ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results are much more frequent in cases of solving multifunctionality. These are namely the need for interim treatment steps, especially for cases of waste treatment where the valuable coproduct is only generated after several steps and the change in inherent properties of e.g. secondary goods; there is not always an (alternative) production route for exactly that secondary good. For these reasons specific approaches have often been developed and additional ones for modelling substitution in end-of-life product and waste treatment. A special case of multifunctionality is the system-system relationship (concept see box in chapter 7.2.2), that under consequential modelling effectively requires the substitution of the short-term marginal


Joint production - substitution/credits in end-of-life and waste treatment models A special case is the waste and end-of-life product recycling that typically requires additional steps:  The modelling of the process steps that condition, modify, transport, etc. the end-of-life products or waste until the valuable function (e.g. a recycled metal bar) is available in a quality and at a place where it is superseding an alternative production (e.g. primary production of this metal bar). These steps are part of the system boundary of the analysed system. In other words: the related inventories are assigned to the analysed system.  The identification and quantification of differences between the function resulting from the end-of-life product or waste treatment, e.g. due to downcycling (e.g. shortened fibres, reduced mechanical performance of polymers, tramp elements in metals, etc.)

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


due to downcycling (e.g. shortened fibres, reduced mechanical performance of polymers, tramp elements in metals, etc.). This can be done in two ways: either by substituting the reduced amount of function that the to-be-substituted co-function replaces (e.g. may 1 kg recycled polymer replace 0.8 kg primary polymer in the analysed application). Or and especially if the specific uses are unknown, the market-price ratio secondary/primary is used to scale down the inventory of the substituting process or system (e.g. if the secondary, recycled polymer has a market value of 0.7 $ per kg and the same, primary produced polymer of higher quality costs 0.9 $/kg, the substituted inventory is reduced by 0.7/0.9, i.e. a factor of 0.778); this is also called "value correction". In this context, also the true joint co-producing processes are to be identified. The detailed provisions are given in a separate chapter in annex 14.5


In this context, also the true joint co-producing processes are to be identified. The detailed provisions are given in a separate chapter in annex 14.5. 7.2.4.7 Initial description of identified processes (Refers to aspects of ISO 14044:2006 chapters 4.2.3.3.2) Especially for the processes of the foreground system, an initial description is required. This will be revised when collecting and documenting the unit process data. Details on documentation are given in chapter 10. Unless the deliverable of the LCA is a unit process data set: For those product flows that connect the foreground system with the background system, a detailed specification including of their function and functional unit is required. Provisions: 7.2.4 Identifying processes in consequential modelling Applicable for those processes in Situation B that have large-scale consequences, and for use in assumption scenarios in Situation B (if consequential elements are included in those)


Fully applicable to all types of deliverables, except for unit processes.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results Provisions: 7.2.4 Identifying processes in consequential modelling Expertise (7.2.4.1) [ISO+] I) SHOULD - Required expertise: Experts in the following domains should be involved in the study, especially for identifying and modelling large-scale consequences: I.a) technology development forecasting (e.g. learning curves, experience curves), I.b) scenario development, I.c) market cost and market forecasting, I.d) technology cost modelling, and I.e) general-equilibrium and partial-equilibrium modelling. II) SHOULD - Policy scenario experts required?: The involvement of domain experts for policy scenarios is recommended regarding their function as setting constraints. In the case policy scenarios are explicitly analysed in the study, such experts should be involved


In the case policy scenarios are explicitly analysed in the study, such experts should be involved. Identifying consequences and constraints to be considered [ISO+] III) SHALL - Modelled consequences: Identify among the following ones those consequences that will be modelled; this step may be taken separately case for each process. Their potential exclusion shall be justified by demonstrating at least argumentative / semi-quantitative that they are not relevant for the results; otherwise the exclusion shall be considered when reporting achieved accuracy (in case of data sets) and when interpreting the results (in case of LCA studies): (7.2.4.2) III.a) Primary market consequences: III.a.i) SHALL - (a) Processes that are operated as direct market consequence of the decision to meet the additional demand of a product (i.e. “consequential modelling of direct consequences; applied for the full system”). This includes among many others also indirect land use effects


“consequential modelling of direct consequences; applied for the full system”). This includes among many others also indirect land use effects. III.a.ii) SHALL - (b) Processes that supersede / complement not required cofunctions of multifunctional processes that are within the system boundary (i.e. “solving multifunctionality by substitution”, reducing the system boundary to exclude the not required function(s)). III.b) Secondary market consequences: III.b.i) SHOULD - Increased demand for a co-product if its market-price is reduced. III.b.ii) SHOULD - Incentive-effects on a process to increase its efficiency due to a higher price for its product(s). III.b.iii) SHOULD - Decreased demand for competing products of a co-product due to the decreased price of the co-product. III.b.iv) SHOULD - Consumer behaviour changes III.b.v) SHOULD - Further consequences should only be included if explicitly addressed in the goal of the study.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results Provisions: 7.2.4 Identifying processes in consequential modelling IV) SHALL - Constraints: Identify the constraints that will be included in the model and that may partly or fully prevent that the marginal process mix as identified along the primary and secondary consequences can directly be used in the system model. The likely specific effect of any included constraint shall be considered when identifying the effective marginal process(es). Their potential exclusion shall to be justified by demonstrating at least argumentative / semi-quantitative that they are not relevant for the results; otherwise the exclusion shall be considered when reporting achieved accuracy (in case of data sets) and when interpreting the results (in case of LCA studies)


The following constraints should be considered (7.2.4.3): IV.a) Existing long-term supply-contracts or co-operations that cannot easily be changed. IV.b) High costs that act as a barrier (e.g. limited mobility of some products due to high transport costs). IV.c) Existing or expected political measures / legal constraints that stimulate perceived positive developments or counteract perceived negative developments. (E.g. a political binding target of X % of energy carrier Y in the fuel mix means that energy carrier X is already pre-set and cannot be assumed to be a long-term marginal product in consequence of the analysed decision.) IV.d) Non-scalability of supply of products or natural resources; including of fully used, dependent co-products of joint production. IV.e) Monopolies, i.e. lack of choice of the supplier or technology


IV.e) Monopolies, i.e. lack of choice of the supplier or technology. IV.f) It is recommended to also consider other constraints in place or expected to be in place that increase, decrease or block a primary or secondary consequence. Identifying the mix of superseded processes /systems [ISO+] V) SHOULD - Stepwise identification of the mix of superseded processes / systems: Identify the processes / systems within the system boundary that are superseded as consequence of the analysed decision on the investigated system(s) 125


For each process the following steps should be applied, starting from the system's functional unit or reference flow to the entire foreground system and following the identified consequences and constraints of a theoretical "supply-chain - use - end-of-life" logic to include identifying as minimum all product and waste flows (or their functional units) that cross the border to the background system126: (7.2.4.4) V.a) Primary market consequence and the size of the effect: First step - consider the primary market consequence and the size of the effect: V.a.i) Identify the processes that are assumed to be additionally operated or taken out of operation as primary market consequence of the analysed decision and the directly related additional or reduced demand for a 125 See also the related decision tree diagram in Figure 21


126 It depends on the chosen background system model solution whether the processes of the background system also need to be individually identified or whether - if embedding the foreground system into an existing background system - this work has been already done.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results Provisions: 7.2.4 Identifying processes in consequential modelling function/product, considering the following: V.a.ii) Size of effect:, EITHER V.a.ii.1) "small" - affecting only the extent of operation of one or more existing processes --> the short-term marginal process(es) are the ones that should be assumed to be superseded, OR V.a.ii.2) "big" - resulting in additionally installed or de-installed capacity - -> the long-term marginal processes are the ones that should be assumed to be superseded


V.a.ii.3) The effect should generally be considered "small", if the annual amount of additional demand or supply is smaller than the average percentage of annual replacement of capacity (see chapter 5.3.6) of the annual supply of that function or system in the given market; if that average percentage is over 5 %, 5 % should be used instead. Otherwise it is "big". The percentage is for orientation only and can be for a given case changed to be smaller or bigger upon the argumentation that the change in demand or supply is directly triggering changes in demand and not only via a marginal accumulative effect in contribution to the general market demand/signal


V.b) Secondary consequences and constraints: Second step - consider secondary consequences and constraints: V.b.i) If the size of the effect of the primary market consequence is "small", check whether the secondary consequences and constraints in the market counteract the primary consequence (rebound), so that the net effect of the consequences is so small that it is not significantly different from being zero. In that case, the "short-term marginal" is best represented by the "average market consumption mix" of the processes / systems (but see next sub-provision). V.b.ii) For the specific case of multifunctionality, a key constraint occurs if the required co-function is an already fully used, dependent co-function of a joint production process (e.g


V.b.ii) For the specific case of multifunctionality, a key constraint occurs if the required co-function is an already fully used, dependent co-function of a joint production process (e.g. copper ore mining with silver as dependent but fully used co-product, egg-laying chicken with the dependent co- "product" chicken being fully used for human food or animal fodder), as additional demand cannot be met by additional supply on a net basis. In that case, the required function/product will have to be produced in another way (e.g. for the above examples: silver from silver mine, or meat-chicken directly raised for food or fodder). V.b.iii) If the size of the effect of the primary market consequence is "big", check next whether secondary consequences and market constraints counteract the primary consequence, so that the net overall effect is not "big" but "small"


V.b.iv) For those processes that are still facing "big" effects, explicitly consider that the affected processes might have been changed by the secondary consequences and constraints. This has to be analysed specifically to


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results Provisions: 7.2.4 Identifying processes in consequential modelling correctly identify the final effect / superseded processes. V.c) Market situation and the cost-competitiveness: Third step - market situation and the cost-competitiveness of alternatives: V.c.i) Market direction, EITHER V.c.i.1) a "growing, stable, slightly declining market" (i.e. declining less than the average equipment replacement rate, OR V.c.i.2) a "strongly declining market" (i.e. declining faster than the average equipment replacement rate). The above named average displacement rate in % is obtained by dividing 100 years by the average or typical life time of the capital equipment, expressed in years


The above named average displacement rate in % is obtained by dividing 100 years by the average or typical life time of the capital equipment, expressed in years. V.c.ii) Based on this: analyse whether the extent of additional demand or supply for the effect "big" is changing the direction of the market, i.e. from a "strongly declining" market to a "slightly declining, stable, or growing" market OR vice versa. V.c.iii) If this is NOT the case, the affected processes / systems are always the "long-term marginal" processes / systems. V.c.iv) For all "small" and "big" cases in addition the cost-competitiveness of alternative processes / systems is relevant: V.c.iv.1) If the market is "growing, stable, or slightly declining", the "short-term marginal" (for "small" effects) and the "long-term marginal" (for "big" effects) are the most cost competitive processes / systems


V.c.iv.2) If the market is "strongly declining" the "short-term marginal" (for "small" effects) and the "long-term marginal" (for "big" effects) are the "least cost-competitive" processes / systems. V.c.v) If in contrast the market direction IS changing, both the least and the most cost-competitive processes / systems are superseded and their specific type and share needs to be identified individually, drawing on the other provisions of this chapter. V.d) Identifying the mix of processes /systems: Final step - identifying the mix of "short-term" or "long-term" marginal processes / systems: V.d.i) In the consequential model, not only one single, short-term or long-term marginal process should be modelled but a mix of the most likely marginal processes, given the high uncertainty of market price forecasts and the often large differences of the environmental profiles among alternative marginal processes


To restrict the model to a single marginal process or system is only justifiable if there are no other, similarly costcompetitive processes or systems and hence the use of a single one is more appropriate. V.d.ii) The final amount of function (process or system) that is superseded shall be approximated considering the combined effect of primary and secondary consequences and constraints.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results Provisions: 7.2.4 Identifying processes in consequential modelling Note that in case the market direction has changed as consequence of the analysed decision, the superseded processes are a specific combination of the least cost-competitive ones and partly the most cost-competitive ones. Further provisions, comments, and recommendation on documentation (7.2.4.5) [ISO+] VI) SHALL - Observe that: VI.a) Part-system and system-system relationships: These need special attention (e.g. for energy related products) and correct inventorying. Note that these cases are modelled identically in attributional modelling


for energy related products) and correct inventorying. Note that these cases are modelled identically in attributional modelling. VI.b) Individual processes within the background system: These may need to be identified as well when identifying significant issues (see chapter 9.2) or if required to meet the specific goal of the study. VI.c) Meet representativeness requirements: The requirements regarding technological, geographical and time-related representativeness shall be met. VII) SHOULD - Indirect land use changes: The appropriate way how to consider indirect land use changes should be developed. If done this shall applying the general provisions on consequential modelling as applicable. This is unless specific provisions would be published under the ILCD. Such provisions might be part of a future supplement. VIII) MAY - Schematic consequential model diagram: It is recommended using the system boundary scheme for overview


Such provisions might be part of a future supplement. VIII) MAY - Schematic consequential model diagram: It is recommended using the system boundary scheme for overview. Schematic decision-consequence and flow diagrams of the most relevant consequences and marginal processes of the system(s) may be used to document the main identified consequences and constraints and the resulting resource bases, technologies, affected markets, etc. This can serve as basis for a data collection planning and later documentation. Note again, that any exclusion of individual processes or activity types shall be justified using the cut-off criteria (see chapter 6.6.3). In principle all processes are to be inventoried that are operated in consequence of the analysed decision. This includes in principle - depending on the system boundary - activities such as e.g. mining, processing, manufacturing, use, repair and maintenance, transport, waste treatment and other purchased services such as e.g


mining, processing, manufacturing, use, repair and maintenance, transport, waste treatment and other purchased services such as e.g. cleaning and legal services, marketing, production and decommissioning of capital goods, operation of premises such as retail, storage, administration offices, staff commuting and business travel, etc. IX) MAY - Initial processes' description: It is recommended to also provide an initial description of the identified unit processes of the foreground system and the detailed functional units of those product and waste flows that link it to the background system. This should complement the documentation of the consequences and constraints and be completed with details during the iterations of the LCI work. (7.2.4.7) Solving multifunctionality of processes and systems (7.2.4.6) [ISO!] X) SHALL - Subdivision and virtual subdivision: Subdivision and virtual subdivision shall be applied in preference to substitution. Provisions see chapter 7.4.2.2127


Provisions see chapter 7.4.2.2127. 127 Observe that virtual subdivison shall not be done if it "cuts" through physically not separable joint processes, as this would distort the substitution.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results Provisions: 7.2.4 Identifying processes in consequential modelling XI) SHALL - Combined production: For cases of truly combined production, the determining physical causality (i.e. the first of the two steps of allocation under attributional modelling) equally applies analogously; see chapter 7.9.3.2. XII) SHALL - Joint production: For joint production, substitution as a special case of system expansion is the preferred solution to multifunctionality. This shall be done as follows: XII.a) The same provisions shall apply as for general consequential modelling of the system


This shall be done as follows: XII.a) The same provisions shall apply as for general consequential modelling of the system. XII.b) Note the specific constraint for already fully used, dependent co-products of joint production: since their production cannot be increased with that same multifunctional process/technology, their additional provision cannot be modelled. Instead, alternative routes need to be modelled for their supply. This means that the determining co-product shall not be substituted. XII.c) If for the not required co-function functionally equivalent alternative processes / systems are operated / provided in a commercially relevant58 extent, the not required co-function shall be substituted with the mix of the superseded marginal processes (excluding the substituted process-route, if quantitatively relevant)


Differences in functionality between superseding and superseded function shall be considered by correction of the actually superseded amount of the superseded process(es) or by market price correction of the superseded process(es)' inventory (if the superseded amount is not known in sufficient detail). XII.d) If such alternative processes / systems do not exist128 or are not operated in a commercially relevant extent, the provided function in a wider sense should be used for substitution129. Note that the substituted processes or products may also have secondary functions. This can theoretically lead to the problem of an eternally self-referring and/or very extensive, multiply extended system. As the amount of these secondary functions and their relevance within the overalls system goes down with each process step, this problem can be avoided / reduced by applying the cut-off rules


As the amount of these secondary functions and their relevance within the overalls system goes down with each process step, this problem can be avoided / reduced by applying the cut-off rules. Substitution for multifunctional processes and systems in reuse / recycling / recovery (7.2.4.6) [ISO!] XIII) SHALL - Recycling, recovery, reuse, further use: Substitution shall be applied for cases of recycling, recovery, reuse, further use: (7.2.4.6, and for all details see annex 14.5) XIII.a) Applying general rules to these cases: Substitution of products recycled or recovered from end-of-life product and waste treatment follows the same rules as 128 E.g. for wheat grain production, many refinery products, etc. 129 E.g. as for NaOH apart from NaCl electrolysis, or if for a mobile phone the individual function SMS would not be available as commercially relevant, separate consumer product


129 E.g. as for NaOH apart from NaCl electrolysis, or if for a mobile phone the individual function SMS would not be available as commercially relevant, separate consumer product. NaOH provides the general function of neutralising agent and hence other, technically equivalent and competing neutralising agents, KOH, Ca(OH)2, Na2CO3, etc. can be assumed to be superseded. For the case of wheat grain and straw production: instead of straw other dry biomass (e.g. Miscanthus grass, wood for heating, etc.) provides equivalent functions and can be assumed to be superseded.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results Provisions: 7.2.4 Identifying processes in consequential modelling for the general cases of multifunctionality. They shall be applied for all cases of waste and end-of-life treatment (i.e. "closed loop" and of "open loop - same primary route" and "open loop - different primary route"). Subdivision and virtual subdivision shall be applied in preference to substitution. Provisions see 7.4.2.2. XIII.b) Specific aspects and steps (true joint process, interim processes to secondary good, recyclability, ...): Specific for reuse/recycling/recovery is that interim treatment steps occur more regularly and that often no truly equivalent alternative process / system exist130. In this context, also the true joint process of the secondary good is to be identified


In this context, also the true joint process of the secondary good is to be identified. Finally, the steps of reuse/recycling/recovery need to be modelled explicitly until the secondary good is obtained that is actually superseding an alternative process / system. The actual mix of superseded processes shall be identified for the given case and along the following steps: XIII.b.i) The true joint process of the secondary good is that process step in the product's life cycle that provides the good with the closest technical similarity to the secondary good; the thereby identified primary good shall not have a lower market value than the secondary good131. XIII.b.ii) The recyclability substitution approach shall be used for substitution. That implies that all interim waste management, treatment, transport etc. steps are to be modelled and assigned to the analysed system including the step that is producing the valuable co-function (e.g. secondary metal bar)


steps are to be modelled and assigned to the analysed system including the step that is producing the valuable co-function (e.g. secondary metal bar). XIII.b.iii) The amount/degree of recyclability shall refer to the actually achieved recyclability, i.e. accounting for all kinds of losses, e.g. loss due to incomplete collection, sorting, recovery, during recycling processing, rejection etc. In short, the recyclability is the %132 of the amount of end-oflife product or waste that is found in the secondary good(s). For practical reasons and for long-living products this should per convention be the currently achieved recyclability for this product (or for new / projected products the achieved recyclability of comparable products in the same market). This can be another reference if the goal of the study explicitly relates to recyclability scenarios. 130 This is as secondary goods often have distinctly different properties from primary produced goods (e.g. recycled aged plastics vs


130 This is as secondary goods often have distinctly different properties from primary produced goods (e.g. recycled aged plastics vs. primary plastics), what makes a clear assignment to the equivalent or most similar process / system more difficult. 131 This serves to avoid a potentially misleading upscaling of the superseded function's inventory in case of applying market value correction when correcting for the functional differences. 132 Note that this % needs to relate to the appropriate property and unit of the secondary good, e.g. Mass in kg for recycled materials, Lower calorific value in MJ for recovered energy, Pieces in number for reused parts, etc.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results Provisions: 7.2.4 Identifying processes in consequential modelling XIII.b.iv) The superseded process(es) / system(s) shall be identified applying the general consequential modelling guidance as detailed in the above provisions133. XIII.b.v) Also here not one marginal process should be used but the average inventories of several of the potential marginal processes. XIII.b.vi) For application-unspecific secondary goods, any reduced technical properties of the secondary good should be corrected in the accredited inventory by using the market price ratio (value correction) of the secondary good to the primary produced replaced function


XIII.b.vii) For application-specific uses of the secondary goods, sufficient functional equivalence with the superseded good shall be ensured and the credited inventory be reduced to the amount that is effectively superseded. In the case this cannot be determined, the market price ratio (value correction) shall be applied as in the application-unspecific case. XIII.b.viii) Especially for the case of "open loop - different primary route" in addition it is to be checked whether commercially relevant alternative processes are operated. Otherwise, the provisions for the general case of solving multifunctionality under consequential modelling shall be applied. XIII.b.ix) The other guidance aspects of this chapter on identifying the superseded processes (e.g. constraints, secondary consequences, etc.) apply analogously


XIII.b.ix) The other guidance aspects of this chapter on identifying the superseded processes (e.g. constraints, secondary consequences, etc.) apply analogously. Note that for scenario formation in comparisons, the various primary and secondary consequences and constraints should be varied jointly when defining "reasonably best case" and "reasonably worst case" scenarios. 7.3 Planning data collection (No corresponding chapter in ISO 14044:2006; addressed in many chapters across the standard) 7.3.1 Overview Based on the scope settings and the initially identified principle data and information needs (see chapter 6.9.2) and the initially identified processes within the system boundaries 133 That means that the earlier named constraint for already fully used, dependent co-products of joint production also applies here: since the production of e.g. a recycled metal as dependent co-product cannot be increased with that same multifunctional process/technology (i.e. by producing more e.g


a recycled metal as dependent co-product cannot be increased with that same multifunctional process/technology (i.e. by producing more e.g. metal goods, what is of course not happening), its additional provision via primary production cannot be assumed. Instead, alternative routes need to be modelled for the supply of the recycled metal. As stated for the general case, the determining co-product shall not be substituted. The following example explains what that means and why for "closed loop" and "open loop - same primary route" cases nevertheless the primary production is to be substituted: Example: the determining coproduct of primary and secondary metal is the primary metal. The secondary metal, after recycling, is the dependent co-product. If this one is fully used in the same or other products and from the perspective of the metal product made of primary metal, recyclability substitution is applied, substituting the secondary good by primary metal


From the perspective of the user of the secondary good "recycled metal", the metal primary production shall not be substituted, but alternative ways of supplying the recycled metal shall be modelled. This alternative way is however - what makes this case apparently specific - the primary production of that metal as this is the only way to increase the availability of the required metal on a net basis. Hence in both cases, primary production is to be substituted, but for different reasons.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results (see chapter 7.2), their actual collection and acquisition is to be planned. As for most steps in LCA work, also the data collection planning is iterative


As for most steps in LCA work, also the data collection planning is iterative. Before the actual planning of data and information collection can be done it is recommended to get clarity on some fundamentally different options and considerations:  Foreground system - specific, average, or generic data?  Background data for attributional and consequential modelling  Need for multi-annual average data or generic data  Primary and secondary data sources  Focus efforts 7.3.2 Foreground system data - specific, average, or generic (Refers to aspects of ISO 14044:2006 chapters 4.2.3.3.2 and 4.3.2.1) Avoiding black-box unit processes When aiming at collecting data for the identified processes within the system boundaries, aim should be to collect data for the actually required processes and not for agglomerates of these with other processes that are not required


This is important for accuracy of the data, for review reasons, as well as for avoiding multifunctionality problems that are otherwise unavoidable. This can be done by either collecting data exclusively for the required processes or, at least in some cases, by virtual subdivision of collected data, singling out the relevant inventory for the required function. Chapter 7.4.2.2 provides the details. Aiming at specific data for the identified processes within the system boundaries Ideally, the final model of the life cycle of a any system would be represented by producer or operator specific data, i.e. modelling the exact life cycle depicting - as far as required for the study - the supply-chain, use, end-of-life (for attributional modelling) or theoretical consequential supply-chain, use, end-of-life (for consequential modelling). In practice, and as a general rule, for foreground processes specific inventory data should be used


In practice, and as a general rule, for foreground processes specific inventory data should be used. This data is typically compiled as primary data from the product/technology developer134, goods producer, or service operator and should include specific secondary data from the tier-one suppliers (incl. waste service suppliers). As initial steps during data collection, generic or average secondary background data may be used to identify the need for more representative or specific data. This fully applies to attributional modelling and to a lesser extent to consequential modelling. For processes that are not expected to be key processes of the system, estimations (e.g. based on modelling from process knowledge) may equally provide a first idea of the process data


For processes that are not expected to be key processes of the system, estimations (e.g. based on modelling from process knowledge) may equally provide a first idea of the process data. Generic or average data for the foreground system in attributional modelling Generic or average data may be more appropriate for processes of the foreground system in case the quality of available specific data is considerably lower and the generic or average data sufficiently represents the process. It is important to note that there is no free choice between producer-specific and average or generic data, but the equivalence / representativeness determines this decision. 134 E.g. data on the use stage of consumer products. Other, independent sources may complement this.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results For attributional modelling, generic or average background data sets can also be used for foreground processes of little quantitative contribution to the overall environmental impact. They also can be used – as parameterised processes or partly terminated systems – for modelling foreground processes that operate standard machines (e.g. trucks for goods transport, injection moulding machines etc.) where only the specific operating conditions need to be adjusted. Generic or average data for the foreground system in consequential modelling For consequential modelling generic or average background data sets may equally help in the foreground system, in case available specific data lacks quality or to fill smaller data gaps


The use of generic parameterised unit process data sets is equally useful as in attributional modelling, if suitable for the specific process / system. 7.3.3 Background data for attributional and consequential models (No corresponding ISO 14044:2006 chapter but relates to aspects of chapters 4.2.3.3.2, 4.2.3.6.2, and 4.3.2.1) Types of background data As detailed in the related box and figure of chapter 6.6.1, the term background system relates to its concept from a data collection perspective135. The types of required background data differ between attributional and consequential modelling. For attributional modelling this type is the market consumption mix of processes / systems. For consequential modelling these are:  mix of "short-term marginal" processes / systems,  mix of "long-term marginal" processes / systems, and All these mixes relate to a specific or generic process, good or service (or a wider group of these) in a given market and a given time


The marginal mixes would either be the most cost-competitive processes or systems (in case of "growing, stable or slightly declining" markets, i.e. declining not more than the average displacement rate of capital equipment) or the least competitive ones (in case of strongly declining" markets; dito). This is unless secondary consequences and constraints change this or even counteract and fully compensate the primary consequences so that the average consumption mix better represents the superseded processes / systems than the marginal mix (respectively in that case both would be identical). Note that for substitution under Situation A and B, somewhat simplified provisions are made (see chapters 6.5.4.2 and 6.5.4.3). 135 Often also foreground processes are affected by an analysed decision. E.g. may a new production technology result in a strongly reduced process steam demand, what poses the question how the current on-site steam producers are affected as consequence


E.g. may a new production technology result in a strongly reduced process steam demand, what poses the question how the current on-site steam producers are affected as consequence. In such cases however one would rather model a scenario of the likely technology to be installed (or decide whether the steam producer would continue to operate at lower load factor) instead of applying a formal and theoretical consequential identification of the processes. This situation is identical to other micro-effect consequences that can be assumed to rather not change the installed processes.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results Unit process, parameterised unit process or LCI results data sets for background use Background data sets can be of different types: LCI results or unit processes (plus variants of these). Both have their clear advantages and disadvantages. It depends primarily on the availability and quality of the specifically required data, but also on the available expertise in modelling and other aspects, which approach is more suitable. If modelled consistently, combinations are equally possible. For both unit process and LCI results a good documentation and a qualified and independent external review or panel review are recommended or may be required, depending on the intended application


For both unit process and LCI results a good documentation and a qualified and independent external review or panel review are recommended or may be required, depending on the intended application. When working with unit processes and wherever possible, “Single operation unit processes” should be preferred for data collection over “Black box unit processes” (see Figure 7). This avoids potential problems of multifunctionality and substantially improves verification/review of the data. For some processes fixed LCI results or unit process inventories may be inadequate. This is if the inventory strongly depends on the specific operating conditions or specific e.g. inputs used. In those cases parameterised unit process data sets or partly terminated system data sets may be required or at least be more efficient and flexible. Examples are e.g. transport processes, flow injection and similar flexible processing machines, waste management processes, etc


Examples are e.g. transport processes, flow injection and similar flexible processing machines, waste management processes, etc. Specific, average, or generic data? Under attributional modelling and for the situation where no specific supplier is used, as well as for data sets further in the background, country/market technology average or generic background LCI sets are more appropriate, but still the data should represent the level of technology (i.e. real market average or – for scenarios - worst case and best case available), to appropriately represent the products in question. Under consequential modelling, average data is not well suitable in theory, unless there is a high uncertainty on which are the superseded processes, what often is the case. 7.3.4 Need for multi-annual average data or generic data Using data that is averaged over several years may also be necessary in cases where a single year is not representative for the general, “current” situation


This applies in cases where data varies considerably among years. This could for example be the case for agricultural products, where e.g. yield, the resulting nitrogen surplus and related emissions, pesticide amounts applied, etc. can differ considerably among years due to different meteorological conditions, disease incidents, and the like. Also the load of industrial plants and e.g. import-mixes of raw materials can vary considerably among years. This especially applies to data that represents a specific producer as the data can be expected to vary stronger than the data of the market mix. Similar as average data also generic data can in such cases often better represent the process or system than specific data. Such situations can be identified along historical data from different years of the analysed or similar process that differ significantly


Such situations can be identified along historical data from different years of the analysed or similar process that differ significantly. It may also be that only for the intended year of modelling specific incidents have occurred that quantitatively have affected the process‟ output or other relevant inventory items in a unique and hence not generally representative way. This case is also one example where average or generic data may have higher overall quality than specific data.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results 7.3.5 Primary and secondary data sources LCI data Based on the specific data needed and the quality requirements, and with the above considerations, the sources for the data and information are to be identified. Consistency and quality as well as quality assurance of data (i.e. review) are important requirements that support valid studies. As already addressed in chapters 6.9.3 and 6.9.4, a wide range of potential LCI data sources exist:  Primary data sources are the producers of goods and operators of processes and services, as well as their associations.  Secondary data sources which either give access to primary data (possibly after remodelling / changing the data) and to generic data are e.g. national databases, consultants, and research groups


 Secondary data sources which either give access to primary data (possibly after remodelling / changing the data) and to generic data are e.g. national databases, consultants, and research groups. The ILCD Data Network helps in identifying suitable secondary sources. It is recommended to well consider the specific data sources, as changing data sources during the process of modelling is likely to not only delaying the work, but also likely to result in considerable additional costs. Other data: recycling rates, statistical data, etc. Similarly as for the LCI data, also for other data the choice of the sources is an important step that should be taken systematically. See also chapter 6.9.4. 7.3.6 Focus on most relevant data and information It is recommended to balance the effort of data collection by the relevance of the respective data and information


See also chapter 6.9.4. 7.3.6 Focus on most relevant data and information It is recommended to balance the effort of data collection by the relevance of the respective data and information. To be efficient and to effectively using the available resources of time and money to provide the best attainable quality, LCA work needs to be focussed, avoiding to get lost in the huge amount of theoretically contributing processes, flows and aspects. Building on existing experience that sufficiently reflects the analysed process or system and that is of high quality is an essential guide. Product Category Rules (PCR) and product-group specific guidance documents can represent this experience


Product Category Rules (PCR) and product-group specific guidance documents can represent this experience. Frequent errors: Wrong focus of data collection It can often be found in LCA practice that the focus of data collection is not properly guided by relevance of that data for the final results: Personal interests in certain processes, lack of experience on what is key for the analysed process or system, no consideration of available experience elsewhere (e.g. as condensed in appropriate and high quality Product Category Rules (PCRs)), getting lost in the many options for substitution and allocation without checking whether it matters from the system's perspective, and many other reasons lead to using up the available time and resources to collect lots of detailed and accurate data for processes or flows that contribute little to the total. At the same time, rough estimate data or gaps remain unsolved for main contributing processes and flows


At the same time, rough estimate data or gaps remain unsolved for main contributing processes and flows. Efficient and effective good practice in data collection requires to focus on what matters. At the same time it should be warned to not entirely rely on existing experience and PCRs, as those sources may have often drawn on other existing experience and so on, without necessarily verifying what else matters. Using high quality experience is necessary, as well as making sure that the experience actually reflects the analysed situation and the specific process or system studied.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results Also the often found entire reliance on readily available background from third-parties, as e.g. included in LCA software (not checking for quality or data gaps in those data, where other third-party data may be needed) contribute to the lack of quality of the results and robustness of conclusions. It is recommended therefore to always foresee that high quality data may have to be also specifically collected or obtained also for key background processes. The following steps are recommended for systematically and efficiently determining quality requirements on LCI data. Unless the quality requirements are defined directly in the goal, this is done only after the first loop of data collection, results calculation, impact assessment, the identification of significant issues, and the evaluation


The requirements may need to be fine-tuned / adjusted in subsequent loops:  For the identification of quantitative LCI data quality needs, determine / estimate the accuracy, completeness and precision of the LCIA results that is required by the intended application, e.g. to allow identifying significant differences among compared alternative products.  Translate these requirements to related requirements at the level of elementary flows by taking into account the impact potentials of the individual elementary flows and by disregarding the uncertainties / inaccuracies associated with the characterisation factors.  Based on the above, use the requirements on the elementary flows to determine the maximum permissible uncertainties, inaccuracies and incompleteness of the overall inventory of the to-be-collected or purchased processes' or systems' inventories


Note that this includes systematic uncertainties from LCI methods and models applied in the system and from assumptions made when setting up the system (e.g. product life cycle model).  Use this information as indicative guidance on quality requirements in the collection or purchase of inventory data (i.e. unit process or LCI results and similar data sets). For third-party LCI data sets it is recommended to consider the following additional quality aspects: appropriate documentation, the use of compatible elementary flows and nomenclature, methodological consistency, and (potentially) a qualified external review. Provisions: 7.3 Planning data collection Differentiated for attributional and consequential modelling. Fully applicable to all types of deliverables, implicitly differentiated


Provisions: 7.3 Planning data collection Differentiated for attributional and consequential modelling. Fully applicable to all types of deliverables, implicitly differentiated. I) SHALL - Identify newly required, study-specific unit processes: Identify for which processes of the analysed system new, study-specific unit processes have to be developed with producer or operator specific primary and secondary data. This is typically the case for the entire foreground system (including for those parts of existing or planned contractual relationships). The use of technical process or flow diagrams is recommended. (7.3.2) II) SHALL - Average and generic data: Identify for which parts of the analysed system the use of average or generic LCI data sets is more appropriate. Note that for a given case, average or generic data may be more accurate, complete and precise also for some processes of the foreground system. If such will be used, this shall be justified. (7.3.2)


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results Note that in case only a single unit process is the deliverable of the LCI study, only data for that process are to be collected, of course, and the provisions apply analogously. III) MAY - Identify data and information sources: It is recommended to systematically identify sources for the required data and information. This includes considering working for the background system primarily with LCI results or with unit process data sets, which both have advantages and disadvantages that are for the given case to be evaluated. Combinations are possible if the data is consistent. Among the LCI data sources, primary and secondary sources can be differentiated. Guiding principle should be the availability and quality of the most appropriate data


Among the LCI data sources, primary and secondary sources can be differentiated. Guiding principle should be the availability and quality of the most appropriate data. Working with well documented and already reviewed data sets is recommended. This supports a correct use of the data sets, a sound documentation of the analysed system, and its review. (7.3.3, 7.3.5) [ISO+] IV) MAY - SI units: It is recommended to aim at collecting data in the Système international d'unités (SI) units, to minimise conversion efforts and potential errors. [ISO+] Note that SI units shall be used for reporting (see chapter 10.2). V) SHOULD - Multi-annual or generic data to be preferred?: Evaluate along the goal of the study whether multi-annual average data or generic data should be preferred over annual average data as better representing the process / system. This applies for processes with strong inter-annual variations (e.g


This applies for processes with strong inter-annual variations (e.g. agriculture; producer-specific data in general), to ensure sufficient time-related representativeness. (7.3.4) [ISO+] VI) MAY - Relevance-steered data collection: It is recommended to steer the effort for data collection by the relevance of the respective data and information. Building on existing experience that sufficiently reflects the analysed process or system and that is of high quality is an essential guide. Product Category Rules (PCR) and product-group specific guidance documents can represent this experience. The following is meant to help focussing data collection efforts


Product Category Rules (PCR) and product-group specific guidance documents can represent this experience. The following is meant to help focussing data collection efforts. The initial data quality and data set quality requirements as identified in 6.9.2 may need to be fine-tuned / adjusted in subsequent loops as follows (but see also chapter 4) (7.3.6): [ISO+] VI.a) For the identification of quantitative LCI data quality needs, determine / estimate the accuracy, completeness and precision of the LCIA results that is required by the intended application (e.g. to allow identifying significant differences among compared alternative products). VI.b) Translate these requirements to related requirements at the level of elementary flows by taking into account the impact potentials of the individual elementary flows and by disregarding the uncertainties / inaccuracies associated with the characterisation factors


VI.c) Use these requirements on the elementary flows to determine the maximum permissible uncertainty, inaccuracy and incompleteness of the overall inventory of the to-be-collected or purchased processes' or systems' inventories. Note that this includes systematic uncertainties from LCI methods and models applied and from assumptions made when setting up the system model. VI.d) Use this information as indicative guidance on quality requirements in the collection or purchase of inventory data (i.e. unit process or LCI results and similar data sets). For secondary LCI data sets it is recommended to consider the following additional quality aspects: appropriate documentation, the use of compatible elementary flows and nomenclature, methodological consistency, and


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results a completed qualified external review. Note that in case the later collected or purchased data sets do not meet the requirements, the results of the study may not meet the overall consistency, quality and review requirements. Note that all publicly accessible data sources shall later be referenced. Various descriptive information shall later be provided for all significant data, such as the data collection process, the age of the data and data quality indicators. 7.4 Collecting unit process LCI data (Refers to ISO 14044:2006 chapter 4.3.2 and aspects of 4.3.3) 7.4.1 Introduction and overview (Refers to aspects of ISO 14044:2006 chapter 4.3.2.1) Introduction For all processes that have been identified (see chapters 7.2.3 or 7.2.4), the inventory data have to be collected


An actual collection of inventory data is typically only required for the foreground system, provided all data in the background system can be sourced from available background databases. Unit process data are at the basis of all LCI work. The provisions for their collection are essentially the same for attributional and consequential LCI modelling. Ideally they relate to a single operation unit process of a specific process (e.g. bulk goods transport performed by a specific 7.5 t truck model). This is what this chapter relates too. However they can also refer to an averaged mix of processes (e.g. market mix of bulk goods transport by all the specific brands of EURO 4, 7.5 t trucks in Germany). This builds on this chapter, with the averaging being addressed in the later chapter 7.7. Or they can be generic in nature and hence depict a process or technology in general rather than its operation in a specific or average way (e.g


Or they can be generic in nature and hence depict a process or technology in general rather than its operation in a specific or average way (e.g. market mix of the same truck type as before, but obtained generically instead of averaging data of the specific truck models, which might not be available). Development of generic data sets is addressed in the later chapter 7.5, with many provisions of this chapter to be applied as well. All these data set types can include parameterisation, yielding technology models as parameterised unit process. Note that all of these can also refer to a set of interconnected single-operation unit processes (e.g. a plant or whole site), i.e. be a black box unit process for which the inventory data is collected136. For this mostly the same provisions apply as for single operation unit processes. Which of these forms of unit processes is used depends on a range of issues


For this mostly the same provisions apply as for single operation unit processes. Which of these forms of unit processes is used depends on a range of issues. These include:  goal and scope of the study (especially type of process / system analysed, intended applications),  data availability and quality, and 136 Note that unless explicitly aim of the study, the collection of single operation unit processes should be aimed at and the collection of black box unit processes should be avoided. Black box unit processes cause difficulties to review and often in addition multifunctionality problems. The latter require extra information and effort to be solved and in any case distort the results to some degree. If during data planning or raw data collection a process turns out to be a black box unit process, one should check whether it can be split by subdivision before data collection or virtual subdivision afterwards.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results available resources (finance, experts). Overview This chapter starts with the main guidance on the initial step of collecting raw data towards obtaining unit processes (7.4.2). This includes the important step of interim quality control and dealing with missing data. The next two subchapters give provisions on a range of methodological issues for elementary flows (7.4.3) and specific process types (7.4.4). The last subchapter 7.4.5 details conventions in naming and other aspects. A more tailor-made, but also much more condensed, technical guidance on the development of LCI data set is given in the separate guidance documents on “Specific guidance document for LCI data sets”. That document builds on the "Provisions" of this general guidance and focuses on the relevant items for LCI data set development


That document builds on the "Provisions" of this general guidance and focuses on the relevant items for LCI data set development. 7.4.2 Basic data collection towards unit processes (Refers to aspects of ISO 14044:2006 chapter 4.3.2.2. and 4.3.3) 7.4.2.1 Introduction and overview (Refers to aspects of ISO 14044:2006 chapter 4.3.2.2


and 4.3.3) Before providing the overarching methodological provisions and nomenclature and other conventions to be applied to processes and flows, in this chapter the provisions and recommendations are given on the basic collection of raw data137 and the way towards unit process inventories:  Avoiding black box unit processes by subdivision or virtual subdivision (7.4.2.2)  Describing what the unit process represents (7.4.2.3)  Types of input and output flows to collect (7.4.2.4)  Data and information types for specific, future and generic data sets (7.4.2.5)  Reference amount of the reference flow (7.4.2.6)  Representativeness regarding operation conditions (7.4.2.7)  Checking legal limits (7.4.2.8)  From raw data to unit process inventory per reference flow (7.4.2.9)  Solving confidentiality issues (7.4.2.10)  Interim quality control (7.4.2.11) , and as an important aspect of this  Dealing with finally missing inventory data (7.4.2.11.3) 137 A more comprehensive guidance


issues (7.4.2.10)  Interim quality control (7.4.2.11) , and as an important aspect of this  Dealing with finally missing inventory data (7.4.2.11.3) 137 A more comprehensive guidance and an approach for systematic documentation of this basic step could be a future work.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results 7.4.2.2 Avoiding black box unit processes by subdivision and virtual subdivision (Refers to aspects of ISO 14044:2006 chapter 4.3.4.2) General approach If the unit process for which data will be collected is a combination of more than one, physically separate process steps, this is a black box unit process; see Figure 8. Black box unit processes can cause difficulties to review. This is the case especially if the process is developed as generic process (see chapter 7.5) and the reviewer would be better able to judge on the level of the single process steps than on an integrated chain of steps. On the other hand and especially for specific data that is based on measurements, the review can well be done on basis of the measured data; subdivision does not help in that case


On the other hand and especially for specific data that is based on measurements, the review can well be done on basis of the measured data; subdivision does not help in that case. At the same time, black box unit processes often cause multifunctionality problems. These require extra information and effort to be solved and in any case distort the results to some degree. If subdividing a multifunctional black box unit process can solve the multifunctionality, this is to be preferred. Subdivided process chains may also be required by the specific application, e.g. a detailed weak-point analysis or ecodesign purpose is more interested in the single contributors and how to reduce their impact than on the value of the absolute, overall results


a detailed weak-point analysis or ecodesign purpose is more interested in the single contributors and how to reduce their impact than on the value of the absolute, overall results. In summary: if during data planning or raw data collection a process turns out to be a black box unit process, one should check whether the process can be split by subdivision and whether that would ease review, improve accuracy and applicability, and avoid multifunctionality. Subdivision is generally done before data collection or virtual subdivision afterwards. Subdivision First choice is to subdivide the concerned black box unit process into its included processes. This subdivision is performed prior to the final raw data collection. The relevant inventory data is collected separately and only for those of the included unit processes that relate to the analysed system


The relevant inventory data is collected separately and only for those of the included unit processes that relate to the analysed system. An example is an assembly hall, where data on electricity, consumables and parts consumption would be collected separately for the different production lines as single operation unit processes. The data on the hall itself, the heating, lighting etc. would also be collected separately as single operation unit processes, while being multifunctional processes, as they serve all production lines. Subdivision is especially important if the black box unit process provides more than one function, i.e. is multifunctional and the resulting single processes are all mono-functional.. If it is theoretically possible in this way to separate the delivery of the targeted good or service from that of the co-function(s), subdivision and sometimes virtual subdivision (see below) are the only approaches that can provide accurate data


In the preceding example this is not possible, as e.g. the hall and the hall heating cannot be modelled separately for the contained lines. Still, the split has probably substantially improved the data accuracy. Partial subdivision If it is not possible to split the black box entirely, a partial subdivision should nevertheless be done. Partial subdivision can lead to two types of results:


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results one or more included processes are singled out as single operation unit processes and one or more included processes are still black box unit processes (e.g. in an integrated site for production of the analysed system Melamine, the included Ammonia production plant and the Carbon dioxide co-product separation and compression can be singled out as separate single operation unit processes, while the Urea and Melamine production plants data are available only jointly.)  only some information can be singled out separately for the analysed function but one or more of the included processes are only partly split up, i.e. the "split" is cutting through a single process step


the "split" is cutting through a single process step. Note that under consequential modelling this form of partial splitting of processes can result in distortions when later substitution is used to separate the processes of the analysed function entirely. Under attributional modelling this form is appropriate. Virtual subdivision There are different possibilities for obtaining the inventory data of the included unit processes: actual data collection (preferable option) and – in many but not all cases – the use of knowledge about the involved processes: This can be the basis to split up the data of the multifunctional process and assign the inventory items to the included unit processes. Such knowledge can e.g. be the simple understanding that emissions to water can only come from the processes that contribute waste water, that certain parts or consumables are only required as input for certain processes, and the like


In some cases, the assignment and virtual subdivision is qualitatively and quantitatively clear and exact, as in the above example. In other cases the subdivision needs to draw on expert judgement and will not be exact while still improving the data quality. E.g. in a manufacturing line several electricity consuming steps might be metered jointly. Along other machine information (e.g. nominal power uptake, load factor, and running time) it may be possible to sufficiently accurately virtually subdivide the black box to the single process steps, even though not exactly. This way, qualitative production / operation system information can be sufficient to subdivide the black box partly and in some cases even entirely and correctly assign all or most of the quantitative information to the single included unit processes


Also in those cases where this “virtual subdivision” cannot provide all single data values, it will often significantly lower the effort, as only remaining missing data needs to be directly collected for the individual included processes. However, note that virtual subdivision can be applied in consequential modelling only if it results in complete separation of the inventory of the analysed function; otherwise the substitution would be distorted. From the perspective of the resulting data sets, virtual subdivision can mean to either generate more than one unit process from the black box data. Or - relevant for black box unit processes that cannot be entirely virtually subdivided but it cuts through the process - the process is not split into more than one, but the subdividable single inventory flows are assigned entirely or partly to the corresponding co-function


Virtual subdivision can also in principle be applied to physically not subdividable processes: in a chemical reactor, organic compounds may be chlorinated with Chlorine, resulting in different co-products with one, two and three chlorine groups. The total amount of Chlorine consumed in the reactions as reactant can be assigned to the co-products in proportion to the amount of Chlorine they have bound. This is also an example of partial subdivision that cuts through the single process step. Note that in this example any surplus of Chlorine and any Chlorine emissions require a separate and typically different approach


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results for solving the multifunctionality. It is reiterated that "cutting" through not further subdividable joint processes, as done in this example, shall not be applied in consequential modelling / substitution as the results would be distorted. Note that virtual subdivision is equivalent to identifying and using the determining physical causality as allocation principle, i.e. of depicting the quantitative inner relationships between the non-functional flows and the co-functions. Provisions: 7.4.2.2 Avoiding black box unit processes by subdivision and virtual subdivision Differentiated for attributional and consequential modelling. Note that these provisions are to be applied to each unit process separately, in case more than one is modelled (e.g. in the foreground system of an analysed system)


in the foreground system of an analysed system). I) SHOULD - Multifunctionality solvable by subdivision?: Investigate whether the analysed unit process is a black box unit process (concept see Figure 7): does it contain other physically distinguishable sub-process steps and is it theoretically possible to collect data exclusively for those sub-processes? Next, check whether subdivision can solve the multifunctionality of this black box unit process: can a process-chain within the initial black box unit process be identified and modelled separately - preferably process step by process step - that provides only the one required functional output? II) SHOULD - Based on the outcome, the following steps should be followed: II.a) If possible subdivide: If it is possible to collect data exclusively for those included processes that have only the one, required functional output: inventory data should be collected only for those included unit processes, i.e. subdivision be performed


subdivision be performed. II.b) If not possible, partially subdivide: If this is not possible (i.e. the analysed unit process contains multifunctional single operation unit processes that are attributed to the required functional output) or not feasible (e.g. for lack of data access or for cost reasons): inventory data should be collected separately for at least some of the included unit processes, especially for those that are main contributors to the inventory and that cannot otherwise (e.g. by virtual subdivision - see more below) clearly be assigned to only one of the co-functions. [ISO+] II.c) If also not possible, virtually (fully or partly) subdivide: If neither subdivision nor partial subdivision is possible or feasible, it should be checked whether it is possible by reasoning to virtually partly or fully sub-divide the multifunctional process based on process/technology understanding


This is the case wherever a quantitative relationship can be identified and specified that exactly relates the types and amounts of a flow with at least one of the co-functions / reference flow(s) (e.g. the specific mechanical parts or auxiliary materials in a manufacturing plant that are only used for the analysed product can be clearly assigned to that product by subdividing the collected data). For those processes where this can be done, a virtual subdivision should be done, separating included processes as own unit processes without separate data collection. [ISO+] Note that under attributional modelling, singling out required process steps from a black box unit process by virtual subdivision can also improve the basis for a subsequent allocation, with more accurate results.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results Note that virtual subdivision is applying the same logic as the physical causality as allocation principle, i.e. of depicting the quantitative inner relationships between the non-functional flows and the co-functions. Note that under consequential modelling, actual or virtual partial subdivision within processes results in distortions in case substitution would later be used to separate entirely the analysed function. III) MAY - Other reasons to subdivide / virtually subdivide?: If according to the initial step of these "Provisions" the unit process is a black box but is not multifunctional, check whether it would improve the reviewability of the data or whether it is required for the intended applications to subdivide or virtually subdivide the process


If so, it is recommended to fully or partly subdivide or virtually subdivide the process. [ISO+] 7.4.2.3 Describing what the modelled unit process represents (Refers to aspects of ISO 14044:2006 chapter 4.3.2.2) Starting from the identification and possibly initial description of the required process (chapter 7.2), describe the actually modelled process in more detail: This includes information of its actual technological, geographical and time-related representativeness and especially the functional unit(s) and reference flow(s), and other quantitative and qualitative information. This information helps in preparation of the actual inventory data collection and quality control. During data collection, quality control etc. it will be fine-tuned towards obtaining the required description and specification of the final process as it has been modelled


During data collection, quality control etc. it will be fine-tuned towards obtaining the required description and specification of the final process as it has been modelled. Frequent errors: Misleading description beyond what is represented by the actual data It can be quite often found that published data sets do not describe what they actually represent (i.e. based on the used data), but what they were intended or are meant to represent. E.g. may the data reflect a single technology, but the data set in such cases is claimed to represent a market mix. Or the data is directly derived from a research study or lab data, theoretical models etc., but is described as being a representative industry process, reflecting average operation at a large scale. This must be avoided by clearly stating what the data set represents. It can of course be that a data set is to some degree representing e.g


This must be avoided by clearly stating what the data set represents. It can of course be that a data set is to some degree representing e.g. a market mix, even though it does not cover all technologies, routes, etc., but this is to be clarified in a prominent place: If combining data from different sources or having otherwise lack of representativeness, this shall be stated in the data set and any accompanying documentation, if published. Note that at the end of the data collection the final documentation of this meta data is to be completed, e.g. naming operating conditions, assumptions made, use of data from other sources, data gaps, achieved completeness and precision of the inventory, etc. Details on documentation are given in chapter 10. Provisions: 7.4.2.3 Describing what the unit process represents Note that these provisions are to be applied to each unit process separately, in case more than one is modelled (e.g. in the foreground system of an analysed system)


in the foreground system of an analysed system). I) SHALL - Characterise the unit process: I.a) Representativeness: Characterise the unit process regarding the technology /


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results technique, geographical / market scope, and the time (e.g. year, plus seasonal / diurnal differentiation, if applicable) it represents and any possibly limited representativeness. This characterisation includes identifying the relevant operating conditions and/or other factors influencing its inputs and outputs to a relevant degree. See chapter 6.8 for details. I.b) Reference flow(s) / functional unit(s): If the deliverable is an LCI study or data set, one or more reference flows are the key identifiers and quantitative reference of the life cycle inventory and documentation. Determine and name the reference flow(s) as the amount of product(s) of the system that provide the function as specified in the functional unit


Determine and name the reference flow(s) as the amount of product(s) of the system that provide the function as specified in the functional unit. For recommendations on product flow naming see document "Nomenclature and other conventions". Also the functional unit(s) should be specified if appropriate and/or technical specifications be given (provisions for different process / system types see chapter 6.4.6). [ISO+] Note that a variety of meta data about the process and/or its product(s) is later to be provided to the user and reviewer, e.g. on its technical applicability, method assumptions, who has modelled it, etc. It is recommended to ensure proper documentation already on level of the single unit process, also if the deliverable is an LCI result or LCA study, by using the ILCD data set format (see also chapter 10 on “Reporting”)


7.4.2.4 Types of input and output flows to collect (Refers to aspects of ISO 14044:2006 chapter 4.3.2.3) Types of flows The final unit process inventory lists input and output flows. These are based on various kinds of data and information and only seldom the collected data can directly be inventoried. This chapter identifies first what is the kind of flows that finally will be found in the inventory, as guidance for orientation: Process inventory data is collected or modelled on input side and output side. Input side flows include elementary flows such as material and energy resources, land use, product flows such as energy carriers, chemicals and materials, consumables, parts and components, semi-finished products, complex products, and services of all kind, and. Output side flows include – next to the one or more product(s) - generated waste, emissions to air, water and soil, and other environmental aspects that may be of relevance for the impact assessment (e.g


noise, nature littering, etc.) and for the given case. Specifically for waste management processes, waste flows will additionally occur on the input side; see chapter 7.4.4.2. The Provisions list the types of flows systematically. Provisions: 7.4.2.4 Types of input and output flows to collect I) SHALL - Types of input and output flows: Quantitative data of all relevant138 inputs and outputs that are associated with the unit process shall be collected /modelled, as far as possible. Where not possible, the gasp shall be documented and if they cannot be overcome be considered when reporting the achieved data quality and when interpreting results of a study. These flows typically include, if relevant for the modelled 138 See Action on "applying cut-off rules" more below in this chapter.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results process / system: I.a) Input of “consumed” products (i.e. materials, services, parts, complex goods, consumables, etc.), as product flows. I.b) Input of wastes (only in case of waste servicing processes), as waste flows. I.c) Input of resources from nature (i.e. from ground, water, air, biosphere, land, etc. and with possible further sub-compartment specifications as required by the impact assessment methodology to be applied), as elementary flows. I.d) Emissions to air, water, and soil (with possible further sub-compartment specifications as required by the impact assessment methodology to be applied), as elementary flows I.e) Other input and output side interventions with the ecosphere (if required by the applied LCIA methods), as elementary flows. I.f) Output of wastes (e.g


I.f) Output of wastes (e.g. solid, liquid, gaseous waste for waste management within the technosphere139), as waste flows. I.g) Output of valuable goods and services provided by the process, as product flows. 7.4.2.5 Data and information types for specific, future and generic data sets (Refers to aspects of ISO 14044:2006 chapter 4.3.2.2. and 4.3.3) Specific data collection - measurements and tailored questionnaires The most representative sources of data for specific processes are measurements directly performed on the process, or obtained from operators by interviews or questionnaires (see morebelow inthis chapter). Seldomly the data can directly be inventoried, but it needs scaling, aggregation or other forms of mathematical treatment to bring them in relation to the process' functional unit(s) and/or reference flow(s). This is addressed in chapter 7.4.2.9


This is addressed in chapter 7.4.2.9. Among others the following types of directly or indirectly measured data and information can be differentiated for existing processes and products:  process or plant level consumption data  bills and stock/inventory-changes of consumables  emission measurements (concentrations plus corresponding off-gas and wastewater amounts)  composition of waste and products, especially the elementary composition and energy content in support of element and energy balances that support quality control and quality improvement (cut-off) Further data sources for specific processes Next to measurements, it is typically helpful (also for cross-checks) or even necessary (to fill gaps) to draw on other data sources. These include: 139 The emissions resulting from waste that is directly discarded into the environment shall be modelled as part of the LCI model, with the processes considered to be part of the technosphere (details see chapter 7.4.4.2).


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results recipes and formulations,  part lists,  patents,  process engineering models,  stoichiometric models,  process and product specifications and testing reports,  legal limits,  data of similar processes, and  BAT reference documents. For future processes and for generic data sets, data and information on existing processes and on models of the future or generic processes need to be used jointly. Future processes - models, foresight, lab data For future processes this will be more on the side of models, drawing on all kinds of available data and information, including e.g.:  process modelling or planning,  patents,  lab data or pilot plant data,  data of existing, similar technologies / techniques,  BAT reference documents, and again  legal limits


Generic data - process and system characteristics For generic data sets technical characteristics of the to-be-modelled processes can often be measured and then averaged towards getting representative parameters for the generic model. Such technical characteristics can be e.g.  principle list of relevant flows of the process or e.g. bill of material and processing level of the good,  efficiency ratios of e.g. energy conversion or yield,  stoichiometric and other physical limits to the range of flow-amount ratios,  ranges of existing technologies / techniques, and again  BAT reference documents, and  legal limits. On the development of generic processes see 7.5


On the development of generic processes see 7.5. Use stage and initial waste management data of consumer products In the case – depending on the goal of the study – also the use stage and initial waste management of a consumer product (either by final consumers or by a service operator) is included in the system boundaries, the data collection faces different challenges than for production processes:


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results In difference to production processes, the way how the product is used is often much less homogeneous and much less well-defined. Very different use scenarios exist. At the same time, consumer products often have an important use stage when they perform their function, e.g. by consuming energy ("energy-using products"), by being related to energy-consumption ("energy related products"), or by having other relevant characteristics (e.g. being potentially problematic regarding initial waste management by the consumer such as waste separation questions, discharge via toilet, etc.). This affects in a similar degree processes operated at final consumers and as supporting processing in business


This affects in a similar degree processes operated at final consumers and as supporting processing in business. Another example - also related to the use stage of products but less obvious - is the use of personal consumer products such as clothes, watches, mobile phones, laptops, and the like: many of them are transported during their use stage, e.g. by car, train, or plane. Their weight, the related fuel consumption, emissions, etc. should be considered in principle, if quantitatively relevant and using e.g. an average or typical transport situation. The data for these steps can in addition to measurements and technical specifications from the producer come from surveys that aim at identifying the representative average or typical user behaviour. This often requires different forms of data collection. Questionnaires and other means to collect data For the data collection, it is recommended to use tailor-made data collection sheets together with specific (e.g


Questionnaires and other means to collect data For the data collection, it is recommended to use tailor-made data collection sheets together with specific (e.g. technical) flow charts to ensure proper inventorying and documentation already on level of the single unit processes. The initial and fine-tuned flow charts that were prepared in context of the scope definition and when identifying to-beincluded processes are useful for this purpose. During data collection and iterative with feedback from the process operator they may be revised to better capture the respective process(es). It is recommended to depict in these flow-charts the level of the desired detail, e.g. singleoperation unit processes and not on the aggregated level of black box unit processes. This supports decisions on the eventual need for subdivision of multifunctional processes and the review of the inventory


This supports decisions on the eventual need for subdivision of multifunctional processes and the review of the inventory. Provisions: 7.4.2.5 Data and information types for specific, future and generic data sets I) SHOULD - Raw data types: Raw data types that should be used for the process, as required: [ISO+] I.a) Measured data collected by/at process operators should be preferred if possible and appropriate. Measurements are not only physical measurements of e.g. emissions but also other specific information for the operated process such as e.g. bills and consumption lists, stock/inventory changes, and similar. I.b) Element composition and energy content of product and waste flows. This data should later be inventoried as flow property information for these flows to support interim quality control, review, and improving data quality. I.c) Various other data can be helpful (also for cross-checks) or even necessary (to fill gaps). These are e.g


I.c) Various other data can be helpful (also for cross-checks) or even necessary (to fill gaps). These are e.g. recipes and formulations, part lists, patents, process engineering models, stoichiometric models, process and product specifications and testing reports, legal limits, market shares and sizes, data of similar processes, BAT reference documents, etc.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results I.d) Use stage information: For modelling the use stage of consumer products and initial waste management, it is recommended to use surveys and studies that analyse the average or typical user behaviour to complement product specifications and user manuals. Information provided in product category rules (PCR) can be supporting. II) MAY - Tailor-made data collection forms: It is recommended to use tailor-made data collection forms together with technical flow charts. Specific data collection forms are recommended over generic forms. [ISO+] 7.4.2.6 Reference amount of the reference flow (Refers to aspects of ISO 14044:2006 chapter 4.3.3) The individual data for the inventory must each be quantitatively expressed as flows per functional unit (e.g


the mass of carbon dioxide that is emitted to air in relation to the reference flow of the system, e.g. 1 MJ lower calorific value heat generated in case of a water boiler). In attributional modelling, the inventories and the model are linearly to the amount of function, i.e. it does not matter whether 1 kg copper wire is used or 100,000 t. In consequential modelling however, the amount of the required or provided function influences whether a small-scale or large-scale situation exists. In modelling of Situation B (see chapter 5.3) it depends on the actual amount in relation to the market size whether large-scale consequences can be assumed to occur. To ease the identification of those processes where this applies, it is therefore recommended to check the amount in context of the system model, e.g. by scaling the model to the total scale of the analysed process in the foreground system


by scaling the model to the total scale of the analysed process in the foreground system. Together with the information of the market size, that is recommended to be documented in any process data set for use in consequential modelling, it can be easily checked - starting from the foreground process and going stepwise into the background system - which processes are affected. To ease reporting, reading, reviewing and jointly using inventory data from different data providers, a convention for the selection of these reference flow properties and reference units is helpful: Unless explicitly differently set by the goal of the study, it is recommended to express the inventory always in relation to "1 unit" of the function of the process / system (e.g. 1 kg "Copper wire XY standard; 0.1 mm"), using the flow properties and reference units as defined in the already named document “Nomenclature and other conventions” (see also chapter 7.4.5). This is unless a different unit (e.g


This is unless a different unit (e.g. one year of production) is explicitly required for the intended applications. If there is more than one function, only one of them can be set to "1 unit" and the other in proportion, of course. Provisions: 7.4.2.6 Reference amount of the reference flow Differentiated applicability to Situations A, B, and C. Differentiated for attributional and consequential modelling. Differentiated applicable for different types of deliverables. Note that these provisions are to be applied to each unit process separately, in case more than one is modelled (e.g. in the foreground system of an analysed system). I) MAY - "1 reference unit" for the reference flow: It is recommended to use the amount of "1 reference unit" of the reference flow (e.g. "1 kg" Copper wire...) and to


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results express the inventory of the process in relation to this amount. This is unless a different amount would be required for the intended application (e.g. "1 year of production" of a site). [ISO+] II) SHALL - Document absolute amount of the central process: For LCA studies under Situation A and B, the absolute amount of the central process in the foreground system shall be documented. The total market size of the function of this process shall be documented. This shall be done with the sufficient precision to later check whether the product or waste flows that link the foreground with the background system and potentially further process steps in the background system or any multi-functional foreground processes need to be modelled under Situation B, i.e


whether the analysed decision has large-scale consequences beyond the foreground system. [ISO+] 7.4.2.7 Representativeness regarding operation conditions (Refers to aspects of ISO 14044:2006 chapter 4.2.3.6.2) General The representativeness regarding operating conditions is part of the technological representativeness: The collection of inventory data is to take into account the full cycle of the process, i.e. in addition to the actual operation of the process also e.g. start, closure, and eventual stand-by times. It may well be that under these special operation conditions, which may not be seen as directly contributing the system, a large share of the emissions occurs. The above applies unless the data set is meant to represent only a partial cycle. The provisions apply analogously to services, i.e. preparation of the work, performing the service, stand-by/waiting times, after-service activities such as e.g. cleaning of the equipment, performing warrantee activities, etc


preparation of the work, performing the service, stand-by/waiting times, after-service activities such as e.g. cleaning of the equipment, performing warrantee activities, etc. In order to get a representative impression of the inputs and outputs associated with the process, they should be quantified for a running time of the process that covers at least one full cycle. The results are then divided by the functional output of the process during this time, hereby directly expressing it in unit process form. For operating plants it is recommended (also in ISO 14044:2006) to use one full year as data basis, to capture these are other issues. Parameterised processes Data used for developing the formulas of for parameterised processes should cover all relevant technical and management aspects of the to-be-represented process


Parameterised processes Data used for developing the formulas of for parameterised processes should cover all relevant technical and management aspects of the to-be-represented process. In principle all these variables that relate one or typically several inputs and outputs to them (or to other inputs and outputs) needs to be covered and expressed in mathematical relations. These variables and the parameters that will later be used to adjust the process to represent the specific way the process is run can be e.g. load-dependent yield and consumption of consumables, input-composition dependent emissions, yield-dependent consumption of products, collection and recycling rates, and many others. Provisions: 7.4.2.7 Representativeness regarding operation conditions Note that these provisions are to be applied to each unit process separately, in case more than one is modelled (e.g. in the foreground system of an analysed system)


in the foreground system of an analysed system). I) SHALL - Full operational cycle of the process, if required: The collected inventory


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results data for a specific process shall as far as possible and required to meet the goal represent the full operational cycle of the process. This includes all quantitatively relevant steps such as e.g. preparation, start, operation, closure, stand-by and cleaning as well as maintenance and repair of the process / system and under normal and abnormal operating conditions. This is unless the data set is meant to represent only a partial cycle. The above applies analogously also to services. The achieved representativeness of the data shall be documented. II) SHOULD - One full year as data basis: For measured data of operated processes, data for at least one full year should be used as basis for deriving representative average data


II) SHOULD - One full year as data basis: For measured data of operated processes, data for at least one full year should be used as basis for deriving representative average data. A sufficient number of samples should be taken and the uncertainty be considered when reporting the precision. III) SHOULD - For parameterised processes: The mathematical relations should represent the relevant changes of the inventory in dependency of the influential parameters, which can be e.g. technical, management, or others. This can include quantitative and qualitative relationships between inventory flows. [ISO+] Note that the mathematical model and its relevant assumptions and limitations later will need to be documented as well. 7.4.2.8 Checking legal limits (No corresponding ISO 14044:2006 chapter) It is additionally advisable to refer to legal limits and reporting obligations that exist for the analysed process or the sector in which the process is operated (or relates to)


All emissions that are specifically regulated should be checked for relevance and – if given – be quantified and reported in the inventory. However, to avoid later questions it is advised to report regulated emissions also in case they are not relevant for the LCIA results. In the case the country where the process is operated does not have legal limits or these are very limited in international comparison, it is advisable to identify the inventory items for which legal limits exist in other countries with stricter legislation (e.g. Japan, the EU, or the USA). The values that are set for legal limits can also be used to check whether the measured data is plausible, and in some cases legal limit values can – after scaling them in relation to the reference flow – also be used as worst case estimate