Decrease in demand saves the supply from least cost effective technology available on the market Decision leads to decrease in demand Decreasing market trend: Decommissioning of least competitive technology is promoted —decrease in demand saves the supply from least cost effective technology available on the market Decreasing market trend: The least cost-efficient technology is no longer needed because of reduced demand—decrease in demand saves the supply from least cost effective technology available on the market After identifying the production technology affected by the change in demand, go to Step 1 again to address other changes created by the assessed decision, if all changes are not already handled.


The table shows that, depending on the combination of the three aspects, the marginal technology can either be the least cost effective technology available on the existing market (6 of the combinations) or the future production technology to be implemented (2 of the combinations). In practice, the marginal technology, especially long term, can be difficult to identify, and this is a potential source of considerable uncertainty in the inventory analysis. The importance of this uncertainty may be investigated by sensitivity scenarios for the different potential marginal technologies. Furthermore, it is possible to create a mix of potential marginal processes, which means that the inventory data becomes a mix of data from the different potential marginal processes, as demonstrated in Sect. 9.5. This approach is used in the ecoinvent database in its version 3 (and higher). Note that in the discussions above, we have mentioned ‘the market’ as one entity

Hauschild et al., LCA: Theory and Practice


9.5. This approach is used in the ecoinvent database in its version 3 (and higher). Note that in the discussions above, we have mentioned ‘the market’ as one entity. However, in reality, there may be many markets for one product, e.g. when the product has high transportation costs compared to the value of the product. In cases where there are many small markets for the same product, the market trend has to be identified in the affected local market. For other products where the transportation costs are lower, there may be only one global market. The spatial nature of a market has to be established as a first task when identifying changes in supply. Secondary consequences and concluding remarks In the presented 4-step guidance we have only addressed the rather ‘direct consequences’ of increased or decreased demands and supplies. However, several derived effects or secondary consequences of these direct consequences may be found


However, several derived effects or secondary consequences of these direct consequences may be found. Depending on the size of these consequences and the scope of the assessment, these may be relevant to consider. Common for each of them is that they are difficult to foresee and even more difficult to quantify. We therefore cannot establish a general procedure for identifying and quantifying these, more than stating that in-depth knowledge on the topic of concern in most cases will be necessary. A few examples of the types of secondary consequences are given below. Additional or reduced production of a product may affect market prices for the product hereby affecting the broader demand for the product. For example, if the assessed decision will lead to the increase in the cost of, say, wheat, the behaviour of other consumers may be to consume less wheat due to this increase. It may also be that due to the increase in price, some consumers will begin to use, e.g


It may also be that due to the increase in price, some consumers will begin to use, e.g. corn instead of wheat, hereby increasing the demand for corn. Changes in market prices may not only affect the consumers but also the producers. In the example above, increases in wheat prices may cause producers to increase the intensity of their production, typically done through increasing the fertiliser use, or through increasing the agricultural area (for more discussions about secondary consequences specifically related to biomaterial production, see Chap. 30). However, it may also be imagined that the increase in price of wheat may cause producers to Life Cycle Inventory Analysis


intensify research related to yield increase, potentially leading to, say, a decrease in area/fertiliser/pesticide use per produced unit of wheat. Other types of ‘secondary consequences’ related not to prices of products but to the time consumption of products can also be imagined for some products. For example, a washing machine may lead to significant time savings for the user. The question is then what this time will be used for. In some cases, what is gained in terms of time savings by various household appliances is to some extent used on other ‘time-consuming’ household appliances, such as TV or videogames. When assessing a washing machine, it may therefore in some cases make sense to include an increase in power consumption from the TV set, or something similar. As may be obvious from the example above, identifying the secondary consequences will in many cases be very difficult and associated with considerable uncertainties


As may be obvious from the example above, identifying the secondary consequences will in many cases be very difficult and associated with considerable uncertainties. Furthermore, these effects are typically far from linear and when certain thresholds are passed a complete shift of parts of the market can be the consequence (e.g. the point where the production cost of wind power makes it fully competitive in certain market segments). Whenever these effects are considered in an LCA, it will often be advisable to make several different scenarios where various realistic possibilities are addressed in order to assess the potential variability of the results (see Sect. 9.6)


9.6). However, despite the problems of identifying these secondary consequences, it is evident that if the goal of the assessment is to get as complete an overview of the consequences of a decision, none of these should be omitted a priori, but should be included if at all considered to be practically possible and important for the outcome of the study. This concludes the introduction and guide to consequential LCA. Readers are invited to consult the Appendix for an example of how to use the 4-step guideline in a case study of the consequences of increasing the supply of biodiesel from poultry fat. As we hope to have demonstrated, consequential LCA is conceptually appealing because it aims to address the consequences of a potential decision


As we hope to have demonstrated, consequential LCA is conceptually appealing because it aims to address the consequences of a potential decision. After all, why bother making an LCA study (or paying for one) if its outcomes are not expected to have a consequence on the physical world? We also hope to have demonstrated that the answers to the many questions that need to be addressed throughout the 4-step guide are often associated with large uncertainties. Even advanced economic models generally do a poor job at predicting concrete consequences in markets following some sort of perturbation (consider how global financial crises tend to take also financial analysts by surprise) and simplifying assumptions have to be applied. These uncertainties are one reason why many LCA practitioners prefer an attributional approach. Its use of average process data and frequent use of allocation is theoretically difficult to defend when the goal of an LCA study is to support decisions (i.e


Its use of average process data and frequent use of allocation is theoretically difficult to defend when the goal of an LCA study is to support decisions (i.e. study the consequence of decisions), which is the case for Situation A and B studies in the terminology of ILCD (see Sect. 7.4). Yet, attributional LCA does not suffer from uncertainties related to economic modelling and is preferred by some LCA practitioners for this reason and considered to be ‘on average more correct than consequential LCA’. This is also part of the reason why ILCD recommends an attributional approach even for goal situation


A where the LCA supports a decision but the scale is small and market elasticities make identification of the marginal product or technology uncertain in many cases. 9.3 Planning and Collection of Data Based on the scope definition and the processes identified to belong within the system boundaries, the collection of data for these processes has to be planned and carried out. The planning has the purpose of balancing the effort of data collection by the relevance of the respective data and information. This is essential in order to avoid wasting time on collecting high-quality data that have a low relevance for the LCA results and/or spend too little time on collecting high-quality data where it is highly relevant for the results. Planning and collection of data are iterative processes, which is why they are addressed together in this section. These processes are an integrated part of the iterative approach to LCA that also involves the calculation of LCIA results


These processes are an integrated part of the iterative approach to LCA that also involves the calculation of LCIA results. For example, the first iteration of LCIA results may guide the practitioner about which data are particularly relevant to focus on in a second iteration. As starting point for data collection, we encourage practitioners to create a table that outlines a plan for the data collection for each process or single data point, see template in Table 9.2 (elements of the table are explained below). Note that the data eventually collected by the practitioner will often diverge from the initial plan due to unforeseen limitations and results of early iterations of LCIA phase that may lead to changes in the data specificity that the practitioner aims at for each individual process or single data point. The table can therefore be adapted accordingly at each iteration and be used in its final version (i.e


The table can therefore be adapted accordingly at each iteration and be used in its final version (i.e. final iteration of the study) to document the metadata behind the LCI data (see Sect. 9.7). The initial planning should be based on the requirements to data representativeness from the scope definition, as well as on the efforts that are expected in order Table 9.2 Template for planning and collection of data Process or single data point Specificity Type Source Access Very high High Medium Low Very low X X Concentration Process engineer Questionnaire Y X Kg/year Academic paper Online search Z X Unit process ecoinvent Database search The structure of the table can follow life cycle stages of the product. Based on Wenzel et al. (1997) Life Cycle Inventory Analysis


to obtain data of a given quality. Data quality is here classified into one of five categories of data specificity shown in Table 9.3. The efforts required to obtain data of a given quality can be estimated for each data point (e.g. a flow quantity) by considering three additional dimensions of the data in Table 9.2: data type, data source and data access. Examples are given for each of these in Table 9.4. The following sub-sections are structured according to the collection of data for each of the five data specificity levels and address challenges that the LCA practitioner commonly faces for each of the three dimensions of Table 9.4. Table 9.3 Classification of data specificity (inspired by Wenzel et al. 1997) Data specificity Explanation Very high Measured directly at specific process site or scaled from measurement High Derived from measurements at specific process site via modelling Medium LCI database process or data from literature specific to actual process, e.g


according to best available technology standard or country average. Specificity may be improved by modifying a process with site-specific data Low Generic LCI database process or data from literature, e.g. covering a mix of technologies in a country or region Very low Judgement by expert or LCA practitioner Table 9.4 Three dimensions influencing the effort required to obtain data Examples and notes Data type Complete unit process Includes all flows scaled to 1 unit of reference flow for process Individual flow to/from process per unit of time X kg/year, covers elementary flows and other flow types Technical or geographic parameters Process pressure, temperature, soil pH, precipitation Concentrations X g/m3 flue gas or waste water to treatment Quantities of products bought per year X kg steel of specified grade (i.e


material flow to process) Use characteristics Temperature of clothes washing, driving pattern of car Sector statistics Sector-average data Economy-wide statistics Infrastructure data, trade data Data source Experts internal to commissioner Process engineers Flow data on internal processes Purchasing department Supplier data Research and development or design Data on product concepts, not yet marketed Experts external to commissioner Researchers Expert in relevant technological domain (continued)


9.3.1 Very High and High Data Specificity The data type to be prioritised is always complete unit processes, because these form the basis of the LCI results. However, for very high and high data specificity, complete site-specific unit processes often do not exist and therefore must be constructed by the practitioner from single data points. For very high specificity, these data points are directly measured input and output flows, i.e. elementary flows from/to the ecosphere and other flows from/to other processes in the technosphere. Ideally, elementary flow data should be gathered in the physical unit matching the characterisation factors to be applied in LCIA (usually ‘kg’) per specific reference flow of the unit process (usually the primary product output). For a CO2 emission (i.e. elementary flow) from an electricity generation process, this would mean an amount of kg CO2 per kWh electricity produced


For a CO2 emission (i.e. elementary flow) from an electricity generation process, this would mean an amount of kg CO2 per kWh electricity produced. Often, a directly measured flow will not be available in this form, but rather as a quantity per unit of time (e.g. kg per year). In this case, the flow needs to be scaled to one unit of reference flow. Figure 9.6 shows an example of how to do this in practice


kg per year). In this case, the flow needs to be scaled to one unit of reference flow. Figure 9.6 shows an example of how to do this in practice. Table 9.4 (continued) Examples and notes Consultants Person having long experience with conducting similar studies Industry representatives Person with broad overview of relevant industry Public Other LCA studies Academic literature, reports commissioned by companies LCI databases ecoinvent, LCAfood LCI models PestLCI Company CSR reports Mentioning of key environmental figures Industry association reports and databases Volumes produced, average elementary flows Legal documents Details on best available technologies, regulatory thresholds National or supranational statistical agencies Mixes of waste treatment, transport, energy, etc


Consumer organisations Average life time of products Data access Online search Google, databases, websites Questionnaire Employees at commissioning company or suppliers Direct dialogue Physical visits to site, email or telephone contact First-hand gathering by LCA practitioner Measurements at site with own equipment The points listed under each dimension are illustrative and not exhaustive Life Cycle Inventory Analysis


Often, a company will not possess all the relevant data required for a unit process, due to the cost of systematically measuring all inputs and outputs. When direct site measurements of flows are not available, the flows can be modelled from other site-specific data, in which case the data quality is high, as opposed to very high. Such other site-specific data can be the concentration of pollutants in effluents (typically wastewater or flue gas). Figure 9.7 shows an example of how to calculate copper emissions to untreated wastewater from the concentration of copper in the wastewater. Another approach is to calculate output flows from site-specific measurements of input flows using a mass balance. Since unit processes, in general, do not gain or loose mass (or energy) over time, the mass of inputs should equal the mass of outputs


Since unit processes, in general, do not gain or loose mass (or energy) over time, the mass of inputs should equal the mass of outputs. So, if a company consumes 10 m3 of natural gas per year, the CO2 emissions can be estimated from the mass of natural gas (calculated using its density) and the stoichiometry of the combustion reaction (natural gas is mainly composed of methane, CH4). A mass balance approach can also be applied to modelling at the level of elements. If for example, a company consumes 950 g copper per unit of reference flow, but one unit of reference flow only contains 928 g copper, then the remaining 22 g per unit of reference flow must leave the process as Material X: 1500kg/year Product Y: 1000kg/year Process Emission of NOx: 500kg/year Divide the flows by the annual producƟon rate (1000 kg/year) Material X: 1.5kg Product Y: 1kg Emission of NOx: 0.5kg Process Fig. 9.6 Example of the scaling of three annual flows to one unit (kg) of reference flow (product Y)


Convert the concentration unit to ”kg/m3” and multiply it with the wastewater flow: 0.0001kg/m3*1000m3/year = 0.1kg/year. Product Y: 2000kg/year Wastewater: 1000m3/year Emission of copper: 0.1mg/L Process Divide all flows by the annual production rate (2000 kg/year) Product Y: 2000kg/year Emission of copper: 0.1kg/year Process Product Y: 1kg Emission of copper: 5*10-5 kg Process Fig. 9.7 Example of the calculation of an emission per reference flow (1 kg of product Y) from a wastewater concentration. Dotted arrows indicate input flows not considered in the example Life Cycle Inventory Analysis


a waste flow to treatment or as an emission. In many cases, a simple back-of-the-envelope mass balance calculation will not suffice, because the relationship between input and output flows is complicated and dependent on many parameters, and it is more appropriate to apply dedicated LCI models. For example, the LCI model PestLCI (Birkved and Hauschild 2006) calculates emissions of pesticides from field applications via different routes (e.g. evaporation, air drift, emissions through drainage pipes and groundwater leaching) based on the application of a specific pesticide to the field, its physical and chemical properties, and a large number of context-specific parameters, such as crop type, time of application, soil pH and slope. Note that the data specificity obtained from LCI models can only be characterised as high if all inputs and parameters are in fact site-specific (and when relevant, time-specific). If this is not the case, the data specificity is lower


If this is not the case, the data specificity is lower. High and very high specificity data (e.g. on elementary flows such as CO2 emissions and freshwater use) are sometimes available in reports, e.g. ‘green accounts’ or CSR reports, published by the company operating the process of interest, but often the source of such data is employees working with or operating the process. These may be process engineers monitoring flow data as part of their daily routine, or they may work in the purchasing department and thereby have knowledge about the amounts of input flows (materials and energy) purchased and the identify of suppliers. The latter may be used to contact suppliers for data specific to processes at their sites and the procedure can, in principle, be repeated several times to obtain company internal data further upstream in the foreground system


Company internal data may be accessed by asking the employees to fill out questionnaires combined with a physical visit to the site, email or telephone contact. This way of obtaining data can be straightforward or require lots of effort depending on the willingness of the employees possessing the data to share them in a relevant format. From our experience, this willingness is generally higher when the commissioner of a study is part of the same company and department as the employee holding the data or if the LCA study has been given attention by the management level in a company. It should be noted that company internal data are sometimes confidential


It should be noted that company internal data are sometimes confidential. In some cases, they are not possible to obtain, but in other cases the confidentiality issues may be handled by the LCA practitioner signing a non-disclosure agreement and reporting any confidential data of importance to the study in a special appendix to the report that is only accessible to a selected group of people (typically including members of a peer review panel if the study is peer reviewed). 9.3.2 Medium and Low Specificity Data For reasons given above it is in practice rarely feasible (nor necessary) to obtain all foreground system data from site measurements, i.e. with high or very high


specificity. A large part of the data collection therefore usually takes place online by searching, identifying and accessing publicly available sources, such as other LCA studies, industry association reports and national statistics. It is also possible to identify, via online searching, data for a process that is very similar to the actual process to be modelled, either because the reference flow of the processes is the same (e.g. the incineration of polypropylene) or similar (e.g. the incineration of polypropylene versus polyethylene). The strategy of extrapolation from data for similar processes is especially useful to ‘fill out gaps’ in a preliminary unit process, but the LCA practitioner must carefully check the representativeness of the process used for extrapolation


For example, if the initial data collection effort has led to a handful of high or very high specificity emission data, but no resource inputs for a process, the remaining flows may be quantified by extrapolation from a similar process. Such similar process can be sourced from scientific papers or other sources, which can document sufficient representativeness (technology, geography, time) and disclose sufficient data to check the agreement with the existing handful of high specificity emission data for the original unit process. A special case of extrapolation is for novel technologies that may not yet operate at industrial scale anywhere at the point in time where the study is to be conducted. Here, an obvious source of extrapolation is laboratory scale processes. It is, however, important to consider how the relationships between the flows of a process changes from laboratory to industrial scale


It is, however, important to consider how the relationships between the flows of a process changes from laboratory to industrial scale. Often the technology of the process will change, not just in size, at the upscaling from lab scale to commercial scale, and this typically leads to increased efficiency (e.g. less input per reference flow output) and changes in the quality of flows. The effort required to access data via online searching depends on the expertise of the practitioner (e.g. familiarity with the terminology of the concerned technical domain) and on how well-studied the phenomena behind the data is. For example, there is generally more publically available data on greenhouse gas emissions than on emissions of synthetic chemicals used for very specific industrial purposes and produced in low volumes. The effort also depends on the number of data points that can be accessed from each source


The effort also depends on the number of data points that can be accessed from each source. A unit process is often composed of more than 100 flows (the majority often being elementary flows). Some sources, such as LCI databases, contain data for all flows making up a unit process, while other sources, e.g. statistical agencies, may only cover a few elementary flows. LCI databases are used to source data for the background system and for the parts of the foreground system where more specific data can or will not be obtained. Table 9.5 presents a non-exhaustive list of LCI databases. Life Cycle Inventory Analysis


Table 9.5 List of process-based LCI databases (not exhaustive) Name Description ecoinvent Swiss database that contains approximately 12,500 processes (version 3) organised under different themes like transport, energy, material production, agriculture, etc. All processes are available as unit- and system-processes and all processes are documented in detail. Updated regularly ecoinvent; www.ecoinvent.org ELCD Database of the JRC of the European Commission, contains more than 300 datasets on energy, material production, disposal and transport Joint Research Centre of the European Commission; eplca.jrc.ec.europa.eu/ ELCD3/index.xhtml Agri-footprint A comprehensive LCI database of feed, food and biomass, containing around 3500 products and processes Blonk Consultants; www.agri-footprint


com LCA Food Danish database containing more than 600 data sets on basic food products and related processes from agriculture, aquaculture, fishery, industry, wholesale and supermarket, including waste treatment processes 2.-0 LCA Consultants and Aarhus University; www.lcafood.dk Swedish National LCA database Contains more than 500 well-documented LCI data sets in SPINE format for a wide range of industrial processes and household goods and services Competence Centre for Environmental Assessment of Product and Material Systems of Chalmers University of Technology; cpmdatabase.cpm. chalmers.se GaBi databases Separate databases mainly based on primary data collection. Cover sectors from agriculture to electronics and automotive industries, textiles and retail, through to services


Cover sectors from agriculture to electronics and automotive industries, textiles and retail, through to services. Contains more than 10,000 Life Cycle Inventory profiles GaBi; www.gabi-software.com/ international/databases/gabi-databases/ LC-inventories Over 1000 process data sets, which are corrections, updates or extensions of ecoinvent v2.2 database, created by ESU-Services and other authors The Swiss Federal Office for the Environment and ESU-services; www. lc-inventories.ch NEEDS Database designed for long-term environmental assessment. Contains around 800 processes of future energy supply systems, future material supply, and future transport services Members of a European research project; www.needs-project.org/ needswebdb/index.php NREL US-American database with around 300 datasets related to the production of materials, components, or assembly in the U.S


National Renewable Energy Laboratory; www.nrel.gov/lci ProBas Comprises more than 8000 datasets on energy, material production, transport and disposal, different data sources and German Federal Environmental Agency; www.probas.umweltbundesamt.de/php/ index.php (continued)


While a number of LCI databases are available and some of them contain high-quality data for specific technologies or industries as shown in Table 9.5, the most comprehensive, and probably most widely used, database is ecoinvent and in the following section we there focus on this database and encourage the reader to look for similar information about other databases using the references given in Table 9.5 as relevant. ecoinvent version 3 contains approximately 12,500 unit processes and each process exists in an ‘allocation, default’ (or APOS: allocation at the point of substitution), an ‘allocation, recycled content’ (or ‘cut-off’) and a ‘consequential’ version


The ‘allocation, default’ version uses price as allocation key as a rule, except for a few processes, where representative physical parameters are used (such as for processes involving co-production of electricity and heat) where markets are judged distorted by, e.g., regulation, and also corrects for fluctuating prices by applying three-year, historical average prices for some processes (Weidema et al. 2013). The cut-off version is identical to the default allocation version, except for the handling of recyclable materials that are cut-off before being sent to recycling. This means that recyclable materials do not bring any benefits to the primary user of the materials and are considered available ‘burden-free’ to recycling processes, and that the impacts attributed to secondary recycled materials are only those of the recycling processes and the associated transportation


By contrast, in the default allocation version secondary recycled materials are also allocated a share of the material’s previous life cycle impacts (based on economic allocation). The existence of the two allocation approaches for recyclable materials in ecoinvent (‘default’ and ‘cut-off’) reflects the fact that there is little consensus on how to perform such allocation in the most reasonable way. The cut-off allocation is the recommended approach in the European Product Environmental Footprint guideline (EC-JRC 2012). The consequential version of ecoinvent uses the long-term marginal technology, which is identified by considering whether a market is increasing (or stable, or slowly decreasing) or rapidly decreasing, in line with Table 9.1


The ecoinvent centre advocates the use of the consequential version of the database not only for large-scale decisions (studied in Situation B studies, according to ILCD), but also for small-scale decisions, which are by definition too small to cause structural changes outside the foreground system, i.e. too small to lead to new equipment Table 9.5 (continued) Name Description data quality. Focuses on processes within Germany LCA Commons More than 18,000 datasets for U.S. agriculture production and agriculturally derived products USDA; www.lcacommons.gov Ökobaudat German database with around 950 environmental product declaration datasets for building materials, building processes and transport processes Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety; http://www.oekobaudat. de/en.html Life Cycle Inventory Analysis


being installed (increase in production capacity) or existing equipment being prematurely taken out of use (decrease in production capacity). Yet the ecoinvent centre argues that the consequential version of the database (which is based on the long-term marginal technology) is “applicable to study the effect of small, short-term decisions, since each individual short-term decision contributes to the accumulated trend in the market volume, which is the basis for decisions on capital investment” (Weidema et al. 2013). In relation to the ILCD-defined decision context situations, the ecoinvent 3 database can, strictly speaking, only be used to model consistently the parts of Situation B studies involving structural changes (using the consequential database) and Situation C2 studies (using the allocation default or cut-off database). However, as noted in Sect. 9.2.2, economic allocation is often the only practical solution to multifunctionality, irrespective of decision context


However, as noted in Sect. 9.2.2, economic allocation is often the only practical solution to multifunctionality, irrespective of decision context. We therefore advise that one of the two allocation versions of ecoinvent is used for Situation A, B (only non-structural changes), C1 and C2. However, the LCA practitioner should check for any multifunctional processes that have high contributions to early iteration LCA results and, where appropriate and technically feasible, manually change the multifunctionality solution in accordance with the scope definition of the study to test its influence on LCA results. Whenever data is sourced by online searches or LCI databases it is important to pay attention to the available metadata describing the characteristics and conditions of the process to evaluate how representative the data is for the actual data needed


Metadata usually specifies the exact technology (or mix of technologies, in the case of average or generic data) involved in a process, the location (e.g. country) of the unit process, the time during which the data applies and relevant operating conditions (e.g. climate). The metadata allows distinguishing between medium and low data specificity (see Table 9.4). Relevant metadata for foreground processes should be reported by the LCA practitioner (see Sect. 9.7) and furthermore considered in the later sensitivity analysis and uncertainty management (see Sect. 9.6). When using a unit process from an LCI database in the foreground system it is preferable to adapt it to make it more representative of the actual process to be modelled to the extent that this is possible (see Sect. 8.7)


8.7). One improvement of the representativeness that is usually possible is to manually change the electricity grid mix that fuels the process to a mix that matches the geographical and temporal scope of the study. Note that such adaptation is not possible if a unit process is ‘aggregated’, meaning that the elementary flows of all processes upstream and downstream have been aggregated, so the reference flow is the only output of the aggregated process (or input, in the case of waste treatment processes) apart from the elementary flows. Aggregated unit processes are often preferred for constructing the background system because the LCA practitioner only needs to include the aggregated processes that link to the foreground processes of an LCI model.


9.3.3 Very Low Specificity Data If efforts to obtain data have been fruitless, one may rely on expert judgement. People may qualify as experts if they are knowledgeable in the technical domain relevant for the data (e.g. plastic moulding) or if they have conducted similar LCA studies themselves in the past. If no expert is available, a last resort is to use a ‘reasonable worst case’ for the calculation of the first iteration of LCA results. A reasonable worst case value may be derived from knowledge of similar or related processes or from correlation or calculation from other flows of the process or other processes. The results will then show if the data is potentially important or negligible (judging against the cut-off criteria identified in the scope definition). In the first case, the practitioner may try again to obtain data of better quality or address the issue in the interpretation of results


In the first case, the practitioner may try again to obtain data of better quality or address the issue in the interpretation of results. In the latter case, the reasonable worst case data may either be kept in the model or removed. Whatever option is chosen it should be reported (see Sect. 9.7) for the sake of other LCA practitioners wanting to use (parts of) the inventory model in future LCA studies. 9.4 Constructing and Quality Checking Unit Processes The data that is collected should represent full operation cycle of the process, including preparatory activities like heating, calibration (with potential loss of materials and products as scrap), operation, idling, cleaning and maintenance. It should also take into account typical scrap rates during operation. This means that the data collection should be based on a longer period of operation, ideally covering several production cycles, perhaps one year’s production


This means that the data collection should be based on a longer period of operation, ideally covering several production cycles, perhaps one year’s production. Sometimes also the impacts from the manufacturing and end-of-life stage of the production equipment are important and then they should also be included in the data collection. When the data has been collected, it is time to construct unit processes. As mentioned, the type of data collected can vary (see Table 9.3) and it is important to ensure that all the data has the right format for a unit process. To reiterate, all data must be in the form of flows. Elementary flows must be in a unit that matches that of the characterisation factors to be applied (‘kg’ in many cases), and all flows should be scaled to 1 unit of the reference flow of a unit process (see Figs. 9.6 and 9.7). Note that unit processes obtained from LCI databases already have the right format and are therefore ready to incorporate in an LCI model (see Sect. 9.5)


9.6 and 9.7). Note that unit processes obtained from LCI databases already have the right format and are therefore ready to incorporate in an LCI model (see Sect. 9.5). 9.4.1 Quality Check of a Unit Process When constructing unit processes there is a risk that they are incomplete and that there are errors in the flow quantities. Incompleteness may be caused by the fact that Life Cycle Inventory Analysis


some flows are not monitored or reported. Errors in flow quantities may be caused by errors in reported measurements (e.g. a technician writing ‘g’ instead of ‘mg’) or errors in the calculation of flows and conversions of units (e.g. if one had forgotten to convert the concentration unit in the example of Fig. 9.7). To avoid (critical) incompleteness and quantitative errors, constructed unit processes should be checked before they are used in an LCI model. Such a quality check can be supported by calculation and interpretation of first iteration LCIA results, e.g. through the identification of the most contributing process and substances. Completeness of flows There are three complementary approaches for validating the completeness of flows. 1. Knowledge of similar processes can help identifying potentially missing flows


Completeness of flows There are three complementary approaches for validating the completeness of flows. 1. Knowledge of similar processes can help identifying potentially missing flows. For example, the LCA practitioner may suspect one or more missing flows, if a unit process for a specific paper production process contains no chlorine containing compounds in the wastewater to treatment and the practitioner knowns from previous experience that chlorine compounds are typically present in the effluent of paper production processes. 2. Knowledge of the nature of a physical transformation in a process can hint what emissions or waste flows to treatment may be missing. For example, NOx gases are known to be formed whenever a combustion process occurs in the presence of nitrogen, the major constituent of atmospheric air. Filters can capture large fractions of generated NOx before it becomes an emission, but usually not every single molecule. 3


Filters can capture large fractions of generated NOx before it becomes an emission, but usually not every single molecule. 3. A qualitative comparison of input and output flows can show if there is disagreement between the elements entering a process and the elements leaving a process. For example, a process cannot emit large quantities of CO2, without inputs of carbon sources in the form of fossil fuels (e.g. coal, natural gas or oil). While using this validation technique it should be kept in mind that some flows entering and leaving a process are elementarily heterogeneous. For example, mercury is a common emission from the combustion of coal due to the mercury content (typically in the order of 0.00001%) of the coal entering the process as a heterogeneous material flow. In this case, the mercury input is ‘hidden’ in the coal input and it would therefore be wrong to assume that a homogenous input of mercury is missing on account of the emission of mercury


In this case, the mercury input is ‘hidden’ in the coal input and it would therefore be wrong to assume that a homogenous input of mercury is missing on account of the emission of mercury. Flow quantities A unit process should obviously not only contain the right flows, but also the right quantities of these flows. A number of validation approaches exist for checking flow quantities. A mass balance is a universal approach because the sum of flows entering a process should amount to the same number as the sum of flows leaving a process since no accumulation occurs inside the process. A mass balance is therefore an efficient way of spotting errors, for example if the mass of outputs is on the order of


1000 times the mass of inputs. Note, however, that flow quantities may be correct, even if the law of conservation of mass seems to be violated. This is because most of the constituents of atmospheric air, e.g. oxygen and nitrogen, are generally not counted as resource inputs in unit processes, in which case the mass of outputs appear larger than the mass of inputs (e.g. due to combustion products such as CO2, H2O and NOx). A mass balance can also be applied at the level of individual elements, but one should be aware of ‘hidden’ elements in heterogeneous flows, as described above. Energy balances can in principle also be used as a validation approach, but this would require calculations of the chemical energy stored in inputs and outputs and quantification of heat lost to the environment, which is often not reported as an emission in a unit process


Following validation based on mass balance a complementary validation based on stoichiometry can be carried out if the process to be validated involves one or more chemical reactions. This serves to check if the ratio between inputs and outputs involved in a chemical reaction is correct. For example, stoichiometry gives us the correct ratio between inputs and outputs in the electrolysis of water in the presence of sodium chloride: 2NaCl + 2H2O ! 2NaOH + H2 + Cl2. The mass (g) of each molecule can then be calculated by multiplying its stoichiometric coefficient (mole) and its molar mass (g/mole). Other validation approaches rely on comparisons to external information. This could be information for similar processes that are expected to contain flows of similar magnitudes as the process to be validated. The external information could also be legal limits


This could be information for similar processes that are expected to contain flows of similar magnitudes as the process to be validated. The external information could also be legal limits. For example, if an emission of nitrogen dioxide corresponds to 100 times a regulatory emission limit, it is a strong indication that there is an error in the emission quantity (note however that many regulatory limits are given as concentrations rather than mass flows, in which case a conversion is needed). Yet another validation approach relies on the first iteration of LCIA results. These are useful for identifying erroneously high flow quantities. For example, if the contribution from a single elementary flow of a single unit process contributes with 99.9% of the impact for an impact category, this is a strong indication that the flow quantity is too high (e.g. due to a factor 1000 unit conversion mistake in a calculation or data entry in the LCA software)


due to a factor 1000 unit conversion mistake in a calculation or data entry in the LCA software). This validation approach can also be used to check for mistakes in the ID of an elementary flow, such as mistakenly using the name ‘dioxin’ for an emission of ‘carbon dioxide’ (dioxin is a group of extremely toxic chemicals). 9.4.2 Using Flow Names Compatible with LCA Software To prepare a unit processes for use in an LCI model it is important that the LCA software used ‘understands’ the identity of the flows of the unit process. If this is not the case, a flow cannot be linked correctly to other processes or characterisation factors (in the case of elementary flows). There have been attempts at harmonising flow names across LCI databases and LCA software, but the LCA practitioner Life Cycle Inventory Analysis


should always check the flow nomenclature of the software used (e.g. SimaPro, GaBi or OpenLCA) and follow this when naming the flows of constructed unit processes. Unit processes of LCI databases (see Table 9.5) are commonly integrated into LCA software, which ensures that their flow names are correct. LCA practitioners may face a situation where an LCA software has no name for a given elementary flow or the CAS-number (Chemical Abstract System number— a unique identifier for a chemical) of an emitted chemical does not exist in the list of flow names in a software. In this case, the LCA practitioner should check if there is a characterisation factor (CF) for the chemical in the LCIA method to be applied in the ensuing LCIA step. If this is the case, the LCA practitioner should create a new flow in the LCA software with a name identical to the name of the CF, so the software can create the link


If this is the case, the LCA practitioner should create a new flow in the LCA software with a name identical to the name of the CF, so the software can create the link. If there is no CF, the LCA practitioner can either calculate the CF on his/her own when guidelines to do so exist (e.g. for the USEtox model; see Chap. 40) or discuss the potential of that substance to contribute to the total environmental impact and to the resulting interpretation of the results. In the case of missing flows that are not elementary flows, these should also be created in the LCA software and used to link processes together. For example, in the case of a waste to treatment flow that is specific to the studied system (part of the foreground system), and therefore not existing in the LCA software, this flow should be created in the software and used to link the process having it as an output to the most appropriate waste treatment process that is available


9.5 Constructing the LCI Model and Calculating LCI Results When all unit processes have been constructed or collected from LCI databases the LCA practitioner can construct the LCI model. Each unit process can be seen as a ‘building block’ in the LCI model, the ‘size’ of which is ultimately decided by the study’s reference flow derived from the functional unit in the scope definition (see Chap. 8). This is because the reference flow decides the quantity required of each unit process-specific reference flow. In other words, each unit process must be scaled to fit the LCI model. Figure 9.8 shows an example of how this is done manually for a simplified system composed of just three unit processes each having just 4 flows. In Fig. 9.8, Process 1 is first scaled to match the reference flow of the study (100 kg of Product X). After the scaling of Process 1, 200 kg of Product Y is required, which Process 2 is scaled according to


9.8, Process 1 is first scaled to match the reference flow of the study (100 kg of Product X). After the scaling of Process 1, 200 kg of Product Y is required, which Process 2 is scaled according to. This means that 240 kg of Product Z is required, which Process 3 is scaled according to, etc. In practice, LCA software can carry out the scaling automatically for the practitioner, when told what the reference flow of a study is. In practice, inventory modelling is normally performed using a dedicated software which supports both the building of the product system model, connecting the


relevant unit processes; the linking to available unit process databases and storing of own processes, and the linking of elementary flows in the inventory results to the relevant characterisation factors for the life cycle impact assessment. Table 9.6 shows some of the widely used software for LCA 9.5.1 Database and Software Specific Aspects As mentioned in Sect. 9.3.2 processes from LCI databases exist in disaggregated and aggregated versions, the difference being that the latter scales all processes upstream and downstream according to the reference flow of the process and aggregates their elementary flows, so that the only output of the aggregated process (or input, in the case of waste treatment processes) that is not an elementary flow is its reference flow


In practice, some LCI databases only provide aggregated Process 3 Process 1 Emission A 432kg Resource A 2.5kg Process 3 Emission A 1.8kg Process 1 Resource B 1kg Emission B 2kg Emission B 200kg Resource A 600kg Resource B 100kg Prod. W 0.3kg Prod. Y 2kg Prod. Z 1kg Process 2 Emission B 1.5kg Resource A 1.3kg Prod. Z 1.2kg Prod. Y 1kg Prod. X 1kg Prod. W 72kg Prod. X 100kg Process 2 Emission B 300kg Resource A 260kg Prod. Z 240kg Prod. Y 200kg (a) (b) Fig. 9.8 Three simplified unit processes unconnected (a) and connected (b) based on a study reference flow of 100 kg of product X (the reference flow of process 1) Table 9.6 Software for performing LCA (non-exhaustive list) Name Information SimaPro Pré Consultants; www.pre-sustainability.com/simapro GaBi Thinkstep; www.gabi-software.com/international/index/ OpenLCA GreenDelta (open access); www.openlca.org/ Umberto Ifu Hamburg; www.ifu.com/en/umberto/ Life Cycle Inventory Analysis


processes, which means that it is not possible to modify them to increase their representativeness for the study. When aggregating processes, the LCI database providers have made choices on how to handle multifunctional processes and how to cut-off the life cycle of the process’ reference flow because including all processes is not practically achievable in process-based LCI modelling (see Sect. 9.1). These choices also relate to solving the issue of closed loops between processes, which occurs if two processes need each other’s outputs as inputs. This issue is commonly solved by matrix inversion (Heijungs and Suh 2002). The way system expansion is performed in the construction of the inventory model depends on the LCA software used, but it is usually simple to implement for the LCA practitioner. For example, in GaBi it is performed by connecting the avoided process as input but with a scaling factor of −1 so that it is computed negatively as a crediting


For example, in GaBi it is performed by connecting the avoided process as input but with a scaling factor of −1 so that it is computed negatively as a crediting. In SimaPro, system expansion is performed by making a direct link to the avoided process in the flow category ‘Avoided products’, and the software automatically accounts for it negatively when processing the assessment. In OpenLCA, which is a free LCA software, system expansion is modelled as an avoided output of a unit process, in practice marking an output flow as ‘avoided product’ by checking a mark in the process. 9.5.2 Calculation of LCI Results The LCI results are the compilation of elementary flows over all the processes that are part of the LCI model (scaled to the reference flow of the functional unit). For the simplified product system in Fig. 9.8 the results would simply be the sum of each of the resources and emissions across all the processes, see Fig. 9.9 describing final LCI results


For the simplified product system in Fig. 9.8 the results would simply be the sum of each of the resources and emissions across all the processes, see Fig. 9.9 describing final LCI results. Product system Product X 100kg Emission A 432kg Emission B 528kg Resource A 960kg Resource B 100kg Fig. 9.9 LCI results for the product system in Fig. 9.8. The aggregated elementary flows of product W (72 kg), that are not shown in Fig. 9.8, are 100 kg of resource A and 28 kg of emission B


In practice, the number of flows and processes is normally huge, but no manual work is typically required from the LCA practitioner as the LCA software can calculate LCI results for a product system with one click of a mouse button. Such LCI results are the basis for the subsequent life cycle impact assessment phase (unless the goal of a study is to simply calculate the LCI results). 9.6 Data Needs for Uncertainty and Sensitivity Analysis Uncertainty and sensitivity analysis is important for the interpretation of LCIA results because they can inform the LCA practitioner on how robust the conclusions of the study are and where future studies should focus to make results even more robust. Chapter 11 is dedicated to these matters and details the theoretical background and the practical use of uncertainty and sensitivity analyses. The following describes the data that needs to be collected during the inventory analysis as inputs for uncertainty and sensitivity analyses


The following describes the data that needs to be collected during the inventory analysis as inputs for uncertainty and sensitivity analyses. Uncertainty analysis allows for the quantification of uncertainties of the final result, as a consequence of the uncertainty of each parameter in the LCI model. To enable an uncertainty analysis, the practitioner must, for quantitative parameters in the foreground system, collect information on their statistical distribution (e.g. normal, log-normal or uniform) and corresponding statistical parameter values (e.g. mean and standard deviation for normally distributed parameters). Sensitivity analysis allows for systematic identification of the parameters that have the highest influence on the LCIA results. The influence of parameters on results is calculated by changing them, one by one, and observing the changes in results. These changes in parameters should reflect uncertainties about the actual product system modelled


These changes in parameters should reflect uncertainties about the actual product system modelled. For quantitative parameters in the foreground system, the practitioner should aim to collect minimum and maximum values, or a low and a high percentile (e.g. 2.5th and 97.5th) when a parameter’s statistical distribution is known (see above), in addition to the default value that is used in the LCI model. For example, a specific farmer may on average apply 2 kg of a specific pesticide to produce 1 tonne of potatoes, but this number may vary from 0.5 to 3 kg, depending on weather conditions. For discrete parameters or assumptions in the foreground system the practitioner should develop a number of sensitivity scenarios. For example, a part of the product system may be located in a different country than assumed in the LCI model and a sensitivity scenario would thus involve differences in energy mix, waste treatment technologies, etc


Note that the data requirements for sensitivity and uncertainty analysis overlap and data collection can therefore be performed in parallel by the practitioner. It often takes more time to collect sensitivity and uncertainty data for some parameters in the foreground system than for others and it may not be necessary to collect data for all processes, depending on the outcome of the first iteration of the analysis. For example, if a process is found to contribute to less than 0.1% of total Life Cycle Inventory Analysis


impacts, then its sensitivity and uncertainty data should generally not be a high priority as illustrated by Fig. 12.3. For the background system, many LCI databases include uncertainty information on processes, which can feed into uncertainty and sensitivity analysis in LCA software. The practitioner therefore needs not to bother about such data in the inventory analysis. 9.7 Reporting The reporting of the inventory analysis should contain six elements: 1. Documentation of LCI model at system level. 2. Documentation of each unit process. 3. Documentation of metadata. 4. Documentation of LCI results. 5. Assumptions for each life cycle stage. 6. Documentation of data collected for uncertainty and sensitivity analysis. Elements 1, 2 and 3 should allow the reader to recreate the LCI results, which are documented in Element 4 (i.e. exigence of reproducibility of the study). Element 5 should allow the reader to judge the reasonability of all assumptions performed (i.e


exigence of reproducibility of the study). Element 5 should allow the reader to judge the reasonability of all assumptions performed (i.e. exigence of transparency) and Element 6 should allow the reader to recreate the uncertainty and sensitivity analysis (exigence of reproducibility and consistency). Below we elaborate on each element and we further refer to the illustrative case on window frames in Chap. 39 for an example of how the inventory analysis may be reported. 9.7.1 Documentation of LCI Model at System Level We propose to use a flowchart that contains all the linked processes in the foreground system for each studied product system and shows their links to processes in the background system. Each process should be named and, depending on the size of the foreground system, flow names and quantities may also be given (this information is, however, not essential, as it will also be given in second reporting element)


Figure 9.10 illustrates how to document a flow chart for a simple, hypothetical LCI model (flow names and quantities not shown). Flow chart should be reported in the main part of an LCA report. Note that only the unit processes of the background system that are linked to (‘neighbouring’) the foreground system needs to be included in the flow chart. These are processes UP1 to UP8 in Fig. 9.10. From this information, the reader


may reconstruct the remaining background system on his/her own by using aggregated versions of the reported ‘neighbouring’ background processes from the relevant LCI database(s). 9.7.2 Documentation of Each Unit Process We recommend the creation of a table for each unit process in the foreground system that contains its name (identical to the one used in the flow chart of the first reporting element) and the names and quantities of all flows (materials, energy, resources, products, waste to treatment and emissions—same units as used in LCA software), see scheme in Table 9.7. We also advocate providing the source of a process or flow (e.g. name of the database where process is from), a reference to the UPa UPb UPc UPd UPe UPf UP6 UP7 UP8 UP1 UP2 UP3 UP4 UP5 Foreground system Background system System boundaries Fig. 9.10 Documentation of LCI model in flow chart. Arrows between unit processes (UPs) indicate material, energy, product or waste flows


9.10 Documentation of LCI model in flow chart. Arrows between unit processes (UPs) indicate material, energy, product or waste flows. Unit processes belonging to the foreground and background system are identified with a letter or with a number respectively. Only the background unit processes neighbouring the foreground system should be included and dotted arrows to and from these processes indicate the existence of additional background processes. Life Cycle Inventory Analysis


section of a report where details of a calculations (e.g. emissions) are provided; and finally, reference to other unit process tables that are input or output to the process of interest. Because the number of processes to be documented is often large, tables like Table 9.7 are usually best reported in an appendix to the LCA report. Table 9.7 Scheme for documenting foreground processes Outputs Quantity Unit Source/note Reference flow (main product or function) Reference flow kg E.g.: input and output flows are not scaled to the functional unit of the product system Other outputs (avoided product or function; waste to treatment) Avoided product 1 – kg E.g. please see Table A1 for the corresponding unit process Waste 1 – kg E.g. ecoinvent ver. 3.0 Waste 2 – m3 E.g. ecoinvent ver. 3.0 Waste n – m3 E.g. ecoinvent ver. 3.0 Emissions (to air; water; soil) Emission 1 – kg E.g. please see “Appendix” for details on calculation of emissions Emission 2 – kg E.g


ecoinvent ver. 3.0 Waste n – m3 E.g. ecoinvent ver. 3.0 Emissions (to air; water; soil) Emission 1 – kg E.g. please see “Appendix” for details on calculation of emissions Emission 2 – kg E.g. please see “Appendix” for details on calculation of emissions Emission n – m3 E.g. please see “Appendix” for details on calculation of emissions Inputs Quantity Unit Source/note Materials Material – kg E.g. please see Table A2 for the corresponding unit process Material – kg E.g. please see Table A3 for the corresponding unit process Material n – m3 E.g. ecoinvent ver. 3.0 Energy Energy 1 – MJ E.g. ecoinvent ver. 3.0; see Table 1 in the main report for the source of values Energy 2 – MJ E.g. ecoinvent ver. 3.0; see Table 1 in the main report for the source of values Energy n – MJ E.g. please see Table A4 for the corresponding unit process Resources Resource – kg E.g. ecoinvent ver. 3.0 Resource – kg E.g. ecoinvent ver. 3.0 Resource n – m3 E.g. ecoinvent ver. 3.0 E.g. ecoinvent ver


ecoinvent ver. 3.0 Resource – kg E.g. ecoinvent ver. 3.0 Resource n – m3 E.g. ecoinvent ver. 3.0 E.g. ecoinvent ver. 3.0 Units are illustrative. Note that for waste treatment processes the reference flow is usually a material input. Note that the column ‘source/note’ is based on fictive examples and references included therein are not a part of this textbook chapter


The flow quantities of process tables should either be scaled to 1 unit of the reference flow of the process (as shown in Table 9.7) or scaled to the quantity of process reference flow required to meet the reference flow of the study (derived from the functional unit). For neighbouring background processes (UP1 to UP8 in Fig. 9.10), the name of the process and the name and version of the database it was sourced from is sufficient, because the reader may use this information to recreate the remainder of the background system. Note that inventory data in the foreground system are sometimes confidential, for example when a manufacturer wants to prevent the details of the production processes to be disclosed to the public or competitors. In terms of documenting LCI results, confidentiality issues can be handled by placing the process tables containing confidential data in an appendix that is only made available to groups of people that are cleared by the supplier of the data (e.g


employees of the organisation commissioning a study and an external critical reviewer). 9.7.3 Documentation of Metadata We recommend reporting metadata according to specificity, type, source and access using the structure of Table 9.2 (introduced for data planning and collection). For easy overview, the rows of the table should be grouped into life cycle stages. The data specificity classification (from very low to very high) for each data point should be transparent, i.e. by writing in the relevant cell why a data point was classified to a given specificity, rather than simply making a cross. The documentation of these metadata should be consistent with the documentation of unit processes described in Table 9.7, and cross-references between the two should be made (e.g. notes and data sources reported in tables documenting unit processes may readily refer to the table with metadata) We advocate reporting metadata in the main part of the LCA report


notes and data sources reported in tables documenting unit processes may readily refer to the table with metadata) We advocate reporting metadata in the main part of the LCA report. 9.7.4 Documentation of LCI Results The LCI results should simply be documented as a list of quantified elementary flows, divided into resources and emissions, i.e. as in Table 9.7. This typically consists of an extensive table, which can be documented as an appendix for readability of the LCA report. Life Cycle Inventory Analysis


9.7.5 Assumptions for Each Life Cycle Stage Due to lack of information and budget constraints, it is common to make several assumptions when constructing an LCI model. For example, data originally planned to be collected in medium or high specificity may end up being collected in low specificity. Thereby assumptions need to be made on what low-quality data can best represent the actual data. For example, should a wastewater treatment process in Vietnam, for which data could not be obtained, be approximated by a process in Thailand, possibly correcting for the Vietnamese electricity mix, or should it rather be approximated by an average process for the entire South East Asian region? All assumptions made during the construction of the LCI model should be transparently documented. We recommend that major assumptions are indicated, when describing the data collection and modelling of each individual life cycle stage, to facilitate cross-comparison with the documentation of metadata


Major assumptions may also be included directly in the table containing metadata. References to the sensitivity analysis should be given for assumptions whose influence on LCIA results are tested by the creation and analysis of sensitivity scenarios (see next subsection). We also recommend that a list of all assumptions, minor and major, be placed in an ‘Appendix’. 9.7.6 Documentation of Data Collected for Uncertainty and Sensitivity Analysis For sensitivity analyses, the LCA report must state which parameters are analysed and whether this is done by calculating normalised sensitivity coefficients (for parameters of a continuous nature) or by the construction of sensitivity scenarios (for parameters of a discrete nature). In the former case, the perturbed values for each parameter must be documented and the basis of these explained (e.g. reported min/max-values, 2.5/97.5 percentiles, or an arbitrary value, such as ±10%)


reported min/max-values, 2.5/97.5 percentiles, or an arbitrary value, such as ±10%). In the latter case, the sensitivity scenarios should be documented and references to the assumptions they are based on made (see previous subsection). For uncertainty analyses, the best practice is to use statistical distributions of parameter values as input to Monte Carlo analysis (see Sect. 9.6), in which case the distributions (e.g. uniform, normal or log-normal) and statistical parameters (e.g. standard deviation) must be documented for each parameter value covered in the uncertainty analysis. If, due to lack of such data, the Pedigree approach is taken, the underlying uncertainty factors and calculated geometric standard deviation for process must be documented. An example was given earlier in Table 9.6.


Appendix: Example of Consequential LCA on Biodiesel Made from Poultry Fat To help you get an overview of the 4-step procedure for performing a consequential LCI (presented in Sect. 9.2.3), an example is here presented, which shows some parts of a consequential LCI looking at the decision to supply additionally 200 tonnes of biodiesel based on poultry fat. It should be noted that this is a constructed example and that the factual claims made may not be completely accurate. To start the procedure, we go to Step 1. Here we are asked to consider whether the assessed decision leads to changes in demand or supply. Clearly, this decision leads to changes in supply. This implies that we move directly to Step 3. Step 3 is based on the assumption that demand is constant, and given that we increase supply of poultry fat biodiesel we therefore have to consider what other products it substitutes


Step 3 is based on the assumption that demand is constant, and given that we increase supply of poultry fat biodiesel we therefore have to consider what other products it substitutes. According to the procedure given in Step 3, we need to identify a user and a satisfying substitute for the user which fulfils the same functions terms of functionality, technical quality, costs, etc. Biodiesel is only used by drivers of diesel vehicles and can be blended with petrochemical diesel or used as a full substitute for petrochemical diesel in ordinary diesel engines. As it is often sold under favourable tax conditions, it seems reasonable to assume that it will substitute ordinary diesel. However, another scenario which may also in some cases be realistic to consider is that it will substitute other types of biodiesel (e.g. based on other substrates)


However, another scenario which may also in some cases be realistic to consider is that it will substitute other types of biodiesel (e.g. based on other substrates). Ordinary diesel and other types of biodiesel can both be produced without constraints (the answer to the second question of the decision tree in Fig. 9.5 is ‘no’) and can therefore both be considered reasonable alternatives. In this example, however, we will only consider the former. Having found petrochemical diesel as a substitute, we go to Step 4 to identify which technology will produce the diesel, which is substituted. Here, we need to consider the trend in the market, the scope of the decision, and whether the decision leads to an increase or decrease in demand. Having addressed these issues, we find that the substituted diesel is produced by the least cost-efficient technology supplying the market at the time of our decision, which we find to be crude oil produced from tar sand


Biodiesel does not contain the same amount of energy per weight unit as ordinary diesel, implying that we will need more biodiesel than diesel to drive a certain distance. The ratio is around 37:42, implying that for each kg of poultry fat biodiesel we produce and use extra, we will reduce the production and use of diesel made from tar sand by 37/42 kg. The production of biodiesel inevitably leads to the co-production of glycerol. When we decide to increase the production of biodiesel by 200 tonnes, we will also increase the production of glycerol by approximately 20 tonne. As this is a result of our decision to produce more biodiesel, it needs to be included in the assessment. We therefore start again in Step 1 by asking the question: “What happens if we Life Cycle Inventory Analysis


increase the supply of glycerol by 20 tonnes?” Being a supply oriented question, we go directly to Step 3, where we are asked to identify products for which glycerol can serve as a substitute, based on relevant functionality, technical quality, costs, etc. Through analysing the biodiesel market, for example through biodiesel journals and experts in the field, we find that glycerol from biodiesel can be used by producers of chemicals, especially for the production of propylene glycol. Hereby glycerol can, after distillation and processing, substitute other feedstock in the production of propylene glycol. Having identified a substitute, we go to Step 4, to identify the propylene glycol production technology affected by the change in feedstock to glycerol. This procedure (not detailed here) allows us to include the avoided production of propylene glycol in our LCI


This procedure (not detailed here) allows us to include the avoided production of propylene glycol in our LCI. When doing so, it is important to identify the processes needed to convert the crude glycerol to propylene glycol and remember to take into consideration the conversion rate. Having considered both the substitution of diesel with biodiesel and conventional propylene glycol with propylene glycol made from glycerol, we have now considered all the downstream parts of the life cycle. However, our decision to supply more poultry fat biodiesel will also create changes in the upstream part of the life cycle: If we want to supply more poultry fat biodiesel, we need more of the constituents included for producing the biodiesel. The demand for these constituents thereby increases. In the concrete case, biodiesel is made from poultry fat and methanol, which are brought to react using a strong base, often sodium hydroxide


The demand for these constituents thereby increases. In the concrete case, biodiesel is made from poultry fat and methanol, which are brought to react using a strong base, often sodium hydroxide. For the sake of simplicity, we will here only consider the increased demand for poultry fat and methanol. Thus, we return to Step 1 and ask: “What happens if I increase the demand for poultry fat?” As this is clearly a question that relates to demand, we go to Step 2. The first part of the decision tree in Step 2 (Fig. 9.4) asks us to consider whether the production of the product is constrained. In this case, this is actually the case, since poultry fat is a low value by-product from the production of other poultry products, mainly meat. The production of poultry fat therefore follows the demand for poultry meat, and additional demand for poultry fat will not result in an additional supply of poultry fat


The production of poultry fat therefore follows the demand for poultry meat, and additional demand for poultry fat will not result in an additional supply of poultry fat. As the assessed decision will lead to an increase in the demand for poultry fat, and as market analysis shows us that poultry fat is already used to the extent the constraint of being a co-product allows (in other words, no poultry fat is wasted), we go to Step 3, to find out which product can substitute our use of poultry fat. Poultry fat is mainly used in the feed industry and through contacts to feed producers we find that they are able to use palm and soybean oil in a certain relationship instead of poultry fat. This implies that if we decide to produce more biodiesel from poultry fat and thereby demand more poultry fat, we will not increase the supply of poultry fat but rather increase the demand for palm and soybean oil


To identify the consequences of the increased demand for these oils, we go through the relevant Steps 2–3 for each of these, but to keep this example relatively simple, we will not go further into documenting these steps. Assuming that we have now fully outlined the processes that change as a result of our increase in demand for palm and soybean oil, we turn to the other main constituent of biodiesel, namely methanol. As noted above, we also increase the


demand for methanol. We therefore again start in Step 1 by asking the question: “What happens when I increase the demand for methanol?” As this is a demand oriented question, we go to Step 2. Here we are first asked whether methanol can be produced without constraints. As this is the case, we go to Step 4. Here we are asked to consider the overall trend in the market, the scope of the decision in comparison to the overall market for methanol, and whether the decision leads to an increase or decrease in demand. Through market studies we find that the trend in the market, which can be considered global, is an increasing production. Secondly, the size of the decision, which in this case is to produce a few hundred extra tonnes of poultry fat biodiesel will amount to very little compared to the overall market volume for methanol. We should therefore identify the short-term marginal producer


We should therefore identify the short-term marginal producer. Given that our decision leads to an increase in demand, we are told by Table 9.1, that the methanol will be produced by the least competitive producer on the market. As there are more or less only producers making methanol from synthetic gas, we assume that the methanol will be produced using this technology. Other inputs and outputs to and from the biodiesel process are handled in a similar way, but to keep the example relatively short, these will not be discussed here. As the example shows, creating a consequential LCI is in many cases a rather laborious task as detailed knowledge is needed about the markets affected by the decision, as for example establishing knowledge about potential substitutes for poultry fat in the feed industry in the example above. Much of the time spent making the LCA will therefore often be used in preparing the consequential LCI


Much of the time spent making the LCA will therefore often be used in preparing the consequential LCI. This chapter is to a large extent based on the ILCD handbook and the ISO standard 14040 and 14044. Due to the scope of this chapter, some details have been omitted, and some procedures have been rephrased to 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, 1st edn. March 2010. EUR 24708 EN. Luxembourg, Publications Office of the European Union (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) Additional References Used in the Text Birkved, M., Hauschild, M.Z.: PestLCI—a model for estimating field emissions of pesticides in agricultural LCA. Ecol. Model. 198, 433–451 (2006). doi:10.1016/j.ecolmodel.2006.05.035 Life Cycle Inventory Analysis


Ciroth, A.: Refining the pedigree matrix approach in ecoinvent: towards empirical uncertainty factors. Presentation at the LCA Discussion Forum Zürich, 13, September 2013 (2013) EC-JRC: Product Environmental Footprint (PEF) Guide. Deliverable 2 and 4A of the Administrative Arrangement between DG Environment and the Joint Research Centre No N 070307/2009/552517, including Amendment No 1 from December 2010. Ispra, Italy (2012) Goedkoop M, Oele M, Leijting J, et al.: Introduction to LCA with SimaPro. Report version 5.2, January 2016 (c) 2002–2016 Pré. (2016). www.pre-sustainability.com Guinée, J.B., Gorrée, M., Heijungs, R., Huppes, G., Kleijn, R., van Oers, L., Wegener Sleeswijk, A., Suh, S., Udo de Haes, H.A., de Bruijn, H., van Duin, R., Huijbregts, M.A.J.: Handbook on Life Cycle Assessment: Operational Guide to the ISO Standards. Kluwer Academic Publishers, Dordrecht (2002). ISBN 1-4020-0228-9 Heijungs, R., Suh, S.: The computational structure of life cycle assessment


Kluwer Academic Publishers, Dordrecht (2002). ISBN 1-4020-0228-9 Heijungs, R., Suh, S.: The computational structure of life cycle assessment. Kluwer Academic Publishers, Dortrecht (2002) Weidema, B.P.: Market Information in Life Cycle Assessment. Environment Project No. 863, vol. 863, p. 147 (2003) Weidema, B.P., Bauer, C., Hischier, R., et al.: The ecoinvent database: overview and methodology. Data Quality Guideline for the Ecoinvent Database Version 3 (2013). www. ecoinvent.org Wenzel, H., Hauschild, M.Z., Alting, L.: Environmental assessment of products. In: Methodology, Tools and Case Studies in Product Development, vol. 1, 544 pp. Chapman & Hall, Kluwer Academic Publishers, Hingham, MA (ISBN:0 412 80800 5) (1997) World Coal Association: Coal and steel statistics 2012 (2012) World Steel: World steel in figures 2015 (2015) 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. Andreas Moltesen Has been working with LCA since 2006 with a particular focus on social life cycle assessment. He has later worked on life cycle assessments of biofuels and is currently particularly involved with life cycle assessments of transport systems. 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. Mikołaj Owsianiak Involved in development and application of life cycle impact assessment methods in sustainability assessment of technologies


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). 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


Morten Birkved With a background in environmental chemistry and LCA, Morten’s primary research activities are focused on the development of quantification methods for ecosphere-technosphere exchanges, general LCI, scope modelling, assessment of buildings and built environments, system modelling and fused assessment forms. 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. Life Cycle Inventory Analysis


Chapter 10 Life Cycle Impact Assessment Ralph K. Rosenbaum, Michael Z. Hauschild, Anne-Marie Boulay, Peter Fantke, Alexis Laurent, Montserrat Núñez and Marisa Vieira Abstract This chapter is dedicated to the third phase of an LCA study, the Life Cycle Impact Assessment (LCIA) where the life cycle inventory’s information on elementary flows is translated into environmental impact scores. In contrast to the three other LCA phases, LCIA is in practice largely automated by LCA software, but the underlying principles, models and factors should still be well understood by practitioners to ensure the insight that isneeded for a qualifiedinterpretationof the results


This chapter teaches the fundamentals of LCIA and opens the black box of LCIA with its characterisation models and factors to inform the reader about: (1) the main purpose and characteristics of LCIA, (2) the mandatory and optional steps of LCIA according to the ISO standard, and (3) the science and methods underlying the assessment for each environmental impact category. For each impact category, the reader is taken through (a) the underlying environmental problem, (b) the underlying environmental mechanism and its fundamental modelling principles, (c) the main anthropogenic sources causing the problem and (d) the main methods available in LCIA. An annex to this book offers a comprehensive qualitative comparison of the main elements and properties of the most widely used and also the latest LCIA methods for each impact category, to further assist the advanced practitioner to make an informed choice between LCIA methods. R.K. Rosenbaum (&)  M


R.K. Rosenbaum (&)  M. Núñez IRSTEA, UMR ITAP, ELSA Research Group and ELSA-PACT Industrial Chair for Environmental and Social Sustainability Assessment, 361 rue Jean-François Breton, BP 5095, 34196 Montpellier, France e-mail: ralph.rosenbaum@irstea.fr M.Z. Hauschild  P. Fantke  A. Laurent Division for Quantitative Sustainability Assessment, Department of Management Engineering, Technical University of Denmark, Kongens Lyngby, Denmark A.-M. Boulay LIRIDE, Sherbrooke University, Sherbrooke, QC, Canada M. Vieira PRé Consultants bv, Amersfoort, The Netherlands A.-M. Boulay CIRAIG, Polytechnique Montreal, Montreal, QC, Canada © Springer International Publishing AG 2018 M.Z. Hauschild et al. (eds.), Life Cycle Assessment, DOI 10.1007/978-3-319-56475-3_10


Learning Objectives After studying this chapter, the reader should be able to: • Explain and discuss the process and main purposes of the LCIA phase of an LCA study. • Distinguish and explain the mandatory and optional steps according to international standards for LCA. • Differentiate and describe each of the impact categories applied in LCIA regarding: – the underlying environmental problem, – the environmental mechanism and its fundamental modelling principles, – the main anthropogenic sources causing the problem, – the main methods used in LCIA. 10.1 In practice, the Life Cycle Impact Assessment (LCIA) phase is largely automated and essentially requires the practitioner to choose an LCIA method and a few other settings for it via menus and buttons in LCA software


However, as straightforward as that may seem, without understanding a few basic, underlying principles and the meaning of the indicators, neither an informed choice of LCIA method nor a meaningful and robust interpretation of LCA results are possible. However, the important extent of science and its inherent multidisciplinarity frequently result in a perceived opacity of this phase. This chapter intends to open the black box of LCIA with its characterisation models and factors, and to accessibly explain (1) its main purpose and characteristics, (2) the mandatory and optional steps according to ISO and (3) the meaning and handling of each impact category. While this chapter is a pedagogical and focused introduction into the complex and broad aspects of LCIA, a more profound and in-depth description, targeting experienced LCA practitioners and scientists, can be found in Hauschild and Huijbregts (2015)


Once the Life Cycle Inventory (LCI) is established containing all elementary flows relevant for the product system under assessment, the next question to answer will be something like: How to compare 1 g of lead emitted into water to 1 g of CO2 emitted into the air? In other words, how to compare apples with pears? Life Cycle Impact Assessment is a phase of LCA aiming to assess the magnitude of contribution of each elementary flow (i.e. emissions or resource use of a product system) to an impact on the environment. Its objective is to examine the product system from an environmental perspective using impact categories and category indicators in conjunction with the results of the inventory analysis. This will provide information useful in the interpretation phase. As the focal point of this phase of an LCA (and also of this chapter), it is a relevant question to ask what is an environmental impact? It could be defined as a R.K. Rosenbaum et al.


set of environmental changes, positive or negative, due to an anthropogenic intervention. Such impacts are studied and assessed using a wide range of quantitative and qualitative tools, all with specific aims and goals to inform or enable more sustainable decisions. In LCA this is an important phase, as it transforms an elementary flow from the inventory into its potential impacts on the environment. This is necessary since elementary flows are just quantities emitted or used but not directly comparable to each other in terms of the importance of their impact. For example, 1 kg of methane emitted into air does not have the same impact on climate change as 1 kg of CO2, even though their emitted quantities are the same (1 kg) since methane is a much stronger greenhouse gas (GHG)


LCIA characterisation methods essentially model the environmental mechanism that underlies each of the impact categories as a cause–effect chain starting from the environmental intervention (emission or physical interaction) all the way to its impact. However, the results of the LCIA should neither be interpreted as predicted actual environmental effects nor as predicted exceedance of thresholds or safety margins nor as risks to the environment or human health. The results of this LCA phase are scores that represent potential impacts, a concept that is explained further on


The results of this LCA phase are scores that represent potential impacts, a concept that is explained further on. The ISO 14040/14044 standards (ISO 2006a, b) distinguish mandatory and optional steps for the LCIA phase, which will all be explained further in this chapter: Mandatory steps: • Selection of impact categories, category indicators and characterisation models (in practice typically done by choosing an already existing LCIA method) !Which impacts do I need to assess? • Classification (assigning LCI results to impact categories according to their known potential effects, i.e. in practice typically done automatically by LCI databases and LCA software) !Which impact(s) does each LCI result contribute to? • Characterisation (calculating category indicator results quantifying contributions from the inventory flows to the different impact categories, i.e


typically done automatically by LCA software) !How much does each LCI result contribute? Optional steps: • Normalisation (expressing LCIA results relative to those of a reference system) !Is that much? • Weighting (prioritising or assigning weights to the each impact category) !Is it important? • Grouping (aggregating several impact indicator results into a group) As already mentioned, it is important to keep in mind that the impacts that are assessed in the LCIA phase should be interpreted as impact potentials, not as actual impacts, nor as exceeding of thresholds or safety margins, or risk, because they are: Life Cycle Impact Assessment


• Relative expressions of potential impacts associated with the life cycle of a reference flow needed to support a unit of function (=functional unit) • Based on inventory data that are integrated over space and time, and thus often occurring at different locations and over different time horizons • Based on impact assessment data which lack information about the specific conditions of the exposed environment (e.g. the concomitant exposure to substances from other product systems) Terminology and definitions are given in Table 10.1. Table 10.1 Essential terminology and definitions Term Definition Source Area of protection A cluster of category endpoints of recognisable value to society. Examples are human health, natural resources and natural environment


Examples are human health, natural resources and natural environment. Hauschild and Huijbregts (2015) Category indicator Quantifiable representation of an impact category ISO (2006b) Category endpoint Attribute or aspect of natural environment, human health or resources, identifying an environmental issue giving cause for concern ISO (2006b) Characterisation model Reflect the environmental mechanism by describing the relationship between the LCI results, category indicators and, in some cases, category endpoint(s). The characterisation model is used to derive the characterisation factors


The characterisation model is used to derive the characterisation factors. ISO (2006b) Characterisation factor Factor derived from a characterisation model which is applied to convert an assigned life cycle inventory analysis result to the common unit of the category indicator ISO (2006b) Ecosphere The biosphere of the earth, especially when the interaction between the living and non-living components is emphasised Oxford Dictionary of English Elementary flow Material or energy entering the system being studied that has been drawn from the environment without previous human transformation, or material or energy leaving the system being studied that is released into the environment without subsequent human transformation ISO (2006b) Environmental impact Potential impact on the natural environment, human health or the depletion of natural resources, caused by the interventions between the technosphere and the ecosphere as covered by LCA (e.g


emissions, resource extraction, land use) EC-JRC (2010a) Environmental mechanism System of physical, chemical and biological processes for a given impact category, linking the life cycle inventory analysis results to category indicators and to category endpoints ISO (2006b) (continued) R.K. Rosenbaum et al.


10.2 Mandatory Steps According to ISO 14040/14044 10.2.1 Selection of Impact Categories, Category Indicators and Characterisation Models The contents of this section have been modified from Rosenbaum, R.K.: Selection of impact categories, category indicators and characterisation models in goal and scope definition, appearing as Chapter 2 of Curran M. A. (ed.) LCA Compendium —The Complete World of Life Cycle Assessment—Goal and scope definition in Life Cycle Assessment pp 63–122, Springer, Dordrecht (2017). The objective of selecting impact categories, category indicators and characterisation models is to find the most useful and needed ones for a given goal


The objective of selecting impact categories, category indicators and characterisation models is to find the most useful and needed ones for a given goal. To help guide the collection of information on the relevant elementary flows in the inventory analysis, the selection of impact categories must be in accordance with the goal of the study and is done in the scope definition phase prior to the collection of inventory data to ensure that the latter is targeted towards what is to be assessed in the end (see Chaps. 7 and 8 on Goal and Scope definition). A frequent difficulty is the determination of the criteria that define what is useful and needed in the context of the study. Some criteria are given by ISO 14044 (2006b), either as requirements or as recommendations. The requirements are obligatory for compliance with the ISO standard, and will therefore be among the focus points of a Critical Review (see Chap. 13 on Critical Review)


The requirements are obligatory for compliance with the ISO standard, and will therefore be among the focus points of a Critical Review (see Chap. 13 on Critical Review). Some of these requirements and recommendations concern LCA practitioners and LCIA method developers alike, while others are most relevant for developers of LCIA methods and of LCA software. The focus is here on the former, i.e. requirements concerning LCA practitioners. Table 10.1 (continued) Term Definition Source Environmental relevance Degree of linkage between category indicator result and category endpoints ISO (2006b) Impact category Class representing environmental issues of concern to which life cycle inventory analysis results may be assigned ISO (2006b) Impact pathway Cause–effect chain of an environmental mechanism LCIA method Collection of individual characterisation models (each addressing their separate impact category) Hauschild et al


(2013) Midpoint indicator Impact category indicator located somewhere along the impact pathway between emission and category endpoint Hauschild and Huijbregts (2015) Potential impact Relative performance indicators which can be the basis of comparisons and optimisation of the system or product Hauschild and Huijbregts (2015) Technosphere The sphere or realm of human technological activity; the technologically modified environment Oxford Dictionary of English Life Cycle Impact Assessment


ISO 14044 (2006b) states that the choice of impact categories needs to assure that they • Are not redundant and do not lead to double counting • Do not disguise significant impacts • Are complete • Allow traceability Furthermore, this list is complemented with a number of obligatory criteria, requiring that the selection of impact categories, category indicators and characterisation models shall be: • Consistent with the goal and scope of the study (when, for example, environmental sustainability assessment is the goal of a study, the practitioner cannot choose a limited set of indicators, or a single indicator footprint approach, as this would be inconsistent with the sustainability objective of avoiding burden-shifting among impact categories) • Justified in the study report • Comprehensive regarding environmental issues related to the product system under study (essentially meaning that all environmental issues—represented by the various impact categories—which a product system may


regarding environmental issues related to the product system under study (essentially meaning that all environmental issues—represented by the various impact categories—which a product system may affect need to be included, again in order to reveal any problem-shifting from one impact category to another) • Well documented with all information and sources being referenced (in practice it is normally sufficient to provide name and version number of the LCIA method used together with at least one main reference, which should provide all primary references used to build the method) ISO 14044 (2006b) recommendations for the selection of impact categories, category indicators and characterisation models by a practitioner include: • International acceptance of impact categories, category indicators and characterisation models, i.e


based on an international agreement or approved by a competent international body • Minimisation of value-choices and assumptions made during the selection of impact categories, category indicators and characterisation models • Scientific and technical validity of the characterisation model for each category indicator (e.g. not based on unpublished or outdated material) • Being based upon a distinct, identifiable environmental mechanism and reproducible empirical observation • Environmental relevance of category indicators Numerous further criteria but also practical constraints beyond ISO 14044 exist and are applied, consciously or unconsciously, often based on experience or recommendations from colleagues. In practice the selection of impact categories, category indicators and characterisation models usually boils down to selecting an R.K. Rosenbaum et al.


LCIA method (or several) available in the version of the LCA software that the practitioner has access to. External factors for this choice will be among other: • Requirements following from the defined goal (see Chap. 7) and specified in the scope definition of the LCA (see Chap. 8) • Requirements by the commissioner of an LCA • Fixed requirements, e.g. for Environmental Product Declarations (EPDs) or Product Environmental Footprints (PEFs) from underlying sector-based Product Category Rules (PCRs) or from labelling schemes (see Chap


24) Practical constraints may, for example, consist of: • Availability, completeness and quality of LCI results required for a specific impact category • Availability, completeness and quality of characterisation models and factors for a specific impact category, including the need to consider specific rare or new impact categories, such as noise, which may only be supported by one or two LCIA methods if at all • If normalisation is required, availability, completeness and quality of normalisation factors for a specific impact category or LCIA method If practical constraints prevent the practitioner from including what has been identified as relevant impact categories, this needs to be made clear in the discussion and interpretation of the LCA results and comments need to be made on whether it may change the conclusions. In the illustrative case on window frames in Chap