Interventions expected to be environmentally irrelevant can be excluded from the environmental profile, but this should be transparently justified in the LCA study report. 4.3.18 Economic flows not followed to system boundary TOPIC LCAs may comprise certain flows that are not specified in terms of environmental interventions, either inputs, like energy or materials, or outputs, like solid waste. Every effort should be made to avoid such flows, in the first place by applying the data estimation methods outlined in Section 3.8. All economic flows that cannot be followed to the system boundary should then be listed in a separate category: ‘Economic flows not followed to the system boundary’. Flows listed in this category should always be described qualitatively (e.g. ‘hazardous waste’ and ‘non-hazardous waste’) and, wherever possible, quantitatively (e.g. truck)


Flows listed in this category should always be described qualitatively (e.g. ‘hazardous waste’ and ‘non-hazardous waste’) and, wherever possible, quantitatively (e.g. truck). CONCLUSIONS All economic flows that cannot be followed to the system boundary, even after application of the data estimation methods outlined in Section 3.8, should be listed in a separate category ‘Economic flows not followed to the system boundary’. RESEARCH RECOMMENDATIONS No research foreseen. 4.4 Classification TOPIC In this step the environmental interventions qualified and quantified in the Inventory analysis are assigned on a purely qualitative basis to the various pre-selected impact categories (see Section 4.2). For a baseline list of interventions, for which characterisation factors have previously been derived, the classification step involves no actual work as these interventions have already been assigned to the various impact categories in this Guide (see Section 4.4 of Part 2b of this Guide)

Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002)


In the case of other interventions the practitioner will have to adopt an appropriate procedure of his own.


Part 3: Scientific background DEVELOPMENTS IN THE LAST DECADE Heijungs et al. (1992) Classification was not a separate step in Heijungs et al. (1992). At that time classification was the name given to the step covering the current ISO steps “Selection of impact categories, category indicators and models”, “Classification” and “Characterisation”. In Heijungs et al. (1992) interventions were assigned implicitly (i.e. not in a separate step) to relevant impact categories by means of associated characterisation factors. Interventions with multiple impacts, i.e. contributing to more than one impact category (parallel or serial), were assigned in their entirety to all relevant impact categories. With respect to the classification step ISO 14042 (2000E) states: “When LCI results are assigned to impact categories, issues associated with LCI results may be highlighted


With respect to the classification step ISO 14042 (2000E) states: “When LCI results are assigned to impact categories, issues associated with LCI results may be highlighted. Assignment of LCI results to impact categories should consider the following, unless otherwise required by the goal and scope: assignment of LCI results which are exclusive to one impact category; identification of LCI results which relate to more than one impact category; distinction between parallel mechanisms, e.g. is allocated between the impact categories of human health and acidification; allocation among serial mechanisms, e.g. may be assigned to ground level ozone formation and acidification. If LCI results are unavailable or of insufficient data quality for the LCIA to achieve the goal and scope of the study either an iterative data collection or an adjustment of the goal and scope is required.” Guinée (1995), Lindfors et al. (1995c), Udo de Haes ed. (1996) and Wenzel et al


(1995c), Udo de Haes ed. (1996) and Wenzel et al. (1997) also discuss the topic of multiple impacts of chemical releases and together distinguish the following four categories of emissions: Emissions with parallel impacts, i.e. emissions of substances that may theoretically contribute to more than one impact category but in practice only to one, e.g. an emission of which may have either toxic or acidifying impacts. Emissions with serial impacts, i.e. emissions of substances that may in practice have successive impacts, e.g. emissions of heavy metals which may first have ecotoxicological impacts and subsequently, via food chains, impacts on human health. Emissions with indirect impacts, i.e. emissions of substances having a primary impact that in turn leads to one or more secondary impacts, e.g


Emissions with indirect impacts, i.e. emissions of substances having a primary impact that in turn leads to one or more secondary impacts, e.g. aluminium toxicity induced by acidification, or methane contributing to photo-oxidant formation, with the produced ozone contributing in turn to climate change, which in turn may contribute to stratospheric ozone depletion. Emissions with combined impacts, i.e. emissions of substances having a mutual influence on each other’s impacts, e.g. synergistic or antagonistic impacts of toxic substance mixes, or and VOC, both of which are required for photo-oxidant formation. In order to avoid double counting, for emissions having parallel impacts it is generally recommended in the literature that the respective contributions of such emissions to relevant impact categories be specified


However, no guidelines are available on how this task is to be performed.ln general, such specification should be performed only in those cases where it really matters (where the contribution of the substance to one impact category substantially lessens its potential contribution to another, e.g. acidification or eutrophication by Rough calculations show that for example, is less relevant in this respect; see Heijungs et al., 1992). If it is unclear how such emissions are to be allocated, as a general recommendation it is advised either to assign them in their entirety to all relevant impact categories or to divide them equally across the categories (e.g. 50/50). If there were one, allencompassing fate and exposure model available covering all impact categories, rather than the diversity of models used for the various impact categories today, parallel impacts would no longer constitute a problem


Current fate and exposure models specify the compartment (or target organism) in which the substance has its principal impact and which impact categories are thus potentially relevant (e.g. emissions to the air may end up in soil or water, with consequent terrestrial or aquatic ecotoxic impacts, respectively). Given that we are still some way from having an all-encompassing fate and exposure model, however, full assignment of emissions having parallel impacts may, for the present, lead to some measure of double-counting. For emissions having serial and indirect impacts the literature generally recommends allocating such emissions in their entirety to all relevant (i.e. serial and indirect) impact categories unless characterisation factors for this purpose are lacking, as in the case of missing (indirect) GWP factors, for example.


Part 3 : Scientific background For emissions having combined impacts the literature generally recommends introducing assumptions regarding background concentrations of the other relevant substances. In practice this is currently only feasible for NOx as a precursor in photo-oxidant formation, but not for synergistic or antagonistic impacts of toxic substance mixes, as knowledge on these issues is virtually entirely lacking. Section 4.4 of Volume 2b lists all interventions for which at least one baseline characterisation factor is available. We advise practitioners to use this list as a default and classify inventory results according to the choices embodied in it. Items not included in the default list should be included using expert judgement and other literature sources. PROSPECTS As indicated above, the subject of parallel impacts may eventually become part an all-encompassing fate and exposure model


PROSPECTS As indicated above, the subject of parallel impacts may eventually become part an all-encompassing fate and exposure model. ] In that case the ‘Classification’ step will be restricted solely to the assignment of interventions to defined impact categories in Section 4.2 (similar to Characterisation now). CONCLUSIONS Emissions having truly parallel impacts are probably rather scarce. As an all-encompassing fate and exposure model is still lacking, it is recommended to assign such emissions in their entirety to all relevant impact categories. Emissions with serial and indirect impacts should also be assigned in their entirety to all relevant impact categories, unless there is insufficient information to do so (e.g. in the case of missing (indirect) GWPfactors) or an overlap between impact categories (e.g. aluminium toxicity induced by acidification, which is already regarded as part of the impact category acidification)


aluminium toxicity induced by acidification, which is already regarded as part of the impact category acidification). Emissions with combined impacts should likewise be assigned in their entirety to all relevant impact categories. In characterisation modeling assumptions must then be made regarding standard concentrations of other relevant substances. Synergistic and antagonistic impacts of toxic substance mixes cannot currently be covered in LCIA. RESEARCH RECOMMENDATIONS Short-term research It is recommended to undertake research to find appropriate default values for the division of parallel interventions, where relevant. Long-term research Research on procedures for assigning new substances to impact categories is recommended


Long-term research Research on procedures for assigning new substances to impact categories is recommended. 4.5 Characterisation TOPIC In the characterisation step of Impact assessment the environmental interventions assigned qualitatively to a particular impact category in classification are quantified in terms of a common unit for that category, allowing aggregation into a single score: the indicator result. DEVELOPMENTS IN THE LAST DECADE According to ISO 14042 (2000E) this step is now concerned only with calculation of the (category) Heijungs et al. (1992) In Heijungs et al. (1992) indicator results were also calculated using characterisation factors: where i is the type of intervention (e.g. substance emission or resource extraction) and its magnitude. indicator results, using the methods described in Section 4.3. For each impact category the (category) indicator result is calculated by multiplying the relevant interventions by their corresponding characterisation factors


For each impact category the (category) indicator result is calculated by multiplying the relevant interventions by their corresponding characterisation factors. Together, these results constitute the ‘environmental profile’: a table showing the indicator results for all the predefined impact categories supplemented by any other relevant information. PROSPECTS No further developments are foreseen.


Part 3: Scientific background CONCLUSIONS For each impact category the (category) indicator results are calculated by multiplying the relevant interventions by their corresponding characterisation factors, according to the formulae elaborated in Sections 4.3.1 to 4.3.16. RESEARCH RECOMMENDATIONS No research is foreseen. 4.6 Normalisation TOPIC ISO 14042 (2000E) defines normalisation as “calculation of the magnitude of indicator results relative to reference information”. The reference information may relate to a given community (e.g. The Netherlands, Europe or the world), person (e.g. a Danish citizen) or other system, over a given period of time. Other reference information may also be adopted, of course, such as a future target situation


a Danish citizen) or other system, over a given period of time. Other reference information may also be adopted, of course, such as a future target situation. While normalisation is considered an optional element of LCIA in ISO 14042 (2000E), in this Guide it constitutes a recommended step of life cycle Impact Assessment The main aim of normalising the (category) indicator results is to better understand the relative importance and magnitude of these results for each product system under study. Normalisation can also be used to check for inconsistencies, to provide and communicate information on the relative significance of the (category) indicator results and to prepare for additional procedures such as weighting or Interpretation (ISO 14042, 2000E)


There exist other definitions of ‘normalisation’, as is the case in multicriteria analysis (where it if often understood as division of the various values in a data set by a single reference value from that set, to express all the values in terms of that reference value). These definitions and the associated methods are not discussed here. DEVELOPMENTS IN THE LAST DECADE ISO 14042 (2000E) states that in selecting the reference system due consideration should be given to the Heijungs et al. (1992) In Heijungs et al. (1992) the normalisation step was mentioned and defined, but not yet numerically elaborated. consistency of the spatial and temporal scales of the environmental mechanisms and of the reference value. It is stated, furthermore, that it may be desirable to use several reference systems, in order to assess their respective influence on the LCIA outcome, and that the normalised results are to be termed ‘the normalised LCIA profile’


(For reasons of consistency in this Guide the term ‘normalised environmental profile’1 is employed; see Glossary). The reference value is the indicator result for a reference system. It is thus for a given impact category the sum of all the interventions associated with the reference system multiplied by the appropriate characterisation factors: with: indicator the indicator result for impact category cat and reference system ref (e.g. in or in the reciprocal of indicator is here referred to as the normalisation factor for impact category cat and reference system ref, 1 This was necessary because ‘LCIA’ refers to the entire Impact assessment phase, which comprises a number of quite distinct steps, such as classification, characterisation, normalisation and weighting, each withits own results.


Part 3 : Scientific background the magnitude of intervention i (emission, resource extraction or land use) associated with the reference system ref(e.g. in or in the characterisation factor for intervention i and impact category cat (e.g. in the normalised indicator result for impact category cat (in yr or in yrcapita); the indicator result for impact category cat (e.g. in kg). characterisation normalised indicator indicator There is currently much discussion regarding appropriate choice of reference systems for normalisation. ISO provides several examples of the type of reference system that can be used for the purposes of normalisation (ISO 14042, 2000E); 1. 2. 3. the aggregate interventions for a given area (global, regional, national or local) in a reference year; examples are the normalisation figures of Guinée (1993) for the world and those of Blonk et al


the aggregate interventions for a given area (global, regional, national or local) in a reference year; examples are the normalisation figures of Guinée (1993) for the world and those of Blonk et al. (1997b) for the Netherlands; the per capita interventions for a given area in a reference year; examples are the per capita figures for Danish and world citizens of Wenzel et al. (1997); a baseline scenario, such as the calculated (category) indicator result for a given alternative product system; no examples of this method are available. Blonk et al. (1997b) have developed a fourth type of reference value: 4. the aggregate interventions associated with the habits of consumption of a particular population in a reference year, for example the data for the Dutch population of Blonk et al. (1997b). The first two approaches are most commonly used


(1997b). The first two approaches are most commonly used. Which is preferable depends on another important choice: normalisation at a single scale level (generally the world, sometimes Europe) versus combined normalisation at the global and regional scales. Normalisation at a single scale level In this option the results for each impact category are normalised against reference data from the same reference area, either aggregate interventions (method 1) or per capita interventions (method 2). The most suitable reference area would then seem to be ‘the world’, as LCAs are not generally sitedependent. The two methods differ only by a constant factor (1 / world population and which method is adopted will depend on the goal of the study


The two methods differ only by a constant factor (1 / world population and which method is adopted will depend on the goal of the study. Reference against aggregate world interventions has the advantage of providing a comparison with the actual aggregate magnitude of the impact categories at stake, while comparison with the interventions of an ‘average world citizen’ shifts the focus of issues to the individual and may therefore be better for communicative purposes. Normalisation data for the world in 1990, Europe in 1995 and the Netherlands in 1997 are available in Huijbregts et al. (in prep.) The EDIP project is currently developing estimates for the contribution of an ‘average world citizen’ to regional and local impacts as reference values (Hoffman & Stranddorf, 1999). At the time of writing of this Guide these data were not yet available, however


At the time of writing of this Guide these data were not yet available, however. Normalisation at different scale levels If, in a particular LCA study, Inventory analysis and Impact assessment for global impact categories are carried out on a global scale and Inventory analysis and Impact assessment for regional impact categories on a regional scale, there are two options for normalisation: the results for each impact category are normalised using reference data from the same reference area (see above); or normalisation is carried out at two scale levels, with the results for global categories being normalised using global reference values and the results for regional categories using the appropriate regional reference values. This approach is more in line with ISO 14042 (2000E). ISO states: “The selection of the reference system should consider the consistency of the spatial and temporal scales of the environmental mechanism and the reference value”


ISO states: “The selection of the reference system should consider the consistency of the spatial and temporal scales of the environmental mechanism and the reference value”. In this second case, simple division by the aggregate interventions per area would not be correct, for the results for global impact categories would then be rated far lower than those for regional impact categories, because aggregate world interventions far outstrip interventions from any one region. The per capita approach (method 2) resolves this problem. The world totals are divided by the total world population and the regional totals by the population of the region in question. An example of an LCA method in which global and regional Impact assessment, normalisation and weighting are combined is the EDIP approach (Wenzel et al., 1997)


An example of an LCA method in which global and regional Impact assessment, normalisation and weighting are combined is the EDIP approach (Wenzel et al., 1997). In EDIP, global impact categories are assessed using global characterisation factors, normalisation is based on global per capita figures, and weighting is based on a distance-to-target approach using global reduction targets. Regional impact categories are assessed using Danish characterisation factors; normalisation is based on Danish per )


Part 3: Scientific background capita figures and weighting (see Section 4.8) is based on a distance-to-target approach using Danish reduction targets. It is stressed once more that if regional normalisation is performed using different reference areas, the results for regional impact categories in regions with relatively high per capita interventions will be rated lower than the same results for regions with relatively low per capita interventions. In such cases, then, not only normalisation but also weighting or grouping (see Section 4.7) of the normalised results should be performed on the same regional basis (Heijungs, 1997b). In this kind a regional set-up, inventory data as well as characterisation, normalisation and weighting factors are therefore required for each and every region involved in the study. Regional characterisation factors for acidification and terrestrial eutrophication are presently available (and POCPs for high and low background concentrations)


Regional characterisation factors for acidification and terrestrial eutrophication are presently available (and POCPs for high and low background concentrations). As yet, however, there is no uniform set of regional reference values that can be applied for these impact categories. For the regions Denmark and the Netherlands these can probably be readily developed based on regional intervention data collected by Wenzel et al. (1997) and Blonk et al. (1997b). For other regions, however, no reference values are available. Regionalised weighting factors are even scarcer. If grouping or weighting are performed on a different regional basis than that used for normalisation, interpretation of the results of the LCA study will become problematical


If grouping or weighting are performed on a different regional basis than that used for normalisation, interpretation of the results of the LCA study will become problematical. Temporal horizon Another point to be considered is the ISO statement that reference system adopted “should consider” not only consistency in terms of spatial scale, but also with respect to “the temporal scales of the environmental mechanisms and reference value”. This is not specified in any further detail. However, it seems undesirable to relate results for impact categories with shorter-term effects, such as photo-oxidant formation, to reference systems for shorter periods. e.g. 5 days, and at the same time relate those for impact categories with long-term effects, such as climate change, to reference systems based on longer periods, e.g. 100 years (Heijungs, 1997b)


e.g. 5 days, and at the same time relate those for impact categories with long-term effects, such as climate change, to reference systems based on longer periods, e.g. 100 years (Heijungs, 1997b). The temporal horizon of the reference system is not the reference point for the impacts occurring in a particular period but for the interventions in that period. As yet, moreover, all available reference values are based on a single period (generally one year). Consistency Last but not least, there is the issue of consistency between the methodological choices made in calculation of normalisation factors and those made in the LCA study in which the normalisation factors are being applied. In principle, the same characterisation factors should be used; system boundaries should be treated similarly, e.g. with respect to dredging1, agriculture and landfills (see Section 3.2); cutoff (see Section 3.8); multifunctionality and allocation (see Section 3.9); etc


with respect to dredging1, agriculture and landfills (see Section 3.2); cutoff (see Section 3.8); multifunctionality and allocation (see Section 3.9); etc. In practice, however, it is often unclear how such choices have effectively been made in the emission figures available for use as a basis for normalisation. The practitioner should at any rate be aware of possible inconsistencies and their potentially major influence on the significance of results and do all he or she can to avoid such inconsistencies wherever possible. If necessary, the normalised environmental profile should be accompanied by appropriate comments. PROSPECTS Besides the various purposes outlined above, normalisation may also serve another function: to ‘compensate’ for interventions on which data are lacking. In the Inventory analysis phase of an LCA it may be convenient for an interested party to ‘forget’ certain emissions or other interventions


In the Inventory analysis phase of an LCA it may be convenient for an interested party to ‘forget’ certain emissions or other interventions. This problem can then be tackled by using a similarly incomplete subset of interventions as a reference for normalisation. If different subsets of interventions are considered for each of the processes examined in an LCA, this would mean performing normalisation for each process individually, prior to aggregation of the data in a single inventory table. This would probably require additional efforts. The subset used for normalisation should then be the same as that employed for the Inventory analysis of the processes in question. In other words, interventions included in the Inventory analysis but with a zero result should be included in the normalisation subset, while interventions ignored in the Inventory analysis should also be excluded. Whether or not such partial normalisation is a useful option should then be examined in further detail


Whether or not such partial normalisation is a useful option should then be examined in further detail. On a second topic, a number of countries are developing (regional) normalisation data sets which will become available in due course. 1 Including dredging as an economic activity (see Section 3.2) implies that the removal of sediments should be regarded as a negative (sediment) emission and the dumping of sediments as a positive emission to soil. The net result is then a shift between compartments of a given amount of substance(s). For practical reasons this shift is now often ignored in the normalisation step.


Part 3 : Scientific background CONCLUSIONS We recommend using normalisation data based on one geographically and temporally well defined reference system, preferably the world for one year (consistent with the temporal coverage of the Goal and scope definition phase of the study), for all impact categories. The method based on aggregate world interventions (method 1) or that based on the interventions of an ‘average world citizen’ (method 2) are both applicable. Which one is chosen depends on the goal of the LCA study and therefore both values are given in this Guide. In Volume 2b Section 4.6, normalisation data for the world in 1990, Europe in 1995 and the Netherlands in 1997 are presented for use as normalisation factors for each impact category for each of the baseline characterisation methods recommended in this Guide (see Section 4.3). These data are based on Huijbregts et al. (in prep.)


These data are based on Huijbregts et al. (in prep.). If other characterisation methods are used the unaggregated data (interventions per reference area and time) can be used to calculate the appropriate normalisation factors for these methods according to the formulae given above. In principle, normalisation might alternatively be carried out at two scale levels, with results for global impact categories being normalised on the basis of global reference values and the results for regional impact categories on the basis of appropriate regional reference values. However, a uniform set of regionally specified reference values is still lacking, as observed above


However, a uniform set of regionally specified reference values is still lacking, as observed above. If different scales are combined: only per capita normalisation data should be used; normalisation data for regional impact categories should be based on the regions where the interventions of the LCA study at stake took place; if grouping or weighting is performed, the regionally normalised data should be grouped or weighted using regional grouping methods or regional weighting factors. Finally, the practitioner should be aware of possible inconsistencies between the methodological choices (with respect to system boundaries and so on) underlying the calculation of normalisation factors and the methodological choices made in the LCA study in which the normalisation factors are being applied. Inconsistencies should be avoided as far as possible


Inconsistencies should be avoided as far as possible. For example, if the practitioner wishes to apply non-baseline characterisation factors, the corresponding set of non-baseline normalisation factors should be applied from the spreadsheet mentioned in Section 4.6 of part 2a of this Guide. RESEARCH RECOMMENDATIONS Short term research: A study to collect adequate world data, based as far as possible on empirical measurements and derived statistics, is highly recommended. The pros and cons of partial normalisation, i.e. normalisation factors based on a subset of interventions, in the case of incomplete datasets (unit processes for which interventions are missing) should be further investigated. These subsets need not necessarily be the same for each unit process. 4.7 Grouping TOPIC Grouping is a step of Impact assessment in which impact categories are aggregated into one or more sets


These subsets need not necessarily be the same for each unit process. 4.7 Grouping TOPIC Grouping is a step of Impact assessment in which impact categories are aggregated into one or more sets. It is an optional element for which two possible procedures are available: sorting and ranking, defined by ISO as follows (ISO, 14042, 2000E): sorting of the category indicators on a nominal basis e.g. by characteristics such as emissions and resources or global regional and local spatial scales; ranking of the category indicators on an ordinal scale, e.g. a given order or hierarchy, such as high, medium and low priority (ranking is based on value-choices). It should be noted that this ISO definition of grouping relates to valuation of the impact categories, not the indicator results. Indicator results can also be ranked without placing any value on the impact category as such. The results are then ranked purely on the basis of the magnitude of the result itself (5 is higher than 3)


The results are then ranked purely on the basis of the magnitude of the result itself (5 is higher than 3). For this type of ranking, the methods developed in the area of multi-criteria analysis may be useful (see also Section 4.8). ISO states, furthermore, that application and use of grouping methods “shall be consistent with the goal and scope of the LCA study and it shall be fully transparent”. As different individuals, organisations and societies may have different values, it is well feasible that different parties will arrive at different ranking results based on the same indicator results. ISO provides no further examples of grouping and ranking methods.


Part 3: Scientific background DEVELOPMENTS IN THE LAST DECADE Heijungs et al. (1992) In Heijungs et al. (1992) grouping in the sense of the ISO definition was not mentioned. Sorting No further developments on the subject of sorting are known to the authors of this Guide. Ranking ISO 14042 (2000E) explicitly mentions the fact that ranking is based on value-choices. In a paper on weighting methods, Finnveden (1999a) identified and reviewed available methods for grouping impact categories in LCA. A variety of criteria are employed for this purpose, summarised by Schmitz et al. (1994) as follows: ‘ecological threat potential; reversibility - irreversibility; global, regional, local; environmental preference of the population; relationship of actual and/or previous pollution to quality goals.’ Based on these criteria these authors group impact categories into five classes, labeled as being from ‘lesser importance’ to ‘very large importance’. Volkwein et al


Volkwein et al. (1996) suggest that impact categories be qualitatively ranked by an expert panel, employing three criteria: scale of impact (anticipated area affected), reversibility time (time required for restoration of damage after the intervention ceases) and actual hazard the (potential) impact (in terms of assessments of actual exceedance of standards, uncertainty about the extent of harm, etc; see Volkwein et al., 1996). Finnveden (1999a) warns that in many cases the results of grouping (and especially ranking) will not be reproducible and that the individual preferences of those performing such an exercise will have a major influence on the overall results of the LCA. It can be argued, moreover, that grouping will generally be useful only in the context of reaching a final decision in a specific LCA study. In such cases, however, there will still be a subsequent need for some kind of weighting, as grouping does not yield a single score


In such cases, however, there will still be a subsequent need for some kind of weighting, as grouping does not yield a single score. PROSPECTS Based on the recent ISO developments it is to be expected that grouping and ranking methods will be further developed and that experience will be gained with application thereof. CONCLUSIONS Grouping is an optional element of LCA according to ISO 14042 (2000E). Grouping is likely to be highly influenced on the personal preferences of practitioners, and little work has yet been done on operationalisation its usefulness is in doubt. Therefore no specific method is recommended in this Guide. No methods have yet been published for ‘sorting’, For ‘ranking’ impact categories, the five criteria used by Schmitz et al. (1994) can serve as a starting point. The criteria used to evaluate impact categories should be described extensively. It is recommended to present the results of grouping as a matrix on the basis of the criteria employed


The criteria used to evaluate impact categories should be described extensively. It is recommended to present the results of grouping as a matrix on the basis of the criteria employed. RESEARCH RECOMMENDATIONS Short-term research In the short term a review should be prepared of grouping and ranking methods used in other tools or in the social sciences that may be useful in the LCA context, too. As the details of whether and how to differentiate between impact subcategories and categories in grouping are as yet unclear, this should also be analysed. Long-term research Some of the methods found in the research mentioned above may be subsequently elaborated for practical application in LCA.


Part 3 : Scientific background 4.8 Weighting TOPIC Weighting is an optional step of Impact assessment in which the (normalised) indicator results for each impact category assessed are assigned numerical factors according to their relative importance, multiplied by these factors and possibly aggregated. Weighting is based on value-choices (e.g. monetary values, standards, expert panel). A convenient name for the result of the weighting step is ‘weighting result’, of which there is generally one for each alternative product system analysed. As a variation, though, weighting may also yield several weighting results per product system, for instance for human health, ecosystem health and resources. The term ‘weighting profile’ is used in this Guide for the overall result of the weighting step: a table showing all the weighting results, supplemented by any other relevant information


The term ‘weighting profile’ is used in this Guide for the overall result of the weighting step: a table showing all the weighting results, supplemented by any other relevant information. Before weighting can be performed, the various indicator results must first be converted into the same units, one possible method for which is normalisation (see Section 4.6). DEVELOPMENTS IN THE LAST DECADE Heijungs etal. (1992) In Heijungs et al. (1992) two weighting methods were discussed: quantitative and qualitative multi-criteria analysis (MCA), but were not elaborated. Rather, their respective advantages and disadvantages were compared with those associated with unweighted comparison. Table 4.8.1: Evaluation of different weighting methods. convincing includes qualitative aspects reproducible less open to discussion unweighted comparison - + + + qualitative MCA + + - - quantitative MCA + - + # +: yes, -: no, #: moderate


convincing includes qualitative aspects reproducible less open to discussion unweighted comparison - + + + qualitative MCA + + - - quantitative MCA + - + # +: yes, -: no, #: moderate. Heijungs et al(1992) state that quantitative MCA is preferable as it provides greater transparency. At the time of writing, this method was used only to a limited extent, if at all, however. Hence, it was recommended to dedicate a separate section in each LCA report to a discussion of the reasons for preferring one product alternative over another. ISO 14042 (2000E) explicitly mentions the fact that weighting is based on value-choices and not on the natural sciences. Under the heading ‘weighting’, ISO again states that “the application and use of weighting methods shall be consistent with the goal and scope of the LCA study and shall be fully transparent”


Under the heading ‘weighting’, ISO again states that “the application and use of weighting methods shall be consistent with the goal and scope of the LCA study and shall be fully transparent”. As different individuals, organisations and societies may have different values, it is possible that different parties will arrive at different weighting results based on the same indicator results. ISO also states that “all weighting methods and operations used shall be documented to provide transparency”. Inventory results and the (normalised) environmental profile arrived at prior to weighting are also to be made available, together with the weighting results. This ensures that (ISO 14042, 2000E): “trade-offs and other information remain available to decision-makers and to others; and users can appreciate the full extent and ramifications of the results.” Finally, ISO states that weighting shall not be used for comparative assertions disclosed to the public


No examples of weighting are provided by ISO. Outside ISO, however, weighting methods have received extensive attention since 1992. In addition, qualitative information, non-weighted indicator results and non-characterised interventions should be also made available as part of the weighting profile (see above; addition by authors of this Guide). Finnveden (1999a) has published a review of weighting methods used in LCA. Since this is the most recent review article, it is summarised here fairly extensively (see textbox below). Finnveden recommends no one weighting method, observing that many methods suffer from serious data gaps. However, he considers the panel and monetary approaches the most promising.


Part 3: Scientific background Finnveden (1999) classifies weighting methods into five main groups, based on Lindeijer (1996) and Braunschweig (1996). Three of these are relevant for the weighting of impact categories in LCA: 1. methods based on monetary values; 2. methods based on (government) standards or targets; 3. methods based on the judgment of an authoritative panel. 1. Methods based on monetary values There exist a wide variety of approaches based on monetary value, which Finnveden classifies as follows: a) Willingness-to-pay methods: Methods based on the willingness to pay a certain amount of money to avoid the occurrence of an intervention, threat or damage, with three basic variations: Individual revealed preferences These include approaches based on the travel costs incurred in going to recreational areas, the price paid for houses in a given area, and variations in wages depending on the risks associated with different types of jobs


This is essentially valuation on the basis of market values. Individual expressed preferences This approach is based on asking individuals to explicitly attach a value to environmental assets (‘contingent valuation method’). Collective revealed preferences These can be derived from political and government decisions. Society’s willingness-to-pay can for example be derived from the expenditure devoted to saving a ‘statistical life’, from the costs incurred to reduce an emission to a set emission limit, or from ‘green taxes’, b) Other monetary methods, i.e. not based on willingness-to-pay These methods are based on estimates of cost, but with no indication that any individual might be willing to pay that cost. Examples are the cost of reducing emissions to a future target level or the cost of restoring damages. In the examples given by Finnveden the difference between ‘collective revealed preferences’ and ‘other monetary methods’ is rather vague


In the examples given by Finnveden the difference between ‘collective revealed preferences’ and ‘other monetary methods’ is rather vague. Since the various kinds of monetarisation methods are concerned with differing kinds of values, the respective methods yield different results and a sum expressed in monetary units cannot simply be compared directly with another sum in the same units. For example, the total economic value measured by the contingent valuation method is typically higher than the value derived from market valuations. Finnveden (1999) describes three monetary methods in more detail: EPS (Steen, 1996), Tellus (1992) and ExternE (EC, 1995a; Van Beukering et al., 1998). Although a weighting method based on monetary valuation is in itself promising, none of these methods could be recommended for LCA (Finnveden, 1999a). The EPS method is a hybrid of the outlined monetary approaches and thus combines different values (market prices and other values)


The EPS method is a hybrid of the outlined monetary approaches and thus combines different values (market prices and other values). As stated, this is not to be recommended, however. The method also has large data gaps, suffers from lack of transparency (e.g. valuation of biodiversity) and contains some scientific, logical and computational errors. Moreover, the EPS method yields results that do not reflect the common perceptions of society. The Tellus method is based on a collective revealed preference approach. The method has several data gaps and elements in need of updating and cannot be used for the impact categories employed in this Guide. The ExternE method is based mainly on contingent valuation methods (i.e. individual expressed preferences), although elements of other valuation methods are also used. The method still has some data gaps and parts in need of updating


individual expressed preferences), although elements of other valuation methods are also used. The method still has some data gaps and parts in need of updating. A further problem associated with methods based on the modeling of damage, such as ExternE (but also Eco-lndicator ‘99; see below), is the major risk of underestimation because of unknown damage pathways (see textbox in Section 4.2). Finnveden (1999) mentions several other monetary methods (e.g. Krozer, 1992; Huppes et al., 1997). However, insufficient details were published to evaluate these properly. Another interesting approach is provided by Beetstra (1998); within the scope of the present Guide it was no no longer possible to properly evaluate this method. 2


Another interesting approach is provided by Beetstra (1998); within the scope of the present Guide it was no no longer possible to properly evaluate this method. 2. Methods based on (government) standards or targets These methods are often referred to as ‘distance-to-target methods’ and differ in the equation used to relate target to weighting factor, the target itself, and the choice to weight inventory results or characterisation results . Finnveden (1999) describes a number of distance-to-target methods, viz. Ahbe et al., 1990 (updated in BUWAL, 1998); Schaltegger & Sturm, 1991; Baumann, 1992; Corten et al., 1994; Goedkoop, 1995; Kalisvaart & Remmerswaal, 1994; Kortman et al., 1994; Wenzel et al., 1997. Finnveden does not recommend using any of these distance-to-target methods because no inter-effect (inter-category) weighting is performed. The relative importance of the actual effects compared is not defined, or implicitly set to unity for each target


The relative importance of the actual effects compared is not defined, or implicitly set to unity for each target. For this reason ‘distance-to-target methods’ are not in fact true weighting methods and their use is therefore often advised against


Part 3 : Scientific background 3. Methods based on the judgment of an authoritative panel In these methods a group of people are asked to provide weighting factors on the basis of their specialist knowledge. This may be by way of a questionnaire, interviews or group discussions among a group of panellists: experts, stakeholders or laymen. A single- or multi-round procedure may be adopted, with or without feedback. Finnveden (1999) describes a number of panel methods (including the Eco-lndicator ‘99 method), viz. Annema, 1992; Kortman et al., 1994; Wilson & Jones, 1994; Nagata et al., 1995; Puolamaa et al., 1996; Lindeijer, 1997; Huppes et al., 1997; Goedkoop & Spriensma, 1999. He does not recommend using any of these panel methods because they have generally been developed for specific cases and it is by no means clear whether they can be universally applied. Moreover, there are significant data gaps in many of these methods and most assian more or less equal weight to all categories


Moreover, there are significant data gaps in many of these methods and most assian more or less equal weight to all categories. Heijungs (1994a) has noted that if weighting is based on an authoritative panel, the panel must be provided with carefully prepared information and questions, results being highly influenced by the manner in which questions are formulated. At the least, the panel must have adequate qualitative and/or quantitative information about the relationship between the chosen category indicators and endpoints (i.e. the environmental relevance of the category indicators). Lindeijer (1996) poses the question whether weighting should be performed on a case-by-case basis using discourse procedures to involve stakeholders, or be based on a universal set of weighting factors


Lindeijer (1996) has no preference for any one weighting method, but distinguishes five criteria of importance for weighting indicator results, as follows: ‘natural science information on the relationship between total burden (‘actual flows’)1 and the extent of actual damages to objects2; foreseeable future trends in flows; reversibility of the damage (including the time for spontaneous reversibility); importance of the damaged object (including scale of effect and substitutability); uncertainty on the extent of damage.’ Lindfors et al. (1995c) discuss and ‘test’ a number of weighting methods. Based on their findings they recommend using not one but several weighting methods. In Finnveden (1997) the author states that consensus will never be achieved on a universal weighting method because there is no consensus in society regarding the fundamental values influencing the choice of valuation method. He therefore recommends developing several methods simultaneously (Finnveden, 1997)


He therefore recommends developing several methods simultaneously (Finnveden, 1997). An entirely different approach is that of Lundie (1999), who evaluates product alternatives based on ranges/distributions of preferences obtained in surveys. These are applied to the indicator results instead of fixed weighting factors, with upper- and lower-bound evaluation being employed to draw conclusions. PROSPECTS There is growing interest in using methods adopted from multi-criteria analysis for life cycle Impact assessment. ‘Classical’ weighting compares alternative product or system alternatives based on a single set of average weighting factors that are used as multipliers for the respective indicator results. Multicriteria analysis focuses more on the ranking of product attributes (i.e. indicator results) and draws conclusions based on these results without weighting


Multicriteria analysis focuses more on the ranking of product attributes (i.e. indicator results) and draws conclusions based on these results without weighting. CONCLUSIONS None of the monetary methods described by Finnveden can be applied to the impact categories elaborated in this Guide. In the EPS method and the Tellus method ‘effect assessment’ and valuation are interwoven, resulting in a factor per intervention rather than per impact category. In the ExternE approach, the weighting factors are based on characterisation methods very different from the ones defined in this Guide. The collective revealed preferences method presented by Huppes et al. (1997) covers only six impact categories and cannot therefore be used for all categories; the frequently important categories ecotoxicity and abiotic depletion are not included. Although there are several distance-to-target methods that cover some of the relevant impact categories, none is applicable to all


Although there are several distance-to-target methods that cover some of the relevant impact categories, none is applicable to all. These methods are based on differing national and international policy targets and are not readily combined. They do have one thing in common, however: the relatively heavy weight 1 In the terminology of this Guide, this is the indicator result for a particular reference area and reference time (see Section 4.6). 2 In the terminology of this Guide, objects correspond to endpoints (see Figure 4.2.2).


Part 3: Scientific background attached to the category of stratospheric ozone depletion. Most of these methods need to be updated to recent standards. Similarly, although certain panel methods cover some of the relevant impact categories, none can be used for all. In the Eco-lndicator ‘99, for example, the weighting factors are based on characterisation methods different from those defined in this Guide and cannot therefore be applied here. Moreover, these panel methods are generally based on panels of stakeholders assembled for a specific case study. In summary, we conclude that there is no complete and authorised weighting set available at the present time that can be used for the weighting of the impact categories elaborated in this Guide. Bearing in mind the above, the following recommendations are made. Avoid weighting wherever possible


Bearing in mind the above, the following recommendations are made. Avoid weighting wherever possible. If the questions formulated in the Goal and scope definition can be adequately answered without weighting, for instance if one product alternative is clearly stands out above all others (e.g. because it scores better on all impact categories) weighting will be unnecessary. Note that according to ISO weighting shall not be used at all for comparative assertions disclosed to the public. If weighting is performed this should be done using a nationally or internationally authorised set of weighting factors covering all relevant impact categories. However, such a set is not currently available. We strongly recommend that a weighting set be developed covering all impact categories and approved by a panel having due international or national authority


We strongly recommend that a weighting set be developed covering all impact categories and approved by a panel having due international or national authority. RESEARCH RECOMMENDATIONS Short-term research In the short term a preliminary set of weighting factors should be developed that can be used for simplified LCA studies. The details of whether and how to differentiate between sub-impact categories and impact categories in weighting are still unclear and should be further examined. Long-term research It should be investigated which methods (individual revealed preferences, individual expressed preferences, collective revealed preferences, expert panel, etc.) are most suitable for deriving a more definite nationally or internationally authorised set of weighting factors covering all relevant impact categories.


Part 3: Scientific background 5. Interpretation 5.1 General introduction In an LCA study the Goal and scope definition provides the initial groundplan of the study. The Inventory analysis supplies the data on relevant processes and interventions on which the assessment is to be based. In the Impact assessment phase the interventions are translated into potential environmental impacts. The final phase of an LCA is Interpretation (see ). According to ISO 14040 in life cycle Interpretation “the results of a life cycle Inventory analysis and - if conducted - of a life cycle Impact assessment (LCIA) are summarised and discussed as a basis for conclusions, recommendations and decision making in accordance with the Goal and scope definition”


ISO 14043 (2000E) defines Interpretation as “a systematic procedure to identify, qualify, check, and evaluate information from the results of the LCI and/or LCIA of a product system, and present them in order to meet the requirements of the application as described in the goal and scope of the study. Furthermore, Life cycle Interpretation includes communication to give credibility to the results of other LCA phases (namely the LCI and LCIA) in a form that is both comprehensible and useful to the decision maker.” The main aim of Interpretation is to formulate the conclusions that can be drawn from the LCA. In addition, this is the place for reflection on the results of the previous phases of the LCA and on the choices that have been made during the entire process of generating these results. It should be clear, however, that Interpretation, no matter how comprehensive it may be, can never replace an external, interactive peer review


It should be clear, however, that Interpretation, no matter how comprehensive it may be, can never replace an external, interactive peer review. Such a peer review is strongly recommended unless the study is explicitly for internal use only.


Part 3 : Scientific background Interpretation as elaborated in this Guide comprises seven steps (see Section 1.4): Procedures (no special Section in this volume; see Chapter 1); Consistency check (Section 5.2, p. 637); Completeness check (Section 5.3, p. 637); Contribution analysis (Section 5.4, p. 637); Perturbation analysis (Section 5.5, p. 638); Sensitivity and uncertainty analysis (Section 5.6, p. 639); Conclusions and recommendations (Section 5.7, p. 643). All these steps should be a regular part of LCA and similar for all levels of sophistication. A further starting point for elaborating these Interpretation steps is ISO 14043 (2000E) with respect to the methodological framework. Here, the ISO recommendations will be further operationalised taking into account the work of SETAC Working Groups and relevant proposals made by other authors. Deviations from ISO will be made only if there are important reasons for doing so


Deviations from ISO will be made only if there are important reasons for doing so. It is recently that the Interpretation phase was introduced by ISO and it is therefore a topic scarcely referred to in previous LCA literature. The steps of the Interpretation phase will therefore not be discussed according to the previously used format of ‘Topic’, ‘Developments in the last decade’, ‘Prospects’, ‘Conclusions’ and ‘Research recommendations’. As explained in Section 1.5, the step dealing with procedures is not discussed separately in this chapter, but in an integrated fashion, for all phases, in Section 1.3. One of the main aims of Interpretation is to check the results of the Inventory analysis and of the Impact assessment against the Goal and scope definition of the study. In general terms, it should be asked whether the results actually answer the questions posed in the Goal and whether the answers are within the defined Scope


In general terms, it should be asked whether the results actually answer the questions posed in the Goal and whether the answers are within the defined Scope. If, for example, the question relates to a future situation and the data used dates back to the early eighties, the results will not be in line with the Scope of the study. The results of the LCA study are confronted with the Goal and Scope in the following steps of Interpretation: Consistency check (Section 5.2, p. 637): The assumptions and models used in the LCA should be consistent with the Goal and scope of the study and consistent among the various product systems reviewed. Completeness check (Section 5.3, p. 637): The parameters describing the system, the data and methodology used in the various phases of the LCA and the results and conclusions of the analysis should all be consistent with the Goal and scope of the study. Sensitivity and uncertainty analysis (Section 5.6, p. 639): The process data sources (e.g


Sensitivity and uncertainty analysis (Section 5.6, p. 639): The process data sources (e.g. representativeness with respect to time, space, technology, etc.) and models used, the methodological choices and assumptions made and data reliability should all be consistent with the Goal and the scope of the study. With respect to reporting on Interpretation, ISO 14043 states: “The report shall give a complete and unbiased account of the study, as detailed in ISO 14040. In reporting the Interpretation phase, full transparency in terms of value-choices, rationales and expert judgements made shall be strictly observed.” This implies that the general requirements of ISO 14040, clause 6 (see Chapter 2) also apply here. For Interpretation the following reporting issues can be derived from Lindfors et al. (1995a): The results of the study should be discussed in relation to aspects that may influence the results. The results of the study should be discussed in relation to earlier, related studies


(1995a): The results of the study should be discussed in relation to aspects that may influence the results. The results of the study should be discussed in relation to earlier, related studies. Conclusions drawn shall be justified by material presented in the report and be based on the whole report. When results and conclusions are presented, aspects that may influence the results shall be mentioned. Biographies and/or current positions of members of the reference panel or review group shall be reported, as relevant. A short resume of the discussions in the reference panel shall be given, with a focus on conflicting views, OR A report from the reviewer(s) on the critical review (see Section 1.3), stakeholder review or validation, OR A statement that an external validation or review process has not been carried out, including a justification of that decision (e.g. since stakeholders have been involved in the conduct of the study).


Part 3: Scientific background 5.2 Consistency check The aim of the consistency check is to determine whether the assumptions, methods, models and data are consistent with Goal and scope of the LCA study, in terms both of the chain embodied in individual product life cycles and among the products compared. ISO provides the following examples of inconsistencies (ISO 14043, 2000E): “differences in data sources, e.g. Option A is based on literature, whereas Option B is based on primary data; differences in data accuracy, e.g. for Option A a very detailed process tree and process description is available, whereas Option B is described as a cumulated black-box system; differences in technology coverage, e.g. data for Option A is based on experimental process (e.g. new catalyst with higher process efficiency on a pilot plant level), whereas data for Option B are based on existing large-scale technology; differences with time-related coverage, e.g


new catalyst with higher process efficiency on a pilot plant level), whereas data for Option B are based on existing large-scale technology; differences with time-related coverage, e.g. data for Option A describe a recently developed technology, whereas Option B is described by a technology mix, including both recently built and old plants; differences in data age, e.g. data for Option A are 5-year old primary data, whereas data for Option B are recently collected; differences in geographical coverage, e.g. data for Option A describe a representative European technology mix, whereas Option B describes one European Union member country with a high-level environmental protection policy, or one single plant”. A final example is differences in functions the two products or options perform. These differences should either be justified or corrected


A final example is differences in functions the two products or options perform. These differences should either be justified or corrected. The influence of differences that cannot be corrected or justified on the results and conclusions should be determined in a sensitivity analysis (see Section 5.6). Involving technological and other experts and comparing the results of the study with those of previous studies on related subjects may be very useful in the consistency check. 5.3 Completeness check One way to spot incomplete or even erroneous data is to have an expert examine the results of the LCA and the way the results were generated. An LCA expert can examine the methodology used in the various phases of the LCA, and the results and conclusions of the analysis, all in relation to the Goal and scope of the study. Besides the LCA expert, technical experts should also have examine the parameters used to describe product systems and the quantitative data employed


Besides the LCA expert, technical experts should also have examine the parameters used to describe product systems and the quantitative data employed. The LCA expert may be able to uncover assumptions or methodological choices that are incompatible with the Goal and scope of the LCA, while technical experts may spot unexpected, missing or erroneous emissions, economical inflows and outflows or product characteristics. Another way in which incomplete or erroneous data can be uncovered is to compare the study with other, similar studies. Again one should focus on the parameters employed to describe the system, the methodologies applied in the various different phases of the study, the data used and the results and conclusions of the analysis, all in relation to the Goal and scope of the study. When comparing two LCA studies great care should be taken that the respective Goal and scope definitions are truly congruent


When comparing two LCA studies great care should be taken that the respective Goal and scope definitions are truly congruent. The above recommendations should be implemented in iteration with the steps ‘Interventions ‘ (4.3.17), ‘Economic flows not followed to system boundary‘ (4.3.18) and ‘Cut-off and data estimation‘ (3.8). Particular attention should be paid to comparisons between alternative product systems. If there is significant variation in the completeness of the data sets of the respective alternatives, the potential influence of this difference should be estimated (using contribution analysis, perturbation analysis or sensitivity analysis, for example; see the following sections). 5.4 Contribution analysis The aim of contribution analysis is to establish the contribution to the overall LCA result of various identifiable elements and parameters. In the production of 1000 litres of milk, for example, the shares might be calculated of: individual processes, e.g


In the production of 1000 litres of milk, for example, the shares might be calculated of: individual processes, e.g. pasteurising; a group of processes, e.g. refrigerated storage; a life-cycle stage, e.g. (dairy) production; the packaging, e.g. the bottle; an intervention, e.g. emissions.


Part 3 : Scientific background In other cases, contribution analysis may focus on specific product properties, such as the power consumption of a refrigerator. Contributions to the overall LCA result can be calculated at different levels: at the level of the weighting results; at the level of the indicator results and/or normalised indicator results; at the level of inventory results, e.g. emissions or resource extractions. Although the questions posed in the contribution analysis may seem quite straightforward, the implications may be more intricate. An example is the question: ‘What is the contribution of the power consumption of a refrigerator to the total score on climate change ?’ The approach would normally be to calculate this consumption in relation to the functional unit, go back a step in the flow diagram and calculate how much is emitted during power generation and, finally, to calculate the contribution of this to the total score on climate change


However, this is only part of the story: what about the emissions of the tankers transporting the oil to the power plant, for example? The simple question we began with thus has answers at different levels: the zero-order answer would take into account only the emissions occurring during the process ‘refrigerator use’. The emissions in this process are zero, however. The first-order answer to the question would take into account the emissions at the power plant, while the second-order would also cover the emissions of tankers and the third-order answer would even consider the emissions occurring during the extraction of the fossil fuels, etc. Normally speaking, in a contribution analysis a rather arbitrary number of steps are taken back in the flow diagram. The contribution analysis establishes the extent to which a particular environmental intervention contributes to a certain environmental score


The contribution analysis establishes the extent to which a particular environmental intervention contributes to a certain environmental score. The contributions of all the environmental interventions associated with a particular process can of course also be summed to calculate the zero-order contribution of this process to the overall LCA result. However, this means ignoring all other linkages to other economic processes. In most cases there will be a second question behind the one formulated above, of the form: ‘Which intervention, economic flow or process can we best change in order to reduce the climate change score of the product system’. This type of question, typical of analysis of improvement options (‘improvement analysis'), can only be properly answered if the linkages among the different processes are covered right up to the highest order


Since normal, zero-order contribution analysis does not take such interlinkage into account it cannot be used to answer this type of question. For this purpose perturbation analysis can be used, the topic of the next section. Contribution analysis can be used to focus the sensitivity analysis on those variables, flows, etc. having greatest influence on the LCA result. Due caution should then be exercised, however. Flows that have been underestimated or disregarded entirely, for example, may initially appear to make little or no contribution to the overall results of the study. Once corrected or included, though, such data may nonetheless affect results significantly. These 'false negatives' will therefore not show up in the contribution analysis as key issues for a sensitivity analysis


These 'false negatives' will therefore not show up in the contribution analysis as key issues for a sensitivity analysis. 5.5 Perturbation analysis In a perturbation analysis1,2 the effects are studied of small changes in the parameters that describe the system on the overall results of an LCA (Heijungs, 1992; Heijungs, 1994). The effects of these small changes are calculated simultaneously for all the flows of a system, i.e. economic flows and environmental interventions. The analysis may be performed at different levels of aggregation: inventory table, indicator results, normalised indicator results or weighting results. All the factors used to calculate the aggregated result are included in the perturbation analysis. For example, if the analysis is performed at the level of weighting results, the characterisation factors, the normalisation factors and the weighting factors are all included


For example, if the analysis is performed at the level of weighting results, the characterisation factors, the normalisation factors and the weighting factors are all included. The outcome of the perturbation analysis takes the form of a list of multiplication factors or multipliers. The values of these multipliers may range from minus infinity to plus infinity for 1 This type of analysis is referred to as marginal analysis by Heijungs et al. (1992). Using this term here would lead to confusion, however, because of the use of the term ‘marginal’ in a different context in the Goal and scope definition and Inventory analysis. 2 It is debatable whether perturbation analysis should constitute a separate step or be included in sensitivity analysis (5.6). In perturbation analysis, so it can be argued, the data used to describe the system are altered. However, these small changes are used only to assess which processes or flows are most important in the system as modeled


However, these small changes are used only to assess which processes or flows are most important in the system as modeled. Thus, perturbation analysis can be said to assess intrinsic system sensitivity rather than the effects of estimated uncertainties in variables or modeling choices.


Part 3: Scientific background economic flows and from -1 to 1 for environmental interventions. In fact the results for environmental interventions are identical to the results of the contribution analysis except for their being expressed as multipliers rather than percentages: a multiplier of 0.98 for an environmental intervention in the perturbation analysis would correspond to a contribution of 98% in the contribution analysis. The main difference between the contribution analysis and the perturbation analysis lies in the fact that the latter is concerned not only with environmental interventions but also with economic flows. This is even more important because of the fact that multipliers of economic flows may exceed unity if internal loops are involved. A multiplier of 4.5, for example, would indicate that an 1% increase in this flow would lead to a 4.5% change in the selected inventory or indicator results


A multiplier of 4.5, for example, would indicate that an 1% increase in this flow would lead to a 4.5% change in the selected inventory or indicator results. It is, of course, clear that this kind of magnification is very important in the context of improvement analysis and in a sensitivity analysis aiming at detecting important issues and refining the LCA study in question. The result of the perturbation analysis is normally a list of processes or flows with associated multiplication factors in decreasing order of significance for a specific type of result (e.g. emission, indicator result for climate change, etc.). Although the mathematics can be quite complicated, the perturbation analysis is relatively easy to implement if a matrix type of calculation method is used. Besides use in improvement analysis, it can help to focus the sensitivity analysis on those variables and (modeling) choices of greatest influence on the results of the study


Besides use in improvement analysis, it can help to focus the sensitivity analysis on those variables and (modeling) choices of greatest influence on the results of the study. In this respect, it can significantly reduce the effort required for gathering uncertainty data, because it identifies which data items are crucial for uncertainty analysis. In interpreting the results of the perturbation analysis due caution should again be exercised, however; as with the contribution analysis described above, ’false negative’ issues will not be identified cf. Section 5.4). 5.6 Sensitivity and uncertainty analysis If LCA is to be usefully employed as a decision-making tool, the robustness of the results must be clear. In the context of sensitivity and uncertainty analyses, a number of technical terms occur again and again. In this textbox, the most important of these are explained


In the context of sensitivity and uncertainty analyses, a number of technical terms occur again and again. In this textbox, the most important of these are explained. Note, however, that terminology is not uniform, so that other meanings for the same terms and other terms for the same concept may be encountered in literature. A basic distinction must be made between accuracy and precision. Data may be precise but inaccurate, for instance when use is made of a high-precision clock which has not been adjusted to local time. Data may also be accurate but imprecise, for instance when a sundial is used. The result of a calculation is sensitive to several sources of uncertainty. We mention uncertainties in the data, for instance when there are several different measurements or estimates of an emission, and uncertainties in the model, for instance due to essentially arbitrary decisions relating to system boundaries, allocation and so on


Data uncertainty may arise because data are in themselves variable, for instance different in London and in Paris, or on Monday and Sunday, or the data may be the outcome of a stochastic process. The former is referred to as variability, the latter leads to sampling error. In addition, data may be measured incorrectly; we can then distinguish random errors from systematic errors. In general, random errors lead to inaccurate data, and systematic errors to imprecise data. Model uncertainty leads to doubts regarding the validity of a result, data uncertainty to doubts on its reliability. Even when results are highly imprecise or inaccurate, they may still be robust. For instance, if product X remains preferable to product Y even when the absolute difference varies wildly under sensitivity analyses, the ranking of these two products is said to be robust


For instance, if product X remains preferable to product Y even when the absolute difference varies wildly under sensitivity analyses, the ranking of these two products is said to be robust. This step of the Interpretation phase assesses the influence on results of variations in process data, (model) choices and other variables. In the sensitivity analysis these changes are deliberately introduced in order to establish the robustness of the results with regard to these variations. In the uncertainty analysis empirical data on the uncertainty ranges of specific data are used to calculate the total error range of the results. In order to assess the robustness of the results information is required on both their validity and their reliability, distinguished as follows: with validity the question to be answered is whether the results are based on sound reasoning or, in LCA, whether the appropriate (e.g


correct representativeness in space, time, technology etc.) process data (sources) and models have been used and whether the appropriate methodological choices and assumptions have been made, all in relation to the Goal and scope of the study. If any controversial choices have been made, the influence of these choices on the results of the study (


Part 3 : Scientific background should be assessed. Variability, as introduced by others in the context of LCA (e.g. Huijbregts, 1998a; Huijbregts, 1998b, Hertwich, 1999), is regarded as being subsumed under validity. When discussing validity it should be borne in mind that assessment thereof is closely related to basic choices (see Section 1.2.2.3); with reliability the question to be answered is whether the parameters and data used are likely to be true or correct, i.e. whether they are to be trusted or believed. The issue of reliability is closely related to that of data quality within the context of LCI (Van den Berg et al., 1999). As indicated earlier, both data (sources) and (modeling) choices should, in principle, be subjected to an analysis to ascertain their validity and reliability


As indicated earlier, both data (sources) and (modeling) choices should, in principle, be subjected to an analysis to ascertain their validity and reliability. Some issues (previously referred to as ‘issues for Interpretation’) to be subjected to sensitivity analyses may have been identified in previous sections under the specific headings of Goal and scope definition, Inventory analysis and Impact assessment. Additional issues can be derived from the results of the contribution analysis and the perturbation analysis. In this step a final selection of issues to be subjected to sensitivity analysis is first made. The number of issues selected may depend on the level of sophistication of the LCA. In a simplified LCA the sensitivity and uncertainty analyses are confined to a limited checklist


The number of issues selected may depend on the level of sophistication of the LCA. In a simplified LCA the sensitivity and uncertainty analyses are confined to a limited checklist. This checklist includes those (model) choices known to be of major influence on the results of the study: product system specification (product composition, trip rates, recycling rates, life span, etc.); allocation rules; characterisation method; weighting method. The consequences of altering the choices vis-à-vis these issues on the results of the LCA should be calculated. Results will always be particularly sensitive to changes in economic flows in close proximity to the functional unit. Thus, the product system specification probably constitutes the most important data for the results of the study. This data should therefore be checked carefully and subjected to a sensitivity analysis


This data should therefore be checked carefully and subjected to a sensitivity analysis. With regard to data uncertainties, in a simplified LCA one should focus on those processes and flows already identified as being of key importance in the contribution and/or perturbation analysis. The data on these processes and flows should be checked and a partial sensitivity analysis performed by varying these data. In a detailed LCA the sensitivity and uncertainty analysis is also confined to a checklist, but a more comprehensive one (based on ISO 14043, 2000E): allocation rules; cut-off criteria; boundary setting and system definition; process data; characterisation method and data; normalisation data; weighting method and data. With regard to data uncertainties, in a detailed LCA one should also focus on those processes and flows identified in the contribution and/or perturbation analysis as being most important


With regard to data uncertainties, in a detailed LCA one should also focus on those processes and flows identified in the contribution and/or perturbation analysis as being most important. The data on these processes and flows should be checked and a partial sensitivity analysis performed by varying these data. As an option for extension the consequences of the (modeling) choices and data uncertainties may be quantified in more detail and partial uncertainty analyses performed. First of all the consequences of different (modeling) choices will have to be assessed, as in detailed LCA. Besides the effects of (modeling) choices, however, the effects of uncertainties in the process data should also be determined. In the most general terms, the question to be answered by an LCA is whether two product systems differ significantly at the level of inventory results, (normalised) indicator results or weighting results


If an LCA is performed without any uncertainty or sensitivity analysis being undertaken, the results take the form of two scores: one for product system A and one for system B. A comparison of the two systems may indicate that the environmental score (e,g, emission of of B is greater than that of A, for example, and the conclusion drawn that A is better than B from an environmental angle.


Part 3: Scientific background However, the question should be whether there is indeed a significant difference between the emissions of the two product systems or whether it is merely an artefact of the uncertainties in the values of the system parameters and in (modeling) choices made. As is evident from the above, the robustness of the end results of an LCA is subject to a large number of validity and reliability issues and the value of the result is not a single point but some kind of probability distribution. Thus, in order to judge this robustness, the combined influence of all the issues mentioned above must be gauged. Several methods have been proposed for this purpose, of which three are discussed here: calculation of extreme values; formal statistics: uncertainty propagation; empirical statistics: Monte Carlo simulation. 1. 2. 3. 1. Calculation of extreme values One apparently simple approach is to calculate extreme values


1. 2. 3. 1. Calculation of extreme values One apparently simple approach is to calculate extreme values. In this calculation the upper and lower values of each parameter are combined to find the upper and lower values of the end result. Heijungs (1996) shows that due to the inherent complexity of an LCA (e.g. the presence of feedback loops) the extremes of the result cannot be predicted by intuition. He also shows that this implies the need to test every combination of upper and lower values and that for an average LCA this task would take a modern PC longer than the current age of the universe. This kind of uncertainty analysis is therefore not of much use in most LCAs. 2. Formal statistics: uncertainty propagation Heijungs (1996) proposes a formal solution using a standard statistical method: propagation of uncertainties. In this case one starts not by determining the upper and lower values of a given parameter but by assuming a particular distribution of the parameter values


In this case one starts not by determining the upper and lower values of a given parameter but by assuming a particular distribution of the parameter values. If a normal distribution is used, the mean and standard deviation of the parameters must be calculated. Although the mathematics is


Part 3 : Scientific background complicated, in itself the method is relatively simple to implement in automated calculation procedures, that is, if matrix calculation is used. This formal statistical approach yields such statements as: with a 95% certainty interval, the emission of product system A is greater than that of product system B. 3. Empirical statistics: Monte Carlo simulation Another technique that can be used to avoid the problems associated with calculation of extreme values is stochastic modeling. This technique can be performed with the aid of a Monte Carlo or Latin Hypercube simulation (Huijbregts, 1998a and 1998b). In both types of simulation a predefined, limited number of combinations (typically 10,000) of random parameters, restricted by their uncertainty distribution, is used to calculate the results


In both types of simulation a predefined, limited number of combinations (typically 10,000) of random parameters, restricted by their uncertainty distribution, is used to calculate the results. The only difference between a Monte Carlo and a Latin Hypercube simulation is that in the former the uncertainty distribution of each parameter must be specified while in the latter the uncertainty distribution is segmented into a series of non-overlapping intervals, each having equal probability. One advantage of stochastic modeling is that, in contrast to formal statistic methods, it is relatively easy to employ a variety of parameter distributions, such as uniform, triangular, normal and log normal. The result of this type of analysis is a frequency chart of possible outcomes. Once a frequency chart has been generated, the same statistical methods used in the aforementioned formal statistics approach can be used to assess whether or not two product systems differ significantly


In all three methods described, information is required on uncertainties in parameter values. This requires a major additional effort, coming on top of actual data collection, in itself already one of the most timeconsuming tasks of LCA. There are two ways to tackle this problem of availability of data on ranges of uncertainty: 1. 2. use rough estimates of standard deviations or upper and lower bounds instead of real values to obtain some kind of subjective probability distribution; focus data collection efforts on those flows of greatest importance for the study results (as determined by contribution and perturbation analysis): a partial uncertainty analysis. As already proposed by Heijungs (1996) and Huijbregts (1998a, 1998b), focusing on key parameters would greatly simplify matters. One way to rank parameters in order of importance for the study results is to use perturbation analysis (see Section 5.5)


One way to rank parameters in order of importance for the study results is to use perturbation analysis (see Section 5.5). This could greatly reduce the amount of information required for a sensitivity analysis. However, the results of both options (i.e. 1 and 2) should be treated with great caution: by introducing subjective and/or partial probabilities one may also introduce an erroneous notion of the probability of the results, while only partial or estimated uncertainty data have been used. Another important issue is that both in the formal statistical approach and in the standard Monte Carlo simulation the variables are assumed to be independent of one another. In practice this is often not the case; fossil fuel inputs are closely related to emissions, for example. Although relationships among variables can, in principle, be taken into account using co-variances, in practice this will be difficult to implement


Although relationships among variables can, in principle, be taken into account using co-variances, in practice this will be difficult to implement. If such dependencies are ignored, however, uncertainties will be overestimated. The approach outlined above can also be used to assess the influence of (modeling) choices. However, current LCA software will not always permit implementation of the required procedures. Such is the case


Part 3: Scientific background with choices concerning allocation, for instance. In order to assess the influence of adopting a certain allocation procedure it should be possible to introduce this change without having to reorganise the whole data set. Use of aggregated process data can therefore lead to problems during Interpretation. 5.7 Conclusions and recommendations1 In this step of the Interpretation conclusions are drawn and recommendations made on the basis of the information gathered in the previous phases of the LCA combined with the results of the previous steps of the Interpretation. In ISO 14043 (2000E) the objective of this step is defined as “to draw conclusions and make recommendations for the intended audience of the LCA or LCI study”. In general the conclusions of any study should comprise the main results of the study and a discussion of the validity and reliability of those results


In general the conclusions of any study should comprise the main results of the study and a discussion of the validity and reliability of those results. In the case of LCA the following items should be included (slightly adapted from ISO 14043). a summary of significant issues; an evaluation of the methodology and results on the basis of the consistency check, completeness check and the sensitivity and uncertainty analysis; the main conclusions as they relate to the Goal and scope of the study, including data quality, predefined assumptions and values, and application-oriented requirements. Firstly, conclusions should be consistent with the results found and with the original Goal and scope of the study2, viz. in line with the limitations of the scope, main data and (modeling) choices, and in line with the limitations of the instrument of LCA itself


in line with the limitations of the scope, main data and (modeling) choices, and in line with the limitations of the instrument of LCA itself. This implies that the conclusions are only valid for the systems analysed and thus not, automatically, for other similar systems that have not been analysed. For example, the results for 1 -litre packaging alternatives for a certain liquid are not valid for similar 1.5-litre packaging alternatives. Separate justification is needed if the conclusions are expanded to other similar systems. It also implies that the conclusions are valid for the main data and (modeling) choices made, and not for an altered system with different data and/or (modeling) choices. If, for example, the system boundary is expanded to include more processes earlier in the chain, the conclusions may no longer retain their validity


If, for example, the system boundary is expanded to include more processes earlier in the chain, the conclusions may no longer retain their validity. Finally, formulating conclusions in compliance with the limitations of the LCA instrument implies that conclusions as to the preferred location of a certain industrial facility cannot be drawn on the sole basis of an LCA. Secondly, the inclusion of the results of the earlier steps of the Interpretation in the formulation of the conclusions is crucial. Performing consistency and completeness checks and sensitivity and uncertainty analyses on data and models is one thing; processing the results of these checks and analyses in the conclusions of a study and formulating recommendations is quite another. There are examples of studies in which sensitivity analyses have been performed, but with absolutely no further processing of the analysis outcomes in the final results


There are examples of studies in which sensitivity analyses have been performed, but with absolutely no further processing of the analysis outcomes in the final results. How, then, should these results be properly taken into account? Inconsistencies, incompleteness and errors should either be corrected or incorporated in the conclusions of the study. The data or parameters to which the conclusions of the study are most sensitive should also be reported. Uncertain data and parameters should be checked and if uncertainties remain, this should be incorporated in the conclusions. One simple but not particularly elegant way of doing so would be to determine for the most dominant data the uncertainty (partial uncertainty analysis) and to add the results of these analyses (see Section 5.6) to a maximum uncertainty range for data. In a similar way the uncertainty could be determined of the most important modeling choices (e.g


In a similar way the uncertainty could be determined of the most important modeling choices (e.g. allocation models and characterisation models for some impact categories) and the results of these analyses could be added again to a maximum uncertainty range for models. Subsequently, further Interpretation is up to the practitioner and/or decisionmaker. A more elegant solution, which is not yet practically available, is to formulate both data and model uncertainties as input uncertainties (e.g. data: 5 ± 0.5 and models as the probability, say 0.333 on model A, B or C) for a Monte Carlo analysis. In this way all model and data uncertainties are aggregated into a total frequency distribution of the end results of a study. This is, however, not yet feasible and may never be. 1 Note that this section does not constitute the overall ‘Conclusions and recommendations’ for the present Guide


This is, however, not yet feasible and may never be. 1 Note that this section does not constitute the overall ‘Conclusions and recommendations’ for the present Guide. Material relating to this aspect is included under ’Research recommendations’ in most of the individual sections of this Guide. 2 If this is not the case, an explicit statement should be made of the problems encountered and the conclusions formulated in such a way as to reflect these problems in a clear and proper manner.


Part 3 : Scientific background Drawing appropriate conclusions is of even greater importance for comparative assertions, in order to minimise opportunistic use of results (‘hired gun’ effect). In this case it will also have to be determined which differences in results are significant in order to be able to conclude that one product alternative is environmentally sounder than another. It is our conviction that even waterproof Interpretation cannot prevent misuse of results of LCA studies, it can only minimise it. It will always be possible to manipulate results or use results to answer the wrong questions. By providing guidelines or checklists, possible misuse can be minimised but not precluded. Therefore, a peer review process and appropriate procedures are in most cases of crucial importance (see Section 1.3).


Part 3: Scientific background 6. References Adriano, D.C. (ed.), 1992. Biogeochemistry of trace metals. Lewis Publishers, Boca Raton. Ahbe, S., A. Braunschweig, & R. Müller-Wenk, 1990. Methodik für Oekobilanzen auf der Basis Ökologischer Optimierung. Schriftenreihe Umwelt. BUWAL Nr. 133, Bern. Albritton, D., R.G. Derwent, I. Isaksen, M. Lal & D. Wuebbles, 1996. Trace gas radiative forcing indices. In: Houghton et al., 1996. Alloway, B.J. (ed.), 1990. Heavy metals in soils. Blackie, Glasgow. Amann, M., I. Bertok, J. Cofala, F. Gyarfas, C. Heyes, Z. Klimont & W. Schöpp, 1996. Costeffective control of acidification and ground-level ozone. Second interim report to European Commission, DG-XI. International Institute for Applied System Analysis, Laxenburg. Andersson-Sköld, Y., P. Grennfelt & K. Pleijel, 1992. Photochemical ozone creation potentials: a study of different concepts. J. Air Waste Manage. 42 (9): 1152–1158. Annema, J.A., 1992


Andersson-Sköld, Y., P. Grennfelt & K. Pleijel, 1992. Photochemical ozone creation potentials: a study of different concepts. J. Air Waste Manage. 42 (9): 1152–1158. Annema, J.A., 1992. Methodology for the evaluation of potential action to reduce the environmental impact of chemical substances. In: SETAC, 1992: 73–80. Anonymous, 1997a. Ökobilanzen – Trends und Perspectieven. Workshop der GDChFachgruppe Umweltchemie und Ökotoxicologie, Frankfurt. Anonymous, 1997b. Dutch consumer patterns and their impact on biodiversity. CREM (Consultancy and Research for Environmental Management), Amsterdam. Anonymous, 1998. Handleiding voor het opstellen van milieurelevante productinformatie (MPRI®): Achtergronden. Versie 1.1. SNPR98001. Stichting NVTB Projecten SNP. Driebergen. Anonymous, 1998a. Proceedings of the International Resource Accounting Modeling Workshop Groningen 16–17 September 1998. IVEM, Groningen. Anonymous, 1998b


Stichting NVTB Projecten SNP. Driebergen. Anonymous, 1998a. Proceedings of the International Resource Accounting Modeling Workshop Groningen 16–17 September 1998. IVEM, Groningen. Anonymous, 1998b. Proceedings of The Third International Conference on EcoBalance – Progress in LCA for a Sustainable Society. November 25–27,1998, Tsukuba. Anonymous, 1998c. Low Doses of Ionizing Radiation: Biological Effects and Regulatory Control. Proceedings of a Conference, Seville, Spain, 17–21 November 1997, Jointly organized by IAEA and WHO. Anonymous, 1999. SETAC working groups 1993–1998. SETAC -Europe news 10 (3): 14–20 Anonymous, 2000a. Integral biodiversity impact assessment system (IBIS). CREM (Consultancy and Research for Environmental Management) report no. 98.309, part II, Amsterdam. Anonymous, 2000b. Biodiversity module for ecolabels. CREM (Consultancy and Research for Environmental Management) report no. 98.309, part III, Amsterdam. AOO, 1995. MER Tienjarenprogramma afval


Anonymous, 2000b. Biodiversity module for ecolabels. CREM (Consultancy and Research for Environmental Management) report no. 98.309, part III, Amsterdam. AOO, 1995. MER Tienjarenprogramma afval. AOO 95–02, Utrecht. Audsley, E., S. Alber, R. Clift, S. Cowell, P. Crettaz, G. Gaillard, J. Hausheer, O. Jolliet, R. Kleijn, B. Mortensen, D. Pearce, E. Roger, H. Teulon, B. Weidema & H. van Zeijts, 1994. Harmonisation of environmental life cycle assessment for agriculture. Final Report of EU Concerted Action AIR3-CT94–2028, Brussels. Ayres, R. U., K. Martinàs & L.W. Ayres, 1996. Eco-thermodynamics. Exergy and life cycle analysis. Working Paper (96/04/EPS), INSEAD, Fontainebleau, France. Ayres, R.U., 1998. Eco-thermodynamics: economics and the second law. Ecol. Econ. 26 (2): 189–209. Azapagic, A. & R. Clift, 1999. Allocation of environmental burdens in co-product systems: product-related burdens (part 1). Int. J. LCA 4 (6): 357–69. Azapagic, A. & R. Clift, 2000


26 (2): 189–209. Azapagic, A. & R. Clift, 1999. Allocation of environmental burdens in co-product systems: product-related burdens (part 1). Int. J. LCA 4 (6): 357–69. Azapagic, A. & R. Clift, 2000. Allocation of environmental burdens in co-product systems: process and product-related burdens (part 2). Int. J. LCA5 (1): 31–36. Azapagic, A. & R. Clift, 1998. Linear programming as a tool in life cycle assessment. Int. J. LCA 3 (6): 305–16. Azapagic, A., 1996. Environment system analysis: the application of linear programming to life cycle assessment. Volume I. Ph.D.-thesis University of Surrey, Guildford. Baitz, M., 1998. Method to integrate land use in life cycle assessment. University of Stuttgart, Stuttgart. Bakker, J. & D. Van de Meent, 1997. Receptuur voor de berekening van de Indicator Effecten Toxische Stoffen (Itox). RIVM rapport nr. 607504003, RIVM, Bilthoven. Barnthouse, L., J. Fava, K. Humphreys, R. Hunt, L. Laibson, S. Noesen, G. Norris, J. Owens, J. Todd, B. Vigon, K


RIVM rapport nr. 607504003, RIVM, Bilthoven. Barnthouse, L., J. Fava, K. Humphreys, R. Hunt, L. Laibson, S. Noesen, G. Norris, J. Owens, J. Todd, B. Vigon, K. Weitz & J. Young (eds), 1997. Life-cycle Impact assessment: The State-ofthe-Art. Report of the Work Group on LCA Impact assessment, SETAC, Pensacola. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27.