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Market information in life cycle assessment

1 Introduction

⁵ This report provides the background for the two guidelines “The product, functional unit, and reference flows in LCA” (Weidema et al. 2003a) and “Geographical, technological and temporal delimitation in LCA” (Weidema 2003). It provides further documentation of the examples provided in these guidelines, as well as additional examples, further explanatory text, scientific background and reference to earlier methodological guidelines. It also expands on specific issues, which were not found to be of sufficient general interest to merit inclusion in the guidelines.

This report and the two guidelines that it supports, carry two key messages:

1. The fundamental rule to apply in all methodological choices in life cycle assessment is that the data used must reflect as far as possible the processes actually affected as a consequence of the decision that the specific life cycle assessment is intended to support. Thus, there is a close link between the goal or application area of the life cycle assessment and the methodological choices. This is elaborated in section 1.1.

2. Life cycle assessments, insofar as they deal with comparing potential choices between alternative products, rely heavily on market information, i.e. information on how the market affects the potential choices and how the markets will react to these choices.

Whenever possible, the above understanding has been converted to practical, step-by-step procedures for including market information when:

• defining the functional unit (chapter 3),
• defining the geographical and technological scope (chapter 4),
• handling co-products (chapter 5),
• forecasting data for processes taking place in the future (chapter 6).

For all these elements of the life cycle assessment methodology, the inclusion of market information leads to improvements, which also reduces the uncertainty of life cycle assessment results. While the methodological improvements are described in this report, the consequences for uncertainty are the topic of a separate report: "Reducing uncertainty in LCI. Developing a data collection strategy" (Weidema et al. 2003).

1.1 The relation between application areas and methodology

The methodological elements listed above are fundamentally determined by the temporal and spatial aspects of the studied systems and by the products and interest groups affected. On this basis, six well-defined application areas can be distinguished (see figure 1.1).



Click on the picture to see the html-version of: Figure 1.1

The decision-maker‘s potential influence on the different processes in the product systems increases towards the top left of the diagram, i.e. as the decision horizon becomes more long-term and as the decision relates to more specific products and geographical areas. This is further illustrated in figure 1.2. For retrospective studies (area A in figure 1.2), there is no choice to influence. For medium-term, tactical studies, high influence on specific processes throughout the life cycle is limited to studies where the product systems are very well-defined and where the decision-maker already at present has a high influence on the other actors in the life cycle (illustrated by area B in figure 1.2). Tactical aspects (i.e. contacts to be made in the product chain) may also be part of the considerations in product development, and the more long-term the development, the more ambitious one may be with respect to obtaining influence (area C). Even on a societal level, it may be possible to influence specific choices throughout the life cycle, when the products are relatively well-defined and have well-defined interest groups (including producers and users), and when the time horizon is long enough to allow the necessary regulative and technical infrastructure to be developed (area D). For the rest of the applications (area E in figure 1.2), the products are either too generic (i.e. includes several products or a group of products) or involve too many interest groups to allow a decision-maker to influence specific choices throughout the life cycle.



Click on the picture to see the html-version of: Figure 1.2

With respect to methodological choices, the most important distinction is that between the retrospective, attributional life cycle assessments of the accountancy type⁶ (typically applied for hot-spot-identification, product declarations and for generic consumer information) and the prospective consequential life cycle assessments, which study the environmental consequences of possible (future) changes between alternative product systems (typically applied in product development and in public policy making) (Tillman 1998, 2000). This distinction is further elaborated in section 1.2.

The application areas (as outlined in figure 1.1) affect the methodology in the following ways:

• The functional unit, which delimits which product alternatives can be included in the study, is affected by the time horizon of the study and by the degree of specification of the studied product (specifically defined products and long time horizons allow more alternatives to be included). This point is elaborated in section 1.4 and further in chapter 3.
• The processes to include in the product systems studied are affected by the distinction between attributional and consequential applications (including either those processes which can be associated with the product according to a chosen rule or those which are affected by a product substitution). This is elaborated in section 1.5.
• Within consequential studies, the technologies to consider and whether to include capital goods, maintenance etc., is affected by the distinctions between small/large and short-term/long-term changes. These distinctions (which are defined in section 1.3) are related to the parameters in figure 1.1, but do not exactly follow the divisions between application areas given there. The way these distinctions affect the technologies to consider is elaborated in section 4.2.
• The method for handling co-products is also affected by the distinction between attributional and consequential applications (attributional applications require economic allocation while consequential applications require system expansion). This is elaborated in section 1.6 and further in chapter 5.
• The methods to use for forecasting future processes is affected by the time horizon and complexity of the studied system (determining whether forecasts should be made by extrapolation, modelling or scenario methods), and by the amount of stakeholders affected (determining whether participatory forecasting is relevant). Furthermore, exploratory and normative forecasting may be relevant for specific applications in product development. This is elaborated in section 1.7 and further in chapter 6.

Thus, for each application area of figure 1.1, we can outline the conditions for the methodological choices to be taken:

• For attributional life cycle assessments:
  
  • As attributional LCA does not apply to comparison of alternative product systems, the functional unit does not play any important role for the assessment, and may therefore be chosen at will.
  • The processes to include are those that are deemed to contribute to the studied product.
  • Co-products are handled by economic allocation, since attributional LCA does not involve changes, which is a necessary condition for applying the system expansion procedure.

Note that when defining the goal and scope of an attributional LCA, one should be aware whether one intends later to use the results for decision making, in which case it should be carefully considered whether it is necessary and worthwhile to perform an attributional LCA or whether a consequential study is adequate and sufficient. See also the discussion in section 1.2.

• For studies of specific products, affecting specific interest groups on a medium (1-5 years) term (for product declarations, hot-spot-identification, marketing claims, and incentives and requirements for suppliers or employees):
  
  • The functional unit shall reflect the current products on the market and their obligatory properties (see definition in chapter 2).
  • The processes to include are those that are affected on short or long term by the decision supported by the results of the study (i.e. choosing the product with the market claim instead of the alternatives, following the incentives or fulfilling the supplier/employee requirements instead of continuing status-quo).
  • Co-products are handled by system expansion.
  • Forecasting of processes is done by extrapolation.
• For studies of generic products (product groups) on a medium (1-5 years) term (for generic consumer information, ecolabelling criteria and product standards, taxes and subsidies):
  • The functional unit shall reflect the current products on the market and their obligatory properties (see definition in chapter 2).
  • The processes to include are those affected on short or long term by the decision supported by the results of the study (i.e. choosing a product with the ecolabel instead of the alternatives, changing behaviour following the taxes or subsidies or fulfilling the product standard instead of continuing status-quo).
  • Co-products are handled by system expansion.
  • Forecasting of processes is done by modelling and participatory methods.
• For studies used to support societal action plans, product legislation and generic performance criteria:
  
  • The functional unit may be broadened to include alternatives assumed relevant under future conditions of availability, price, and product information.
  • The processes to include are those processes, which are affected by the decisions supported by the results of the study (typically large, long-term consequences).
  • Co-products are handled by system expansion.
  • Forecasting of processes is done by modelling and scenario methods.
• For studies used in product development and for enterprise specific performance criteria:
  
  • The functional unit may be broadened to include more alternatives in all parts of the product chain, when assumed to be controlled by the decision maker and relevant under future conditions.
  • The processes to include are those processes, which are affected by the decisions supported by the results of the study (long-term consequences, small or large).
  • Co-products are handled by system expansion.
  • Forecasting of processes is done by modelling and scenario methods. For processes where a large degree of control is assumed, also exploratory and normative methods may be applied (see chapter 6 for definitions).

Unfortunately, the above recommendations are not in complete accordance with the recommendations from the Dutch methodology project (Guinée et al. 2001), which was carried out simultaneously with the Danish project of which this report is a result. In spite of close agreements on many important basic concepts (see Guinée 1999) we did not succeed in reaching consensus on the specific recommendations to be given in our respective guidelines. The main differences between the guidelines are that the Dutch guideline restricts its recommendations to a baseline situation (applications with small, long-term consequences), and recommends an intentional disregard for market mechanisms and their consequences. In relation to the definition of the functional unit, the latter recommendation implies an assumption that there are no changes in consumer behaviour in relation to product substitutions, such as the so-called “rebound effect”, and that differences in consumer prices do not induce the consumer to spend more or less money on other products. In relation to system delimitation, it implies an assumption that all processes will react to changes in demand in proportion to the revenue obtained for the production (i.e. that relative supply elasticities equals relative prices) without any side-effects, so that the affected technology will be the average of the currently installed technology, and so that co-production does not lead to substitution and may therefore be handled by economic allocation. The argument for this intentional disregard for market mechanisms is apparently that a full modelling of market mechanisms is not practicable, and that using an incomplete model of market mechanisms may introduce large uncertainties in the modelling. Thus, the Dutch guideline opts for an incomplete description rather than an uncertain description of the markets. In this way, there is an inconsistency between the Dutch recommended methodology and the application area for which it is suggested (consequential studies), as also pointed out by several of the international reviewers (Guinée 1999). In section 2.4 we continue the discussion on the issue of market modelling, and provide further arguments for intentionally including market mechanisms and their consequences.

1.2 Discussion of attributional versus consequential LCA

The relevance of attributional LCAs has been questioned (Weidema 1998b, Wenzel 1999), because the ultimate goal even of hot-spot-identification and product declarations is to improve the studied systems:

• If an attributional hot-spot-identification identifies a number of improvement options, a consequential assessment is still needed to assess the consequences of implementing the improvements, so one might as well perform a consequential study in the first place.
• If product declarations are used by the customer to make a choice between several products, this choice should ideally be based on the environmental consequences⁷ of this choice (i.e. a specific, medium-term, prospective study), not on the historical impact caused by the products⁸.
• Likewise, if generic consumer information affects the behaviour of the consumer, this behavioural change should ideally be based on the environmental consequences of this change (i.e. a generic, medium-term, prospective study).

Click on the picture to see the html-version of: Figure 1.3

Even a question that appears retrospective at first sight (like “If I look at the world as it is now, what is the environmental contribution of car driving?”, Guinée 1999, p.5) does not appear to have a meaningful answer, except if we reformulate it as a hypothetical “historical, consequential”: “What would the world have looked like now, if we had removed car-driving?” Such historical consequential questions can be answered by applying the same consequential methodology as for prospective questions, but using current or historical data. As pointed out by Guinée et al. (2001, part 3, p. 14), outside such a consequential context, there is no objective way to separate the system of car driving from the rest of the technosphere (i.e. to draw the dotted line in the left circle in figure 1.3), since all product systems are ultimately linked (i.e. there are so many lines crossing the dotted line in the left circle in figure 1.3 that the drawing of this line will imply a number of normative cut-offs). Thus, outside of a consequential context, any separation of product systems will be inherently normative and will therefore have to be included in the question asked, i.e.: “Providing we use method X for dividing car driving from the rest of the technosphere, what is its environmental contribution?” implying that the question carries the premises for its own answer. Such questions, and the LCAs that are used to answer them, may therefore more correctly be termed attributional (Heijungs 1997, Frischknecht 1998, Hofstetter 1998) than retrospective, since they deal rather with the juridical issue of allocation or attribution of guilt, blame or responsibility, than with the natural science issue of analysing causalities and consequences, and since such questions of guilt, blame or responsibility may pertain to the future as well as the past. The term retrospective, if used at all, should then rather be used for the “historical consequential” applications (see also figure 1.4). The point made here is not that attributional questions are meaningless, but that it is impossible to give meaningful, objective answers to such questions. In terms of uncertainty, this may be considerable in consequential LCA, since current uncertain knowledge is used to assess future consequences. However, this uncertainty can be estimated and controlled, while the error that is inherent in attributional LCA is fundamentally unknowable and uncontrollable.



Click on the picture to see the html-version of: Figure 1.4

In the past, life cycle assessments have primarily been applied to consequential questions, and practitioners have sought to adjust their methodologies to reflect this objective. However, attributional methodologies have often been applied, because adequate consequential methodologies have been missing. We hope that the market-based methods presented in this report will help practitioners to apply a consequential approach more consistently throughout their life cycle studies.

The relevance of attributional LCAs have been defended with a number of different arguments, which will be treated separately here:

• Attributional LCA may be used as a pedagogical introduction to a life cycle study, since at first sight it may appear simpler: All that is needed is knowledge on current or potential suppliers and customers – other market relations may be disregarded, and data need only be collected from enterprises in one’s own supply chain. This may be useful in the early stages of a life cycle study, where there is a need simply to explore the life cycle, to increase the understanding of the product chain (Tillman 2000). An attributional LCA may pinpoint the processes and relations most important to influence in a product system (known as “hot-spot-identification”). However, this could equally well (and maybe even more sensibly) be done with a consequential LCA that tells about the consequences of producing, using, and disposing a quantity more or less of the investigated product. And this would even provide more relevant information on what parameters guide the behaviour of the investigated product systems.
• To operate an LCA-based system for environmental product declarations, there must be a generally accepted set of rules for how to perform such LCAs. Tillman (2000) doubts whether it will be possible to establish the necessary consensus within a consequential approach to LCA since this implies system expansion (see chapter 5) and use of marginal data (see chapter 4), including “an approach as to which marginals and in which way the system should be expanded.” The present report, and the twoguidelines that it supports, is nevertheless a report on an attempt at providing such consensus. And in response to Tillman’s doubt, it appears equally questionable (if not more so) that the necessary consensus and acceptance can be obtained for an attributional approach to LCA that needs to apply and justify arbitrary allocations and choices of which averages to use.
• Additivity between individual parts of a life cycle (enabling a producer to add his own environmental exchanges to those reported by his suppliers) and completeness (in the sense that only negligible parts of the product system are omitted) are both features that Tillman (2000) use as arguments for using attributional LCA. However, in the sense described here by Tillman, both additivity and completeness are also features of consequential LCA as described in this report.
• Attributional LCA are also said to be applicable in situations where no specific change is planned, as may be the case e.g. for hot-spot-identification, for setting priorities that do not immediately involve a change, or where the scale or products involved in a substitution are unknown, while it is questioned (also by Tillman 2000) how this could be done in a consequential LCA. However, a consequential LCA may very well assess the consequences of production, use and disposal of a defined quantity more or less of the investigated product. This can be done independently for any product, without prior knowledge on the specific comparison that each assessment may later be used for. Later, when specific comparisons are required, these may be obtained simply by subtracting the individual product systems. These comparisons will be valid as long as the product quantities studied are small. For larger quantities it is of course important to include any influence on the boundary conditions.
• System expansion as an important method in consequential LCA to handle systems with multiple products (see chapter 5) is thought by Tillman (2000) to imply “a larger system and thus more data to collect.” Since most LCA databases are currently based on average data without concern for market mechanisms, an LCA based on available data and default cut-off criteria will of course be easier and less time consuming to perform than a consequential LCA that must rely on not readily available market data. However, in an LCA that involves specific data collection, the procedures for consequential LCA suggested in chapters 4 and 5 specifically reduces the size of the system to investigate by excluding all processes that do not change as a consequence of the change in demand for the product under study. In contrast, a product system in attributional LCA must include, for every step (tier) of the life cycle, all specific suppliers to the previous tier (and even more individual suppliers when average data are used). Our experience shows that for more detailed LCAs that place a large demand on specific, high-quality data, the additional time spent in collecting market data (see section 2.5) will quickly be outweighed by the timesaving in having fewer processes from which to collect detailed environmental data.
• In an assessment of policy options, the decision-maker may be interested in how to change or influence the markets, and is therefore not interested in limiting the analysis to the predicted market reactions to the potential decision, as implied by a study of actual consequences. In LCAs performed for a decision-maker with a long time horizon and a strong influence on the actors and markets in the product chain (such as studies by a market-leader or studies aimed at societal action plans and legislation), the flexibility of attributional LCA to include any process of interest, may better reflect the actual flexibility of the decision maker. However, even in such cases, where the normal market mechanisms are overruled, the market-based procedures of consequential LCA (see the following chapters) will still provide a good framework for explicitly documenting this dominating influence of the decision-maker.
• Because consequential LCAs only look at the consequences of changes, it may misrepresent the “signal value” implied in a demand for an environmentally improved product if this demand does not lead to an immediate change. An example of this may be the initial immature market for ecological foods, where an increase in demand may not lead to an increase in production, because of the transaction costs of the initial small quantities or because of the time it takes to implement the new technology on the farms. An LCA should give credit to such a demand even if it does not lead to changes in production in the short term, because the combined demand of many actors would be able to overcome the outlined constraints. An attributional LCA can give such a credit, since the attribution is not dependent on any assessment of actual changes. The answer of consequential LCA is to expand the scale and time horizon for assessing the consequences so that the long-term reaction of the market to the change in demand is indeed included, and the credit therefore assigned (see section 4.3).
• Attributional LCA may be used in a context where the decision-maker wishes to support, be part of, or otherwise be associated with what is deemed to be a “good” system, or to be dissociated with what is deemed to be a “bad” system (Ekvall 1999, 2000, Ekvall et al. 2001a, b). For example, the decision-maker may wish to be associated with companies that use renewable energy sources, disregarding whether this leads to increased production of renewable energy or not. A consequential LCA would not be able to provide the sought-after information, since it only takes into account the actual consequences and therefore only gives a credit for renewable energy when an increase in the capacity of renewable energy can be expected (see section 4.3). If no change is expected in the composition of the overall output, for example when the renewable energy source is constrained, as is the case with hydropower in Europe, the consequential LCA does not give any credit (see, however, the exception dealt with in the previous bullet). In this situation, an attributional LCA may well give a credit for a supply of hydropower, simply because it is the association with the “good” system that is credited, and not whether there is any overall change in environmental impact. It should be noted that in the opposite example, where the decision maker wishes to dissociate from what is deemed a “bad” system, e.g. one associated with hazardous chemicals or ionising radiation, the consequential and the attributional LCA would both be able to supply the desired information, since it is very few systems that are downwards constrained, which implies that an explicit reduction in demand would indeed have consequences for these “bad” systems. Ekvall (2000, Ekvall et al. 2001b) seek to justify attributional LCA by referring to rule ethics (as opposed to utilitarian or situation ethics which would support consequential LCA) but acknowledges that its application would require an agreement on what is regarded as “being associated with.” This agreement would amount to a rule for allocating or attributing guilt, blame or responsibility, which cannot be made on objective grounds, as noted above. Furthermore, the concept of “being associated with” is hardly meaningful beyond a few steps backwards or forwards in the supply chain, thus rendering LCA too sophisticated a technique for identifying the relevant associations. Nevertheless, it is natural that a commissioner of a life cycle study may feel that it is more relevant to study the processes in the immediate supply chain than those actually affected by the product substitutions. It is important to clarify whether the interest of the commissioner is really in the environmental impacts of products (i.e. in LCA) or more in the environmental impacts of the supply chain as such, since the latter interest may be better handled through supply chain management.
• Ekvall et al. (2001a, b) provide a specific thought experiment where consequential LCAs would lead to an undesirable effect: The lack of credit for using hydropower may provide an incentive to create a separated, sub-optimised market for hydropower where this credit could be justified. However, the thought experiment depends on two conditions being simultaneously fulfilled, namely that the environmentally preferable process or technology is more competitive (cheaper) than the environmentally less preferable, and absolutely upwards constrained in its ability to change its capacity as a result of a change in demand. In practice, we have not been able to identify any other examples than hydropower, where these conditions occur simultaneously. Nevertheless, this is a real and undesirable effect of consequential LCA, which cannot be avoided but only alleviated or internalised by applying in this situation an additional scenario in which the separated, sub-optimised market is assumed realised, implying thus a credit to the users of the environmentally preferable technology. When so applied, this scenario would work counter to its own fulfilment and counter to the described undesired effect. This isolated undesirable effect of consequential LCA does not in itself constitute an argument for a more general use of attributional LCA.

If a company or product chain in an expanding market has several production lines, some older more polluting and some new less polluting, it may appear with a below average environmental performance in an attributional LCA that use average data, while a consequential LCA that focus only on the new production lines that will be installed, may show a performance equal to the rest of the market, since all actors on the market typically install the same new technology. It may be argued that an attributional LCA will provide an incentive for improvement of the older, more polluting production lines, in order to better compare with “green” competitors that have only newly installed production lines, while the consequential LCA does not provide the same incentive. The attributional LCA can be said to reward the newcomer that is not burdened with the old technology, while the older factory is punished for having been in business longer. However, there is a way for the older factory to avoid this, namely by separating the old and the new parts of the factory, selling the products from the old production lines to the general customer, and selling the now competitive products from the new production lines on the “green” market. This restructuring would not change the overall environmental impact and the attributional LCA would not have provided any more incentive for improvement than the consequential LCA. In contrast, consequential LCA does provide a real possibility to reward improvements in older production lines even when these are not immediately affected by changes in demand. This is possible if the producer actively links his improvements in the older production lines to increases in sales. Thereby, the customer buys both a product from the new production line and a share of the improvements of the older production lines, which obviously provides a better environmental performance than just buying a product from the new production line. In fact, such cross-subsidising between production lines need not be limited to the production lines within the same company or product chain, if the money is better spent on environmental investments elsewhere. However, to be credible, such cross-subsidies should binding and verifiable (e.g. contractual) and their existence preferably verified by an independent third party. In this way, consequential LCA allows any production to obtain an environmental “credit” when consciously affecting a specific production, while those who only do what everybody else do, obtain the same LCA result as everybody else, no matter how good or bad their average performance.

The understanding of consequential life cycle assessment as a tool for decision-support as opposed to a tool for documentation and attribution of guilt, blame or responsibility, implies a focus on the importance of system boundaries and market data. This implies also a focus on the problems involved in verifying such information, including the involvement of stakeholders, critical assessment of sources, and peer review. This is common to other decision-support tools, which are also not expected to result in unambiguous information, but rather different scenarios where the different assumptions are documented.

1.3 Product substitution

In a consequential, comparative life cycle assessment, the object of study is the environmental impacts of a potential product substitution, i.e. the replacement of one product or group of products with another product or group of products, fulfilling the same needs of the customer. We define a product also in terms of its production process, which implies that a product substitution will always imply one or more process substitutions (understood as process changes or complete replacements), and that a process substitution can also be seen as a product substitution, even when the product itself is unchanged (e.g. in terms of its physical properties).

Product substitutions may occur anywhere in the life cycle, from raw material substitutions, over substitutions in the production and use stages, to substitutions between alternative waste handling options.

In this context, several authors (Clift et al. 1998, Frischknecht 1998, Tillman et al. 1998) have suggested that a distinction between foreground and background processes can be useful. However, we have found it necessary to define these terms more strictly⁹, to understand that:
- a foreground process is a process whose production volume will be affected directly by the studied change,
- a background process is a process whose production volumes will not be affected or only be indirectly affected (i.e. only through the market) as a consequence of the increase or decrease in demand as a result of the studied change.

Life cycle assessments are typically limited to study the effects of substitutions at one specific stage in the life cycle, the range of possible substitutions at that stage being delimited by the functional unit (i.e. the functional unit typically does not specify what choices to make at other stages). The reason for this is that life cycle assessments are typically aimed at situations where the influence of the decision-maker is limited to the specific substitution studied. (i.e. most processes are in the background).

However, if the decision-maker is able to affect substitutions at different stages in the life cycle (i.e. using foreground processes for these), these substitutions may - both in principle and in practice - be specified by the functional unit, thus including simultaneously all possible choices in the study.

Even when the decision-maker is not able to directly influence any substitutions elsewhere in the life cycle (i.e. when most processes are in the background), the studied substitution at one stage in a life cycle (the foreground) may still lead indirectly to product substitutions in other life cycle stages (in the background), due to the change in demand implied by the initial substitution. These substitutions are then not included in the functional unit, but the expected result of the substitutions (in terms of affected processes and their technologies) is simply included when modelling the product systems.

Put very briefly, using the terminology of foreground and background processes: Product substitutions in foreground processes may be included in the definition of the functional unit, while substitutions in background processes are simply accounted for by including the affected processes and technologies when modelling the product systems. See also figure 1.5 and the explanatory text to this figure.



Click on the picture to see the html-version of: Figure 1.5

Explanations to figure 1.5: The substitution studied may be at the use stage (to use product A or Product B for the function P), at the production stage (to produce product A by route A¹ or A²), at the raw material stage (to use raw material R¹ or R²) or at the disposal stage. However, the choice of a specific product (say B) will typically imply a choice of production route and raw materials (R²) that is not put into question. It is only when the decision maker (in the case of the choice A or B, the user is the decision maker) has an influence on the choice of production and/or disposal route and/or raw materials use, that the other choices (e.g. A¹ or A² and R¹ or R²) can be included by the definition of the functional unit (e.g. specifying: "P produced using raw material R²", or the more conditional specification: "P produced with optimal raw material choice," which allows a comparative investigation of different raw materials). This is illustrated by the sphere of influence S². Usually, the influence of the decision-maker is more limited, typically to the choice between different products at the previous stage in the product chain (S¹). In this case, the functional unit is simply specified as "P" without indication of any specific conditions of production or disposal. Nevertheless, these choices will still be made by other decision-makers in the chain. So what will be included in the life cycle study is the expected result of these choices, i.e. the expected route of production and disposal as chosen by the decision-makers for these stages of the life cycle.

Figure 1.5. Product substitutions in relation to the sphere of influence of the decision-maker

Relating this to the application areas in figure 1.1, it can be seen that the conditions for a large area of influence (S¹ in figure 1.5) is limited to the upper left-hand corner of figure 1.1, as can also be seen in figure 1.2, namely for long-term, strategic applications involving relatively well-defined products from enterprises with a relatively large (expected) influence on the different actors in the life cycle.

For a thorough understanding of a specific product substitution, information is required on:

1. The extent of the studied substitution, where:

• small¹⁰, short-term substitutions affect only capacity utilisation, but not capacity itself,
• small, long-term substitutions affect also capital investment (installation of new machinery or phasing out of old machinery),
• large substitutions affect also the determining parameters for the overall technology development, i.e. the constraints on the possible technologies, the overall trends in the market volume, or the production costs of the involved technologies, so that the studied substitution in itself may bring new technologies into focus.

1. 2. The market segment affected, as determined by the obligatory product properties (i.e. properties that a product “must have” for a customer in that segment to accept the products as comparable and thus substitutable).
2. Product availability, i.e. whether the market situation actually allows a choice between the products to be made (markets and/or production technologies may be constrained by market failures, declining markets, regulations, or shortages in supply of raw materials or other necessary production factors).
3. The positioning properties of the products ("nice to have"), as well as price and information, which influences the degree to which a potential product substitution will actually be realised.

This is further elaborated in chapter 2 of this report.

1.4 Defining the functional unit

The functional unit plays several roles in a life cycle study:

• First, it serves as a reference unit, to which all other data in the study relates.
• Secondly, it reflects the amount of substitutions that the decision maker desires to influence, as outlined in section 1.3 (see especially figure 1.5),
• Thirdly, it is the basis of equivalence, when comparing different product alternatives in consequential studies.

For the latter role, the obligatory product properties must always be taken into account. To obtain a precise and unambiguous definition, it has proven useful to analyse in detail the actual obligatory product properties required by the relevant geographical markets and market segments.

A company-internal study comparing different options in the product development, may define additional properties as obligatory for their own brand, although they are only regarded as positioning properties on the general market (and would be determined as such in a more generic life cycle assessment comparing this brand with other brands).

Whether the other aspects of product substitution (availability, positioning product properties, price, and information) should also be taken into account depends on the time horizon of the study. In studies with a long time horizon (e.g. product development or strategic management), it may be reasonable to compare two products, for which substitution cannot be immediately realised, but where it is assumed that substitution will be realised under specific, future conditions of availability, price and product information. The shorter the time horizon of the study, the less relevant it is to include product alternatives, for which substitution is not likely to be realised under the present market conditions.

Two products may be compared even when they differ with respect to positioning properties. If these positioning properties can be determined to fulfil specific functions, equivalence between the products under comparison must be ensured by treating these functions as co-products (see section 1.7 and chapter 6).

1.5 Market-based system delimitation

¹¹As mentioned above, the processes to include in a consequential life cycle study - and the technologies of these processes - are the processes and technologies actually affected by the studied product substitution (as defined by the functional unit). To identify the processes affected, all four types of information on product substitution mentioned in section 1.3 are relevant. In chapter 4, we present a step-wise procedure for identifying the affected processes through a formalised treatment of the last three types of information.

Figure 1.6 can be used to illustrate the difference between such a consequential, market-based system delimitation, and the more traditional system description based on an attributional or accountancy approach, where material and energy flows are followed mechanically from process to process. In the figure, it is shown how a change in volume of one process (process 1 to the right) leads to a change in the demand for one of the raw materials to this process. However, many different technologies or processes can meet the specifications for this raw material supply. This is illustrated by the fully drawn processes to the left, which together make up the suppliers to the market. Now, the traditional system delimitation will either include an average of all these processes, weighted by their respective production volumes, or just include that specific process, which represents the current supplier to process 1, here illustrated by the fat box.

When applying an average, the result can be seriously affected by the delimitation of the market on which the average is taken. For example, it will make a large difference whether you regard the Nordic electricity market as one (relatively closed) market, so that Danish electricity consumption is calculated as an average of Danish, Finnish, Swedish and Norwegian electricity production, or whether it is assumed that Denmark is a market in itself (which is often seen in life cycle assessments). If we choose to look at the average for Denmark, which is not a closed market, it is decisive whether the average is calculated from the Danish production alone or whether you take into account the exchanges with the neighbouring markets, and how you take this into account, e.g. whether you calculate with Danish production plus import-mix (in periods with much available hydropower in Norway and Sweden), with Danish production plus import-mix minus export-mix (in periods with little hydropower available) or just Danish production plus net import/export (thus disregarding transit-trade). For Switzerland, having a large degree of transit-trade, Ménard et al. (1998) have shown how such different assumptions affect the average from 21 g CO₂ (Switzerland’s own production) over 140 g CO₂ (Switzerland plus import minus export) to 500 g CO₂ (UCPTE average, in that UCPTE can be regarded as a relatively isolated electricity market like the Nordic). The recommendation of Ménard et al. (1998) is to use the model that disregards transit-trade (48 g CO₂) with the argument that this best reflects the actual market conditions. It should be clear from this example that averages can be highly debatable, and possible arguments for preferring one average over the other is actually often market-based. This may in itself be regarded as a serious argument for taking the full consequence, and use a truly market-based system delimitation instead of the average approach.

Click on the picture to see the html-version of: Figure 1.6

A market-based system delimitation will first determine the actual geographical and temporal market boundaries (see section 2.1), which in the electricity example will lead to the identification of the Nordic and the UCPTE markets as being the relevant electricity markets.

Within each such market, a market-based system delimitation will then - instead of considering averages - investigate whether any of the processes delivering to the market are constrained in their capacity to change as a result of a change in demand from process 1 (figure 1.6). These constrained processes are marked with C’s.

It should be noted, that also in a market-based system delimitation, the directly delivering process (the fat box) may well come into play. However, this requires that the change in demand overcome the constraints on the process, so that its production volume is actually affected. Thus, the change in demand must to some extent put the market forces out of play to ensure that a capacity adjustment is actually taking place in that specific process. This may especially be the case if the customer has a controlling influence on the supplier (possibly in the form of a monopoly position).

Another aspect of the market-based delimitation is that it investigates whether the change is so large that it gives room for new technologies (illustrated by the perforated box in the upper end of figure 1.6) or that it can affect one or more of the identified constraints, so that a C-marked technology can anyway come into play.

Now, if the technologies/processes in figure 1.6 are arranged in such a way that the most economical are at the top (this is often also the newest and most efficient ones, but this depends also on the cost structure, including the wage level) and the least economical at the bottom (often the older, less efficient), it will typically be either the upper or the lower unconstrained process that will be affected by a change in demand – depending on whether the market is expanding or shrinking. Contrary to the average, we are rather concerned with the extremes here.

If we now focus on the situation with an expanding market, where the possible (non-C-marked) processes are found in the upper part of figure 1.6 inside the perforated box, the final step in the market-based system delimitation is to look at the expected long-term marginal production costs of these technologies/processes (the figures in the boxes). With adequate respect for non-monetarised aspects (flexibility, quality, knowledge), the technology/process with the lowest expected long-term marginal production costs (marked with an arrow) can now be pointed out as the one that will be affected by the change studied.

The outlined procedure is explained in more detail and illustrated with numerous examples in chapter 4.

1.6 Handling co-production

When a process is related to more than one product, how should its exchanges be partitioned and distributed over the multiple products? This has been one of the most controversial issues in the development of the methodology for LCA, as it may significantly influence or even determine the result of the assessments.

The ISO standards on life cycle assessments requires a step-wise procedure to be applied. Besides the obvious solution of subdividing the unit process into separate processes each with only one product, whenever this is possible, the ISO procedure (ISO 14041, clause 6.5.3) consist of three consecutive steps:

• First, when possible, the system should be expanded “to include the additional functions related to the co-products”,
• Secondly, if the above is not possible, “the inputs and outputs of the system should be partitioned between its different products or functions in a way which reflects the underlying physical relationships between them; i.e. they shall reflect the way in which the inputs and outputs are changed by quantitative changes in the products or functions delivered by the system”. Clearly, this is a description of causal relationships, implying that the co-products can be independently varied (i.e. a situation of combined production).
• Finally, “where physical relationship alone cannot be established or used as the basis for allocation, the inputs should be allocated between the products and functions in a way which reflects other relationships between them. For example, input and output data might be allocated between co-products in proportion to the economic value of the products.” Although not stated explicitly, it can be seen from the parallel wording to the second step that the relationships referred to here should also be causal in nature, which is further emphasised by the only example provided, namely that of economic value of the products, which can be seen as the ultimate cause for the existence of the process. Economic value is so far the only causal relationship that has been found to fit this last step of the ISO procedure.

The two first steps of the ISO procedure are only relevant for consequential studies, since they rely on an analysis of relative changes in the output of the co-products and an adjustment of the systems to yield the same output (see also figure 1.6). This means that for attributional life cycle assessments, where such system adjustments are not possible, co-product allocation by economic relationships is the only option left.

In consequential, comparative studies where a co-product does not appear in similar quantity in all systems under study, it is necessary to expand the studied systems, so that they all yield comparable product outputs. The processes to include when making such system expansions must be those processes actually affected by an increase or decrease in output of the byproduct from the systems under study (see figure 1.6).



Click on the picture to see the html-version of: Figure 1.6

Explanations for figure 1.6: The two original systems to the left are producing product A either without by-products (system 1) or with the by-product B. System expansion (illustrated in the systems to the left) is performed with the following rationale: If system 2 substitutes system 1, more B will be produced for the same quantity of A. This additional amount of B will substitute another existing production of B, which must then be added to system 1 to take this effect into account. Here, the difficult task is to identify which existing production of B will be substituted. If system 2 is substituted by system 1, less B will be produced, thus requiring a new substitute production to be added to system 1. Here, the difficult task is to identify which production of B will be the substitute.

Thus, to identify the processes for a system expansion, one may apply the procedure mentioned in section 1.5 for identifying the processes and technologies actually affected by a product substitution. In chapter 5 it is demonstrated that when applying this formal procedure, system expansion is always possible, i.e. it is always possible to identify those processes that will be affected by a shift between the studied systems. Obviously, the identification can be made with more or less precision, but even an uncertain identification of the affected processes gives a more useful result than an arbitrary allocation according to e.g. economic relationships between the co-products.

From the observation that system expansion is always possible for consequential studies, and never for attributional studies (leaving only the option of economic allocation for such studies), we obtain a much simpler description of the procedure for co-product handling than the description in ISO 14041, although leading to the same result as when following the ISO procedure.

Also other suggestions for allocation procedures, such as the recycling allocation procedure using material grades (Wenzel 1998, Werner & Richter 2000) and the so-called 50/50 procedure for recycling allocation (Ekvall 1994), can be shown to be simple procedures for system expansion relevant in situations of limited information.

1.7 Forecasting processes

Obviously, forecasting is only relevant for prospective life cycle assessments, where the description of the product systems should reflect the relevant time horizon. It is relevant to forecast:

• the future market conditions determining which future product substitutions will take place,
• the geographical and technological conditions of the future processes, and
• the future environmental exchanges of these processes.

As illustrated in figure 1.7, short and medium term (1-5 years) forecasts for specific product systems may be based on simple extrapolation of trends and historical data. For long-term (5-25 years) forecasts, and forecasts for decisions on less specific systems (e.g. the general disposal system of society), it becomes increasingly relevant to use modelling methods, such as trend impact analysis, which adjusts the extrapolations with the expected impact of mechanisms analogous to those determining past events. For generic studies, aimed at influencing many stakeholders (e.g. ecolabelling), it may be relevant to use participatory methods incorporating the insight and opinions of experts and stakeholders. Scenario methods, incorporating several parallel forecasts, are most relevant for systems used in long-term, strategic studies for both societal decisions and product development. The product development process may also benefit from the systematic creativity in exploratory methods, which combine analytic techniques dividing a broad topic or development into increasingly smaller subtopics or consequences, and imaginative techniques aimed at filling all gaps in the analytical structure. For long-term, strategic applications, involving relatively well-defined products from enterprises where the decision maker is expected to have a large degree of control over the future and the different stakeholders involved, it may be relevant to apply normative forecasting, which investigates how we want the future to be and how to obtain this goal.



Click on the picture to see the html-version of: Figure 1.7

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⁵ An early version of this introduction was presented to the 3rd International Conference on Ecobalance, Tsukuba 1998.11.25-27 (see Weidema 1998b).
⁶ Also known as status-quo or descriptive LCAs as opposed to the consequential LCAs, which are also known as change-oriented, effect-oriented or comparative (Ekvall 1999). In principle, attributional LCAs may also be performed in an estimated future situation, and consequential LCAs may describe the consequences of a historical decision. We therefore generally use the terms attributional and consequential rather than terms that signal a temporal context.
⁷Although Ekvall (2000) and Ekvall et al. (2001a, b) argue that choices could also be based on other premises than environmental consequences, in which case an attributional LCA based on these premises could be relevant (see also the further text in this section).
⁸ The issue of product declarations is dealt with in more detail in section 4.7, since the potential ambiguity in the purpose of this application make it useful as a touchstone for methodological debates.
⁹ It is worth noticing that in the following methodological explanations, we have not relied on these terms but only used them in brackets to show the places where these terms can be used. Our point in doing this is to show that, even with our more precise definition, the terms are not necessary, and since they are often used without a precise definition, they may be more misleading than guiding. We therefore suggest that these terms should not be used in general for systems descriptions.
¹⁰ In earlier presentations of the procedure to identify the processes or technologies affected by a substitution (e.g. Weidema et al. 1999), the term “marginal” was used extensively to signify small changes and the processes they affect. In this report, as well as in the guidelines, we now generally avoid the term, as it is in everyday-language used in many different meanings and may therefore give rise to confusion. We suggest to use it only to distinguish between small (marginal) substitutions, where an increase and a decrease will affect the same process, and large substitutions where this may not be the case.
¹¹An early version of this section was published in Guinée (1999, pp. 33-46).

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