typically not be structurally changed by the decision-maker commissioning a study (e.g


From a management perspective, processes in the background system can typically not be structurally changed by the decision-maker commissioning a study (e.g. a company), because the decision-maker is only a minor customer and therefore can only exert limited power or because the suppliers are anonymous to the customer like the case of copper which is bought on the global metal market (an exception is Situation B studies where the decision-maker has influencing power on the background system, see Chap. 7). The distinction between foreground and background system is especially useful for planning data collection for the inventory analysis (see Chap. 9) and for making recommendations as part of the interpretation of LCA results (see Chap. 12). 8.2.4 Life Cycle Inventory Model and Results A life cycle inventory (LCI) model aims to link all unit processes that are required to deliver the product(s) studied in an LCA (glueing together the product system)


Figure 8.3 shows an example of an LCI model for a generic product. An LCI result is an inventory of the aggregated quantities of elementary flows, separated into resources and emissions, from all the unit processes within the system boundary. These elementary flow quantities must be correctly scaled to the assessed product by considering the extent to which the function of each unit process is required to deliver the studied product (see Chap. 9). 8.2.5 Life Cycle Impact Assessment LCIA is composed of selection of impact categories, classification and characterisation, normalisation and weighting (the latter two are optional steps according to ISO). Chapter 10 details these steps and only their main characteristics and purposes are presented here


Chapter 10 details these steps and only their main characteristics and purposes are presented here. Selection of Impact Categories, Classification and Characterisation The first step of LCIA involves selecting the impact categories that are relevant to consider in the LCA (considering the goal and scope of the study) and classifying the elementary flows of the LCI results into these impact categories. Scope Definition


The classification is based on the identification of the environmental issues that each elementary flow can contribute to, such as water depletion, non-renewable resource depletion, climate change or freshwater eutrophication. The purpose of the next step, characterisation, is to translate the LCI results (quantities of elementary flows aggregated across all unit processes of an LCI model) into indicator scores for the different impact categories. This essentially reduces a list of hundreds of quantified flows (the LCI results) to a manageable number of indicator scores (typically around 10 or fewer) with a clear environmental meaning, which is practical when comparing the environmental performance of two or more products. Normalisation Normalisation is an optional step under ISO 14044:2006 to support the interpretation of the impact profile from the characterisation


Normalisation Normalisation is an optional step under ISO 14044:2006 to support the interpretation of the impact profile from the characterisation. Normalisation means that indicator scores for all impact categories are expressed in a common metric, typically the annual contributions to total environmental impacts of an average person. This serves mainly three purposes: (1) for decision-makers to better understand the magnitude of characterised results by relating them to a common familiar and external reference, (2) to check for errors in the assessment resulting in unreasonably low or high normalised results and (3) to pave the road for weighting. Weighting Like normalisation, weighting is an optional step under ISO 14044:2006 to support the interpretation of the impact profile


Weighting Like normalisation, weighting is an optional step under ISO 14044:2006 to support the interpretation of the impact profile. In weighting, the (typically normalised) indicator scores for the different impact categories are made comparable by assigning weights to each impact category that is intended to reflect their relative importance. This relative importance is inherently subjective and can be based on the opinion of experts, policymakers or the general public (or a combination of these). Weighting allows calculating a single indicator score by summing all the weighted impact scores. This is often considered useful by decision-makers wanting to understand which product system performs best “overall” in a comparison


This is often considered useful by decision-makers wanting to understand which product system performs best “overall” in a comparison. The detailed choices on impact assessment methods and factors are made in the impact assessment phase of the LCA but it is necessary to select the impact categories in the scoping phase to ensure that the inventory analysis collects data on all elementary flows of potential relevance for the selected impact categories. 8.3 Deliverables The types of deliverables should directly reflect the intended applications of results, as defined in the goal definition. To be compatible with the ISO 14044 standard an LCA study must include an impact assessment, and most LCA studies have two deliverables, the LCI results and the LCIA results. Some LCA studies (e.g. collection of data for unit process databases) only involve the construction of a life cycle inventory (LCI), in which case the only deliverable is the LCI results


Some LCA studies (e.g. collection of data for unit process databases) only involve the construction of a life cycle inventory (LCI), in which case the only deliverable is the LCI results. In any case, LCI results should be documented with full transparency (see Sect. 9.7) to


ensure reproducibility of the LCA study and potentially allow elements of the underlying LCI model to be used as data sources for other LCA studies, if results are publicly released. LCIA results must be documented by the numerical values of the characterised results for each impact category covered. If normalisation and weighting of characterised results is carried out (see Sect. 8.2.5) the results of these steps must also be documented numerically. 8.4 Object of Assessment 8.4.1 Functions All LCAs study one or more product systems composed of many unit processes that are active throughout the life cycles of the product system(s). To study these systems the functions they provide must be understood. Indeed, LCA is the environmental assessment of needs fulfilment focusing on functions first and then on the products needed to provide these functions


Indeed, LCA is the environmental assessment of needs fulfilment focusing on functions first and then on the products needed to provide these functions. An LCA study should thus first define the functions from the perspective of the user (later the perspective will change when secondary functions are to be defined, see Sect. 8.5). For example, two different energy technologies may be compared on the basis of the function they provide of enabling the delivery of electricity to households (through a common distribution system). Functions are especially important to understand when comparing two or more product systems because a comparison is only fair and meaningful if the compared systems provide (roughly) the same function(s) to the user


For example, a tablet and a newspaper both provide the function of a news media, but because the tablet provides more functions (access to other websites, word processing and other software) a direct comparison of environmental impacts of a newspaper and a tablet would not be meaningful. An LCA must therefore always be anchored in a precise, quantitative description of the function(s) provided by the analysed product system. In the illustrative case on window frames in Chap. 39, the windows are compared based on their function of allowing daylight into a building. 8.4.2 Functional Unit To support a fair and relevant quantitative comparison of alternative ways of providing a function, knowledge of the functions provided by the alternative product systems must be used to define a functional unit


A functional unit defines the qualitative aspects and quantifies the quantitative aspects of the function, which generally involves answering the questions “what?”, “how much?”, “for how long/how many times?”, “where” and “how well?”. For example, a comparison of Scope Definition


outdoor paints may be based on the functional unit: “Complete coverage of 1 m2 primed outdoor wall for 10 years in Germany in a uniform colour at 99.9% opacity”. This is not to say that all LCAs on paint should have this functional unit. In other cases, for example, a particular colour or sheen may be considered an important function and should be included in the functional unit. It is important to understand that the functional unit should always include a function and not simply be a physical quantity, such as 1 kg, 1L or 1 MJ. For example, it would be wrong to compare paints on the basis of a functional unit of “1L paint”, since an identical quantity of different paints may deliver different functions, e.g. in terms of area of wall that can be covered, or the quality and duration of the coverage. Figure 8.4 illustrates how this functional unit is composed of answers to the five questions presented above


Figure 8.4 illustrates how this functional unit is composed of answers to the five questions presented above. It is important to define the functional unit right because it significantly influences the way LCA is performed, its results and interpretation, especially in comparative studies (see Sect. 8.9). This is because the functional unit serves as a reference point for deciding which unit processes to include and to what extent they are drawn upon. It is therefore essential to ensure that the functional unit fully captures the relevant functional aspects of the studied systems. In the following paragraphs, we provide some guidance for defining a correct functional unit. To get started, two concepts from the product development field are generally useful. These are obligatory properties and positioning properties. The obligatory properties are features that the product must possess for any user to perceive it as a product (e.g


These are obligatory properties and positioning properties. The obligatory properties are features that the product must possess for any user to perceive it as a product (e.g. ability to cover and protect the wall against the weather for an outdoor wall paint) and may also include legally required features (e.g. a car must have seat belts). These can usually be expressed in technical terms. The positioning properties, on the other hand, are optional features of a product, which can be used to position it as more attractive to the consumer in the competition with other similar products. Examples include price, colour, comfort, convenience, image, fashion and aesthetic aspects of the product. Positioning properties often vary from consumer to consumer as opposed to obligatory properties. Tables 8.1 and 8.2 show an example of obligatory and positioning properties for an outdoor wall paint and the window frame case study (Chap. 39), respectively


Tables 8.1 and 8.2 show an example of obligatory and positioning properties for an outdoor wall paint and the window frame case study (Chap. 39), respectively. After having listed the obligatory and positioning properties they need to be transformed into the functional unit, i.e. they should be used to address the Complete coverage of 1 m2 primed outdoor wall for 10 years in Germany in a uniform color at 99 .9 % opacity What? How much? What? For how long/ how many times? Where? How well? Fig. 8.4 Example of a functional unit composed of five questions


questions “what?”, “how much?”, “for how long/how many times?”, “where” and “how well?”, as in the example of Fig. 8.4. When defining the functional unit it is useful to distinguish between its quantitative and qualitative aspects. The quantitative aspects always make up the answers to the “how much?” and “for how long/how many times?” questions and often take part of the answer to the “how well?” question. In the example of an LCA on shopping bags quantitative functional aspects may be the volume (“how much?”), the number of shopping trips that the bag should be used for (“how long/many times?”) and strength, i.e. the weight that can be carried (“how well?”). For products that are continually in use (e.g. a fridge or a paint) the “how long/many times?” question should be addressed in the form of the time during which the product is in function (as in the paint example of Fig. 8.4). For products that are not in use all the time (e.g


8.4). For products that are not in use all the time (e.g. clothes, mobile phones) the “how long/many times?” question should instead be addressed by specifying the intensity of the use, either as the total duration of use (e.g. 1000 h) or the number of times that the function is provided (e.g


50 shopping trips for the Table 8.1 Derivation of functional unit on the basis of obligatory and positioning properties of an outdoor wall paint Obligatory properties Positioning properties Cover wall with uniform colour Protect wall against rain, sun and microalgae Provide surface that is easy to clean Meet health requirements for application Drip-free application Many different colour tones to select from Water-based Well covering (needs only one application) Functional unit Complete coverage of 1 m2 primed outdoor wall for 10 years in Germany at a uniform colour at 99.9% opacity Reference flow 0.67 L of water-based paint A (needs two applications and a re-paint every 21⁄2 years) 0.15 L of water-based paint B (low content of water, only needs one application and lasts 5 years until re-paint is required) Table 8.2 Derivation of functional unit on the basis of obligatory and positioning properties of windows Obligatory properties Positioning properties Allow daylight into a building


is required) Table 8.2 Derivation of functional unit on the basis of obligatory and positioning properties of windows Obligatory properties Positioning properties Allow daylight into a building through a physical barrier Protection from outdoor climate (thermal and noise insulation) Allow ventilation between indoor and outdoor Provide aesthetic functionality to the building Protection against breaking into the building Functional unit Allow daylight into a building through a physical barrier, equivalent to light being transmitted through an area of 1.23  1.48 m2 with visible light transmittance of at least 0.7, for 20 years Reference flow 0.5–0.67 window frames, depending on material 1 window pane Paint for maintaining surface of window pane (dependent on frame material) Scope Definition


shopping back example above). In the window frame case study the “how well?” question was partly addressed quantitatively by defining a visible light transmittance (the fraction of light that a window allows into the building) of at least 0.7 in the functional unit. The magnitude of the quantitative aspects in the functional unit can be chosen more or less arbitrarily. However, for the users of an LCA, it often makes the most sense to relate it to the magnitudes of typical use by a person, a family or a community. In the example of Fig. 8.4 it would be less intuitive to relate to a functional unit involving the complete coverage of 1 km2 primed outdoor wall, while a good magnitude in the functional unit for a study of waste incinerators could be the household waste generated by the municipality in one year. The qualitative aspects cover the way in which the function is provided and are often not easily quantifiable and sometimes not even clear-cut


The qualitative aspects cover the way in which the function is provided and are often not easily quantifiable and sometimes not even clear-cut. The “what?” and “where” questions require qualitative answers. In the example of Fig. 8.4 the “what?” question is answered by “complete coverage of primed outdoor wall” and the “where?” question by “Germany”. Other qualitative aspects are often used to answer the “how well?” question. These could be legal requirements, e.g. fire safety measures in a car or an office building, or technical standards, e.g. RAL code 3020 for the colour of paint. References to relevant legal requirements and technical standards in the functional unit are helpful, because they ensure comparability through adherence to the standard. To fully address the “how well?” question subjective or ambiguous elements related to user perception (e.g. fashion) are often important to include, to ensure comparability of different products


To fully address the “how well?” question subjective or ambiguous elements related to user perception (e.g. fashion) are often important to include, to ensure comparability of different products. For example, products may be discarded by users although they still fulfil their technical functions because they are no longer perceived as fashionable. For this reason, it is important to understand which aspects of a studied product’s function, including non-technical aspects such as fashion, that are perceived as important by users. LCA practitioners carrying out a study are therefore advised to consult the users of the product or service that is studied to ensure that the definition of the functional unit captures their perception of the product’s functionality. Those non-technical aspects that differ between compared products should either be included in the functional unit or considered separately in the interpretation phase of the LCA (see Chap. 12)


Those non-technical aspects that differ between compared products should either be included in the functional unit or considered separately in the interpretation phase of the LCA (see Chap. 12). The authors of this chapter have over the years encountered many types of mistakes in the definition of functional units. Box 8.1 provides selected examples of such mistakes and explains what is wrong with them and what needs to be considered to prevent making them. Box 8.1: Common Types of Mistakes when Defining the Functional Unit 1. Assuming that same physical quantity of product equals the same function: Example: “1 kg of packaging material” Explanation: A physical quantity, such as mass, is not a function. The mass required to provide a packaging function often depends on the material.


As an example, glass and PET in beverage packaging have different densities and physical properties, and different masses will therefore be required for providing the same function. To prevent mistakes like this, the functionality of the product should be considered (for example, what is the functionality of packaging?). 2. Being overly restrictive: Example: “Enable watching of television with a 30 W power consumption for 10,000 h” Explanation: A fixed power consumption is (except in special cases) not relevant to the user of a television and means that only televisions with that exact power consumption can be included in a study. To prevent mistakes like this, it must be ensured that the functional unit only covers what relates to the function of the product (to watch television). 3


To prevent mistakes like this, it must be ensured that the functional unit only covers what relates to the function of the product (to watch television). 3. Incorrect use of technical standards or legal requirements: Example: “Driving 1000 average person-kilometres in a diesel passenger car that fulfils the Euro 6 standard and therefore emitting less than 0.08 g NOX per kilometre (Euro 6 standard) during use” Explanation: Often products can demonstrate compliance with the law or a voluntary standard when completing a test that does not represent the actual conditions of the product’s use. A passenger car complying with the Euro 6 standard may emit more NOX than 0.08 g/km, depending on the driving pattern, climate, etc. A misinterpretation of a technical standard in the functional unit can therefore lead to mistakes in the LCI (in this case, underestimated NOX emissions). To prevent mistakes like this, the condition of the use must be considered


To prevent mistakes like this, the condition of the use must be considered. Generally, a reference to a technical standard in the functional unit does not need to be accompanied by the exact meaning of the technical standard, as this will be dealt with in the LCI modelling step. It must be stressed that a solid insight in the relevant technological domain is required to define a meaningful functional unit. For example, good knowledge about biofuels, nanomaterials or remediation of contaminated sites is required to define meaningful functional units for these technologies. Chapters 26–36 discuss the application of LCA, including the definition of functional units, for a wide range of technological domains. 8.4.3 Reference Flows When the functional unit has been defined, the reference flows can be determined. A reference flow is the product flow to which all input and output flows for the processes in the product system must be quantitatively related


A reference flow is the product flow to which all input and output flows for the processes in the product system must be quantitatively related. In other words, the Scope Definition


reference flow is the amount of product that is needed to realise the functional unit. For example, as shown in Table 8.1, 0.67 L of paint A is required to realise the functional unit in Fig. 8.4, while the same functional unit is realised with 0.15 L of paint B. The reference flow is typically different qualitatively and quantitatively for different products compared on the basis of a functional unit, due to differences in product properties and characteristics (e.g. viscosity and tear resistance of a paint). The reference flow is the starting point for the ensuing LCI analysis phase of an LCA (see Chap. 9), because it determines all the product flows required throughout the life cycle of the product system studied and their associated elementary flows (resource uses and emissions). It is very important not to confuse a reference flow with a functional unit (see Example 1 in Box 8.1). The former can only be known when the latter is correctly defined


It is very important not to confuse a reference flow with a functional unit (see Example 1 in Box 8.1). The former can only be known when the latter is correctly defined. One should, for example, never base an LCA on the comparison of 1 L of two different paints, unless a correctly defined functional unit has shown that the reference flows of the compared paints are quantitatively identical. It is important to understand the use situation in order to correctly define reference flows. For example, to define reference flows in a comparison of a disposable cardboard cup and a ceramic cup, the LCA practitioner must understand the number of times the two cups are used before they are discarded and how the ceramic cup is cleaned (by hand or dishwasher, and the associated consumption of detergent and water and its temperature). Tables 8.1 and 8.2 include functional unit and corresponding reference flows for the example of outdoor wall paint and the window frame case study (Chap


Tables 8.1 and 8.2 include functional unit and corresponding reference flows for the example of outdoor wall paint and the window frame case study (Chap. 39), respectively. 8.5 LCI Modelling Framework and Handling of Multifunctional Processes This part of the scope definition deals with the choice of an appropriate LCI modelling framework and ways to handle multifunctional processes. These choices must be made in accordance with the goal definition, particularly the identified decision context (Situation A, B or C, see Sect. 7.3), and they have a strong influence on the inventory analysis, the LCA results and their interpretation. 8.5.1 Secondary Functions and Multifunctional Processes To understand why different LCI modelling frameworks exist we first need to consider that a product system often delivers other types of function than the type dealt with in Tables 8.1 and 8.2


The functions of Tables 8.1 and 8.2 all relate to obligatory or positioning properties and are intended functions made available to product users by, e.g. companies selling the products. They are called primary functions. In addition to those, secondary functions can also emerge in the life cycle


of a product system. Secondary functions are unintended functions that usually have low or no relevance to the users of a product, meaning that they are not contributing to the obligatory or positioning properties. Instead, secondary functions are relevant to other systems of the technosphere that the studied product system interacts with. The existence of secondary functions reflects the fact that some processes are multifunctional. A process is multifunctional when it provides more than one function, meaning that it either delivers more than one product output and/or provides more than one service. An example of a multifunctional process that delivers more than one product output is animal husbandry where the cow may deliver both milk, meat, hide, bone meal and other products with an economic value


The production of the hide is an example of a secondary function of the husbandry from the perspective of the user of a bottle of milk, since hide is neither an obligatory nor a positioning property of the milk. An example of a multifunctional process that both deliver more than one product output and provide more than one service is waste incineration. It provides the multiple services of getting rid of many different types of wastes (the obligatory property) and can deliver both electricity and heat while doing so. Thus, secondary functions of a product that is disposed of by incineration are the production of heat and electricity. These secondary functions are relevant from the perspective of the energy system that the product system interact with because a change in the volume of discarded products that is incinerated leads to a change in the amount of energy generated from incineration


Multifunctional processes constitute a methodological challenge in LCA, which is based on the idea of analysing individual product systems based on the primary functions they provide in order to determine the environmental impact from the product. In the real world, there is hardly any product system that exists in isolation. As soon as a by-product arises from a multifunctional process (e.g. animal husbandry), it is economic common sense to try to utilise it, often in a different context from the product system being analysed in the LCA. This means that the process becomes part of another product system as well, and that the environmental impacts from the process can no longer be fully ascribed to the product system studied. 8.5.2 The ISO 14044 Hierarchy to Solving Multifunctionality In order to solve multifunctionality issues, the ISO 14044 standard presents a hierarchy of solutions


8.5.2 The ISO 14044 Hierarchy to Solving Multifunctionality In order to solve multifunctionality issues, the ISO 14044 standard presents a hierarchy of solutions. These solutions can both be used to make different product systems functionally comparable and to represent a single product system in a hotspot analysis. The levels of the hierarchy are presented below and the hierarchy is summarised as a decision tree in Fig. 8.5. Chapter 9 shows how to use to ISO hierarchy in practice when constructing an LCI. Scope Definition


Subdivision of Unit Process First choice is to try to solve this problem through increasing the resolution of the modelling by dividing the multifunctional unit process into minor units to see whether it is possible in this way to separate the production of the product from the production of the co-product, and if so exclude the subprocesses that provide the additional functions from the product system, see Fig. 8.6. An example of subdivision is when a factory produces two products


8.6. An example of subdivision is when a factory produces two products. Here, the subdivision approach may lead to the realisation that the factory actually contains a For each, can you sub-divide the multifunctional process ? Identify secondary functions Can you identify conventional or most probable alternative way to produce secondary functions? Perform subdivision of the process Cut off the subprocess providing the secondary function Integrate the secondary function into the system boundaries (displacement/ avoidance of impacts) Partition the environmental flows and associated impacts between the primary and the secondary functions, and cut off the part related to the secondary functions Can you perform meaningful allocation using causal physical relationship? Perform system expansion Perform allocation using physical causality Yes Yes Yes No No Can you perform meaningful allocation using representative physical relationship? Perform allocation using another parameter (e.g


economic) Perform allocation using representative physical parameter Yes No No Fig. 8.5 ISO hierarchy for solving multifunctionality presented in a decision tree


number of processes and that the processes needed for the production of the first product are physically separated from the processes needed for the production of the second product. This approach to solving multifunctionality does not always work. Even if you zoom to the molecular level of a cow, it is not possible to physically separate the metabolic processes in the cow that lead to the production of milk from the ones that lead to the production of meat or hide. System Expansion If subdivision fails to solve the multifunctionality problem, the ISO standard recommends trying to solve the problem by system expansion. In a comparison of two processes, this means expanding the second process with the most likely alternative way of providing the secondary function of the first process


In a comparison of two processes, this means expanding the second process with the most likely alternative way of providing the secondary function of the first process. In the comparison of power plant 1, which has district heating with co-generated heat as a secondary function, with a power plant 2, which only produces electricity, this means expanding the system of plant 2 with the most likely alternative way or combination of ways of providing district heat in that region (see Fig. 8.7). Expansion of system 2 with the alternative way to produce the secondary function of system 1 is equivalent to subtracting the alternative way from system 1 (which provides the function). This is also called to credit system 1 with the inputs and outputs which are avoided when its secondary service replaces this alternative production


This is also called to credit system 1 with the inputs and outputs which are avoided when its secondary service replaces this alternative production. In the case of district heating being the secondary function, system expansion would thus be the same as crediting the power plant, which produces the district heat, through subtracting the impacts from the most likely alternative way of producing this heat as illustrated in Fig. 8.6. In Fig. 8.6 equation B follows from equation A by subtraction of the alternative way of district heating from both sides of the equal sign. The approach of system expansion is thus mathematically equivalent to crediting for avoided production. Crediting for avoided production is typically used to account for secondary functions in a hotspot analysis where there is not a comparison of two alternative systems


Crediting for avoided production is typically used to account for secondary functions in a hotspot analysis where there is not a comparison of two alternative systems. For example, a product system that includes incineration can be credited for the avoided impacts from the production of heat and electricity by subtracting the avoided elementary flows in the inventory of the process (see Chap. 9 for technical details). In the milk example, system expansion can be performed by Materials Emissions to air, water and soil Product 1 Product 2 Materials Emissions to air, water and soil Product 1 Product 2 Unit process Fig. 8.6 Solving the multifunctionality problem by increasing the modelling resolution and sub-dividing the process into minor units which can unambiguously be assigned to either of the functional outputs Scope Definition


crediting the milk for the avoided impacts from alternative production of beef and other co-products. This alternative production might be the raising of cattle in a pure beef production system (which includes hides and other low-value co-products). Note that quality differences between dairy cow meat and cattle meat means that they may not be functionally equivalent. This may require the application of a value correction factor to the crediting. An important task in system expansion is to identify the process (or combination of processes) which is superseded by the co-product. This relates to the decision context (Situation A, B or C1/C2) identified in the goal definition (Sect. 7.4) and will be dealt with in Sect. 8.5.3. Allocation Sometimes it is not feasible to obtain complete functional equivalency between the compared systems or to isolate the primary function of a process from the secondary functions through system expansion


This may be the case when there is no alternative way to produce the secondary functions. A classic example of such a multi-output process is a petrochemical refinery with a variety of different organic substances as output without any mainstream alternative routes of production for these. It may also be the case when the most likely alternative route also has secondary functions, creating the need for further system expansion introducing alternative routes for the new level of secondary functions, which again may have secondary functions, creating the need for further system expansion and so on


In the milk example, the alternative production of meat from raising of cattle for example leads to the co-production of horn (for example used in jewellery Combined heat and power plant 1 1.5 GWh heat 1 GWh electricity Power plant 2 1 GWh electricity Alternative production of district heating 1.5 GWh heat Combined heat and power plant 1 1.5 GWh heat 1 GWh electricity Alternative production of district heating 1.5 GWh heat Power plant 2 1 GWh electricity (a) (b) Fig. 8.7 Equivalent modelling approach when dealing with multifunctionality. a System expansion: to ensure equal functionality system 2 is expanded to include the secondary function of system 1. b Crediting: system 1 is credited for the production of the secondary function, in order to have equal functionality of system 2


production), which cannot be produced in isolation and for which there may not exist a functionally equivalent material. When system expansion is not feasible, or when it is in conflict with the goal definition (for Situation C2, see below), the ISO 14044 standard recommends dividing the inputs and outputs of the multifunctional process or system between the different products or functions. This is called allocation. If possible, the allocation should be performed in accordance with the underlying causal physical relationships between the different products or functions, reflecting the way in which the input and output quantities are affected by changes in the quantities of products or functions delivered by the process or system


For example, in the hypothetical example of a waste incineration plant that incinerates two waste inputs, batteries and plastic, emissions of the toxic metal cadmium from the process will originate entirely from the batteries, given that the plastic stream contains no cadmium and that cadmium cannot be formed in the waste incineration process. This conclusion on the origin of cadmium, based on deductive reasoning, could also have been reached empirically by measuring changes in cadmium emissions in response to changes in waste inputs (e.g. a doubling of cadmium emissions would be expected from a doubling of battery inputs). A causal physical relationship can thus be established and cadmium emissions can be allocated 100% to the batteries. In the case of the milk example, the International Dairy Association recommends that physical allocation be based on the different physiological feed requirements for an animal to produce milk and meat (IDF 2010)


In the absence of a causal physical relationship between the products, the ISO standard recommends performing the allocation according to representative parameters. This is possible when co-products provide identical or similar functions. In the case of a waste incineration plant that delivers both heat and electricity as output, the exergy content of the two flows may, depending on the study context, be used as a representative physical parameter or allocation key, because it reflects the potential of each energyform to perform mechanical work. Here, it is important that the representative physical parameter actually represents a common function of the co-products. In the example of an agricultural process that produces both wheat and straw, the energy content of the two flows can only be used as a representative parameter if they are both intended as animal fodder (a common function)


If instead, the wheat is intended as food for humans this choice of representative parameter would be wrong (food for humans deliver many more functions than energy, e.g. vitamins and taste). When no common representative physical parameter can be identified for the different outputs, another relationship must be found between them. As an example, the ISO standard mentions an economic relationship, and indeed, this is a frequently applied allocation parameter. In economic allocation the inputs and outputs of the process or system are divided between its products according to their respective economic values, e.g. determined as their long-term average market prices, or some shadow price in cases where there is no market, e.g. for intermediary products


determined as their long-term average market prices, or some shadow price in cases where there is no market, e.g. for intermediary products. A justification for the use of economic allocation is that products are produced due to an incentive of financial income, and that a co-product with a market value close to 0 should be allocated a correspondingly low share of the non-product flows of a Scope Definition


process, compared to a primary product with a high market value. In the extreme situation where the value of the co-product is zero, its allocated share of the inputs and outputs also becomes zero in accordance with the fact that a zero-value output is not a co-product but waste and should be modelled as such. 8.5.3 LCI Modelling Framework: Attributional and Consequential LCA Traditionally, there have been two main LCI modelling frameworks: attributional and consequential modelling. In the ILCD guidelines, these were adapted to match the four decision context situations (i.e. A, B, C1 and C2). Understanding the difference between attributional and consequential modelling and when to use what has been one of the most difficult aspects of LCA, and there is still no consensus on this issue within the LCA community


In addition, some aspects of the terminology defined in the ILCD guidelines, in particular with regard to the definition and settings of attributional modelling, are inconsistent with the traditional views within the LCA community, thus adding more confusion to the matter (Ekvall et al. 2016). Below we first offer an explanation of the two modelling frameworks, including their handling of multifunctional processes and the use of average or so-called marginal LCI data (to be explained below). Where relevant we specify discrepancies between the ILCD guidelines and the traditional views. Table 8.3 summarises the explanation and discrepancies. We then provide guidance in compliance with the ILCD guidelines for selecting the LCI modelling framework with consideration to the goal definition. Attributional LCI modelling was initially the common practice when LCA development caught pace in the early-mid nineties


Attributional LCI modelling was initially the common practice when LCA development caught pace in the early-mid nineties. The overall aim of attributional modelling is to represent a product system in isolation from the rest of the technosphere or economy. The question addressed by attributional LCA can be said to Table 8.3 The meaning of the attributional and consequential modelling frameworks and their handling of multifunctionality LCI modelling framework Question to be answered Handling of multifunctional processes when subdivision is not possible Modelling of background system Before ILCD ILCD Attributional What environmental impact can be attributed to product X? Allocation System expansion or allocation Average processes Consequential What are the environmental consequences of consuming X? System expansion System expansion Marginal processes


be “what environmental impact can be attributed to product X?” or “what environmental impact is product X responsible for?” As hinted by these questions, there is an element of subjectivity involved in attributing impacts to a product system or deciding the impact responsibility of a product system. This subjectivity arises in the act of artificially separating the studied product system from the rest of the economy. This separation is artificial because many, if not most, product systems interact with other products systems through multifunctional processes, meaning that they, as explained in the previous section, cannot be described as physical entities in isolation. For example, from a strict physical perspective, the product system of a bottle of milk cannot be described in isolation and the assignment of processes that the product system is seen as “responsible for” therefore involves choices


Before the ILCD guidelines came into place attributional modelling was generally associated with allocation as the approach to solving the issue of multifunctional processes, provided that subdivision (the preferred solution of the ISO hierarchy) was not possible. By contrast, ILCD in some cases recommends solving multifunctionality by system expansion within an attributional modelling framework (see below). Besides the issue of multifunctionality, attributional LCA is also associated with the use of average processes in the background system, which reflects the modelling of an average supply chain. In practice, this means that a market mix is used. This could be for the global aluminium market or the electricitymarket of a nation


In practice, this means that a market mix is used. This could be for the global aluminium market or the electricitymarket of a nation. The former is composed of a range of bauxite mines with different ore grades and processing facilities that employ different production technologies, while the latter is composed of different energy conversion technologies, such as the combustion of coal, natural gas, oil and biomass, the harvesting of wind and solar power and the use of nuclear power. As an example, Fig. 8.8 shows the Danish electricity consumption mix in 2014. Consequential LCI modelling was developed around the year 2000 to eliminate the weakness inherent in the attributional LCA modelling framework due to the attempt to artificially separate a product from the rest of the economy. Its overall aim is to describe the changes to the economy caused by the introduction of the studied product system, i.e. the product system’s consequence


Its overall aim is to describe the changes to the economy caused by the introduction of the studied product system, i.e. the product system’s consequence. Consequential LCI modelling thus aims to answer the question “What are the environmental consequences of consuming X?” For example, a consequential LCA of a bottle of milk would attempt to model how the market responds to the change in demand for milk represented by the functional unit of the study (e.g. involving a milk volume of 1 L or a specified nutritional value). This is a very different approach than attributional modelling because the change in the economy can look very different than the representation of the isolated bottle of milk system. For example, the increased demand for milk may lead to an increase in the capacity for milk production (i.e. the numbers of cows giving milk), which in turn may lead to a reduction in the production of some meat (e.g


the numbers of cows giving milk), which in turn may lead to a reduction in the production of some meat (e.g. beef from raising cattle) due to the increasing supply of meat from dairy cows. This corresponds to handling the multifunctional process of milk production by system expansion. A consequence of increased consumption of milk may therefore be a reduction in environmental impacts from the avoided Scope Definition


production of beef from cattle, which is somewhat counterintuitive. The market may also be influenced by an increased demand for a product in other cases than multifunctionality. For example, if an additional kg is demanded of a fish species that is already fished at its maximum level permitted by regulation (a production constraint) a consequence may be an increase in the production of another protein source that is not constrained, such as chicken, and the environmental impacts following this increase. The examples show that consequential modelling to a large extent relies on a good understanding of and ability to model the dynamics of the economic system, which requires a markedly different way of thinking than the engineering perspective on product supply chains that historically has been in the core of LCA (see Chap. 3)


3). Contrary to attributional LCA, consequential LCA is not associated with the use of average processes for modelling the background system, but instead with the use of marginal processes. These are the processes that are employed or taken out of use as a response to an increase or decrease in the demand for a product, respectively. In the example of the Danish electricity system, the short-term marginal process will never be solar or wind, because solar irradiance or the wind are natural processes that cannot be “turned up or down” in response to a short-term change in electricity demand. Instead, the short-term marginal process in this example is a combustion process because it is possible to quickly adjust the rate at which something (e.g. coal or natural gas) is combusted in response to a change in electricity demand


coal or natural gas) is combusted in response to a change in electricity demand. The short-term marginal is often the combustion of natural gas, because this is a more expensive way of generating electricity than coal and thus sensitive to changes in Import from Germany Import from Norway Import from Sweden Natural Gas Oil Waste Wood chips Coal Wind Others 34% 14% 2% 5% 4% 14% 1% 14% 10% 3% Fig. 8.8 Danish electricity consumption mix in 2014 (low voltage, e.g. for domestic consumption). Imports from neighbouring countries can be further broken down into energy sources (Treyer and Bauer 2013)


electricity prices caused by changes in electricity demand (often, natural gas is only used during peak demand when a relatively high electricity price makes this technology economically viable). However, the relevant marginal processes to include in an LCI model are not always the ones that are affected as an immediate consequence of a decision, i.e. short-term marginal processes. Long-term marginal processes may be more relevant if a decision leads to large changes in supply or demand. Long-term marginal processes represent changes in the installed production capacity in response to the projected development of electricity demand. Often it is difficult to identify a single long-term marginal process, which is why a mix of potential long-term marginal processes is often used. Figure 8.9 shows such a mix for the long-term marginal electricity technology in the Danish market. See Chap. 9 on the identification of short- and long-term marginal processes


Figure 8.9 shows such a mix for the long-term marginal electricity technology in the Danish market. See Chap. 9 on the identification of short- and long-term marginal processes. It can be seen that fewer electricity production processes are part of the mix in Fig. 8.9 for consequential modelling than the mix in Fig. 8.8 for attributional modelling. For example, waste as an electricity source is not part of the consequential mix and this is because the long-term planning of waste incineration is thought to consider projections in future waste volumes (the primary function of waste incineration is to “get rid of” solid waste) rather than projections in future electricity demand. On the other hand, the construction of new wind turbines and coal-fired power plants (and to a very small extent, hydropower plants and rooftop photovoltaic panels) are thought to consider projections in future Danish electricity demand


When to consider short- versus long-term marginal processes in consequential LCA and how to identify these are still being debated in the LCA community. Note that while the background system is modelled differently in attributional and consequential LCA, the foreground system is overall modelled in the same way, the only exception being the handling of multifunctional processes. Fig. 8.9 Danish market mix of long-term marginal electricity processes (low voltage, e.g. for domestic consumption) (Treyer and Bauer 2013) Scope Definition


8.5.4 Recommended Modelling Choices for the Identified Decision Context ILCD provides recommendations for model choices for each of decision contexts (Situation A, B, C1 and C2) identified as part of the goal definition (see Chap. 7). These recommendations are the outcome of a comprehensive consultation process within the LCA community. Since different actors with different views have had a saying in this consensus process leading up to the ILCD recommendations, they are somewhat internally inconsistent, as pointed out by, for example, Ekvall et al. (2016). Below we present the recommendations for each decision context and make notes about the parts that are disputed. Table 8.4 summarises the recommendations. Situation A Situation A concerns micro-level decision support (see Chap. 7) and the consequence of a decision (e.g. the introduction of a new product on the market) is therefore of interest


Situation A Situation A concerns micro-level decision support (see Chap. 7) and the consequence of a decision (e.g. the introduction of a new product on the market) is therefore of interest. Ideally, the marginal process should therefore be identified and used for all background processes (such as electricity supply) and cases of multifunctionality (e.g. of an incineration process) should be handled by system expansion with marginal processes, provided that subdivision is not possible (see Sect. 8.5.2). This ideal for Situation A is logically consistent with a consequential modelling framework. Yet, ILCD recommends using an average market consumption mix for background processes and in cases of system expansion in the background system. ILCD terms this attributional modelling, although system expansion was previously associated with consequential modelling, as mentioned above


ILCD terms this attributional modelling, although system expansion was previously associated with consequential modelling, as mentioned above. The main reason for diverging from the ideal is that for the small changes studied under Situation A it can be very difficult to identify marginal processes, i.e. to understand the long- and short-term consequences on the market of introducing a small change in its composition of product systems. The actual market behaviour in response to small changes may also not be well-represented by simple mathematical Table 8.4 Summary of ILCD recommendations on LCI modelling choices Decision context LCI modelling framework (ILCD terminology) Handling of multifunctional processes when subdivision is not possible Modelling of background system Situation A Attributional System expansion Average processes Situation B Mix of attributional and consequential System expansion Mix of long-term marginal processes for processes structurally changed


Average processes in all other cases Situation C1 Attributional System expansion Average processes Situation C2 Attributional Allocation Average processes


equations, which makes it difficult to model what will actually happen, short-term and long-term, when, for example, a light-bulb is turned on, compared to a situation where it is not turned on. There is therefore a risk of using wrong marginal processes and this is problematic because LCA results are often quite sensitive to the choices of marginal process (e.g. natural gas vs. wind for electricity supply). These considerations have lead ILCD to pragmatically recommend using average processes in the background system. It must be mentioned that some LCA experts prefer to pursue the ideal by using marginal processes in Situation A studies, which conflicts with the presented ILCD recommendations. Situation B Situation B concerns meso/macro-level decision support (see Chap. 7)


Situation B Situation B concerns meso/macro-level decision support (see Chap. 7). ILCD recommends the same modelling choices as for Situation A, with the exception that background processes in the studied product system that have been identified as being affected by structural changes as consequence of the analysed decision are recommended to be modelled as mix of the long-term marginal processes. The logic behind this exception is that marginal processes for suppliers that experience structural changes are easier to identify than marginal processes for suppliers that just experience changes in terms of the volume of products they deliver. The reason for the focus on the long-term marginal is that the consequences studied under Situation B are generally long term


The reason for the focus on the long-term marginal is that the consequences studied under Situation B are generally long term. Still, identifying the correct long-term marginal processes in Situation B can be challenging and this is why it is pragmatically recommended to use a mix of possible long-term marginal processes, rather than actual long-term marginal processes, such as the mix for electricity shown in Fig. 8.9. Chapter 9 addresses the calculation of such a mix. In light of the uncertainty involved, we advise to model the LCI using a range of different mixes to analyse how sensitive results are to the estimated mix (see Chap. 12). As for Situation A studies, some LCA experts prefer to pursue the ideal of using a fully consequential approach by only using marginal processes (either single process or a mix) in Situation B studies, which conflicts with the presented ILCD recommendations


Situation C Situation C relates to accounting, meaning that studies are not to be used to directly support decisions and are of purely descriptive nature, often describing what has already happened. Situation C1 considers interactions with other systems and ILCD recommends handling this interaction via system expansion (for solving multifunctional processes where subdivision is not possible) and use of average processes in the background system. This means that the recommendations of ILCD in practice are similar for Situation A and C1, even though the modelling ideals of Situation A and C1 are different. Situation C2 disregards interactions with other systems and ILCD therefore recommends that allocation be systematically used to solve multifunctional processes, provided that subdivision is not possible. Note that this conflicts with the ISO hierarchy, according to which system expansion should be performed when possible instead of allocation. Scope Definition


8.6 System Boundaries and Completeness Requirements System boundaries demarcate the boundaries between the studied product system and (1) the surrounding economy (technosphere) and (2) the environment (ecosphere). “Completeness requirements” is a related concept that can be used to determine what processes should be included within the system boundaries to reach the degree of completeness in the product system modelling that is needed to be in agreement with the goal of a study (see details below). The setting of the system boundaries can have a large influence on LCA results because they determine the unit processes from which environmental impacts should be quantified. At this point in the scope definition, the system boundaries should be represented in a diagram that provides an overview of which parts of the studied product system(s) that are included and which are excluded


An appropriate level of detail in this diagram is the life cycle stages (such as production, manufacturing, transportation, retail, use and disposal) or the main processing steps. It is often useful to start with the process or life cycle stage that delivers the reference flow and then expand upstream and downstream. See Fig. 8.10 for an example diagram for the study of a steel sheet used to prevent accidents during roadworks. Note that the diagram does not need to contain individual unit processes, as this full level of detail will only be achieved in the actual construction of the inventory model (Chap. 9). Extraction of iron ore Crude steel production Steel rolling Use on road Collection for recycling System boundaries Pig iron production Extraction and production of materials Manufacturing Use Disposal Landfilling (a) (b) Fig. 8.10 a Example of system boundaries diagram for the life cycle of a steel sheet used to prevent accidents during roadworks


8.10 a Example of system boundaries diagram for the life cycle of a steel sheet used to prevent accidents during roadworks. Only the main process steps in the life cycle are shown. b Illustration of steel sheets in use


8.6.1 Ideal System Boundaries Ideally, within the system boundaries should be all the unit processes required to deliver the reference flow(s) defined by the functional unit. In cases where multifunctionality is handled by system expansion, this also includes processes from other systems that interact with the studied system. System boundaries should ideally be set so that all flows crossing them are elementary flows (resources and emissions). In other words, no material, energy, product or waste to treatment flows should cross the system boundaries. Ideal system boundaries thereby contain all the unit processes used to deliver the reference flow(s) by (1) generating energy and products (materials for other unit processes) from extracted resources and (2) treating waste flows to the point where the only outputs are emissions. Figure 8.11 illustrates an ideal system boundary for a simple hypothetical product system containing just fifteen unit processes


Figure 8.11 illustrates an ideal system boundary for a simple hypothetical product system containing just fifteen unit processes. In this case, the inventory model is fully complete, because all unit processes needed to deliver the reference flows are inside the system boundaries. Outside the system boundaries lies the rest of the technosphere (not shown in Fig. 8.9), i.e. the total body of other product systems in the global economy, and the ecosphere, i.e. which is affected by resource uses and emissions from the technosphere. Ecosphere Technosphere System boundaries Reference flow Legend Process Product or waste flow Elementary flow Fig. 8.11 Setting of system boundaries for a simple hypothetical product system. The boundary contains all the unit processes required to deliver the reference flow (bold), and the only flows crossing the system boundaries are elementary flows (blue). Note that the rest of the technosphere is not shown Scope Definition


8.6.2 Reasons to Divert from Ideal System Boundaries There are three reasons to divert from working with ideal system boundaries: First, if a study does not take a full life cycle perspective the rule of only allowing elementary flows to cross the system boundary does not apply. A study taking a full life cycle perspective aims to cover all the processes that are needed to deliver the function(s) of interest upstream (extraction and production of raw materials and manufacturing) and downstream (disposal) to the use stage. By contrast, a so-called “cradle-to-gate” study is an example of a study not taking a full life cycle perspective because the system boundary ends at the gate of the factory where the studied product is produced. In this case, the product flow thus crosses the system boundary, as shown in Fig. 8.12 (based on the simple hypothetical product system shown in Fig. 8.11)


In this case, the product flow thus crosses the system boundary, as shown in Fig. 8.12 (based on the simple hypothetical product system shown in Fig. 8.11). The goal definition’s intended applications of results decides whether a full life cycle perspective should be taken (see Chap. 7). This decision is usually also reflected by the functional unit (see Sect. 8.4.2). Second, in comparative studies it is justified to exclude identical processes if they deliver identical quantities of services (energy, materials or treatment of waste) in the systems studied. For example, in the illustrative case study on window frames (Chap. 39) comparing four windows, the processes involved in cleaning the Ecosphere Technosphere Reference flow System boundaries Legend Process Product or waste flow Elementary flow Fig. 8.12 Setting of system boundaries for a simple hypothetical product system in a cradle-to-gate assessment


8.12 Setting of system boundaries for a simple hypothetical product system in a cradle-to-gate assessment. In this case, the reference flow (bold) is crossing the system boundaries, to the rest of the technosphere; in addition to the elementary flows (blue) entering or leaving the ecosphere


windows throughout their use stages were excluded from the system of each window because users are expected to clean the windows, that all have the same surface area, in the same way, using the same amount of water and detergent. While this kind of exclusion is allowed for comparisons between systems, it prevents a proper hotspot analysis because it is unknown how much the omitted processes contribute to overall environmental impacts. Third, constructing an LCI model with ideal boundaries is practically impossible. This is because the number of unit processes actually required to deliver a reference flow is often, even for simple products, enormous: Typically, unit processes require around 5–10 material or energy inputs that each needs to be produced by a unit process that in itself requires around 5–10 material or energy inputs, etc


Furthermore, many product systems include examples of infinite loops where one process A requires input from another process B to deliver an output that is needed by process B to produce the input to process A. Every step back in a value chain represents a step back in time and ideal system boundaries would therefore need to encompass a large part of industrial history, which is not practically possible to model. Yet, amongst the enormous number of unit processes that should ideally be included in the system boundaries, only a minority actually have a quantitatively relevant contribution to the environmental impacts of the studied product system. For example, the ballpoint pens used by employees at a coal-fired power plant obviously have an insignificant contribution to the environmental impacts of a unit of power generation. Therefore, all LCA studies in practice cut-off some unit processes that are actually needed (although to a very limited extent) to deliver the reference flow