XVI.e.ii) If using non-generic LCIA methods upon justification as indicated more above. This shall result exclusively in a differentiation of the already selected, generic LCIA methods, unless a best attainable consensus can be found among involved stakeholders on selection of another set of already available non-generic LCIA methods. The normalisation basis and weighting set shall remain unchanged. 98 This is not required for use of non-generic LCIA methods and for additionally included single elementary flows / characterisation factors, unless this would relevantly change the results, what by default can be assumed to be not the case.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how 6.8 Representativeness and appropriateness of LCI data (Refers to aspects of ISO 14040 chapter 4.2.3.6.2) 6.8.1 Introduction and overview (Refers to aspect of ISO 14044:2006 chapter 4.2.3.6.2) Introduction LCI data quality can be structured by representativeness (composed of technological, geographical, and time-related), completeness (regarding impact category coverage in the inventory), precision / uncertainty (of the collected or modelled inventory data), and methodological appropriateness and consistency. For details and illustrative graphics see annex 12 on data quality aspects and indicators. Within the concept of “Data quality”, the representativeness of the LCI data is a key component

European Commission – JRC, ILCD Handbook: General Guide for LCA (2010)


For details and illustrative graphics see annex 12 on data quality aspects and indicators. Within the concept of “Data quality”, the representativeness of the LCI data is a key component. It is the aim of LCA to reflect the existing physical reality of an existing supply chain (attributional modelling) or the forecasted physical reality of a theoretical future supply chain steered by market mechanisms in consequence of analysed decisions (consequential modelling). This means that the life cycle models are to be in accordance with what actually happens or can be expected to happen, to the extent possible. On a system's level, the inventory data must be representative of the processes, which actually relate to the life cycle of the system (e.g. product). Representativeness and appropriateness The ability of the inventory data to represent the environmental impacts of a system can be differentiated into two closely related aspects: representativeness and appropriateness99


The first aspect, the representativeness, addresses how well the collected inventory data represents the “true” inventory of the process for which they are collected regarding technology, geography and time. E.g. may some of the flow information be taken from similar processes, older data sources, another country, be estimated or missing, etc.; such data lacks representativeness to some degree. The second aspect, the appropriateness refers to the degree to which a process data set that is used in the system model actually represents the true process of the analysed system. E.g. when Danish office paper of 2005 is required as input to an analysed system, a process data set for “Danish newsprint paper of 2006” is fully appropriate regarding geography while limited in technical and somewhat limited in timerelated appropriateness. In system models, the data has to be both sufficiently representative and appropriate


In system models, the data has to be both sufficiently representative and appropriate. There is hence the representativeness of a unit process data set inventory for the represented process(es) and the appropriateness of a (unit process, LCI result, etc.) data set for a required function / product on the system level. Combined, this results in the overall representativeness of the LCI result inventory for the analysed system. Overview Representativeness is classically looked at from technological (chapter 6.8.2), geographical (chapter 6.8.3) and time-related perspective (chapter 6.8.4), while these three are closely interrelated. 99 Note that here, same as in common LCA practice, both aspects are also jointly covered by the term “representativeness”.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how It depends on the specific case, which aspect is most important: Data with good geographical and technological representativeness can be more appropriate in some instances than using the most recent data (time-related representativeness). This is to be identified for the given case: how different is the process inventory for the different geographical situation and different technology and how fast does it change with the years due to technological progress and changes in the background system? In general, it can be found - same as across all data quality aspects - that the weakest of the appropriateness components determines (i.e. lowers) the overall quality. Note that in attributional and consequential modelling representativeness refers to different technologies (and sometimes geography), as to be explained


lowers) the overall quality. Note that in attributional and consequential modelling representativeness refers to different technologies (and sometimes geography), as to be explained. 6.8.2 Technological representativeness (Refers to aspect of ISO 14044:2006 chapter 4.2.3.6.2) Technological representativeness of process and product Technological representativeness relates to two interlinked aspects: the process step (i.e. the activity) and its product (i.e. the result of the activity that represents the functional unit of the process) that are both to be explicitly considered. Terms and concepts: Technological representativeness of process and product Introduction The technological representativeness of a process or system identifies how well the inventory data represents it regarding its true technological or technical characteristics that are documented in the descriptive information of the data set or report


Aspects of technological representativeness of a process The specific technology that is used and the way in which it is operated, strongly influences the environmental impacts of the process, as to be expressed in its inventory. This applies to both the inputs (e.g. consumed energy, materials, used services) as well as the outputs (e.g. the process-specific emissions) that can differ considerably among technologies producing the same e.g. product. This is especially the case for highly variable processes. (For the difference between variance and variability - see annex 12.2 under "Variance vs


product. This is especially the case for highly variable processes. (For the difference between variance and variability - see annex 12.2 under "Variance vs. variability".) The number of aspects that can for the given case be decisive for the inventory is very extensive: the raw material basis, route of synthesis, the intermediaries used, the nature of the process in terms of enclosure, abatement techniques, etc., the speed of services, the load factor and other parameters of highly variable processes such as waste treatment and transport, internal recovery rates, etc. Aspects of technological representativeness of the product The specific product that is to be represented by a data set or used in another system as input (e.g. a specific type of steel, a distinct kind of service) can differ in many technological and other aspects, i.e. its specifications. The applicability of the product for a specific purpose (i.e


a specific type of steel, a distinct kind of service) can differ in many technological and other aspects, i.e. its specifications. The applicability of the product for a specific purpose (i.e. the appropriateness of its functional unit) differs typically very much among often only seemingly similar products. At the same time the environmental impact can differ hugely (as the case e.g. between technical quality silicon and chip-grade silicon). It appears not useful to try listing all potentially relevant aspects. All quantitative and qualitative aspects of the functional unit and specification can matter for a given case. Often forgotten in practice are the following with the list by no means being exhaustive: purity of


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how material (e.g. technical quality silicon vs. chip-grade silicon), processing depths of materials (e.g. metal bar vs. metal foil), specific treatments (e.g. surface treatment, coating, etc.), durability / life time, quality of a service, the actual composition of a material (e.g. a polymer recipe with not only the main resin but also with fillers, colouring agents, stabilisers, curing agents, etc.), the specifically achieved recyclability of a product compared to average recyclability of the contained material(s), etc. Furthermore also the specific process or mix of processes and routes that has produced the product in question (e.g. the product hydrogen either from steam conversion of natural gas, or from geothermal energy, or solar electricity,...). Hence the combined representativeness of product and process(es) needs additional attention


Hence the combined representativeness of product and process(es) needs additional attention. Background system data for consequential vs. attributional models The technological representativeness of the different activities throughout the life cycle of the analysed system is a key feature of a valid LCA. This applies to both attributional and consequential modelling alike. Note however that these two modelling principles may require substantially different processes in the background system that are to be represented by the used LCI data sets: For attributional modelling, technology-specific data of the supply-chain should be foreseen for the foreground system and average market consumption100 mix data for the background system. These are ideally the primary data and the secondary data of the suppliers and of the downstream users (e.g. further processors, use stage, recyclers) of the e.g. product, if the system covers the full life cycle. Secondary data from e.g


further processors, use stage, recyclers) of the e.g. product, if the system covers the full life cycle. Secondary data from e.g. third-party database providers, being specific, generic or average data can be used also in parts of the foreground system. This should be done only if for the given case those data is more accurate, precise, and complete. This can be if primary data and suppliers' data is of little completeness or representativeness (e.g. regarding operating conditions). Note that this can be checked only in the subsequent iterative steps of the LCI work, of course. For consequential modelling, the same data should be foreseen for the foreground system. Here this should include the suppliers' technology-specific data of the contractually fixed or planned supply-chain links to the foreground system. The appropriate short-term or long-term marginal technological mixes (see chapter 7.2.4) should be foreseen to be used for the background system


The appropriate short-term or long-term marginal technological mixes (see chapter 7.2.4) should be foreseen to be used for the background system. The named long-term mix only applies to those processes under Situation B that face "big" changes in consequence of the analysed decision, and - optional - for the assumption scenarios. The technology mix of marginal processes shall be foreseen to be identified depending among others on the market direction and the cost-competitiveness of potential marginal processes. The identification of the short-term and long-term mixes is not straightforward and needs the introduction of the related concepts first. For the detailed provisions, please see chapter 7.2.4.4. 100 That also applies if a market production mix data set is developed (e.g


For the detailed provisions, please see chapter 7.2.4.4. 100 That also applies if a market production mix data set is developed (e.g. "PP granulate produced in Germany in 2005": the fact that the data set is to represent the production mix would be achieved by combining the representative mix of producing technologies (and sites) of that market according to their production share. For the data in the background system of the individual routes nevertheless the respective consumption mix data are to be used (e.g. here: crude oil mix consumed in Germany for propylene production).


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how Average vs. specific mode of operation, cycle step, etc. Check along the goal of the study and the intended applications whether the data needs to represent a specific way of operation or aspect of the technology / technique (e.g. a specific load factor for transport, or a specific start, closure etc. cycle step of a process, etc.).. This can be if the goal requires deviating from the average, typical or integrated technology / technique operation. This aspect closely relates to the explanations in the "terms and concepts" box above. Non-scalable processes / systems in Situation A and B Where attributional modelling is to be used for the main system model in Situations A and B, a specific provision requires to use consequential aspects regarding the technological representativeness: if the process / system is not scalable (e.g


hydropower production in many countries) the average market mix of technologies (here: for electricity production) is to be used and not the specific supplier / technology (here: for hydropower). This is unless the user of such a not freely scalable process can demonstrate that the production is actually quantitatively increased in consequence of its specific demand, what can also be via import from not limited supply. If such an identified actual increase is only meeting a part of the demand, only that share can be modelled using the specific process data and for the remainder the market mix is to be used. This is necessary as under these conditions big differences often occur between attributional and consequential processes that at the same time can be systematically and reproducibly avoided by using the market mix instead. Figure 16 illustrates this situation of limited/non scalability. Figure 16 Limited or non-scalability of supplies in a market


Figure 16 illustrates this situation of limited/non scalability. Figure 16 Limited or non-scalability of supplies in a market. The example of hydropower; illustrative. If the used amount of hydropower is equal or close to the usable amount, an additional demand in hydropower can be assumed to not result in more hydropower being produced; Hydropower is not relevantly up-scalable in that case. The usable amount can be restricted by other than technical factors. Here this might be nature protection or legislative restrictions. If such factors are changed and more strict this can result in an absolute nonscalability, as the used amount would be "frozen" by these other factors or might even be stepwise reduced. In the example of electricity procurement, a consequential modelling would require the use of the mix of marginal technologies to be used. If – as in the example of hydropower – the specific procured technology hydropower is not scalable in production (as e.g


If – as in the example of hydropower – the specific procured technology hydropower is not scalable in production (as e.g. in Germany), the consequential demand for electricity is not resulting in additional hydropower installed but this is only resulting in a virtual shifting of electrons from the electricity market Technical potential (100%) Usable Used


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how mix to the specific supplier. Using hydropower data would substantially change the results, while not being justified in the decision-making context of Situations A and B. Provisions: 6.8.2 Technological representativeness Applicable to Situation A, B, and C, differentiated. Differentiated for attributional and consequential modelling. Fully applicable for LCI results, LCIA results, and LCA studies. For unit processes only required to complete the system model for quality control. Note that these provisions will be applied only in the LCI phase. I) SHALL - Good technological representativeness: The overall inventory data shall have an as good as required technological representativeness, meeting the goal requirements of the study


I) SHALL - Good technological representativeness: The overall inventory data shall have an as good as required technological representativeness, meeting the goal requirements of the study. (See also the accuracy requirements identified in chapter 6.9.2; note that technological, geographical and time-related representativeness are closely interrelated). For both analysed processes and systems, this includes all quantitative and qualitative aspects of the functional unit(s) and/or reference flow(s), and/or technical specification(s). This applies especially for those aspects, that matter in terms of leading to relevant differences in the LCI data. II) SHALL - Specific way or mode of process?: Identify along the goal of the study and especially the intended applications whether the data needs to represent a specific way or mode of operating the technology / technique (e.g. a specific load factor for transport, or a specific start, closure etc


a specific load factor for transport, or a specific start, closure etc. cycle step of a process, etc.), if this differs from the average, typical or integrated operation. [ISO+] III) SHALL - Different technologies for attributional and consequential modelling: Note that attributional and consequential modelling often require very different processes (and to some degree also systems) for the background system. But see the simplifications set for all Situations, except for the processes that face "big" changes in Situation B (chapter 6.5.4): [ISO!] III.a) Attributional modelling: It should be used: III.a.i) Foreground system: Technology-specific primary data for the foreground system and for the specifications of the products and wastes that connect the foreground system with the background system. Secondary data of the actual suppliers / downstream actors should be preferred to other (third-party) secondary data


Secondary data of the actual suppliers / downstream actors should be preferred to other (third-party) secondary data. Technology-specific, generic or average data from third-parties should be used in those parts of the foreground system where this for the given case is of higher quality (i.e. more accurate, precise, complete) than available technology-specific primary or secondary data from suppliers / downstream actors. III.a.ii) Background system: Average technology as market consumption101 mix data should be used. 101 This also applies if a market production mix data set is developed: the fact that the data set is to represent the production mix would be achieved by combining the representative mix of producing technologies of that market according to their production share. For the data in the background system of the individual routes nevertheless the respective consumption mix data are to be used.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how Provisions: 6.8.2 Technological representativeness III.b) Consequential modelling: It should be used: III.b.i) Foreground system: The same applies as described above for attributional modelling. Here this includes the suppliers' / downstream actors' technology-specific secondary data of the contractually fixed or planned supply-chain. III.b.ii) Background system: The short-term or long-term marginal technology mixes should be used, as appropriate for the applicable Situation A, B, C1, and C2. Among these, the named long-term technology mix only applies to those processes under Situation B that face "big" changes in consequence of the analysed decision, and - optionally - to the assumption scenarios


The technology mix of marginal processes should be identified, depending among others on the market conditions and the cost-competitiveness of the potential marginal processes. The detailed provisions and terms / concepts are given in chapter 7.2.4. III.c) Using not fully representative data: For both attributional and consequential modelling, not fully technologically representative data can be used only along the following conditions: III.c.i) For LCI and LCIA data sets / non-comparative LCI/LCA studies: The use of not fully technologically representative data is justifiable only if this is not relevantly changing the overall LCIA results compared to using fully representative data; otherwise the lower achieved representativeness shall be documented in the data set / report. For data provided for a competitor's product, lower representativeness shall not lead to higher overall environmental impacts of the LCIA results calculated for that product


For data provided for a competitor's product, lower representativeness shall not lead to higher overall environmental impacts of the LCIA results calculated for that product. For data provided for own products or for products without any competition situation (e.g. generic data from consultants or research projects for general background use), lower representativeness shall not lead to lower impacts of the overall LCIA results calculated for that product. III.c.ii) For comparative LCA studies: The conclusions or recommendations of the study should not be affected, as far as possible. Otherwise the lower achieved technological representativeness shall explicitly be considered when drawing conclusions and giving recommendations. Especially shall the use of less representative data not relatively disfavour any competitors' products to a relevant degree. Note that this can be implemented only in the subsequent iterative steps of the LCA work


Note that this can be implemented only in the subsequent iterative steps of the LCA work. IV) SHALL - Non-scalable supplies: For the life cycle model of Situation A, B, and C1, the following shall be applied: if the supply of a specific required function (e.g. product) cannot relevantly be increased in the analysed market and due to inherent constraints (e.g. as for hydropower in many countries) the market consumption mix of the specific function that the product provides (e.g. electricity in the above example) shall be used as far as possible, and not the data for the specific supplier/product. To not contradict the provisions on solving multifunctionality, this provision does not apply to required cofunctions.[ISO!]


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how 6.8.3 Geographical representativeness (Refers to aspect of ISO 14044:2006 chapter 4.2.3.6.2) Introduction The geographical representativeness of a process or system identifies how well the inventory data represents it regarding the location (e.g. market, site(s), region, country, etc.) that is documented in the descriptive information of the data set or report and where it is operated, produced, or consumed. Identifying the appropriate geographical scope of LCI data The level and type of technology that is applied, and the conditions under which it runs (e.g. in terms of surrounding climate or national legal requirements on emission limits), are influenced by the geographical location of the process


in terms of surrounding climate or national legal requirements on emission limits), are influenced by the geographical location of the process. Also to identify the mix of marginal processes (for consequential modelling) and the background data (for both attributional and consequential modelling) a correct identification of the geographical scope is required. Apart from those processes where site or producer specific data is required, the data typically relates to a market. The below box briefly introduces the market concept: Terms and concepts: Market Market delimitation In difference to other geographical concepts such as countries or regions, markets often have a different delimitation. The market in its sense for LCA is the unit that allows buyers and sellers to exchange any type of goods and services


The market in its sense for LCA is the unit that allows buyers and sellers to exchange any type of goods and services. Markets can be usefully differentiated - geographically, - temporally, and - in customer segments (for the related concept "niche market" and its limitations in interpretation of related LCA study results see chapter 5.2.2). The geographical scope is typically not exact as imports and exports occur across the market border. A useful delimitation is that no relevant amounts of such occur, respectively that imports and exports are considered when modelling e.g. consumption mixes for a given market. Reasons for the forming of markets are mainly - political (legislation especially on competition and product requirements such as material bans, product safety, etc., technical and other standards, taxes, and subsidies), and - cultural (recognised markets by producers and service providers)


- natural geographical aspects play a role when de facto isolating markets via barriers to transport (islands, large distances in general especially for low value/weight goods and for services that require human presence) and climate aspects of related products. Markets can geographically be equal, smaller or larger than a country. Temporal market segmentation is relevant for many services but also certain goods (e.g. intraday segmentation such as night time / base load electricity consumption, seasonal segmentation such as agricultural products and tourist industry). Also the temporal segmentation is not always exact, as some aspects can be overcome via storage and transport (e.g. of fruits from the tropics to the moderate climate zones in the cold season, or solar power storage e.g. as hydrogen).


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how Types of market related data set types In LCA the main market related data set types of relevance are the market production mix, market supply mix, and the market consumption mix. The related text and figure in chapter 7.7 explain their relationship. Average or generic data that represent the consumption mix are the most widely required ones, while production mix data can be of interest for associations and for countries. For niche markets see chapter 5.2.2 The geographical coverage of the LCI data should represent the smallest, appropriate geographical unit, depending on the goal of the LCI/LCA study and the intended applications. If e.g


If e.g. the use of an energy-using consumer product in France would be the scope of the data set, the corresponding electricity market consumption mix (which is not automatically France102) and French product use conditions were to be considered, i.e. not European or Global average conditions. Generally, the degree of geographical or supplier-data differentiation, is to be decided considering the decision relevance and knowledge of decision makers about the market or specific supplier. In attributional modelling this can be e.g. whether a material consumed in Malaysia has a specific producer that would need to be represented. Or whether it is from an unknown origin such as e.g. electrolytic raw copper which in Europe is usually traded on the London Metal Exchange with an European average origin. In the latter case, an average European consumption market data set would be most appropriate to apply for all European countries, as there are de facto no national markets


In the latter case, an average European consumption market data set would be most appropriate to apply for all European countries, as there are de facto no national markets. In consequential modelling that would be the (short-term or long-term) marginal consumption market mix that would be operated as a consequence of a decision. This means that even if e.g. a material is currently predominantly produced for the national market, consequential modelling may identify one or more other countries as source if the growing demand is met by additional imports. Transfer of inventory data from a different geographical context The use of data from one geographical area or specific supplier to another one is appropriate only if the differences in the environmental impacts have no or little relevance for the overall representativeness of the inventory


This is only given if applied technologies of that process, the way it is operated, abatement technologies, as well as the background system of that process (e.g. the raw material route, waste treatment, etc.) are very similar or at least result in very similar inventory values. An example is when the use of a technology-specific Thai unit process data for a certain waste water treatment process with the same treatment efficiency but operated in another country in e.g. Vietnam would result in only insignificantly different inventories of the overall system (e.g. cloth washing) in which the waste water treatment process is used. Another example is the case that the production inventory of a consumer product may differ only 102 Electricity markets are relatively difficult to delimit, given the internationally connected grids. In addition and related to the time-representativeness, it matters whether the named consumer good would be operated only at peak hours (e.g


In addition and related to the time-representativeness, it matters whether the named consumer good would be operated only at peak hours (e.g. an electric toothbrush) or continuously (e.g. a fridge) or only during night time at base load (e.g. an electric storage heater). These latter aspect is to be considered obviously under time-related representativeness (see related chapter).


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how insignificantly among different African countries, because they all imported from the same producing country, e.g. Japan103. The above illustrates that next to the transferred unit process or LCI result data also process parameter settings are to reflect the correct geographical scope. This includes also management parameters such as e.g. achieved recycling rates. Frequent errors: Use of LCI data with another geographical scope A frequent error in LCI/LCA studies is that data that represents one country is directly used for another country. Or that only limited adjustments are done (e.g. replacing only the electricity background data) without analysing which other adjustments may actually be relevant. Different markets and countries differ not only e.g. in the mix of energy carriers used (e.g


Different markets and countries differ not only e.g. in the mix of energy carriers used (e.g. share hard coal, natural gas, nuclear power, etc.), but also the technologies how these energy carriers are converted to e.g. electricity, the way how these technologies are operated, the installed and operation of abatement technologies (if any), the sources / routes of the e.g. energy carriers and many others. What may look similar on a more general level is in fact often substantially different. It is to be stressed that using not sufficiently representative data renders the whole LCI/LCA study invalid and misleading. While in practice limitations in data availability often require such transfer / adjustment of data, this is only valid and useful if the resulting data actually represents what it intends / claims to represent. A related risk is that the data that is used for another market may already from the beginning not be complete, i.e


A related risk is that the data that is used for another market may already from the beginning not be complete, i.e. it may even mislead focus for own data collection. An in-depths technical understanding of the to-be-represented processes is hence key also for any transfer and adjustment of data from other markets. Finally, If the differences and hence the main inventory values are known (what was argued to be indispensible in any case), there is little need to use data from other markets, except for rough cross-checking. With an enlarged pool of consistent and quality-assured LCI data sets in the ILCD Data Network, the availability of consistent data should stepwise and substantially be improved over time and the need to use less representative data be minimised. Relationship geography of LCI and of LCIA While the above relates to the general geographical scope of where processes are operated, the inventory items typically need a different differentiation (e.g


in which environmental media an emission goes). The default compartments are given in the separate document "Nomenclature and other conventions" and are implemented in the ILCD reference elementary flows. Also the environmental issues of concern for the activity can vary with the geographical setting: the relevance of the use of e.g. water and construction materials that are typically extracted and used at a local to regional scale, is thus highly variable between regions. The impact assessment and interpretation may have to take this into account. This requires that elementary flows that act differently depending on where they are emitted (e.g. sulphur dioxide and particle emissions, while not carbon dioxide) would need to be reported spatially differentiated. This would allow the use of impact assessment methods with characterisation factors on e.g. resource-depletion that reflect the spatial differentiation


This would allow the use of impact assessment methods with characterisation factors on e.g. resource-depletion that reflect the spatial differentiation. However, only limitedly spatially differentiated impact assessment models are available (e.g. differentiating emissions to fresh water and sea water). Until models and factors have 103 In that case the Japanese production or export data is correct as it represents the consumption market mix of all these countries.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how been developed and tested in practice also for further sub-compartments and emissionsituations or even location-specific, the use of spatially differentiated elementary flows directly in the data set inventories is discouraged. For the time being, the spatial information should be kept and documented separately (e.g. as second inventory set) to be able to adjust data and data sets later, if needed. This applies analogously to time-specific models, methods, and factors. Please also note that the exact degree and way of spatial differentiation is still to be determined in LCIA context, i.e. whether to divide by national boundaries (countries), natural geographical units or sub-units (continents and landscape zones), sub-compartments of the environment (e.g. different types of water bodies), emission situations (e.g


different types of water bodies), emission situations (e.g. in areas with high or low human population density), or by geographical coordinates via a global impact grid, etc. This will need to be closely coordinated with data availability especially in industry, LCI modelling needs, review questions, and software and database management implications. Provisions: 6.8.3 Geographical representativeness Applicable to Situation A, B, and C, differentiated. Differentiated for attributional and consequential modelling. Fully applicable for LCI results, LCIA results, and LCA studies. For unit processes only required to complete the system model for quality control. For LCI results, LCIA results, LCA studies: be aware that the declared geographical scope of all later to be used inventory data needs to enable a correct impact assessment. This is to be checked especially carefully if a nongeneric impact assessment (e.g


This is to be checked especially carefully if a nongeneric impact assessment (e.g. with differentiated characterisation factors by country, region or even site) is applied. Note that these provisions will be applied only in the LCI phase. I) SHALL - Good geographical representativeness: The overall inventory data shall have an as good as required geographical representativeness, according to the goal of the study (see the accuracy requirements identified in chapter 6.9.2). This applies especially, where this matters in terms of relevant differences in the LCI data of different geographical scope. II) SHALL - Different geographical scope for attributional and consequential modelling: Note that attributional and consequential modelling may require processes/products of a different geographical scope in the background system


But see the simplifications set for all Situations, except for the processes that face "big" changes in Situation B (chapter 6.5.4): [ISO!] II.a) Attributional modelling: It should be used: II.a.i) Foreground system: Site or producer/provider specific data for the foreground system, supplier-specific data for the products that connect the foreground with the background system. Generic data of geographical mixes can be used also in parts of the foreground system if for the given case justified as being more accurate, precise, and complete than available specific data (especially for processes operated at suppliers). II.a.ii) Background system: Average market consumption mix data for the background system. II.b) Consequential modelling: It should be used: II.b.i) Foreground system: Site or producer/provider specific data for the


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how directly controlled processes of the foreground system, suppliers' site specific data of the contractually fixed or planned supply-chain of the foreground system plus for the products and wastes that connect the foreground with the background system. Generic data of geographical mixes can be used also in parts of the foreground system if for the given case justified as being more accurate, precise, and complete than available specific data (especially for processes operated at suppliers). II.b.ii) Background system: The short-term or long-term marginal geographical mixes should be used for the background system, as appropriate for the applicable Situation A, B, C1, and C2. The geographical mix of the marginal processes should be identified, depending among others on the market conditions and cost-competitiveness of the potential marginal processes


The geographical mix of the marginal processes should be identified, depending among others on the market conditions and cost-competitiveness of the potential marginal processes. The detailed provisions and terms/concepts are given in chapter 7.2.4; but check for the simplified provisions for the applicable Situation A, B or C in chapter 6.5.4. II.c) Using not fully representative data: For both attributional and consequential modelling, not fully geographically representative data can be used only along the following conditions: II.c.i) For LCI and LCIA data sets / non-comparative LCI/LCA studies: The use of not fully geographically representative data is justifiable only if this is not relevantly changing the overall LCIA results compared to using fully representative data; otherwise the lower achieved representativeness shall be documented in the data set / report


II.c.ii) For comparative LCA studies: The conclusions or recommendations of the study should not be affected; otherwise the lower achieved geographical representativeness shall explicitly be considered when drawing conclusions and giving recommendations. Especially shall the use of less representative data not relatively disfavour any competitors' products in a relevant degree. 6.8.4 Time-related representativeness (Refers to aspect of ISO 14044:2006 chapter 4.2.3.6.2 and 4.3.2.1) Introduction and overview Technology changes over time. What has been best available technology 10 years ago may today be the average technology, or even already outdated in sectors of rapid technological progress (e.g. IT, solar-electric systems, etc.). The average technology from 10 years ago may already be decommissioned or only contribute a small share to the current market mix, except for sectors with long-running production plants (e.g. for many basic materials, power plants, etc.)


for many basic materials, power plants, etc.). Thus, the temporal representativeness is closely linked to technological representativeness. The inventory of a process or system that is to represent a certain time context (e.g. present or near future situation, “2025”, or for a baseline scenario for accounting “1990”) is to be based on data that sufficiently appropriately represents that declared time. That is especially important for the quantitatively most relevant contributors to the overall environmental impact. Note that the time representativeness of the data to be used should also be in line with the intended application.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how The represented year, especially on system level The represented year of a system data set cannot always be determined straightforward: the single data values already for unit processes may stem from different sources and years. On a higher level, the unit process data sets that are combined in a system often represent different years. Which year a data set represents, is to be determined by looking at the different ages of the main contributing data (in case of unit processes) or unit processes (in case of LCI results). Weighing their contribution and age and reflecting the speed of changes of the different technologies / techniques over time the best represented year can be given by expert judgement. Figure 27 illustrates this concept


Figure 27 illustrates this concept. Frequent errors: Misleading/wrong use of "time representativeness" It is important to note that the time representativeness always refers to the actual time represented and determined e.g. by measurement, NOT the time when a used secondary data source had been published or the modelling / calculation year of the unit process or LCI results. It is a frequent error in LCA to confuse this fundamentally different age information, including when declaring in a misleading way the claimed time-representativeness of distributed LCI data sets. Reflecting on what has been said before on the speed of technology development in different industries, data of several years age, may still be sufficiently representative. Data sets should therefore show an “expiry year” after which they can be assumed to be not sufficiently representative any more and typically will need revision


Data sets should therefore show an “expiry year” after which they can be assumed to be not sufficiently representative any more and typically will need revision. If such data is nevertheless used in a study, the data have a lower than declared timerepresentativeness and it is to be judged how strongly this effects results, conclusions, and recommendations. Intra-annual and intra-day variations Another time-aspect, which may need to be considered in special cases, is the difference of inventory data in the course of the year (especially hot and cold season) and the day (daytime / night). It is to be checked along the goal of the study whether such intra-annual or intra-day specific data might be needed (e.g. on night-time electricity base-load data for charging electric car batteries over night). Intended application and required time-representativeness The need for time-related representativeness is very much influenced by the intended application and its requirements on e.g


Intended application and required time-representativeness The need for time-related representativeness is very much influenced by the intended application and its requirements on e.g. future validity of the results and conclusions that can be drawn from the LCA results: e.g. for studies in support of procurement and especially of products with a short life time, the use of data with a 1 year validity may be fully sufficient. Ecolabel criteria are typically revised regularly (e.g. every three years), and the need for future validity of data sets used to support the identification and quantification of the criteria is thus confined to this time horizon. Decisions made in the ecodesign of long-living products may be valid for 10 years. In the extreme, LCAs made to support decisions on choice of products with a long life-time (e.g


Decisions made in the ecodesign of long-living products may be valid for 10 years. In the extreme, LCAs made to support decisions on choice of products with a long life-time (e.g. production plants, houses) or answering strategic questions may be required to provide conclusions and recommendations that strive at being valid for 20 to 30 years into the future. This points to the need in such cases to use futurerelated foreground scenarios and background data for the use / operation and end-of-life stages of these systems rather then present/recent ones.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how Time-representativeness of future and past processes Many studies of high relevance - e.g. those under Situation B - relate to the future. Also the processes along the life cycles of long-living products and e.g. interventions from landfills under Situation A relate to different time-horizons, including the long-term future. The following general provision applies for all future and past processes104: Data should be as time-representative as possible and any lack of representativeness shall be documented and considered in the results interpretation. Limited time-representativeness in comparative studies shall not relatively disfavour any competitors' products. This can be operationalised as follows:  Processes operated within 5 years into the future or past: - The most recent data that is still valid for that to-be-represented time should be used


This can be operationalised as follows:  Processes operated within 5 years into the future or past: - The most recent data that is still valid for that to-be-represented time should be used. In the case the data is already outdated (e.g. is not sufficiently valid anymore for the year of recycling), new data should be collected or obtained.  Processes operated more than 5 years into the future or past - Fully time-representative data, i.e. forecasting data (or, for processes in the more remote past: historical data) should be used. - As second option, and especially for attributional modelling, the mix of the Best Available Technologies (BAT)105 should be used


- As second option, and especially for attributional modelling, the mix of the Best Available Technologies (BAT)105 should be used. - As third option the present / most recent available data can be used, along the following conditions: ° The use of less time-representative data should be justifiable only if not changing relevantly the LCIA results of the LCI/LCA study compared to using fully timerepresentative data; otherwise the lack of time-representativeness shall be documented. ° For comparative studies the conclusions or recommendations of the study should not be affected; otherwise the lack of time-representativeness shall be considered explicitly when interpreting the results. Especially shall the use of less timerepresentative data not relatively disfavour any competitors' products in a relevant degree. Related but also different from the question of time-representativeness of a process is the question of how to inventory the future interventions (e.g


Related but also different from the question of time-representativeness of a process is the question of how to inventory the future interventions (e.g. emissions from landfills). Another related issue is carbon storage and delayed emissions (e.g. in bio-based goods or from longliving products). These two topics are discussed and guidance is provided in chapter 7.4.3.7. Provisions: 6.8.4 Time-related representativeness Fully applicable for LCI results, LCIA results, and LCA studies. For unit processes only required to complete the system model for quality control. 104 Note that all this applies analogously for past processes, if they are part of the analysis and model


For unit processes only required to complete the system model for quality control. 104 Note that all this applies analogously for past processes, if they are part of the analysis and model. 105 BAT mix example: If the present technology-routes mix for end-of-life product treatment of the analysed product would be 60% incineration with energy recovery and 40% closed-loop material recycling, the BAT mix would combine 60% of the BAT technologies for incineration with energy recovery and 40% of the BAT technologies for material recycling.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how Note that these provisions will be applied only in the LCI phase. I) SHALL - Good time-related representativeness: The overall inventory data shall have an as good as required time-related representativeness, according to the goal of the study (see the accuracy requirements identified in chapter 6.9.2). This applies especially, where this matters in terms of relevant differences in the LCI data that represent a different time. Note that the represented year of a process or system shall refer to the actually represented year and not the year when the data set was calculated or the year of publication of used secondary data sources


II) SHALL - Specific seasonal or diurnal situation?: Check along the goal of the study and the intended applications whether the data needs to represent a specific seasonal or diurnal situation, if this differs from the average annual data. [ISO+] III) SHOULD - Time-related representativeness of future processes: For processes that run more than 5 years in the future or past from the time of study (e.g. of the use and end-of-life stage of long-living products or in case of backward looking analysis), fully time-representative future/past scenario data should be used, if possible. If this is not possible: [ISO!] III.a) BAT and recent data: For both attributional and consequential modelling, Best Available Technology (BAT) mix data should be used as second option, if BAT data can be argued to be sufficiently representative for the required time. The most recent data are the third option


The most recent data are the third option. III.b) Using not fully representative data: Not fully time-representative data can be used only along the following conditions: III.b.i) For LCI and LCIA data sets / non-comparative LCI/LCA studies: The use of not fully time-representative data is justifiable only if this is not relevantly changing the overall LCIA results compared to using fully timerepresentative data; otherwise the lower achieved timerepresentativeness shall be documented in the data set / report. III.b.ii) For comparative LCA studies: The conclusions or recommendations of the study should not be affected; otherwise the lower achieved timerepresentativeness shall explicitly be considered when drawing conclusions and giving recommendations. Especially shall the use of less time-representative data not relatively disfavour any competitors' products in a relevant degree. Note that time-related inventorying issues and how to inventory e.g


Especially shall the use of less time-representative data not relatively disfavour any competitors' products in a relevant degree. Note that time-related inventorying issues and how to inventory e.g. carbon storage and delayed emissions is necessarily addressed in the LCI chapter 7.4.3.7. 6.9 Types, quality and sources of required data and information (Refers to aspects of ISO 14044:2006 chapter 4.2.3.6.2) 6.9.1 Introduction and overview (Refers to aspect of ISO 14044:2006 chapter 4.2.3.6.2) During the initial scope definition and in preparation of the subsequent work, the main types and sources of data and other information should be identified. These initially identified


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how types and sources will be more detailed and often also revised during the iterative steps of inventory data collection and modelling, impact assessment, and interpretation. Types of required data and other information comprise - depending on the deliverable and type of study - e.g. inventory information, statistical data, technical process / system information, market information, allocation-related information, as well as legal and other boundary conditions. Note that LCIA methods are required (at least for supporting the quantification of the achieved data completeness / cut-off). Also normalisation data and weighting factors may be required. For identifying the data and information needs and suitable sources, the required overall data quality is the key measure


Also normalisation data and weighting factors may be required. For identifying the data and information needs and suitable sources, the required overall data quality is the key measure. It has been derived directly or indirectly from the goal of the LCI/LCA study in the chapters on completeness / cut-off criteria (6.6), representativeness (6.8), and precision (6.9.2). The equally relevant methodological appropriateness and consistency relates to the various method-related chapters of this document. For quality of third-party data sets that may be required, additional quality aspects relate to documentation, nomenclature and review. Unless the required precision is directly fixed in the goal (e.g


Unless the required precision is directly fixed in the goal (e.g. “modelling of a high-quality LCI data set of maximum XY% overall uncertainty (or: for each single impact category)") or unless specific, previous experience exists, the quality requirements on inventory data can only be identified after the first rough model of the life cycle has been established. It is then revised in context of the iterative improvement of the inventory (see Figure 5). 6.9.2 Data quality needs in light of the intended applications (Refers to aspects of ISO 14044:2006 chapters 4.2.3.6.2 and 4.4.2.2) Relative relevance of accuracy, completeness, and uncertainty/precision Data quality is composed of accuracy (i.e. representativeness and methodological appropriateness and consistency), precision / uncertainty and completeness of the inventory106. All of these contribute to the overall quality and typically the weakest of them determines (lowers) the overall data quality


All of these contribute to the overall quality and typically the weakest of them determines (lowers) the overall data quality. In general in LCA, the relatively lowest quality can typically be found regarding representativeness, methodological appropriateness and consistency (especially on system level), and completeness. These show the greatest need for improvement in LCA practice. Also, LCI related information often lacks quality, e.g. actual market prices in case of economic allocation under attributional modelling and forecasted market prices in support of identifying marginal processes in consequential modelling. The uncertainty of the data (that relates to stochastic uncertainty of measurements) in contrast is argued to often be of comparatively less relevance in practice, although it must not be disregarded, of course, as it can well lower otherwise high quality data


Determining data quality requirements for single data values in view of the aggregated LCIA results Data quality of LCA starts from the quality of the single inventory data values, and goes back even beyond to the raw data obtained. The required overall quality of the single data values and unit processes typically can only roughly be derived from the goal and the related overall quality requirements: the required overall quality on data set or system level is to be identified first. Only then these requirements can be translated to the level of the elementary 106 For the concepts and components of data quality and data set quality see annex 12.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how flows for which data is collected in the inventory. This can generally be done only after the first iteration of the LCA, i.e. not initially: This translation requires knowing the elementary flows' characterisation factors for the different impact categories and bring this information together with knowledge of the inventory. E.g. Climate change may be an important impact of an analysed system. In the contribution analysis it may turn out that one specific process contributes with e.g. about 95 % to the overall Climate change impact potential due to a high emission of methane. In this case it is very important to have a high quality on the data for this emission. In contrast, the emissions of e.g. CO2 from transport, energy conversion processes, etc


In this case it is very important to have a high quality on the data for this emission. In contrast, the emissions of e.g. CO2 from transport, energy conversion processes, etc. as part of the same system can be far less precise, as they in total contribute only little to the overall impact. While it requires initial analysis of the life cycle, in practice it is often only a rather limited number of emissions and processes that relevantly contribute to the overall impacts. It is key to correctly identify and focus on these in the described iterative approach; this is described systematically in chapter 4


It is key to correctly identify and focus on these in the described iterative approach; this is described systematically in chapter 4. 6.9.3 Inventory data needs and sources (Refers to aspect of ISO 14044:2006 chapter 4.2.3.6.2) Introduction For an LCA study, two types of data are usually required:  specific inventory data on the one or more newly to be developed process(es) of the foreground system, and  average or generic (for attributional modelling) or (mix of) marginal processes (for consequential modelling) for the background system. It is important that all foreground and background data used in a LCI/LCA study are methodologically consistent and that the overall quality requirements for the analysed system are met. Note that the required processes and hence data sets for attributional modelling are typically different from those for consequential modelling


Note that the required processes and hence data sets for attributional modelling are typically different from those for consequential modelling. Primary data (towards developing specific unit processes) For specific unit process data measurements at the operated processes are the as preferred option. In practice a range of other data sources is helpful (e.g. for cross-checks) or even necessary (e.g. in case of missing data). These are e.g. patents, process engineering models, stoichiometric models, process and product specifications and testing reports, legal limits, data of similar processes, BAT reference documents, and many others. As this is an operational and case-specific question it is addressed in the LCI chapter 7.3. Available data sets (primary and secondary) It is recommended to prefer well documented third party data sets as a good documentation supports correct use, quality assessment, and eases review


Available data sets (primary and secondary) It is recommended to prefer well documented third party data sets as a good documentation supports correct use, quality assessment, and eases review. Equally, for secondary background LCI data sets pre-verified data (e.g. via the ILCD Data Network) are recommended, as this reduces the efforts of own verification/review: the data itself will not require any additional review, only the correct selection and use in the analysed system model is to be reviewed. Note that for published studies the required level of review (e.g. independent external review or independent review panel) may differ for non-comparative LCA studies and comparative studies. See chapter 6.11.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how Sources for available inventory data are very diverse:  LCI data providers for the foreground system are typically the developer or producer and/or operator of the analysed process or system and their suppliers. Often market average data is provided by business associations; this data is typically of use for the background system. These industry sources are also named "primary data suppliers".  Secondary data providers, typically for the background system, are national and international LCI databases, consultants, and research groups. The ILCD Data Network gives access to all ILCD compliant data from any kind of data provider. By working with an ILCD compliant and appropriate documentation, using the same nomenclature and elementary flows, etc. such data eases to work in line with the ILCD Handbook


By working with an ILCD compliant and appropriate documentation, using the same nomenclature and elementary flows, etc. such data eases to work in line with the ILCD Handbook. More details on LCI data and information collection and modelling are addressed in the LCI chapter 7. 6.9.4 Other inventory-related data and information needs and sources (Refers to aspect of ISO 14044:2006 chapter 4.2.3.5) Depending whether attributional or consequential modelling principles and the related allocation or substitution approaches are to be applied, in addition to inventory data itself further data and information may be required to support the application of these methods. Which type of data is required strongly depends also on the type of deliverables of the LCI/LCA study and the specific case. Examples are market mix data of technologies, import/export statistics, and recycling rates


Examples are market mix data of technologies, import/export statistics, and recycling rates. Furthermore, especially for consequential modelling: long-term economic market competitiveness of technologies and related future market price scenarios, user behaviour data / surveys and models on reactiveness of different consumer groups, policy scenarios and their effect on future markets, experience and learning curves of technologies, and many more. Statistical agencies provide among others production, import/export and market statistics data. Equally market organisations and business associations provide such statistical data as well as other product-related information such as e.g. recycling rates and recycled contents. Regarding market prices and scenarios, technology foresight, policy scenarios, user behaviour and others, specialised research and consulting organisations, governmental organisations and business organisations work on these topics


The most suitable sources are to be identified in context of the specific LCI/LCA study. 6.9.5 Impact assessment models and factors, normalisation basis, and weighting set needs (Refers to ISO 14044:2006 chapters 4.2.3.6.2 and 4.4.2.2) Regarding the identification of suitable LCIA methods see chapter 6.7. Sources for LCIA methods are e.g. dedicated LCIA method developers or national and international LCA projects. Regarding the identification of suitable normalisation data and weighting sets see chapter 6.7.6. Sources for normalisation and weighting data are national LCA projects and respective recommendations of governmental bodies.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how Provisions: 6.9 Types, quality and sources of required data and information Applicable to Situation A, B, and C, implicitly differentiated. Differentiated for attributional and consequential modelling. Fully applicable to all types of deliverables, implicitly differentiated. Some of the steps can be done only after the first iteration. I) MAY - Overview of the principle types of data and information: It is recommended to prepare an overview of the principle types of data and information that will be required depending on the type of deliverable of the LCI/LCA study, unless this is done in the later step on "Planning data collection" (chapter 7.3). Depending on the study, these are e.g


Depending on the study, these are e.g. technical information of the analysed process(es) or system(s), use and end-of-life management data/information, raw inventory data for foreground processes, statistical data e.g. on international trade, market delimitation information and other market characteristics, generic or average background LCI data sets, LCIA methods data sets, normalisation and weighting data, legal and other boundary conditions, etc. The previous scope chapters should be re-checked, including on different data representativeness needs for attributional and consequential modelling. (6.9.1) Note: the detailed inventory-related data needs will be identified in the Life Cycle Inventory work (see 7.3). II) SHOULD - General requirements on data and data set quality: Determine the general requirements on data and data set quality (details, terms and concepts see annex 12)


II) SHOULD - General requirements on data and data set quality: Determine the general requirements on data and data set quality (details, terms and concepts see annex 12). Regarding newly collected LCI data this means the needs for representativeness, completeness, and precision. For third-party LCI data sets in addition method appropriateness and consistency, the use of ILCD-consistent elementary flows and nomenclature, appropriate documentation, and (potentially) an external review. (6.9.2) Note that unless the quality requirements are directly quantified in the goal, the initial data and data set quality requirements can be set only after the first loop of data collection, results calculation, impact assessment, the identification of significant issues, and the evaluation. This is described in more detail in chapter 4. These requirements will typically need to be revisited and refined in the subsequent iterations


This is described in more detail in chapter 4. These requirements will typically need to be revisited and refined in the subsequent iterations. III) SHOULD - Potential sources for the required data, data sets, and information: It is recommended to already identify potential sources for the required data, data sets and information, as far as possible. Details are decided in chapter 7.3 on "Planning data collection" (6.9.3, 6.9.4): III.a) Well-documented data: Well-documented data and data sets should be preferred to allow judging the data appropriateness for use in context of the analysed system and to enable the (potential) critical reviewer to be able to perform an independent verification (6.9.3). [ISO!] Note that if the deliverable of the study is intended to support comparisons, a minimum documentation scope is specified; see chapter 10.3.3


[ISO!] Note that if the deliverable of the study is intended to support comparisons, a minimum documentation scope is specified; see chapter 10.3.3. III.b) Pre-verified data: It is recommended to prefer the use of externally and independently pre-verified data and data sets, as this provides an assurance of the claimed quality and reduces the effort and costs for review of the LCI/LCA work (6.9.3). [ISO+] Note that different types of critical review are mandatory for different types of deliverables and applications (see 6.11). Note: The ILCD Data Network is one suitable source for primary and secondary LCI data sets and potentially for


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how LCIA methods. The related requirements make these data especially suitable for working in line with the ILCD Handbook. Statistical agencies, trade associations, governmental bodies, consultants and research groups are potential sources for data, data sets, and information. 6.10 Comparisons between systems (Refers to ISO 14044:2006 chapter 4.2.3.7) 6.10.1 Introduction and overview (Refers to aspect of ISO 14044:2006 chapter 4.2.3.7) A comparison of (product) systems is already an application of LCA, while it is covered within the general LCA standards ISO 14040 and 14044:2006. For valid comparisons a few additional aspects are to be considered. Studies that involve comparative assertions that are foreseen to be published must meet additional requirements in order to be valid, fair and hence non-misleading


Studies that involve comparative assertions that are foreseen to be published must meet additional requirements in order to be valid, fair and hence non-misleading. ISO 14040 and 14044:2006 pose a number of further requirements on such studies. Apart from the issues addressed in this chapter these relate to review and reporting; see chapters 11 and 10.3.3. This is reflecting the consequences that the comparative use of LCA results may have for other companies, institutions and stakeholders that are not directly involved in the study. A life cycle impact assessment shall be performed for studies intended to be used in comparative assertions intended to be disclosed to the public. Terms and concepts: “comparison” vs. “comparative assertion disclosed to the public” “Comparison” relates to the LCA-based comparison of the overall environmental impact of two or more systems that may or may not provide the same function. Such studies can be worked out on-demand in an LCA study, be based e.g


Such studies can be worked out on-demand in an LCA study, be based e.g. on available EPDs, apply ecodesign tools, etc. The results are used either internally for decision support or are published. Of interest here are the cases that are published. “Comparative assertion” in contrast means that the superiority, inferiority or equality of alternatives is claimed based on the LCA. The addition “disclosed to the public” means that these conclusions of superiority or equality are published to the general public (i.e. are made available outside a small and well defined list of actors that were involved in the LCI/LCA study). The term "comparative study" covers in this document both cases, i.e. both assertive and non-assertive studies that compare alternatives


The term "comparative study" covers in this document both cases, i.e. both assertive and non-assertive studies that compare alternatives. 6.10.2 Strengthening affected stakeholders in non-assertive comparisons and multi system type studies Types or comparisons and affected stakeholders The following types of comparisons can often be found in published LCA studies:  systems or processes with the same or similar functional unit are compared with each other (e.g. different brands of 20'' TV sets, or: potato cropping in country X comparing integrated, conventional, biological, and low input farming)


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how variants of a system are evaluated (e.g. design or material alternatives of a brandunspecific, i.e. generic or brand X specific kitchen chair)  a contribution or weak point analysis of a specific system is done (e.g. analysing the impact share of production stage vs. use stage vs. end-of-life stage of a vacuum cleaner Z, or: of the main contributing processes, materials, energy carriers, or services, etc. of a vacuum cleaner Z)  a multi-system type study analysis several systems with different functional units or functions (e.g


of a vacuum cleaner Z)  a multi-system type study analysis several systems with different functional units or functions (e.g. a basket-of-products type study of an average citizen of country A, or: a prioritization study on the most impacting products in a country B, or: the overall environmental impact (or: per average citizen) of countries A, B, and C is compared) ISO 14044:2006 has a set of stricter requirements for studies that compare systems and make assertions on superiority, inferiority (and implicitly also: equality) of the compared systems. This is to strengthen the interests of the affected stakeholders, avoiding the misuse of LCA in market competition


In the above examples the affected stakeholders are (at least):  the different TV set producing companies (first bullet, first example)  the farmers and the downstream production chain that produce/use potatoes from the respective farming methods (first bullet, second example)  the producers of the alternative materials (second bullet)  the producers of the product groups that show the highest impact in the basket-ofproducts and the prioritization study (forth bullet, first and second example)  the governments/people of the compared countries (forth bullet, last example)  In the case of the system-internal weak point analysis (third bullet) it is argued that the potential effect on the producers/service providers is limited, as related to a small market share Strengthening stakeholder interests The publication of comparisons without claiming superiority of one alternative while showing e.g


the results on level of impact indicators leaves it to the recipient to draw the conclusions of superiority/inferiority. This can be understood to be a misleading use of LCA, as the conclusions affect the "loosing" entities that represent the compared systems. This can be via purchase decisions, impacts on the image, political measures that build on such studies, etc. To protect the affected stakeholders, such studies shall at least state that the study does not support to draw conclusions or recommendations on the superiority or equality of any of the analysed systems. Finally, to avoid misinterpretation by non-technical audience or the general public, the study shall meet the same review and other requirements that apply to "comparative assertions disclosed to the public". To avoid this, the ISO requirements on "comparative assertions disclosed to the public" shall also be applied to "product comparisons disclosed to the public"


To avoid this, the ISO requirements on "comparative assertions disclosed to the public" shall also be applied to "product comparisons disclosed to the public". Exceptions are contribution and weak point analysis type studies on specific products / brands (see example in third bullet in the above list of types).


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how 6.10.3 Considered alternatives, the functional unit, and assumptions (Refers to aspect of ISO 14044:2006 chapter 4.2.3.7) Studies on systems that are meant to be functionally comparable In the classical case of comparative studies, the aim is to conclude the superiority, inferiority or equality of the compared alternatives and - typically - come up with recommendations. Two aspects related to the “what is compared” issue are important for those studies that look into system that are meant to be comparable: the equivalence of the functional unit of compared alternatives and the non-misleading selection of the compared alternatives. The equivalence of the functional units was already addressed in chapter 6.4.7. It is required for comparative LCAs that are to be published


The equivalence of the functional units was already addressed in chapter 6.4.7. It is required for comparative LCAs that are to be published. In the case that some of the aspects of the functional unit differ significantly between the systems, it shall be ensured that:  either the functions that the compared systems provide, are still seen as sufficiently comparable by the main stakeholders affected by the LCA study and the product users  or the sufficient comparability is to be achieved by the respective provisions for attributional modelling (typically, but with exceptions: allocation) and for consequential modelling (typically, but with exception: system expansion). Details for Situations A, B, C107 see chapter 6.5.4. For both options a close involvement of stakeholders and product users (or their representatives) is to be foreseen


Details for Situations A, B, C107 see chapter 6.5.4. For both options a close involvement of stakeholders and product users (or their representatives) is to be foreseen. Selection of compared alternatives As to the inclusion or exclusion of compared alternatives, it should be ensured that the comparative assertion is not misleading by leaving out existing or even widely used alternative products that may perform environmentally clearly better than the compared alternatives. In the case such alternatives are left out, this shall be highlighted visibly in the interpretation including when drawing conclusions and giving recommendations, as well as in the executive summary. Selection of specific scenarios to be compared Often also the application context of the products is to be considered carefully as part of the functional unit, as it may render products with the same general functional unit to perform differently: E.g


may a hybrid-vehicle with internal combustion engine and propulsion battery / electric motor perform somewhat better than a conventional vehicle with internal combustion engine, if analysed for an average use pattern. If long-distance overland transport is looked it, it may however perform clearly less good, while it may perform much better for predominantly inner-city transport. I.e. first the general technical specification of such products needs to be transformed into a functional unit that considers average or specific operation conditions of the product. Please note however that for comparative assertions that will be published, the choice of a specific application context may fulfil the criteria of 107 Note that product comparisons usually imply a decision-context. This again implies system expansion to be used here. The use of the attributional approach of allocation is only applicable in the foreseen exceptions and the cases of Situation C (e.g


This again implies system expansion to be used here. The use of the attributional approach of allocation is only applicable in the foreseen exceptions and the cases of Situation C (e.g. accounting of progress over time in the environmental performance of specific products, product groups or functions) that need an adjustment for functional equivalence. Check carefully along the applicable goal situation A, B, or C, which approach is to be chosen (see chapter 5.3).


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how misleading goal definition, e.g. by using very unusual application contexts. Studies that look into a-typical or otherwise specific scenarios shall highlight this fact visibly in the interpretation including when drawing conclusions and giving recommendations, as well as in the executive summary. Durability Among the positioning properties the durability of the product plays a special role, as it is directly related to the product‟s functional unit, and addressed there. An example is e.g. a wall hanging kitchen cupboard comparison for a house of 40 years use. Alternative A of 10 years life-time needs to be replaced three times to provide the same functional unit as another one with 15 years life time that needs replacement twice


Alternative A of 10 years life-time needs to be replaced three times to provide the same functional unit as another one with 15 years life time that needs replacement twice. Such has to be quantitatively considered, using the technical life-time of the alternatives as basis for published comparative assertions. The above example illustrates a second issue: the selection of the functional unit (here e.g. "providing wall-hanging kitchen cup-board space of X m3 for 40 years") can result in advantages/disadvantages for compared alternatives due to the specific values chosen. In the above example the 40 years relatively disfavour product B, because the three sets that are required to provide the functional unit for the defined 40 years still function for another 5 years (three times 15 years = 45 years). The same can apply to the chosen amount of m3, as just another aspect of the above example


The same can apply to the chosen amount of m3, as just another aspect of the above example. To ensure a fair comparison, the chosen functional unit shall reflect a well justified typical or average case and be agreed with the affected stakeholders in a best attainable consensus. Other life-time considerations should be considered in the scenario analysis, such as fashion life-time, mechanical integrity life time, technical innovation life time, cost of reuse vs. replace considerations etc. Note that legally required minimum guarantees are usually not suitable. Note also that in comparisons of product alternatives with different life times, the replacement of the alternative with the shorter life time will usually be done with a newer model that is technically equivalent and available at the time of replacement. This should be considered explicitly in the model, unless a different agreement can be achieved among the affected stakeholders


This should be considered explicitly in the model, unless a different agreement can be achieved among the affected stakeholders. Other qualitative aspects of the functional unit Depending on the specific system, a range of other qualitative system properties plays a relevant role; this is to be evaluated for the given case. Examples are e.g. cleaning, servicing, repair needs, but a range of other kinds of positioning properties are to be checked. 6.10.4 Methodological, assumptions and data consistency (Refers to aspect of ISO 14044:2006 chapter 4.2.3.7) Of particular importance is to ensure consistency of the methods, assumptions and data used in the LCA study for all compared systems


Consistency is crucial when defining the functions, functional units and reference flows, the system boundaries, the requirements on representativeness (time-related, geographical and technological), completeness and precision of LCI data, the LCI modelling principles and approaches applied, as well as applied LCIA methods.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how 6.10.5 Data quality requirements (Refers to aspect of ISO 14044:2006 chapter 4.2.3.7) In studies comparing systems, the overall data quality requirements depend on the relative difference of the overall environmental impact between the compared systems: For an LCA performed e.g. in support of ecodesign decisions, comparing two or more alternative designs, the requirements may be modest if one of the alternatives has much lower impacts than the others. The initial overall data quality requirements are hence to be revised when the results of the first calculation of inventory and impact assessment are available. Regarding the completeness, for comparative assertions, and next to the overall environmental impact the cut-off criteria shall be applied also to mass and energy


Regarding the completeness, for comparative assertions, and next to the overall environmental impact the cut-off criteria shall be applied also to mass and energy. 6.10.6 Identical parts of the compared systems (Refers to aspect of ISO 14044:2006 chapter 4.2.3.7) When looking at alternatives in specific parts of otherwise identical or similar systems, the rest of the compared systems is often108 identical. Examples are comparing material alternatives for parts of a product, or comparing alternative electricity sources in the energy efficiency in the use stage of an electricity using product. If the sole purpose of such a comparison is to decide which system has the lowest environmental impact, as is often the case in applications for product improvement in ecodesign or for procurement, all those parts of the systems that are identical, can be left out when drawing the system boundaries. This can drastically reduce the effort for the LCA study


This can drastically reduce the effort for the LCA study. However, this is only possible, when they are actually identical: even apparently identical parts may in fact not be identical: E.g. the same amount of the same aluminium alloy used in the same component of two alternative product models may be left out. This shall not be done if the alloy is used in different components of these models, as the inventories of the alloys are only partly correlated in the second case. They should hence be kept in and their partial correlation shall be considered when interpreting differences. Note that the intended applications may not permit to leave out identical parts, e.g. if also the total overall impact is required or if the share of impact of parts in relation to the total shall equally be analysed, etc


if also the total overall impact is required or if the share of impact of parts in relation to the total shall equally be analysed, etc. 6.10.7 Scenarios in support of comparisons (Refers to aspect of ISO 14044:2006 chapter 4.2.3.7) Reasonably best case / most likely case / reasonably worst case scenarios (plus optionally other scenarios) shall be performed for comparison of systems: data and method assumptions are varied to investigate the robustness of the results. Such scenarios support the later results interpretation. For comparative micro-level decision support studies (i.e. Situation A), examples for such method and data assumptions are inventory data values, parameters, relevant flow properties, relevant system properties / aspects of the functional unit, but also method assumptions including method approaches such as allocation, the mix of superseded processes used in substitution, and the like; the "shall" provisions of this document shall still be met however


These data and method assumptions are to be identified among the "significant aspects" (see chapter 9.2). 108 Note however that changes in specific parts of a product can induce other changes that must be explicitly considered (see related box on part-system relationships in chapter 7.2.2).


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how Uncertainty calculation shall be used to support the comparison of systems, especially to identify whether differences can be considered significant or too small to justify the superiority of one system over the other. For comparative meso/macro-level decision support studies (i.e. Situation B), a more extensive us of scenarios is necessary to ensure that the decision support is robust. In difference to Situation A, in Situation B and here exclusively for the assumption scenarios also the shall provisions of this document can be changed. That means e.g. that also fully consequential or fully attributional scenarios can be performed, if the affected stakeholders come to a best attainable consensus on their integration and definition (see also chapter 7.2.4.2 and 7.2.4.3)


Next to uncertainty calculations, also scenarios can be used to help capture the reliability of the data results of Situation C studies. 6.10.8 Carbon footprint studies and other selected comparisons (No corresponding ISO 14044:2006 chapter) The remainder of chapter 6.10 on comparisons of systems equally applies to Carbon footprint studies, except for limiting the question of significance to the Climate change relevant emissions. Note however that published comparisons or comparative assertions based on Carbon footprint or other selected indicators or impact categories shall be justified by demonstrating that the compared alternatives do not differ in other relevant environmental impacts to a degree that would change the conclusions and/or recommendations of the comparison. Otherwise such studies are considered misleading. Provisions: 6.10 Comparisons between systems Note that restrictions apply to studies under Situation C1 and C2 for use in decision support


Otherwise such studies are considered misleading. Provisions: 6.10 Comparisons between systems Note that restrictions apply to studies under Situation C1 and C2 for use in decision support. Differentiated for attributional and consequential modelling. These provisions are mandatory (shall) only for comparative LCA studies that analyse more than one system or system variants. It is recommended to also apply them analogously to non-comparative LCA studies that include a system internal contribution / weak point analysis. These provisions also apply to LCI studies and data sets that are intended to be used in context of comparative studies (e.g. as background data). These provisions are planning items that need to be considered in the later LCI, LCIA and Interpretation phases and for reporting and review


as background data). These provisions are planning items that need to be considered in the later LCI, LCIA and Interpretation phases and for reporting and review. Note: these Provisions partly compile provisions from other chapters and reproduce them here in a condensed way; the complete and binding conditions are found in the referenced chapters. For all comparative studies: I) SHALL - Non-assertive, comparative studies: The ISO 14044:2006 provisions for comparative assertions shall also be applied to non-assertive, comparative studies. Both types together are grouped under the term "comparisons" in this document. (6.10.2). [ISO!] II) SHALL - Consistency: All elements of the scope definition shall be addressed consistently for all systems to be compared, as far as possible. Otherwise, the lack of consistency shall be reported and be considered explicitly when interpreting the results,


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how Provisions: 6.10 Comparisons between systems giving conclusions or recommendations. Especially: (6.10.3) II.a) LCI model: The compared system models shall be constructed in an analogous way applying the same rules for system boundaries, LCI modelling principles and method approaches. II.b) Assumptions: Methodological and data assumptions shall be made in an analogous way. II.c) Data quality: The achieved completeness, accuracy and precision of the data shall be sufficiently similar for the compared systems. III) SHALL - Uncertainty and accuracy calculations: Calculations on the stochastic uncertainty and accuracy shall support this analysis. This is not required if uncertainty calculations have already been used to derive the reasonably best and worst case scenarios


This is not required if uncertainty calculations have already been used to derive the reasonably best and worst case scenarios. (6.10.4) IV) SHALL - Completeness / cut-off: The cut-off % that has been defined in chapter 6.6.3 shall also be met for mass and energy, next to for the overall environmental impact. V) SHALL - Excluding identical parts: If included processes / systems of the compared systems are identical for all alternatives, they may be left out of all models. Included processes / systems that are similar but not identical shall remain in the model, but their partial correlation shall be considered when interpreting differences. [ISO+] Note that the intended applications may not permit to leave out even identical parts. Note that even apparently identical parts may only be left out of the comparison if they are truly identical. E.g. the same amount of the same aluminium alloy used in the same component of two alternative models may be left out


E.g. the same amount of the same aluminium alloy used in the same component of two alternative models may be left out. This shall not be done if the alloy is used in different components of these models, as the inventories of the alloys are only partly correlated in the second case. (6.10.5) VI) SHALL - LCIA to be performed: A Life Cycle Impact Assessment shall be performed for LCI or LCA studies intended to support comparative studies that are intended to be published. VII) SHALL - Impact coverage limitations (e.g. Carbon footprint): Comparison studies based on selected indicators or impact categories (e.g. Carbon footprint based comparisons) shall highlight that the comparison is not suitable to identify environmental preferable alternatives, as it only covers the considered impact(s) (e.g. Climate change)


Climate change). This applies unless it can be sufficiently demonstrated that the compared alternatives do not differ in other relevant environmental impacts to a degree that would change the conclusions and/or recommendations of the comparison if those other impacts would be included in the analysis. Such demonstration should draw on robust approximations for the analysed system and/or robust information derived from detailed and complete LCA studies available for sufficiently similar systems. System / product group specific guidance document and Product Category Rules (PCR) may provide such robust information. The above shall be investigated in any case and if other environmental impacts were identified as being relevant in the above sense, they shall be named in the report. (6.10.8) [ISO!]


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how Provisions: 6.10 Comparisons between systems For studies on systems with similar functional units: Comparisons shall be made based on the system's reference flows. VIII) SHALL - Functional equivalence: The compared systems shall have the same (or only insignificantly different) functional unit in terms of both the primary function and possible secondary functions, as far as possible


In the case that some of the aspects of the functional unit(s) differ significantly between the systems, it shall be ensured that: (6.10.2) VIII.a) either the functions that the compared systems provide are still seen as sufficiently comparable by the main stakeholders affected by the LCA study, VIII.b) or the sufficient comparability is to be achieved by the respective method approaches for consequential modelling or attributional modelling109, as to be applied for the respective Situation (see chapter 6.5.4). For consequential modelling this approach is system expansion. IX) SHOULD - Selection of compared alternatives: The study should include - next to the foreseen alternatives - potentially environmentally better market relevant and available alternatives, as otherwise the study would be considered misleading


If such alternatives are not included, this shall later be highlighted in a prominent place of the conclusions and recommendations, as well as in the executive and technical summary chapters of the report, pointing to this fact. For studies on niche products, see chapter 5.2.2. (6.10.2) [ISO+] X) SHOULD - Selection of production, operation and use scenarios: To ensure a fair comparison, the chosen functional unit should reflect well-justified typical or average production / operation / use scenarios; it shall be agreed with the affected stakeholders in the best attainable consensus. If a-typical or otherwise specific scenarios need to be compared in line with to the goal definition, compared, this fact shall later be highlighted in a prominent place of the conclusions and recommendations and executive summary chapter of the report, pointing to this fact. (6.10.2) [ISO!] XI) SHOULD - Modelling replacements over time: For cases where a system (e.g


(6.10.2) [ISO!] XI) SHOULD - Modelling replacements over time: For cases where a system (e.g. a product) needs to be replaced to meet the required duration of performance of the compared functional unit, the replacement should consider that potentially a newer model or system in general will replace the initially used model. This is unless a different agreement can be achieved among the affected stakeholders. This provision analogously relates to the need of repeating a service. XII) SHALL - Indicative only (The exact and complete provisions are given in chapter 6.5.4.2). Situation A - Assumption scenarios and uncertainty calculation: For comparative micro-level studies (Situation A): each compared scenario shall be complemented with assumption scenarios of reasonably best and reasonably worst cases. This can be optionally extended to further assumption scenarios within the 109 Comparisons also can occur in accounting type studies (e.g


This can be optionally extended to further assumption scenarios within the 109 Comparisons also can occur in accounting type studies (e.g. across product groups in basket-of-product type of studies), while these shall not be used for decision support that would lead to e.g. purchases or policy measures based on superiority or inferiority of the compared alternatives.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 6 Scope definition - what to analyse and how Provisions: 6.10 Comparisons between systems reasonably best and worst cases. Uncertainty calculation shall be performed, unless such has already been used to derive the reasonably best and worst case scenarios. The interested parties shall be involved in achieving a best attainable consensus on the definition of the reasonably best and reasonably worst assumption scenarios. The assumption scenarios can in principle vary all methods, data and assumptions except for the "shall" provisions. (6.10.7) XIII) SHALL - Indicative only (The exact and complete provisions are given in chapter 6.5.4.3)


(6.10.7) XIII) SHALL - Indicative only (The exact and complete provisions are given in chapter 6.5.4.3). Situation B - Assumption scenarios and uncertainty calculation: For comparative meso/macro-level studies of Situation B: the scenarios for each of the analysed alternatives shall apply the modelling guidance of Situation A, except for process that are affected by large-scale consequences of the analysed decision. The assumption scenarios can in principle vary all methods, data and assumptions including the "shall" provisions, but excluding the shall provisions of ISO 14040 and 14044. (6.10.7) XIV) SHALL - Involvement of interested parties in review: For their involvement in the critical review, see chapter 6.10 and separate guidance document on "Review schemes for LCA". [ISO!]