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. This presents a dilemma of the system scoping. You should include within your system boundaries the processes that matter, i.e. contribute significantly to the overall impacts from the product system, but how can you determine whether a process matters before you know what the total impacts are and can relate the impacts from the process to this number? The solution to this dilemma lies in the iterative approach to LCA that was introduced in Sect. 6.3 and presented for inventory modelling in Sect. 9.3. Figure 8.13 shows examples of excluded product flows. 8.6.3 Completeness Requirements: Quantitative or Qualitative? Completeness requirements are understood quantitatively by the ILCD guideline as the share (%) of a product’s actual environmental impact that a study aims to capture


From this understanding, completeness requirements would, for example, be lower for a study that intends to provide an initial screening of hot spots for a company to familiarise itself with the concept of life cycle thinking (e.g. 70%), than for a study that intends to provide an environmental product declaration (EPD) for Scope Definition

Hauschild et al., LCA: Theory and Practice


consumers to consider environmental aspects of their purchasing (e.g. 90%). In an LCA guiding the choice between two product designs, the completeness requirement depends on the difference in impact between the product designs. If there is a large (expected) difference, the requirement to completeness would be lower than if the product designs have very similar impacts. In practice, it is often difficult to derive a quantitative completeness requirement from the, generally qualitative, goal definition. In addition, a quantitative completeness requirement is often not helpful for deciding whether a specific process should be included in the system or can be cut-off. To know whether a process can be cut-off one must know how much that process contributes to the total LCIA results for the product system. In other words, one must include the process to figure out if it can be excluded


In other words, one must include the process to figure out if it can be excluded. To circumvent this paradox, some LCA practitioners take a more practical approach by deriving a mass-based cut-off criterion,such as 0.1% from quantitative completeness requirements. This would mean that processes delivering flows with a mass of less than 0.1% of the reference flow can be cut-off. We do not recommend following this approach blindly, because flows that are quantitatively small may still lead to large impacts and therefore have to be included in the modelling. For example, a low mass share of gold in a laptop can account for relatively large impacts due to mining Ecosphere Technosphere System boundaries A/S A/S A/S Legend Excluded product or waste flow Allocation/ Substitution Process Product or waste flow Elementary flow Reference flow Fig. 8.13 Setting of system boundaries for a realistic product system


8.13 Setting of system boundaries for a realistic product system. In this case, some processes are not included within the system boundaries (cut-off), as illustrated by the excluded product and waste flows. The exact system boundaries depend on whether allocation or substitution is performed in the handling of multifunctional processes


activities, and a small quantity of radioactive waste, e.g. from hospital equipment, can require extensive waste treatment, and therefore be associated with environmental impacts that should not be neglected. Due to the limitations of working with quantitative completeness requirements, we here advocate a qualitative approach. This means specifying the parts of a life cycle that must be included in the system boundaries and arguing why cutting off other parts is acceptable. For example, an LCA practitioner may know from similar LCA studies or previous experience that transportation between the use stage and the waste management stage, or business trips of the employees of a tier-one supplier are negligible. For new LCA practitioners, it can be difficult to create reasonable completeness requirements and it is therefore always important to explicitly report and justify them


For new LCA practitioners, it can be difficult to create reasonable completeness requirements and it is therefore always important to explicitly report and justify them. Applying the iterative approach, the omission of any processes should be justified in a sensitivity analysis after the inventory analysis and impact assessment. If the sensitivity analysis indicates that the process may be important with the chosen completeness requirements, it should be included (and perhaps refined) in the next iteration. We stress that an LCA practitioner should not blindly apply “default” qualitative completeness requirements, such as disregarding the production and maintenance of infrastructure, to any study, but always base the requirements on a case-specific assessment. This is to avoid cutting off parts of a life cycle that are important in the specific study, although they may typically not be important


This is to avoid cutting off parts of a life cycle that are important in the specific study, although they may typically not be important. As with most items of the scope definition, completeness requirements are meant to guide the initial LCI analysis, but during this analysis unforeseen limitations may mean that the requirements are in practice not possible to follow. The LCA practitioner can either handle this situation by modifying the completeness requirements in a new iteration of the scope definition or by explicitly documenting in the LCI analysis the parts of the LCI model that do not fulfil the completeness requirements. 8.7 Representativeness of LCI Data It is the aim of LCA to reflect physical reality


8.7 Representativeness of LCI Data It is the aim of LCA to reflect physical reality. This means that the model should represent what actually happens or has happened to the extent possible, and the unit processes applied to model the product system must be representative of the processes which are actually used in the analysed product system (in case of attributional LCA) or affected due to the introduction of the assessed product on the market (in the case of consequential LCA). Typically, parts of a foreground system will be based on data (elementary flows, etc.) collected first-hand by the LCA practitioner, e.g. from the company commissioning the study. This primary data is, provided that it contains no errors, per definition representative of the specific process occurring at the time that the data was collected


This primary data is, provided that it contains no errors, per definition representative of the specific process occurring at the time that the data was collected. Other parts of the foreground system and the entire background system, on the other hand, are constructed from other than first-hand data sources and when doing so it is important to consider how representative the chosen or Scope Definition


constructed unit processes are of the actual unit processes that they are models for. Representativeness of LCI data can be understood in three interrelated dimensions: geographical, time-related and technological. Based on the goal definition and knowledge about the studied product system, the scope definition must provide guidance and requirements for the inventory analysis with respect to representativeness of LCI data, as explained below for each dimension of representativeness. Besides serving as a guide for carrying out the inventory analysis, the representativeness of data should also be used in the interpretation of the results to reflect upon the extent to which the product system model corresponds to reality (Chap. 12). 8.7.1 Geographical Representativeness The geographical representativeness reflects how well the inventory data represents the actual processes regarding location-specific parameters


12). 8.7.1 Geographical Representativeness The geographical representativeness reflects how well the inventory data represents the actual processes regarding location-specific parameters. Geographical representativeness is important to consider because two processes delivering the same product output, but taking place in two different locations (e.g. nations), can be quite different in terms of the other flows (elementary flows, energy flows, material flows and waste to treatment). Differences between unit processes can be caused by geographical differences, such as local climate and proximity to natural resources, and regulatory differences, such as energy taxes and emission thresholds. In addition, when a mix of processes (market mix for attributional LCA and mix of marginal processes for consequential LCA) is used to model the background system or perform system expansion, the location of the mix used in the model versus the actual location of the mix must be considered


For example, the electricity mixes of Denmark (mainly coal and wind power) and Sweden (mainly nuclear and hydropower) vary quite a lot; despite the close proximity of the two countries, see Fig. 8.14. This can in part be explained by geographical differences (Sweden has mountains and therefore a potential for generating hydropower—Denmark is flat) and in part from social and political differences (Sweden has nuclear power plants— Denmark does not, largely due to public resistance). Due to the importance of geographical representativeness the LCA practitioner must in the scope definition define the geographical scope of the processes, or combinations of processes, taking place in the product system. The starting point should be the foreground system, where the locations of processes are typically known with high certainty


The starting point should be the foreground system, where the locations of processes are typically known with high certainty. The LCA practitioner can then proceed to defining the geographical scope of upstream and downstream processes that typically are more uncertain the more “process steps” from the key processes they are in the model. The appropriate resolution of the geographical scopes (e.g. city, region, nation or continent) depends on factors such as the spatial coverage of regulation (typically following national borders), geographical variations (e.g. weather, climate) and the spatial extent of markets (some markets are very local, while others are global).


Table 8.5 shows the geographical scope for life cycle stages and processes in the illustrative case of window frames (Chap. 39). During an inventory analysis, it is common that some unit processes cannot be obtained for the described location, such as a specific country. In these cases, the LCA practitioner must choose the most representative unit process to approximate the actual unit process based on his or her knowledge of geographical variations in central factors such as climate, regulation and markets. For example, in a study Fig. 8.14 Swedish (a) and Danish (b) electricity consumption mix in 2014 (low voltage, e.g. for domestic consumption). Imports from neighbouring countries can be further broken down into energy sources. ‘Others’ is an aggregation of all energy sources contributing less than 1% to the mix (Treyer and Bauer 2013) Scope Definition


involving clothes washing in Vietnam the unit process for a certain waste water treatment process in Thailand may be a good approximation for a Vietnamese unit process if the treatment efficiency is the same, because of the climatic similarities between the two countries. If needed, the proxy process may be adjusted to better represent the actual process of the product system. Chapter 9 elaborates on the choices related to geographical representativeness when constructing an inventory model. The influence of a low geographical representativeness on the conclusionsof the study must be evaluated in the interpretation of the LCA results (see Chap. 12). 8.7.2 Time-Related Representativeness Just as two processes delivering the same product output can be different if they occur in different locations, they can also be different if they occur at different times


This is due to technological innovation and development, which often tends to lead to more efficient processes over time, meaningless input (energy, material and resource flows) and sometimes also less unwanted by-products (waste to be treated and emissions) per unit of output. The time-related representativeness reflects how well the inventory data represents the actual processes regarding the time (e.g. year) they occur. Technological innovation is “faster” in some sectors than others


year) they occur. Technological innovation is “faster” in some sectors than others. Therefore, a unit process that reflects the situation 10 years prior to the occurrence of the process in the product system can have a high time-related representativeness if it belongs to a mature sector with little technological Table 8.5 Geographical scope for life cycle stages and central unit processes in the window frames case study Stage Window type Wood Wood/aluminium PVC Wood/composite Materials Metal ores: not known Crude oil: Norway, Russia, Middle East Forestry: Finland – – Manufacturing Glass pane: Sweden Wood frame: Scandinavia Wood frame: Scandinavia PVC frame: Germany Composite frame: Germany Other elements: Europe Other elements: mainly Europe Other elements: mainly Europe Other elements: mainly Europe Assembly: Denmark Use (heat supply) Mainly Scandinavia, Germany Mainly Scandinavia, Germany Europe Mainly Scandinavia Disposal The same as the use stage


innovation (such as the pulp and paper industry); but it can have a low representativeness if it is part of a sector with rapid technological development, such as IT, energy (with the growing focus on decarbonisation) and waste treatment (with the focus on waste avoidance and recycling of materials). In line with the requirements to define the geographical scope of processes, LCA practitioners must in the scope definition define the time frame of the processes in the different stages of the life cycle. Figure 8.15 gives an example of how time frames can be represented. These times are largely influenced by the expected lifetime of the studied product(s). For example, in a study involving furniture the expected lifetime, from consumer purchase to disposal, is decisive for the time at which waste treatment can be expected to occur. In other cases, the lifetime of installed capacity in the foreground system has a great influence on the time frames


In other cases, the lifetime of installed capacity in the foreground system has a great influence on the time frames. For example, in a study involving a decision to construct a new incineration plant, the number of years that it is planned to operate (typically 20–30 years) is decisive for the timing of the involved unit processes. In all cases, the intended application of results and reasons for carrying out a study, as stated in the goal definition, can guide the time-related requirements. In the illustrative case study on window frames, the time frame of the manufacturing and use stage is estimated to be 5 and 20 years, respectively. Following the formulation of the time-related requirements, the LCA practitioner must attempt to obtain the highest overall possible time-related representativeness when constructing the inventory model, within the time or budget constraints of the LCA study


When comparing the time aspects of the obtained inventory data with the time-related requirements it must be noted that the time at which a dataset was published is usually not equivalent to the time for which its data is valid (several years may pass between the first-hand collection of data and the publication of the data). In the foreground system the focus should be on those processes taking place in the future that the results of an initial iteration show to be important and that is also expected to change relatively rapidly (see above). The available current or past data for these processes can be used to project how they will evolve in the future. For example, the electricity mix of the future might be projected from past trends along with plans issued by public authorities that govern the electricity system. See also Chap. 21 on prospective LCAs and technological foresight


See also Chap. 21 on prospective LCAs and technological foresight. Regarding the background system, LCA practitioners usually have to make do with the most recent process contained in the LCI database used, while considering any trade-offs Disposal Use Manufacturing Product development Fig. 8.15 Example of time frames expressed for different life cycle stages Scope Definition


with geographical representativeness. The influence of a low time-related representativeness on the conclusions of the study must be evaluated in the interpretation of the LCA results (see Chap. 12). In comparative studies it is important to investigate whether there is a risk that differences in time-related representativeness for the compared alternatives can lead to a bias that favours one product system over the others. This could, for example, be the case in a comparison of two technologies if the data of one technology is older (in terms of the year they are valid for) than the data of the other technology. Just as some LCIA methods are spatially differentiated, there are also LCIA methods that are temporarily differentiated, meaning that their results are affected by the timing of elementary flows (see Chap. 10)


10). So far, this LCIA practice has been limited to mainly distinguishing between “short-term” and “long-term” emissions, which is, for example, relevant when including landfilling processes, from which some emissions are projected to occur hundreds or even thousands of years after the landfilling of a given material. In addition, some climate change indicators consider when an emission occurs, which, for example, enables quantification of the benefits of temporary carbon storage. In specific cases, the difference of inventory data in the course of the year (especially hot and cold season) and the day (daytime/night) are relevant for a study. It is to be checked along the goal of the study whether such intra-annual or intra-day specific data might be needed (e.g. on night-time electricity base-load data for charging electric car batteries overnight)


on night-time electricity base-load data for charging electric car batteries overnight). In all cases, the time-related information for elementary flows required by the LCIA methods chosen in the previous step of the scope definition should guide the data collection and output format of the inventory analysis. 8.7.3 Technological Representativeness Two identical products can be produced using two different technologies and thereby be associated with different (sets of) unit processes and related flows. For example, crude steel can be produced using an electric arc furnace (EAF) or a basic oxygen furnace (BOF), which are two very different technologies involving different inventory flows. Technological representativeness reflects how well the inventory data represents the actual technologies involved in the studied product system. Technological representativeness is interlinked with geographical and temporal representativeness


Technological representativeness is interlinked with geographical and temporal representativeness. For example, the technology mix involved in the production of electricity (coal power, natural gas, nuclear power, windmills, etc.) varies in space (e.g. from country to country) and over time. The LCA practitioner must use his or her knowledge about the product system to ensure (to the highest degree possible) that it is modelled using unit processes that reflect the actual technologies involved. It is important to ensure that the unit processes modelled in the system are in fact internally technologically compatible, meaning that the product output of one process should meet the quality requirements for input materials of the next process in the system. For example, if a unit process requires


steel that is stainless and heat resistant as material input, then it is incompatible with the product of a unit process producing basic grade steel without these properties. The scope definition should therefore contain a list of technologies that are known to be involved in the foreground system and in those parts of the background system for which such knowledge exists (typically energysupply, waste management and transportation), specifying representativeness requirements. This list should be partly based on the outcome of the geographical scope and time frames in terms of where and when processes are taking place. 8.8 Preparing the Basis for the Impact Assessment The planning of the impact assessment in the scope definition has two main purposes


8.8 Preparing the Basis for the Impact Assessment The planning of the impact assessment in the scope definition has two main purposes. The first is to ensure that it is done in accordance with the goal definition and the second is to prepare for the inventory analysis where the elementary flows (resources and emissions) that should be included depend on the impact categories to be covered in the LCIA. These elementary flows may also depend on the particular LCIA methods that are used to model these impact categories because different LCIA methods can cover different elementary flows. Planning how to perform the LCIA prior to the life cycle inventory analysis therefore helps ensuring that the right data is being collected in the cycle inventory analysis. A brief guidance on the planning of the LCIA is given in the following sections. Chapter 10 gives a comprehensive introduction to the science behind LCIA and how to report results


A brief guidance on the planning of the LCIA is given in the following sections. Chapter 10 gives a comprehensive introduction to the science behind LCIA and how to report results. 8.8.1 Selection of Impact Coverage According to the ISO 14044 standard for LCA, the selection of impact categories to be covered by an LCA “shall reflect a comprehensive set of environmental issues related to the product system being studied, taking the goal and scope into consideration”. This means that all environmental impacts where the product system has relevant contributions must be included in the impact assessment, unless the goal definition explicitly states otherwise. The latter is the case, e.g. in carbon or water footprinting studies, and in such studies the limitations imposed by the narrow impact coverage should be stressed in the goal definition and addressed the interpretation of results


Other valid reasons to exclude one or more impact categories from the assessment is when an initial iteration of the LCA shows that they do not contribute to the differentiation between the alternatives in a comparative LCA, or when they have a negligible contribution to the overall impacts, estimated by aggregating indicator scores for different impact categories to a single score following normalisation and weighting (see Sect. 8.2.5). In this case, the excluded impact categories must be listed as deliberately omitted in the scope definition of Scope Definition


the LCA report with reference to the outcome of the initial iteration. Transparency on the selection of impact categories is essential to avoid an “interest-driven” selection of impact categories where impact categories are excluded, e.g. because they disfavour the product produced by the commissioner of a study in a comparative analysis. 8.8.2 Selection of LCIA Methods To support the choice between alternative LCIA methods that can be used to calculate an indicator score for the same impact category, ILCD has developed six criteria for evaluating the methods: 1. Completeness of scope: how well does the indicator and the characterisation model cover the environmental mechanisms associated with the impact category under assessment? 2. Environmental relevance: to what extent are the critical parts of the impact pathway included and modelled in accordance with the current state of the art? 3


Environmental relevance: to what extent are the critical parts of the impact pathway included and modelled in accordance with the current state of the art? 3. Scientific robustness and Certainty: how well has the model been peer reviewed, does it represent state of the art, can it be validated against monitoring data and are uncertainties reported? 4. Documentation, Transparency and Reproducibility: how accessible are the model, the model documentation, the characterisation factors and the applied input data? 5. Applicability: are characterisation factors provided for the important elementary flows for this impact category in a form that is straightforward to apply? 6


Applicability: are characterisation factors provided for the important elementary flows for this impact category in a form that is straightforward to apply? 6. Stakeholders’ acceptance: has the model been endorsed by competent authorities, are the model principles and applied metric understandable for users of the LCA results in a business and policy contexts? These criteria can be difficult to apply for LCA practitioners that are not experts in LCIA modelling, but further insight can be gathered in Chaps. 10 and 40 gives an overview of available LCIA methods, discusses their main differences and how they perform on the six criteria. In practice, an LCA practitioner will often rely on the use of software to model the product system and perform the impact assessment and then simply calculate LCIA scores for all the impacts categories that are made available in the software as part of an LCIA method


An LCIA method is a collection of impact categories that aims to have a broad coverage of environmental issues, and it is typically developed by one research group (Hauschild et al., 2013). If several LCIA methods are available, it may be useful to apply more than one to test the sensitivity of the results to the choice of LCIA method (see Chap. 11). This is an easy way to explore the sensitivity of LCIA results because calculating results for multiple impact


categories in LCA software essentially takes the same time as calculating results for a single impact category. For some LCA studies, no LCIA method may cover an environmental impact that is considered relevant. In such cases, the LCA practitioner can choose to develop an LCIA method on their own and this development should be guided by the six criteria above. Often, however, the development of a new impact category is not feasible for an LCA practitioner, due to budget constraints and limited knowledge of the impact pathway. The potentially relevant environmental impacts that are not covered by the impact assessment should be highlighted in the scope definition and considered qualitatively in the interpretation of results (Chap. 12). An important aspect related to compatibility between the collected elementary flow of the life cycle inventory analysis and the ensuing LCIA is the degree of spatial differentiation of the LCA study


An important aspect related to compatibility between the collected elementary flow of the life cycle inventory analysis and the ensuing LCIA is the degree of spatial differentiation of the LCA study. Spatial differentiation essentially means taking into account where an elementary flow occurs. This information is relevant for many impact categories, because the sensitivity of the environment towards 1 unit of elementary flow differs from place to place (see more details in Chap. 10). Many popular LCIA methods are (still) spatially generic. Yet, spatially differentiated methods have over the years increased in numbers and quality and their use may therefore increase in the future. If it is chosen to use spatially differentiated methods it is important to collect spatial information for the elementary flows in the life cycle inventory analysis (e.g. name of nation, watershed ID or grid cell defined by GIS coordinates) that is compatible with these methods


name of nation, watershed ID or grid cell defined by GIS coordinates) that is compatible with these methods. Normalisation and weighting are optional LCIA steps under ISO 14044:2006, and as part of the scope definition the LCA practitioner should decide whether normalisation and weighting is needed. Are the steps relevant for the intended application(s) and target audience of the LCA study (see Goal definition in Chap. 7)? Normalisation is usually beneficial to aid the understanding of results if the target audience are not experts, and weighting is required if an aggregation of impact scores across the environmental impact categories is intended. On top of normalisation, an LCA practitioner may thus choose to include weighting, if the commissioner of a study has specifically asked for single score results. The decision to perform normalisation and weighting can also influence the choice of LCIA method since not all methods support these steps


The decision to perform normalisation and weighting can also influence the choice of LCIA method since not all methods support these steps. A detailed description of normalisation and weighting is given in Chap. 10. 8.9 Special Requirements for System Comparisons Many LCA studies compare systems, e.g. when two or more products fulfil the same function as captured in the functional unit. The ISO 14044 standard poses a number of special requirements for the scope definition of comparative studies to ensure that the systems can actually be compared: “Systems shall be compared using the same functional unit and equivalent methodological considerations, such as performance, system boundary, data quality, allocation procedures, decision Scope Definition


rules on evaluating inputs, and outputs and impact assessment. Any differences between systems regarding these parameters shall be identified and reported”. When a comparative study is intended to conclude on the superiority or equivalence of the compared alternatives in terms of their environmental performance, and to make these conclusions publically available, the standard identifies it as a “comparative assertion intended to be disclosed to the public”. For such applications of LCA, the standard requires that these points shall be evaluated in a critical review performed by a panel of interested parties (see Sect. 8.10 and Chap. 13). These special requirements reflect the consequences that the comparative use of LCA results may have for other companies, institutions and stakeholders that are not directly involved in the study and they are intended to prevent the misuse of LCA in market competition


To prevent misleading LCA results and the misuse of LCA in comparative assertions, the ILCD guideline furthermore requires that: • The uncertainties involved must be evaluated and communicated when one product system appears to have a lower environmental impact for one or more impact categories than another, see Chaps. 11 and 12 for details. • In the case where the goal definition prescribes a comparison based on a single indicator (e.g. carbon footprint) the LCA study must highlight that the comparison is not suitable to identify environmental preferable alternatives, as it only covers the considered impact(s) (e.g. climate change). This applies unless it can be sufficiently demonstrated that the compared alternatives do not differ in other relevant environmental impacts to a degree that would change the conclusions of the comparison if those other impacts would be included in the analysis


Such demonstrations may be in the form of other LCA studies available for sufficiently similar systems. 8.10 Need for Critical Review A critical review is performed by experts not involved in making a study. A critical review is sometimes required (e.g. for studies with the intended publication of results), but even when there is no formal requirement a critical review is useful for improving the quality and credibility of a study. Chapter 13 deals specifically with the critical review stage of an LCA, presents the different types of critical review and explains for what kind of LCA studies (with reference to the goal definition) these are needed. It is, however, useful already during the scope definition to decide whether a critical review is needed or intended


It is, however, useful already during the scope definition to decide whether a critical review is needed or intended. If a review is required or intended, the scope definition should furthermore specify the form of the review in order to allow the documentation and reporting of the study to be tailored to meet the later requirements from the peer reviewers. It should also, in the scope definition, be decided whether the review should be performed on the final draft of the LCA report or whether it should be


done in an interactive process throughout the performance of a study. In this case, the reviewers are given the opportunity to comment on the goal and scope definition prior to the onset of the inventory analysis, and possibly on interim results of the impact assessment and interpretation before the final reporting so that their comments can guide the process of the LCA. 8.11 Planning the Reporting of Results Product systems can be very complex, and choices are often made during the LCA that can influence the conclusions. To reduce the risk of erroneous and misleading use of the LCA, it is essential that the reporting is clear and transparent with a clear indication of what has and what has not been included in the study and which conclusions and recommendations the outcome supports. The reporting of an LCA study should target the audience as it is specified in the goal definition


The reporting of an LCA study should target the audience as it is specified in the goal definition. Depending on whether the study is comparative and public, the ILCD guideline identifies three reporting levels: 1. Internal use by the commissioner of study; 2. External use by the third party, i.e. a limited, well-defined list of recipients with at least one organisation that has not participated in the study. 3. Comparative studies to be disclosed to the public. Due to the sensitive nature of comparative assertions based on LCA, there are a number of additional reporting requirements to level 3 studies. No formal requirements apply to level 1, but it is recommended to follow the requirements for level 2. Chapter 38 shows all the elements that an LCA report should cover, according to level 2 and 3, and proposes a sequence of these elements, and the reporting of the case study on window frames in Chap. 39 demonstrates the application of the template in a comparative study


39 demonstrates the application of the template in a comparative study. 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. 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 (2006a) ISO.: Environmental Management—Life Cycle Assessment—Requirements and Guidelines (ISO 14044). ISO, the International Organization for Standardization, Geneva (2006b) Scope Definition


Additional References Used in the Text Ekvall, T., Azapagic, A., Finnveden, G., et al.: Attributional and consequential LCA in the ILCD handbook. Int. J. Life Cycle Assess. 21, 293–296 (2016). doi:10.1007/s11367-015-1026-0 Hauschild, M.Z., Goedkoop, M., Guinée, J., et al.: Identifying best existing practice for characterization modeling in life cycle impact assessment. Int. J. Life Cycle Assess. 18, 683– 697 (2013). doi:10.1007/s11367-012-0489-5 IDF.: A common carbon footprint approach for dairy. The IDF guide to standard life cycle assessment methodology for the dairy sector. http://www.idf-lca-guide.org/Public/en/LCA +Guide/LCA+Guidelines+overview (2010). Accessed 2 Aug 2016 Treyer, K., Bauer, C.: Life cycle inventories of electricity generation and power supply in version 3 of the ecoinvent database-part I: electricity generation. Int. J. Life Cycle Assess. 3, 1–19 (2013)


Int. J. Life Cycle Assess. 3, 1–19 (2013). doi:10.1007/s11367-013-0665-2 Weidema, B.P., Bauer, C., Hischier, R., et al.: The ecoinvent database: overview and methodology. Data quality guideline for the ecoinvent database version 3. www.ecoinvent. org (2013) 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. 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). Alexis Laurent working with LCA since 2010 with a strong LCIA focus, particularly on normalisation aspects


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. 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. Andrea Corona materials engineer with a focus on sustainability assessment of biobased products and composite materials. Working with LCA from 2012. Main interests include life cycle engineering, product development and eco-design. Michael Z. Hauschild involved in development of LCIA methodology since the early 1990s


Working with LCA from 2012. Main interests include life cycle engineering, product development and eco-design. 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.


Chapter 9 Life Cycle Inventory Analysis Anders Bjørn, Andreas Moltesen, Alexis Laurent, Mikołaj Owsianiak, Andrea Corona, Morten Birkved and Michael Z. Hauschild Abstract The inventory analysis is the third and often most time-consuming part of an LCA. The analysis is guided by the goal and scope definition, and its core activity is the collection and compilation of data on elementary flows from all processes in the studied product system(s) drawing on a combination of different sources. The output is a compiled inventory of elementary flows that is used as basis of the subsequent life cycle impact assessment phase. This chapter teaches how to carry out this task through six steps: (1) identifying processes for the LCI model of the product system; (2) planning and collecting data; (3) constructing and quality checking unit processes; (4) constructing LCI model and calculating LCI results; (5) preparing the basis for uncertainty management and sensitivity analysis; and (6) reporting


Learning Objectives After studying this chapter the reader should be able to: • Collect and critically evaluate the data quality of an LCI. • Construct a unit process from first-hand gathered data. • Build an LCI model using either attributional or consequential approach and explain the differences between the two approaches. • Explain what data is required for uncertainty and sensitivity analyses and how to collect these data. • Document an LCI model, including unit processes and LCI results. A. Bjørn (&)  A. Moltesen  A. Laurent  M. Owsianiak  A. Corona  M. Birkved  M.Z. Hauschild 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, Montréal, QC, Canada © Springer International Publishing AG 2018 M.Z. Hauschild et al. (eds.), Life Cycle Assessment, DOI 10.1007/978-3-319-56475-3_9


9.1 During the life cycle inventory (LCI) analysis phase of an LCA the collection of data and the modelling of the flows to, from and within the product system(s) is done. This must be in line with the goal definition (see Chap. 7) and (to the extent possible) meet the requirements derived in the scope definition (see Chap. 8). The LCI result is a list of quantified elementary flows crossing the system boundary of the studied life cycle and it is used as input to the subsequent LCIA phase (see Chap. 10). Insights that the LCA practitioner gains when conducting the LCI analysis are also commonly used to adjust the requirements of the scope definition, e.g. when unforeseen data limitations lead to the need for a modification of the completeness requirements (see Sect. 8.6.3)


when unforeseen data limitations lead to the need for a modification of the completeness requirements (see Sect. 8.6.3). Typically, the LCI analysis is the phase that requires the most efforts and resources from the LCA practitioner, and it is rarely practically possible to collect the highest quality of data for all processes of the LCI due to the unreasonable high cost that would be involved. Fortunately, it is also rarely needed in order to meet the goal and support the intended applications of the LCA. Therefore, the inventory analysis requires a structured approach to ensure that time is being spent on collection of data for those parts of the product’s life cycle that are most important for the overall impacts from the product system. Several iterations between the LCI and LCIA phase are normally needed to meet the goal of the study, with each iteration providing insight into which inventory data are the most important for the LCA results (see Chap. 6)


6). In this chapter, we provide practical guidance on how to perform an LCI analysis using an iterative approach to LCA. We will focus on providing detailed guidance for the four decision contexts (A, B, C1 and C2) in line with the ILCD guideline. The chapter is structured around six steps of an LCI analysis: 1. Identifying processes for the LCI model 2. Planning and collecting data 3. Constructing and quality checking unit processes 4. Constructing LCI model and calculating LCI results 5. Preparing the basis for uncertainty management and sensitivity analysis 6. Reporting. Before digging into the details, we note that this chapter teaches how to construct an LCI using knowledge about the industrial processes taking part in a life cycle and the physical flows connecting them. This is called a process-based (or bottom-up) approach to inventory modelling


This is called a process-based (or bottom-up) approach to inventory modelling. A complementary approach to constructing an LCI is to model the life cycle inventory for the product from a macroscale perspective by drawing on a combination of (1) information on elementary flows associated with one unit of economic activity in different sectors and (2) national statistics on the trade of products and services between sectors. This is called environmentally extended input–output analysis (EEIO) and in contrast to the process-based approach it can be seen as a top-down approach to inventory modelling. The strength of EEIO is that a completeness of 100%, in theory, can be


achieved in the sense that no processes need to be cut-off due to missing data or budget constraints. The two main weaknesses of the EEIO approach are (1) that the coverage of elementary flows is rather limited, compared to the process-based approach and that (2) the resolution of many products and services is quite low due to the heterogeneous nature of many sectors, as defined by national trade statistics. Chapter 14 deals with IO-LCA and in particular how to use EEIO to complement and guide process-based LCA. This chapter will make references to EEIO, when the approach can complement the process-based approach. 9.2 Identifying Processes for the LCI Model This first step of the LCI details the coarse initial system diagram made under the scope item System boundaries (see Sect. 8.6) and draws upon the related completeness requirements. The outcome of the step is a detailed depiction of the foreground system, i.e


8.6) and draws upon the related completeness requirements. The outcome of the step is a detailed depiction of the foreground system, i.e. all the processes it is composed of and their links, and the processes of the background system ‘neighbouring’ the foreground system, i.e. where links to LCI database processes will be established. 9.2.1 Detailing the Physical Value Chain For all decision contexts (A, B, C1 and C2—see Sect. 7.4) the approach to identifying processes is to start with the reference flow and construct the entire foreground system process by process: 0. The unit process having the reference flow, as product output, should first be identified (or unit processes, in the case of more than one reference flow). This is termed a ‘level 0’ process. In a study where a window is the reference flow, the level 0 process is the assembly of the window. 1. The processes required to deliver flows that will be physically embodied in the reference flow should then be identified


1. The processes required to deliver flows that will be physically embodied in the reference flow should then be identified. These are termed “level 1” processes. In the window example, examples of level 1 processes are the production of glass and the window frame. 2. The processes required to deliver flows that perform a supporting function to the level 0 process (i.e. not becoming physically embodied in its output) should then be identified. These are termed ‘level 2’ processes. In the window example, examples of level 2 processes are the supply of electricity used in the assembly of the window or the transportation needed to deliver the flows of the level 1 processes to the level 0 process. 3. The processes required to deliver services to the level 0 processes should then be identified. These are termed ‘level 3’ processes. In the window example, examples of level 3 processes are administration and marketing. Life Cycle Inventory Analysis


4. The processes required to produce and maintain the infrastructure that enables the level 0 process should then be identified. These are termed ‘level 4’ processes. In the window example, examples of level 4 processes are production and maintenance (oiling, replacing and repairing parts) of the assembly machines. After having identified level 1, 2, 3 and 4 processes belonging to the level 0 process (the reference flow), Step 1–4 is then repeated for each these processes. This procedure is illustrated in Fig. 9.1 for the window example. 0) Window assembly 1) Window glass 1) Window frame 2) Electricity supply 3) Admin., R&D 3) Marketing 4) Assembly machine 1) Sand 1) Limestone 2) Natural gas 3) Admin. 3) Business travel 4) Furnace Downstream Use 2) Transp. 2) Heat 2) Cleaning 2) Transp. Waste management 4) Furnace for glass recycling 2) Transp. 2) Electricity supply 4) Incin. plant for frame 3) Admin. Upstream * Fig


2) Heat 2) Cleaning 2) Transp. Waste management 4) Furnace for glass recycling 2) Transp. 2) Electricity supply 4) Incin. plant for frame 3) Admin. Upstream * Fig. 9.1 Procedure for identifying processes of the foreground system, exemplified in the study of the life cycle of a window. The starting point is the process that delivers the reference flow, ‘0) Window assembly’. The foreground system is then populated process by process by proceeding upstream and downstream from the reference flow. Unlinked arrows present on some processes indicate the existence of other processes that were not included in the figure. Use and Waste management are in italic, because they represent life cycle stages, rather than actual processes. The star at ‘Waste management’ indicates the existence of multifunctional processes, i.e. glass recycling and incineration of window frame. Abbreviations in the figure: Incin incineration, Transp transportation, Admin administration, R&D research and development


glass recycling and incineration of window frame. Abbreviations in the figure: Incin incineration, Transp transportation, Admin administration, R&D research and development. Numbering and colour code, identify the process level for the different foreground processes


Processes downstream, i.e. in the use and waste management stages, should be identified in a similar fashion. The procedure is, in principle, repeated until the foreground system is completed and can be linked to LCI database processes of the background system, as described later in this chapter. When carrying out this procedure, the LCA practitioner should identify all multifunctional processes, because they have to be handled next. Note that the step of identifying processes for the LCI model and the step of planning and collection of data are somewhat interrelated. For example, data collected for a given process may lead to the realisation that one or more upstream processes are different than the ones previously (assumed) identified. During data collection the LCA practitioner may, for example, realise that a plastic component is actually produced from biomaterials rather than petrochemicals, as was initially assumed


During data collection the LCA practitioner may, for example, realise that a plastic component is actually produced from biomaterials rather than petrochemicals, as was initially assumed. The identified processes in this first inventory step should therefore be considered preliminary. In practice, many processes belonging to level 3 and 4 will end up being entirely omitted from an LCI model, because their individual contribution to the indicator score is expected to be insignificant and because data can be hard to find, at least when using the ‘bottom-up’ (=process-based) approach to constructing inventories. In such cases, the environmental impacts of product systems are systematically underestimated by various degrees. It is an important task of the inventory analysis and consecutive impact assessment to ensure that this underestimation does not violate the completeness requirements for the study


It is an important task of the inventory analysis and consecutive impact assessment to ensure that this underestimation does not violate the completeness requirements for the study. Chapter 14 shows how IO-LCA can complement process-based LCA to better cover the impacts from level 3 and 4 processes. 9.2.2 Handling of Multifunctional Processes Section 8.5.2 presented the ISO hierarchy for solving multifunctionality, i.e. processes in the product system that deliver several outputs or services of which not all are used by the reference flow of the study. According to this hierarchy, the preferred solution is subdivision of the concerned process, and if this is not possible, system expansion and, as a last resort, allocation. Below, examples are given for how to carry out each solution in practice


Below, examples are given for how to carry out each solution in practice. This guidance is primarily relevant for the foreground system because multifunctionality has typically already been handled for the processes in the LCI databases that are used to construct the background system. Some LCI databases exist in different versions, according to how multifunctionality has been solved (see Sect. 9.3 below). For the background system this reduces the job of the LCA practitioner to just source processes from the appropriate version of the LCI databases. Yet, even in the background system, the LCA practitioner may sometimes have to solve multifunctionality manually when no appropriate solutions exist in the used LCI databases. We note that many waste treatment processes are multifunctional because they both offer the function of managing (often heterogeneous) waste streams and the function of providing Life Cycle Inventory Analysis


product flows, such as recycled materials or electricity. We refer to Chap. 35 on application of LCA to solid waste management systems for more details on how these special cases of multifunctionality are solved in LCA practice. Subdivision When possible, subdivision should always be the solution to multifunctionality. Unit processes can be defined at many levels of detail and for the use in LCA there is no point in detailing them beyond what is needed for the modelling purpose in the LCA. This may mean that by increasing the detail applied in the modelling, the multifunctionality may be revealed as artificial. For example, a process that encompasses an entire factory producing two different products may have been identified from the procedure detailed in Sect. 9.2.1


For example, a process that encompasses an entire factory producing two different products may have been identified from the procedure detailed in Sect. 9.2.1. If this factory is in fact using different and independent machines and work stations for manufacturing the two products, the initial process can, by introducing additional detail in the modelling of the process, be subdivided into two or more processes that each contribute to the production of only one of the products, see Fig. 9.2. Note that it is often not possible to fully physically divide a process according to the co-products. In the factory example room lighting, room heating and administration (all level 3 processes, according to Sect. 9.2.1) may not be possible to divide between the co-products. In such cases, subdivision needs to be supplemented with or replaced by another solution to multifunctionality. Note also, that in practice data availability often determines whether subdivision is possible


Note also, that in practice data availability often determines whether subdivision is possible. In the factory example, it may be that data only exist for the electricity consumption of the entire factory, i.e. the consumption of each machine is unknown and in this case, subdivision would be practically impossible. In addition, there are many situations where the creation of the co-products is integrated into the process in a way that impedes the multifunctionality to be addressed by subdivision. This is the case for many biological and chemical processes. 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. 9.2 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


System Expansion System expansion is second in the ISO hierarchy. As explained in Sect. 8.5.2, system expansion is mathematically identical to crediting the studied product system with the avoided production of the secondary function(s) that would alternatively have been produced and delivered somewhere else in the technosphere. When modelling a life cycle inventory, the technique used to perform crediting varies between LCA software (see Sect. 9.5). The identification of avoided processes depends on the decision context. For Situation A and C1 a market mix is used, which corresponds to the average process used to supply the entire market (see Sect. 8.5.4). To calculate a market mix, one needs to know the amount of product or service that is produced and delivered to the relevant market by each process at the time when the secondary function is delivered by the studied product system (see Fig. 8.13)


8.13). So, for example, if recycled steel is a co-product of a studied life cycle and the two processes for producing steel, electric arc furnace (EAF) and a basic oxygen furnace (BOF), delivered 60 and 140 million tonnes, respectively, in the relevant market and reference year, then the market mix would be 30% EAF and 70% BOF (World Steel 2015). The LCI model should thus be credited with a constructed process composed of 30% of the flow quantities associated with the production of 1 unit of EAF steel and 70% of the flow quantities associated with the production of 1 unit of BOF steel. It is important to identify the correct market for each system expansion. The correct market must reflect the geographical and temporal scope (see Sect. 8.7). Note that some goods and services are sold in global markets due to the low cost of transportation relative to their value (e.g. gold), while other goods and services are sold on local or regional markets due to high transportation cost (e.g


gold), while other goods and services are sold on local or regional markets due to high transportation cost (e.g. some biomaterials and water) or regulation. Information on volumes produced and delivered to markets can often be obtained from reports or databases of industry organisations (e.g. the World Steel Association in the example of recycled steel). In consequential modelling (parts of Situation B, see Sect. 8.5.4), the avoided process is not a market mix, but the marginal process (or a mix of marginal processes) and its identification is explained in Sect. 9.2.3. Allocation Allocation is the third and last option in the ISO hierarchy. As mentioned in Sect. 8.5.2 allocation should, when possible, be based on (1) causal physical relationship, followed by (2) a common representative physical parameter and, as a last resort, (3) economic value. The causal physical relationship approach is possible when the ratio between quantities of co-products can be changed


The causal physical relationship approach is possible when the ratio between quantities of co-products can be changed. Consider again the above example of a factory producing two products (x and y), where only the total electricity consumption is known. Here it would be possible to derive the electricity consumption of x and y by collecting data on production volumes and total electricity consumption at two points in time, where the relationship between the produced quantities are different. This could lead to the following simple system of equations: Life Cycle Inventory Analysis


Time 1 : 10 tonnes  X þ 20 tonnes  Y 1⁄4 10:000 kWh ð9:1Þ Time 2 : 10 tonnes  X þ 40 tonnes  Y 1⁄4 12:000 kWh ð9:2Þ Here, X and Y represent the electricity consumption of product x and y (kWh/tonne) and by solving the equation system, one finds that X is 800 kWh/tonne and Y is 100 kWh/tonne. If time 1 is representative for the unit process to be applied in the LCI model, then 80% (10 tonnes * 800 kWh/tonne divided by 10.000 kWh) of the factory’s electricity consumption should be allocated to product x. Note that this 80% allocation factor should not blindly be applied to allocate the remaining flows (e.g. consumption of heat and emissions of NOx) between product x and y, for which the causal physical relationships may be different. Note also that allocation according to a causal physical relationship is in many cases not possible, because the ratio between co-products or co-services for many processes cannot be changed


Note also that allocation according to a causal physical relationship is in many cases not possible, because the ratio between co-products or co-services for many processes cannot be changed. For example, it is not for practical purposes possible to reduce or increase the production of straw, while keeping the production of wheat constant. The representative physical parameter approach is possible when co-products provide a similar function. For example, in the case of a fractional distillation process of crude oil, a similar function of many of the co-products (e.g. diesel, petrol, kerosene, propane and bunker oil) is to serve as a fuel to drive a process performing mechanical work, and therefore exergy, which can be interpreted as the maximum useful work, is an appropriate representative physical parameter. The parameter values of each co-product can typically be obtained from physical or chemical compendiums (e.g. in the case of exergy values)


The parameter values of each co-product can typically be obtained from physical or chemical compendiums (e.g. in the case of exergy values). Once the values have been obtained, calculating the allocation factor is straightforward. For example, if co-products x, y and z are produced in quantities 1, 3 and 6 kg and if their representative physical parameter values are 10, 1, and 0.5 per kg, then the total parameter value would be 16 i.e. (1 * 10 + 3 * 1 + 6 * 0.5) and the allocation factor for product x would be 62.5% (1 * 10 divided by 16) and so on. Note that in the distillation process case, the functions of the co-products are not entirely identical. Airplanes cannot fly on bunker oil, and bitumen, one of the co-products, cannot be used as a fuel. Allocating according to a representative physical parameter is therefore not ideal, but may be the best solution, compared to other allocation approaches


Allocating according to a representative physical parameter is therefore not ideal, but may be the best solution, compared to other allocation approaches. This example illustrates that there is often not a single correct allocation approach and the choice of approach therefore depends on the judgement of the LCA practitioner. The sensitivity of the LCA results to this judgement may be investigated in a sensitivity analysis applying different possible allocation factors, as explained in Sect. 9.6. Note that it is very important to choose a representative parameter that is actually representative for the function of all co-products. For example, mass is not a representative parameter for the co-production of milk and meat from dairy cows because the functions of milk and meat are not their mass. In this case, some measure of nutritional value would be a more representative parameter.


The economic value approach is recommended as a last resort and is generally easy to carry out due to the abundance of price data on goods and services. Prices may be obtained by contacting the company running the multifunctional process in question or from the stock exchange in case of global markets, e.g. for some metals. For some co-products there may not be a market because they need to go through additional processing before they are sold. In that case, the LCA practitioner should calculate a shadow price. For example, straw, a co-product of wheat production, needs to be baled before it is sold, and the economic value of baled straw must therefore be subtracted the cost to the farmer of baling the straw to calculate the shadow price of the unbaled straw leaving the multifunctional process of wheat production. Note that the prices of most goods and services are volatile to varying degrees


Note that the prices of most goods and services are volatile to varying degrees. It is therefore recommended to calculate average values for the time period that is relevant to the temporal scope of the study (see Sect. 8.7.2). Once the economic values have been determined, allocation factors are calculated in the same way as the above generic example for the representative physical parameter approach. It should be noted that although allocation by economic value is the last resort according to ISO, it is widely used in practice. This is because the other solutions to the handling of multifunctional processes are often not possible due to the nature of the multifunctional process or due to lack of the required information and data to identify the relevant process for a system expansion or to determine a causal physical relationship, or a common representative physical parameter. By contrast, the price data needed to carry out economic allocation is generally available


By contrast, the price data needed to carry out economic allocation is generally available. For this reason, economic allocation is done by some LCA researchers recommended as a default solution to multifunctionality, e.g. by the Dutch CML Guideline (Guinée et al. 2002), and the LCI database ecoinvent comes in a version where allocation by economic value is systematically applied to all multifunctional processes (see Sect. 9.3.2 below). 9.2.3 Consequential Modelling In most cases, a consequential LCI will include other processes than an attributional LCI for the same product system. The attributional LCI includes the processes which the assessed product ‘sees’ from its journey from the cradle to the grave. If, for example, the assessed product is a plastic cup, the start of the journey will be some extracted crude oil, which through a sequence of production processes will be processed into plastic


If, for example, the assessed product is a plastic cup, the start of the journey will be some extracted crude oil, which through a sequence of production processes will be processed into plastic. This will then be transported to the shop, be bought by a user, who will use it once and then discard it, after which it will be transported to, say, an incinerator and burned. In the attributional LCI, each of these processes: the production of crude oil, the conversion into plastic, the transport and incineration will be included. The consequential LCI is different; the goal of the assessment is to identify the environmental impacts caused by a decision, for example the decision to buy a Life Cycle Inventory Analysis


plastic cup. The processes that change due to a decision may not be the same that a product ‘sees’ throughout its product life (see Fig. 9.1). The following example may make this easier to understand. Assume now for the sake of the example that we have reached the peak in oil production: we simply cannot economically extract more oil than we are already doing. This implies that the decision to, say, use this plastic cup will not result in an increase in the production of oil, as this is already at its maximum. What happens instead may be that the price of oil will go up due to the increase in demand (which in this example is going to be extremely small due to the small amount of oil needed to produce the cup. However, here it is the principle that is of interest). The increase in price may cause other users of oil to reduce their use, or find a substitute for their use of oil


However, here it is the principle that is of interest). The increase in price may cause other users of oil to reduce their use, or find a substitute for their use of oil. In this example we will assume that some oil users will find natural gas a suitable substitute and these users will therefore increase their demand for natural gas to compensate for the decreased availability of oil. This implies that, given these assumptions, an increase in the demand for oil created from the increase in demand for plastic cups will not result in an increase in the production of oil, but rather in the production of natural gas. The consequential LCI will therefore not include an extraction of oil, but rather an increased extraction of natural gas. This line of thinking obviously does not only relate to the oil used in the production of the plastic, but to all the inputs used when the plastic cup is produced


This line of thinking obviously does not only relate to the oil used in the production of the plastic, but to all the inputs used when the plastic cup is produced. Another very important difference between the attributional and consequential LCI is that in an attributional LCI the normal procedure is to assume that the electricity consumed in the production of the plastic cup is produced by all the suppliers on the market, depending on their market share. In a consequential LCI, this is different: If we increase the demand for electricity in the market, it is most likely that not all the suppliers are going to increase their production to meet the increase in demand. The reason is that the most cost-efficient producers will already produce at full capacity. This is for example going to be the case for nuclear power plants. This means that if we increase the demand for electricity, we will not influence the extent of the production from the nuclear power plants


This means that if we increase the demand for electricity, we will not influence the extent of the production from the nuclear power plants. Rather, we will influence other types of power plants, for example natural gas power plants, which are more expensive to operate (per kWh), and which will therefore only produce during peak load situations (when electricity prices are higher). The same thinking is applied when studying the effect of increasing or decreasing demands for other products than electricity. Rather than including an average of the producers in the market in the LCI, as is done in the attributional LCI, it is the ‘marginal’ producers, which are included in the consequential LCI. A marginal producer is a producer who will change its supply due to small changes in demand. A final important difference between the attributional and consequential LCI lies in the handling of multifunctional processes (see Sect. 8.5.2)


A final important difference between the attributional and consequential LCI lies in the handling of multifunctional processes (see Sect. 8.5.2). In a consequential LCI, the multi-output processes are always handled by system expansion (if subdivision is not possible). Based on the outline above, there are generally three different tasks in a consequential LCI:


1. To identify whether an increase or decrease in demand for a product will actually lead to corresponding increases or decreases in supply for that product. As illustrated with the oil and gas example above, this is not necessarily the case. 2. To identify which production technology will be affected by the change in supply of products. This is most likely not going to be an average of the production technologies on the market, but rather one or a few operating on the margin. 3. To identify which product substitutes which. This is relevant when changing demands for a product whose production is constrained, such as oil in the example above. It is also relevant for the handling of multi-output processes, where it involves identifying the product that will be affected (substitute or be substituted) by a co-product from a multi-output process


From the discussion above, it can be seen that if we want to perform an attributional LCI, we can do so simply on the basis of knowledge about the product and the parts that it includes: we need to know about how plastic cups are made, used and discarded. However, if we want to perform a consequential LCI, besides the technical knowledge about how the plastic cup is produced, used and discarded, we also need knowledge about how the market reacts to an increase (or decrease) in demand and supply. As can be imagined, answering how the market reacts is easier said than done. What will actually happen if I increase the demand for this or that? Modelling the reactions of the market is a very complex task—just ask any stockbroker! Outlining what will happen is therefore necessarily somewhat uncertain, especially if the assessment addresses decisions in the more distant future


However, to ease the answering of these questions we will in this chapter outline a range of ‘rules of thumb’ developed for identifying the processes that are likely to change due to a decision. As outlined above, the goal of the consequential LCA is to answer questions of the type: “What are the environmental consequences if ...?”. As the environmental consequences that are considered arise from changes in production of products, this overall question answered in the consequential LCA can basically be translated to “What changes in the production of goods if we demand/supply more/less of X(, Y, Z, ...)?”. We continue asking this question until we have covered all induced changes. For example, in the case where we want to assess what happens if we use a plastic cup, we basically want to increase the demand for plastic cups


For example, in the case where we want to assess what happens if we use a plastic cup, we basically want to increase the demand for plastic cups. We therefore start by asking: “What will happen if I increase the demand for plastic cups?” If what happens most likely turns out to be that additional cups will be produced, then the follow-up question will be: “What will happen if we produce additional plastic cups?” The overall approach of identifying processes to include in a consequential modelling of the product system is to repeatedly ask this question for each step upstream and downstream from the reference flow (see Sect. 9.2.1) until all changes have been covered. Life Cycle Inventory Analysis


We recommend solving this task by following a 4-step procedure shown in Fig. 9.3. Depending on the concrete case, one or more steps can be skipped (as will be explained below). Step 1: Change in demand or supply? When performing a consequential LCI, full elasticity of supply is generally assumed. This implies that a change in demand for some function will lead to a change in supply of products that can fulfil this demand, but that change in supply, will not lead to a change in demand. There will therefore be a difference between the market effects of changing demand and changing supply, as will be visible in the steps below. First step in the procedure is therefore to consider whether the question at hand addresses a change in demand or supply; e.g


First step in the procedure is therefore to consider whether the question at hand addresses a change in demand or supply; e.g. are we assessing the question: “What happens if I demand more/less of X?” or the question “What happens if I supply more/less of Y?” Note that handling a co-product from a multi-output process in the studied life cycle relates to changes in supply of this co-product, and therefore is related to the latter type of question. If the assessed decision relates to changes in demand, go to Step 2. If it relates to changes in supply, go to Step 3. 1. Change in demand or supply? 2. Identify constraints in the market 4. Identify production technology affected by change in demand 3. Product substitution Supply Demand Not constrained Constrained Substitute identified Fig. 9.3 4-Step approach for identifying affected process in a full consequential LCA


Step 2: Identify constraints in the market If we increase (or decrease) our demand for X, the market will, according to standard economic theory, respond by increasing (or decreasing) the supply of X. In many cases, at least on a short term, there will not be a one-to-one relationship between increases in demand and supply. The reason is that an increase in demand will often result in an increase in price, implying that some users may stop using the product and potentially find a cheaper substitute product. Hereby, the supply and demand will reach a new steady state, which will often not entirely correspond to the initial demand plus the increase. Despite that these thoughts about price elasticity have been introduced in LCA literature, for simplicity, the default assumption here will be that the increase (or decrease) in demand will spur an equally large increase (or decrease) in supply, which is also the most common assumption in consequential LCI


However, in many cases markets face various constraints and other market imperfections. An increase (or decrease) in demand will therefore not always lead to an increase (or decrease) in supply. Market limitations may be of a legal, economical, technical or physical nature. For example, straw used for co-firing in power plants is, due to the transport cost to value ratio, not transported far from the production site. Moreover, as there is limited production capacity of straw in a given area, an increase in demand within this area will in many cases not result in an increase in supply. Another example may be the demand for recycled metals, which are often constrained by the amount of waste input to recycling processes, in which case an increase in demand will not result in an increase in supply of recycled metals. Other constraints may be due to legally set boundaries for how much of a certain good may be produced


Other constraints may be due to legally set boundaries for how much of a certain good may be produced. If the production already fills the boundaries, a small increase or decrease in demand will also not have any effect on supply. There are thus a number of situations where the default assumption—that an increase or decrease in demand results in an increase or decrease in supply—may not hold true. In these situations, the market is constrained, and a central task will be to identify how existing or potential users will handle the increase or decrease in demand. In the example above with an increased demand for recycled metal, a reasonable assumption may be that existing users of the recycled metal will use virgin metal instead. In other words, an increase in the demand for a product already produced at maximum will not lead to an increase in supply, but more likely make existing users find a substitute


In other words, an increase in the demand for a product already produced at maximum will not lead to an increase in supply, but more likely make existing users find a substitute. A guideline for identification of which products can substitute which is provided under Step 3. The assessed decision may also lead to a decrease in the demand for a product, which is only produced to a certain amount. If this product is already fully utilised, a reasonable assumption may be that a decrease in demand for the product in question will not lead to a decrease in the supply of this product, as other users will use up the freed supply. In the metal example above, this could imply that the freed supply of recycled metal will be used up by a user of virgin metal, in total lowering the demand of virgin metal, while keeping the utilisation of recycled metal at the same level. Life Cycle Inventory Analysis


It may also happen that the freed supply resulting from a decreased demand does not lead other users to utilise the product. If this is the case, it can be assumed that less will be produced of the product, or if the product is a co-product of another and more valuable product, and its production therefore bound, it may end up as waste, implying that a decrease in the demand for the product will simply imply more waste. It may seem an enormous task to try to identify whether all the commodities included in the life cycle are constrained in their production. However, in practice the assumption will often be that a product is constrained if: • It is a co-product from a process that has another more valuable product, as it will never be the less valuable product that will control the overall output of the production (e.g. waste from a slaughterhouse that may be utilised for biodiesel production is constrained by the amount of meat produced). • Its production is limited by regulation (e.g


waste from a slaughterhouse that may be utilised for biodiesel production is constrained by the amount of meat produced). • Its production is limited by regulation (e.g. regulation may set a limit for the overall annual catch of commercial fish species). • Its production is limited physically (for example the production of wood on an island is restricted by e.g. forest area and a high cost of transportation may mean that import is not an economic option). Identifying whether a commodity is produced as a less valuable by-product will often be quite easy, but the identification of both regulatory and physical constraints may be more difficult. It will in many cases require knowledge about the specific market in which the change in demand is made, which will often require advice from experts. Furthermore, one must know whether the production capacity for the product, for which demand is changed, is already filly utilised


Furthermore, one must know whether the production capacity for the product, for which demand is changed, is already filly utilised. Figure 9.4 presents a decision tree for dealing with potential market constraints. In case of unconstrained markets, go directly to Step 4. Constrained markets must in some cases (see Fig. 9.4) be studied in Step 3 first to identify what other users prefer as a substitute (in the case of increased demand) or which substitute other users stop using (in the case of decreased demand). Step 3: Product substitution As noted in Step 1 it is commonly assumed in consequential LCA that supply follows demand. This implies that if we change supply, we will not change the demand but rather affect the competition between suppliers to cover the demand


This implies that if we change supply, we will not change the demand but rather affect the competition between suppliers to cover the demand. For example, if we reduce the supply of crude oil on the market, it is assumed that the crude oil users will attempt to find a substitute for the crude oil, creating a demand for other products satisfying the same service as offered by the crude oil. The demand for the service that the crude oil is providing is thereby assumed to be constant, but there is a change in the way the demand is met. Following this assumption about demand driven consumption, changes in how the demand is met may arise if the supply of a product is changed, or if we change demand for a product whose production is constrained (as explained in Step 2).


In each of these cases, we need to identify the substitutions that occur in the market, like in the above example where gas substitutes oil. The question that we will address in this step of the consequential LCI is: “How do we identify which product substitutes which?” In order to identify which products can substitute which, there are two aspects that we have to consider: • The products must deliver the same service(s) for the product user. • The product working as a substitute has to be available. Below we will address each of these two issues. Identifying a satisfying substitute for the product user A product may provide different services for different users, implying that one product may be a fully satisfying substitute for one user, but completely useless for another


Thus, to identify which product can substitute which, we first need to identify the product user who is likely to find a substitute due to an increase in Is the production of the product constrained? •Is it produced as a low-value by-product? •Is its production constrained by regulation? •Is its production physically constrained? •... Yes No Will the demand increase or decrease as result of decision? Increase Decrease Is the product already used to the extent the constraint allows? Is the product already used to the extent the constraint allows? Yes No Other user finds substitute Other user uses product instead of alternative Study market to identify what the product substitutes/is substituted with – handled in Step 3 Go to Step 4 Is the product a low value by-product? Yes No Waste is reduced. Include the reduced waste handing in LCA. Production is increased. Go to Step 4 (product modelled as if unconstrained). Yes No Is the product a low value by-product? Yes Waste is increased


Include the reduced waste handing in LCA. Production is increased. Go to Step 4 (product modelled as if unconstrained). Yes No Is the product a low value by-product? Yes Waste is increased. Include the increased waste handing in LCA. Production is decreased. Go to Step 4 (product modelled as if unconstrained). No Fig. 9.4 How to identify constrained production and how to handle it Life Cycle Inventory Analysis


demand or decrease in supply or who decides to use the product instead of a substitute due to a decrease in demand or increase in supply, i.e. the marginal user. However, in reality, identifying the marginal user may be very difficult. Therefore, if a market analysis shows that the product is used in significant amounts for several different purposes, it is advised to make different scenarios for each of these potential substitutions. This can feed into sensitivity and uncertainty analysis of an LCA (see Sect. 9.6 and Chap. 11). In this case, this step should be followed for each of the scenarios. Having identified the marginal product users and what they use the product for, the next step is to identify what can be used as a substitute for the product by the different marginal users


Having identified the marginal product users and what they use the product for, the next step is to identify what can be used as a substitute for the product by the different marginal users. Identifying what will be a satisfying substitute for a specific user will in most cases require a large amount of background information about the market where the substitution will take place, and hence involve some elements of uncertainty. However, for a product to work as a substitute, it needs to fulfil the same functions for the user. As outlined in Weidema (2003), these may relate to: • Functionality, related to the main function of the product • Technical quality, such as stability, durability, ease of maintenance • Costs related to purchase, use and disposal • Additional services rendered during use and disposal • Aesthetics, such as appearance and design • Image (of the product or the producer) • Specific health and environmental properties, for example non-toxicity


Apart from the basic functionality of the product, which can be seen as an obligatory property of the product (see Chap. 8), the importance of these properties will to a large extent depend on the product user. If the product user is a company using the product in its production, the functionality and technical quality will normally be the most important, for some companies accompanied by health and environmental issues. For consumers, on the other hand, issues like aesthetics and image may have a high priority. It should be noted that there may be not one but several products that work as a substitute for a product. If it is possible to identify the distribution between the alternative product substitutes, the consequential LCI should be based on this. If this is not possible, it may be necessary to develop several scenarios for each of the likely substitutes. Product availability Ensuring that the substitute has the necessary functionality, however, is not enough


Product availability Ensuring that the substitute has the necessary functionality, however, is not enough. The substitute also has to be available. A substitute is unavailable if constrained and already used to the extent that the constraint allows. To identify whether the substitute is available, we need to perform parts of Step 2 (included in the decision tree below), which also had as a goal to identify the availability of a product. As the discussion of how to perform this identification is going to be the same as under Step 2, the reader is referred to this section for further explanation.


Figure 9.5 presents a decision tree for identifying product substitutes. As an additional consideration, it should be noted that in some cases one product will not substitute another directly. For example, the production of biodiesel leads to the co-production of glycerol which contains salts and other impurities. Before it Does the product have the necessary properties to be considered a substitute by the user? Consider where relevant functionality, technical quality, costs, additional services, aesthetics, image, and health - and environmental properties. Is the production of the potential substitute constrained? • Is it produced as a low-value by-product? • Is its availability constrained by regulation? • Is its availability physically constrained? • ... Will the demand for the potential substitute increase or decrease as result of decision? The product is not a substitute. Identify alternative product Find the most significant users of the product through market analyses


Identify alternative product Find the most significant users of the product through market analyses. Go through the following procedure for each of the users. The product is a substitute. Remember to consider whether there may be other alternatives. Then, go to Step 4 to find the production technology affected. Yes No Yes No Increase Decrease Is the potential substitute already used to the extent the constraint allows? Yes No The product is not a substitute. Identify alternative product Fig. 9.5 Procedure for identifying possible substitutions of products as consequence of changes in supply or demand Life Cycle Inventory Analysis


can be sold on the glycerol market, it therefore needs to be distilled. In this case, and in others where additional treatment is needed for the product to be considered as a substitute, these additional treatments need to be included in the LCI. Also, it should be noted that in some cases a product substitution may create a cascading substitution effect (not captured by Fig. 9.5 for simplicity). E.g. if a decrease in demand for product A leads to other users using product A instead of B (substitutes), which is a waste product that is fully used (nothing goes directly to waste management), this can lead to other users using product B instead of C (substitutes), and so on and so forth. Step 4: Identify production technology affected by change in demand If the product for which the demand is changed is not limited in supply, it will normally be assumed in a consequential LCI that supply follows demand in a one-to-one relationship


The question is, however, which production technology will be affected by the change in demand. Identifying this technology is the purpose of this step. In many cases, similar products can be produced with very different environmental impacts. Just think of electricity that may be produced from wind turbines or coal fired power plants. It is therefore in many cases important to identify not only that the production of a certain product will change as a result of the assessed decision, but also to identify as accurately as possible which supplier, and hereby which production technology will be affected by the change in demand. For doing so, three issues need consideration: The size of the change in demand created by the decision, the trend in the market and whether the assessed decision leads to an increase or a decrease in demand. These issues will be discussed below


These issues will be discussed below. Size of change When identifying which technology will be affected by the change in demand, it is important to distinguish two different perspectives: The immediate production perspective and the perspective relating to changes in production technologies in the market. Consider the following example of electricity generation: Some technologies cost more to run than others. The production of electricity from gas turbines is, for example, often more expensive than electricity produced from coal. This implies that only coal power will be used, when the capacity of the installed coal power plants is sufficient to cover the demand. However, when the demand increases above what can be supplied by the coal power plants, gas power plants will start to produce


However, when the demand increases above what can be supplied by the coal power plants, gas power plants will start to produce. From an immediate production perspective, the concrete technology that will supply the demand will depend on the cost efficiency of the production technologies with available production capacity—the least cost efficient will be only be used to supply peak load. However, this is only the immediate consequence of the decision. If the electricity consumption in the given market in general is increasing or stable, a decision leading to an increase in demand will push for an increase in the installed power production capacity. In other words, the decision will have an effect on installed


capacity. Assume now that the planned implementations of power plants in the market are wind turbines. The long-term effects of increasing the demand will then be a corresponding increased implementation of wind turbines. The difference between the immediately affected production technology, known as the ‘short-term marginal’ and the effect on the installed production technology, known as the ‘long-term marginal’ may be very large—in the example above, the difference was between coal and gas power and wind. It can therefore be a very important decision for the results of the LCA whether the short or long-term marginal is used in the LCI. The general rule has been to use the long-term marginal when the assessed decision is creating large changes in demand, and use short-term marginal when the assessed decision creates small changes in demand


A change in demand is in this context considered small, if it is smaller than the average percentage of annual replacement of capacity (often around 5%, see below). The argument is that these small changes will be part of the general trend in the market and therefore be handled by the trend in the market. The signal they send is therefore considered too small to overcome the threshold for a structural change in production capacity. The difference in the size of changes assumed in the LCA is in fact what makes Situation A and B studies different in the ILCD classification (see Sect. 7.4). Trend in the market The electricity example above relates to the situation where the market trend points towards a stable or increasing demand


7.4). Trend in the market The electricity example above relates to the situation where the market trend points towards a stable or increasing demand. However, if the market trend is rapidly decreasing, the long-term marginal response to a decision that leads to an increase in demand will not be an increase in the implementation of more wind turbines but rather the continued use of coal or gas power plants that would otherwise have been taken out of operation. In this market, the demand caused by the assessed decision will thereby make the existing least competitive technology stay longer on the market. The distinction between whether the trend in a decreasing market is slowly decreasing or rapidly decreasing depends on whether the decrease happens below or above the average replacement rate for the production technology


For example, a market trend would be characterised as rapidly decreasing if it decreases by 10% per year, while the average replacement rate for the production technology is 5%. Note that a replacement rate of 5% means that production plants are designed to operate for 20 years, which is quite common, depending on the technologies involved. The reason for making this distinction in market trends is that for increasing, stable or slowly decreasing market trends there is a need for implementation of new production technology, and changes in demand will therefore affect this implementation rate. For rapidly decreasing market trends, however, the decrease is faster than the decommissioning rate for the technology, implying that production plants would be taken out of use before their design life time. In such cases (small) changes in demand will not lead to changes in implementation of new Life Cycle Inventory Analysis


technology (e.g. wind turbines), but merely to the changes in the speed of decommissioning (e.g. coal or gas power plants). Increase or decrease in demand The electricity generation example above relates to the situation where the assessed decision leads to an increase in demand, and the general trend in the market is either on the increase or decrease. However, the assessed decision may also lead to a decrease in demand. If the assessed decision leads to a large decrease in demand in a market with an increasing market trend, the implementation of new technologies will be postponed, implying that existing least cost effective technologies will continue to be used for a longer time. As showed in the discussions above, there are three aspects that need to be considered, and since each of them has two possible outcomes, there is a total of eight possible combinations. Not all combinations were discussed above, but they follow the same logic


Not all combinations were discussed above, but they follow the same logic. Table 9.1 summarises the discussions above and gives an outline of how to perform the identification of which technology is affected by a change in demand for all eight combinations. Table 9.1 Identification of the technology which will be affected by a change in demand (i.e. the marginal technology) Long-term marginal Short-term marginal Decision leads to increase in demand Increasing market trend: Implementation of new production technology is promoted—increase in demand is supplied by the production technology to be implemented in the context Increasing market trend: Less cost-efficient technology will be used to supply increase in demand— increase in demand is supplied by least cost effective technology available on the market Decision leads to increase in demand Decreasing market trend: Decommissioning of least competitive technology is delayed


Increase in demand is supplied by least cost effective technology available on the market Decreasing market trend: Less cost-efficient technology will be used to supply increase in demand. Increase in demand is supplied by least cost effective technology available on the market Decision leads to decrease in demand Increasing market trend: Implementation of new production technology is delayed. Decrease in demand saves the supply from production technology to be implemented in the context Increasing market trend: The least cost-efficient technology is no longer needed because of reduced demand