When speaking of a function, one may indeed have in mind minor functions in the private sphere, like ‘writing and sending a letter’. However, there is nothing to prevent us from widening the scope to include broad societal functions, like ‘transporting 2 million commuters between the city of Chicago and the suburbs’. Both minor and major functions should in principle be covered by LCA, although the practical methods employed in the analysis may be very different. It is only through an explicit restriction of scope that we can opt for a certain breadth of function as the topic of LCA. The stated purpose of LCA allows for two quite distinct modes of Interpretation. In the first, the question of interest is the contribution of a particular way of fulfilling a certain function to the entire spectrum of environmental problems as they currently exist or are being created


Using LCA to answer this question is referred to as doing a descriptive LCA, although other terms like retrospective LCA, level 0 LCA, and status quo LCA are also encountered in the literature. In the second mode of interpretation the emphasis is on change. The analysis then addresses the environmental changes resulting from a change from or to a particular way of fulfilling a certain function. This change may assume a variety of forms, illustrated by ‘drinking one more beer’ and ‘drinking a different brand of beer’. The use of LCA for answering this second type of question is referred to as doing a change-oriented LCA, or sometimes a prospective LCA, or a level 1 (or 2 or 3) LCA. Both types of LCA Interpretation are deemed useful, difficult or state-of-the1 Products usually comprise both goods and services. Functions may be seen as services.

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


Part 3 : Scientific background art, depending on the perspective of the study in question as well as personal preferences. There is even evidence that while most LCAs are intended to be change-oriented, actual practice is descriptive, or that current practices in fact coincide. Whatever the case, in a chapter treating the foundations of LCA we consider that the two should be kept conceptually separate and discussed as such, even though the later chapters will focus on the change-oriented mode. 1.1.3 The questions addressed by LCA Our basic point of departure is a decision situation in which a choice is to be made between a number of alternatives. We distinguish between the questions to be answered and the way the answers can be produced. The questions are the starting points, and one or more simple or complex models are the means for answering them


The questions are the starting points, and one or more simple or complex models are the means for answering them. The questions are of the form: What are the environmental1 effects of choosing option A rather than option B for fulfilling a certain function? Restricting ourselves to the Inventory phase, effects are to be considered in terms of environmental interventions2. We distinguish three main types of question, related to three main types of decision:3 occasional choices, concerning one-off fulfilment of a function; structural choices, concerning a function that is regularly supplied; strategic choices, on how to supply a function for a long or even indefinite period of time. What specific questions are to be answered for each type of choice? Without pretending to be complete, we would consider the following questions relevant for developing a systematic approach to answering them


The category of occasional choices refers to one-off decisions made by individuals that have a negligible influence on society. Concrete examples include: mode of travel: should I take the high speed train or the plane to my meeting in Paris next week? beverage containers: should I use a china cup or a paper cup in the lunch facility I happen to be visiting today? waste management: should I put this particular piece of waste paper in the paper container or the organic wastes container? The time horizon of the decision and function fulfilment is short. In contrast, the time horizon of effects in the chain may be quite long. In specifying the effect chain, one may take into account simple or more complex mechanisms, simple or complex model forms, one may use one or several models, etc. A current choice does not influence the past, so capital investments like building the aircraft to be used next week play no part in occasional choices


A current choice does not influence the past, so capital investments like building the aircraft to be used next week play no part in occasional choices. However, the extra passenger may lead to earlier replacement of the aircraft, which then leads to earlier and possibly greater environmental interventions. Similar lines of reasoning can be developed for the other examples. It should be noted that occasional choices may be frequently repeated, as when a business traveller chooses between train and plane on every business trip. The same applies to the manager of a waste treatment plant making daily decisions on optimum operating conditions, depending on the incoming flow of waste. If a decision can be placed in such a framework, it is no longer an occasional choice. The category of structural choices refers to choices made by individuals as well as firms relating to decisions that affect society in a limited and readily reversible manner


The category of structural choices refers to choices made by individuals as well as firms relating to decisions that affect society in a limited and readily reversible manner. Examples include: mode of travel: should I take the high speed train or the plane to my weekly meetings in Paris? beverage containers: should I use a china or paper cup in the lunch facility at my office, every working day? waste management: should I put my daily flow of old newspapers in the dustbin or the paper recycling container? In this case the time horizon of function fulfilment may be long. The context for my decisions in terms of facilities required is given in the short term but the constraints are eased each year, as investments and wear alter the capacities of the various technologies installed. For instance, even a small but continuous flow of extra discarded paper will lead to adjustments in investments at paper mills


For instance, even a small but continuous flow of extra discarded paper will lead to adjustments in investments at paper mills. The choices illustrated are for consumers, but they may similarly apply to business enterprises, as when a company decides its employees are to make their business trips to Paris by train. A company’s decision to give its employees a lease car or a free annual public transport ticket is also in this category. The essential 1 Here, we might add “and other” to allow for coverage of social and economic effects. 2 Non-environmental effects would fit into the same framework. 3 This categorisation of questions is close to, but nevertheless differs in some respects from the categories shortterm optimisation, mid-term improvements and long-term societal change.


Part 3: Scientific background feature is that the choice is reversible, involving no major investments, for instance. This sets limits on the scale of the decision. Thus, if a government were to decide that every citizen be issued with a free public transport ticket, this would require major investments in the railway system, which is to be regarded as irreversible in practice, and we would then categorise such a decision as a ‘strategic choice’. This final category of strategic choices refers to choices by individuals, firms and governments relating to decisions that affect society in a substantial and practically irreversible manner


This final category of strategic choices refers to choices by individuals, firms and governments relating to decisions that affect society in a substantial and practically irreversible manner. Examples include: mode of travel: should the government invest in high speed railroads or in airports? beverage containers: should society as a whole opt for re-usable china luncheon crockery rather than throw-away cups and plates? waste management: should cities introduce separation of waste flows into different fractions, with a concomitant set of waste processing and recycling facilities? The time horizon of the activities involved in function fulfilment is longer for strategic choices than for occasional choices. Structural choices have a similar or more limited time horizon. There may be a delay in the start of the activities in question, as with the investments required in the high speed train example


Structural choices have a similar or more limited time horizon. There may be a delay in the start of the activities in question, as with the investments required in the high speed train example. The time horizon of functioning may be limited to a few years, as with lunchroom kitchens, or may extend to centuries, as with rail infrastructure. Regarding the effects in the chain, one can again opt to consider a shorter or longer period, for example with respect to the time taken for recycling effects to fade off. Again, one or several models may be used, incorporating simple or complex mechanisms and with a simple linear or complex non-linear form. The essential feature is that the choice leads to extensive changes with a high degree of irreversibility, since the investments are so large that it is very unlikely that such a decision will easily be reversed. It may be difficult to adequately categorise all the choices encountered in practice


It may be difficult to adequately categorise all the choices encountered in practice. Though certain choices may initially appear to be structural, they may be argued to constitute strategic decisions. For instance, if airline companies base their investment plans on current aircraft usage, one extra passenger weekly may lead to more investments. Similarly, an occasional choice for a fluorescent lamp is a decision for 5 years, with possibly even repercussions for power plant investment plans. It is consequently not so important in which category a given concrete choice is placed. What is important, rather, is that the user of LCA is aware of the fact that every category of question highlights certain aspects while ignoring others. In this Guide we concentrate on the structural choices. That does not imply we consider the occasional and strategic choices uninteresting or beyond the LCA domain. Indeed, we see these as representing useful questions


That does not imply we consider the occasional and strategic choices uninteresting or beyond the LCA domain. Indeed, we see these as representing useful questions. However, they require a modeling set-up deviating markedly from that needed to support structural choices. We have therefore chosen to leave the elaboration of these categories of choices to other projects. In our opinion the approaches developed by Clift and co-workers and by Weidema and co-workers may be useful for LCAs having occasional choices as their starting point. Strategic choices, on the other hand, call for an approach that would draw more heavily on elements of scenario analysis and partial or general equilibrium modeling. The methodology of LCA The terms ‘method’ and ‘methodology’ are often used to indicate what essentially is the method. A method is a structured way to achieve a certain goal: to measure the toxicity of a compound, for example, or construct a bridge


A method is a structured way to achieve a certain goal: to measure the toxicity of a compound, for example, or construct a bridge. A method consists of rules, recipes, formulas, descriptions and so on. Even the Mosaic commandments can be seen as providing a method to live a life in accordance with the principles of God. In any case, a method is not a scientific undertaking. It may be the result of such an undertaking, however, and it may also guide such an undertaking. Methodology, on the other hand, also referred to as the philosophy of science, is a science that studies means of developing methods that can be labeled scientific. More specifically, epistemology studies the sources of our knowledge. The source of the Mosaic commandments is divine revelation. This source of knowledge will not be considered scientific; some will deem it superior to scientifically derived knowledge, while others will deem it inferior or simply not believe it


This source of knowledge will not be considered scientific; some will deem it superior to scientifically derived knowledge, while others will deem it inferior or simply not believe it. Intuition, tradition, narratives and historical accounts are other sources of knowledge that are not scientific, although that does not mean they are ‘false’. Surprisingly perhaps, both approaches (i.e. science-based and not science-based) are possible with LCA. They in fact lead to two different methods for LCA, and correspond to two different Interpretations of the objective of LCA. On the one hand stands the methodology whereby which the adequacy of LCA theory is determined by comparing theoretical predictions with actual, observed phenomena. Here, LCA is interpreted as part of science, as using the scientific method, be that interpreted as nomological-deductive (Nagel), 1.1.4


hypothetical-deductive (Popper) or inductive (Carnap)1. The only thing that is accessible for testing empirical relations and therefore able to provide grounds for confirmation or refutation is observed change. If an additional unit of product function is superimposed on a constant background of economic activity and environmental pollution, the change in observed activity and pollution can be argued to be due to the additional unit of function. In practice, autonomous changes in economic activity and pollution may be so large that we cannot speak of a constant background. We therefore have a violation of the ceteris paribus assumption. In many fields of science, like biology and sociology, however, this is a quite common state of affairs. A frequently adopted approach is then to analyse the complicated structure in question by way of a number of simpler steps. These steps then correspond to portions of accepted models, disciplines or causal relationships


These steps then correspond to portions of accepted models, disciplines or causal relationships. Relevant examples in the context of LCA are process technology, micro-economics, multi-media fate models, dose-response functions and so on. The important point to bear in mind is that these previously established ‘building blocks’ are assembled as a means of predicting the changes in economic activity and environmental pollution due to an additional unit of product function. In other words, the approach is suitable as an epistemological basis for the change-oriented variant of LCA. For the normative element of LCA, the tools are not those of science but are in a similar vein, including multi-criteria analysis, multi-objective decision-making and, more generally, decision theory. On the other hand stands the methodology for dealing with descriptive LCA. This is not amenable to the scientific method (nomological deduction, hypothetical deduction or induction)


On the other hand stands the methodology for dealing with descriptive LCA. This is not amenable to the scientific method (nomological deduction, hypothetical deduction or induction). There is no possibility, not even in theory, of testing empirically the contribution to economic activity and environmental pollution of a unit amount of product function. We can neither confirm nor refute statements based on a descriptive LCA. However, some of these statements will certainly be deemed more logical than others. It may be possible to design a set of rules for developing a method for doing descriptive LCA studies which is internally consistent and more or less in accordance with a number of intuitive ideas. The rules forming the basis of such a method are the axioms (postulates, principles) and the method developed from it follows by deduction, employing theorems (propositions) requiring rigorous proof


The rules forming the basis of such a method are the axioms (postulates, principles) and the method developed from it follows by deduction, employing theorems (propositions) requiring rigorous proof. Given a consistent set of axioms, a method may be classified as false or true in a logical sense. It may be that certain propositions are undecidable, that is, their truth or falsehood cannot be established by means of the axioms. However, a different but equally consistent set of axioms may well lead to a different method, producing different but equally true LCA results. In applying these two methodologies there may be a substantial overlap of activities. When models are constructed according to the empirical-scientific method, there will be an array of central theoretical concepts. These may be interpreted as hypotheses, which can be refuted, or may be interpreted as axioms, which may or may not be accepted for certain reasons


These may be interpreted as hypotheses, which can be refuted, or may be interpreted as axioms, which may or may not be accepted for certain reasons. Statements derived from these central concepts or axioms are true in the empirical science sense if the central concepts have been corroborated, in the axiom sense if they seem ‘right’. An example is the 100% rule in allocation, which may be interpreted as a reasonable or just axiom, or (in a specific model structure) as an empirical rule based on the conservation of mass and energy. 1.2 Modeling aspects of LCA 1.2.1 Introduction In the previous section we have seen that there are two fundamentally different methodologies for developing methods for LCA. These two indeed lead to a number of methods within each of the two modes, descriptive and change-oriented. For the descriptive mode of LCA this was already concluded when discussing the variety of options for choosing a set of axioms


For the descriptive mode of LCA this was already concluded when discussing the variety of options for choosing a set of axioms. However, this open character of the method also holds true for the change-oriented mode of LCA. Although there is one ultimate benchmark for testing the predictions of change-oriented LCA, it is clear that this benchmark is useless in practice owing to the complex nature of autonomous developments in society, the economy and the environment. A plethora of unforeseen events mean that a ceteris paribus assumption will never hold for the full LCA model. Even in the absence of such extreme events as economic depressions, volcano eruptions or civil wars, technological change, economic growth, cultural development of tastes, shifts in taxing structure, changing infrastructure and so on are so ubiquitous that the consequences of decisions we wish to observe are mere ripples in a stormy pond


In practice, therefore, comparison of LCA predictions with reality is unattainable. In addition, when a choice is to be made between several alternatives for a given 1 The reader should bear in mind that the term ‘deductive’ in the realm of logic, for deriving conclusions from premises, embodies a different approach (see below) and that the term ‘complete induction’ is likewise used in a different sense in mathematics. Part 3 : Scientific background


Part 3: Scientific background function system, it might only be the effects of the adopted course of action that are investigated. As the effects of the rejected alternatives will never materialise, the grounds for the choice will be based on modeling assumptions, even though the consequences of implementing one alternative might be analysed empirically. Not only is society in toto too complex to analyse and in too great a flux to assume it in any way constant. It must also be acknowledged that predicting the environmental consequences of a simple unit change in the economy is an undertaking of tremendous complexity. The pathways in the economy and environment are long, interwoven and full of time lags and non-linearities. Every prediction will be based on a model, which simultaneously embodies incorporated knowledge and deliberate ignorance


Every prediction will be based on a model, which simultaneously embodies incorporated knowledge and deliberate ignorance. In other words, the predictions made with a change-oriented LCA are based on model calculations, and a model is a simplified representation of real mechanisms and phenomena. The choice of where to introduce which simplification is partly subjective. Different researchers may wish to use different models, corresponding to different assumptions and different simplifications. These researchers will therefore produce different answers to the same change-oriented question. Since there is only a platonic view of the benchmark of reality, it is hard to judge these different answers on the basis of their truth content. The best solution here is to state as explicitly as possible the assumptions and simplifications introduced in modeling the environmental consequences of a given change. Modeling choices are not entirely subjective, however


Modeling choices are not entirely subjective, however. Depending on the questions asked, some models may be more appropriate than others. If one is interested in short-term effects of a given choice, short-term relations should play a role, including changes in capacity usage of given installations in the model. If the interest is in long-term consequences, the model should indicate consequences of changes in installed capacities. If both shortterm and long-term consequences are to be examined, a dynamic model predicting effects in time is the most appropriate. As change-oriented LCA constitutes the principal focus of the remainder of this Guide, the rest of this section will be concerned mainly with an explicit treatment of our arguments for constructing a particular LCA model and a consideration of theoretical and practical modeling aspects


The theoretical part considers such issues as the general structure of the model and the main directions in which simplifications can be introduced, while the practical part is devoted to several pertinent problems in LCA: potential and actual impacts, non-linearities, spatial differentiation, average and marginal data and software. 1.2.2 Theoretical modeling aspects 1.2.2.1 The reality behind the model It is useful to gain a clearer idea of what kind of reality is supposed to be reflected by the LCA model. For the sake of simplification, we shall restrict our discussion to the Inventory analysis; an extension to Impact assessment is, in principle, straightforward. When studying any change in environmental interventions it is necessary to specify: a time pattern, e.g


When studying any change in environmental interventions it is necessary to specify: a time pattern, e.g. distinguishing between short-term changes and changes in the longer term; a reference situation (or more precisely: time path) in which the changes induced by the alternative investigated do not take place, that is the autonomous pattern or a reference alternative. Figure 1.2.2.1.1 illustrates what is involved in a change-oriented analysis. We see a time pattern of emissions.1 There is an autonomous pattern governed by a complex pattern of economic cycles, environmental regulation, population growth, cultural influences, natural disasters, civil wars and so forth. At a certain moment in time an economic actor has the power to decide to implement a choice, which is to be supported by an environmental analysis. This may be a producer redesigning his production process, a consumer buying a certain product, an authority granting a permit, and so on


This may be a producer redesigning his production process, a consumer buying a certain product, an authority granting a permit, and so on. From onwards Figure 1 shows two lines: one with some alternative being implemented and one with the reference alternative The analysis is thus between two parallel systems; it is not a before-after comparison but a with-without comparison. It is assumed here that there is an immediate effect at and that there is a complicated difference between the two lines at later stages. This complicated difference is due to upstream and downstream 1 For explanatory purposes, this is a one-dimensional conception, representing emissions of for example, or damage to the ozone layer or an aggregated weighting result. The focus of the present discussion on Inventory analysis means that emissions will be the typical entity considered, hence the symbol e


The focus of the present discussion on Inventory analysis means that emissions will be the typical entity considered, hence the symbol e. The generalisation to a multi-dimensional set of environmental consequences, including resource use, is straightforward.


Part 3 : Scientific background consequences that are introduced with different time-lags. Note that upstream processes occur only after the decision has been implemented. An example may clarify this further. A consumer who decides to drink one extra cup of coffee, buying it from a coffee machine, first causes an increase in electricity and water demand. With a delay of a few hours, he will use the toilet, again using electricity and water, but now also involving the waste-water treatment plant. With a delay of a few days, filling of the coffee machine will take place, with some extra filling of coffee (and perhaps sugar and milk) and the cup reservoir required. The same applies to the disposed cup. After days to weeks, the signal of extra coffee consumption will have reached the producers of coffee, milk, sugar and cups, and the waste treatment facilities. These will presumably produce extra coffee, milk, etc. to compensate for the extra use


These will presumably produce extra coffee, milk, etc. to compensate for the extra use. Next they may also be induced by this and similar signals to adjust their longer-term production and/or investment plans. This latter, indirect effect may also reach the producers of electricity and water, the coffee machine maintenance firm and the firm that empties the waste bins. For instance, it may be the case that an extra waste bin is already being delivered a day later, from stock. We may even extend this example, with bizarre consequences, analogous to the image used to illustrate chaos theory: the butterfly in Peking which causes a storm in New York. For instance, my personal coffee consumption may cause imports to exceed the threshold for constructing a new freight airport. Nobody knows at what levels chaos and instability occur. Similar examples may be conceived for any choice: product design, packaging material, mode of transport, etc. There may be all kinds of primary, secondary, etc


Similar examples may be conceived for any choice: product design, packaging material, mode of transport, etc. There may be all kinds of primary, secondary, etc. effects invoked at quite different moments of time. A general formula for expressing the effects of making a certain choice can be expressed as: where is the point in time at which the difference between the two emission levels is evaluated. represents this difference, and can hence be interpreted as the difference between the emission level with and without the choice being implemented. In principle, two future states are predicted, one for the reference situation and one following the choice being made and implemented. In the expression above, denotes the point in time at which the emission levels are predicted and the difference between them is evaluated. An obvious question is what should be chosen for A point in time is an infinitely short lapse of time


An obvious question is what should be chosen for A point in time is an infinitely short lapse of time. It may be that one of the processes of the life cycle is actually operating at that point in time, but most of the processes will take place at different moments in time. Therefore, opting for one point in time will discard many emissions from the life cycle, and is therefore not compatible with the principles outlined in the section on the purpose of LCA. It is natural to take a broader time span, for instance taking into account all substances released during the first day or month or decade after the moment of decision. If we wish to cover instantaneous effects as well as effects on the time scale of hours, days and years, we must switch from one moment in time to either the modeling and analysis of time series, or to an integration over an interval of time


In this section we shall discuss the first option, while the second option will be considered as one form of simplification and is therefore discussed later, in the section on model simplifications.


Part 3: Scientific background Modeling time series comes down to predicting for a (possibly large) number of intervals for t. To give a concrete example, we could model the emission of during the first week following the decision, during the second week, during the third week, and so on. Or we could do so on a annual or daily basis. Within each time interval, the emissions would be integrated over that time interval. If required, time intervals might be unequal, for instance the first hour, the next week, the next year, and so on. Within an economic-environmental analysis, a quite detailed level of distinct time intervals is readily defensible. The fate and effects of volatile compounds may differ between morning, noon, evening and night. And aircraft noise is clearly perceived differently during day and night time. For certain chemicals, a seasonal distinction between summer and winter seems more appropriate


And aircraft noise is clearly perceived differently during day and night time. For certain chemicals, a seasonal distinction between summer and winter seems more appropriate. Hence, while modeling time series may appear straightforward from a theoretical perspective, in practice it leads to massive data requirements, an immensely complex model and a tremendous quantity of results. The discussion on the temporal domain can be repeated for the spatial domain. In Figure 1.2.2.1.1 there is one independent variable: time. However, we may add three more mode-independent variables, corresponding to geographical longitude, latitude and elevation above ground level. This also means that we now express an emission as e(x, y, z) rather than e(t). There are again three principally different options: to restrict the analysis to a single location, to model spatial differentiation within selected regions and to integrate over the entire spatial domain


The first option is of definite interest for local policymaking, but again seems incompatible with the life cycle concept of wishing to include upstream and downstream effects, which almost by definition take place somewhere else on the earth. In the second option, a number of regions are distinguished for specifying where emissions take place. Again, from an economic-environmental point of view, this may be very relevant indeed. Emissions of acidifying compounds in Scandinavia lead to quite different effects from similar emissions in the Sahara. For volatile organic solvents, a distinction between indoor and outdoor emissions seems appropriate (Potting & Blok, 1994). And for there is a distinct difference between urban, rural and marine areas. As with time series, fine-meshed spatial series increase environmental relevance, but lead to spiralling data requirements, ever more complex models and extremely extensive analysis results


As with time series, fine-meshed spatial series increase environmental relevance, but lead to spiralling data requirements, ever more complex models and extremely extensive analysis results. 1.2.2.2 The need for a simple model A model is a simplified representation of part of reality. In the context of LCA, two basic modeling issues are of particular importance. LCA deals with complex, interwoven networks of industrial, agricultural, household and waste management activities dispersed over many locations and potentially spanning many decades. The mechanisms governing the dynamics of these activities are of a technical, economic, social, cultural and political nature. The mathematical relationships that describe these real mechanisms are, by principle, non-linear and dynamic and will often exhibit hysteresis and irreversibility. No such a model of ‘true reality’ exists, and an LCA model must inevitably introduce a multiplicity of crude simplifications


No such a model of ‘true reality’ exists, and an LCA model must inevitably introduce a multiplicity of crude simplifications. LCA concentrates on the function system for a particular product life cycle, although it is well known that economy and technology are such that any two products are at some stage connected through a common process. The LCA model should therefore be capable of dissecting a product life cycle from this interconnected complex. In particular, this procedure for isolating an individual product life cycle requires the setting of system boundaries and an appropriate allocation procedure. Another option is to incorporate ‘all other processes’ in a more aggregated fashion, using input-output tables, for example (Lave et al., 1995). Ideally, one would like to see the environmental interventions (and/or impacts) associated with a particular choice specified in terms of both location and time


Ideally, one would like to see the environmental interventions (and/or impacts) associated with a particular choice specified in terms of both location and time. For theoretical reasons this is extremely difficult, if not impossible, however. LCA practitioners are therefore happy to overcome the modeling difficulties while maintaining reasonable accuracy for the sum total of environmental interventions, integrated over all locations and infinite time in an assumed steady state. To date, little attention has been paid to aspects of spatial differentiation or specification of dynamic patterns in time, of the form ‘emission in Dublin on June 16th’. This has meant that LCA is sometimes said to exclude a priori specifications of space and time, and that spatially differentiated LCA or dynamic LCA is almost a contradiction in terms


This has meant that LCA is sometimes said to exclude a priori specifications of space and time, and that spatially differentiated LCA or dynamic LCA is almost a contradiction in terms. In this Guide we take a more liberal stance, regarding spatial and temporal integration as two possible steps in the inevitable process of constructing a workable LCA model. Several other simplifications have also been introduced. Some of these relate to model structure (e.g. linearisation of process characteristics and aggregation of individual companies along sectoral lines), others to mechanisms (e.g. ignoring demand elasticities or changes in use patterns by consumers). Again, we stress that although these simplifying steps facilitate the modeling exercise and may be a


Part 3 : Scientific background normal feature of LCA practice, the explicit incorporation of, say, non-linear relationships and economic mechanisms by no means contradicts the aim or principles of LCA. Hence, where this Guide proposes certain simplifications to the LCA model, this does not preclude additional efforts being made to develop more sophisticated models. This will hold particularly for strategic decisions having large-scale implications, and in such cases dynamic, non-linear, complex modeling may be imperative. This issue will not be considered in the present Guide. 1.2.2.3 Main model simplifications Although the ultimate aim is obviously to develop a high-quality model having adequate discriminatory power and yielding a consistently valid and reliable outcome, in practice this aim is constrained by a shortage of data, theoretical expertise and the capacity to handle complexity. Inevitably, simplifications must be introduced


Inevitably, simplifications must be introduced. While omission of economic mechanisms and spatial detail makes for great simplification, however, it reduces the quality of the analysis results. When making predictions about the functioning of economic processes and their interrelationships, it is obviously a substantial loss to disregard economic mechanisms. The same holds for a wide variety of environmental effects that may be highly dependent on the location of the activities in question. Below, we propose several simplifications to the life cycle Inventory analysis model for use in supporting structural decisions. This exposition should be regarded as providing reference standards for two levels of LCA complexity suitable for different types of decision situation. We first provide a reference standard for a detailed type of LCA, which can be seen as a stepping stone towards an ideal generic method


We first provide a reference standard for a detailed type of LCA, which can be seen as a stepping stone towards an ideal generic method. As already mentioned, deviations from these simplifications are perfectly legitimate if such a need is felt. Such deviations may indeed lead to major improvements in model quality. However, a more simplified model may be perfectly legitimate in situations where the key differences between alternatives are very clear or where a rough indication is a good enough starting point. Alongside the detailed LCA we thus also specify, in Chapter 2 below, a reference for a more simplified version of LCA. Such ‘best available practice’ types of LCA are ‘best’ only in the given historic context and the databases and software that history has spawned. Two of the main directions for simplification follow directly from the discussion on the temporal and spatial detail of the analysis


Two of the main directions for simplification follow directly from the discussion on the temporal and spatial detail of the analysis. The alternative to modeling time series is to integrate over the entire time horizon. Technically speaking this is not in itself a simplification, as the time series must first be modeled before integration is possible. In practice, however, integration opens the road to further simplification. If linear models are used, for example, it becomes irrelevant when exactly in time specific activities occur. In integration, the basic quantity of interest shifts from the difference between two emission levels at distinct points in time to the time-integrated difference between these two emission levels. Mathematically: where and delimit the time period covered by the analysis. represents this time-integrated difference. Observe that in Equation (1.2.2.1.1) and interrelationships and in Equation (1.2.2.3.1) have different dimensions


represents this time-integrated difference. Observe that in Equation (1.2.2.1.1) and interrelationships and in Equation (1.2.2.3.1) have different dimensions. If the emission e is measured in kg, for instance, will likewise be expressed in kg, while will have the unit kg×yr. It seems natural to opt for full time integration when dealing with consequences occurring throughout the life cycle. In other words, taking and as integration boundaries seems to be optimal in relation to the stated purpose of LCA. However, it is sometimes advocated to restrict the time horizon from infinity to 100 years, for example. The main arguments given are data availability, policy relevance and uncertainties with respect to the very distant future. Especially in discussions regarding the impacts of landfill and the toxicity of heavy metals and other persistent chemicals this issue is sometimes raised


Especially in discussions regarding the impacts of landfill and the toxicity of heavy metals and other persistent chemicals this issue is sometimes raised. An alternative here is to retain an infinitely long time horizon but to discount the future by means of a temporal discount rate that gives less weight to events occurring, at least in the model, in the distant future. An operational formula here is: where r is the temporal discount rate. Typical values of r range from 0 to 5%. A discount rate of 5% implies that the importance of effects is halved every 14 years, which means that effects occurring over 100 years hence are virtually ignored In Equation (1) the implicit value judgement is that effects now and in the future are valued the same. In Equation (2) the same can be achieved explicitly by setting r = 0.


Part 3: Scientific background The compromise between taking a single point in time and modeling time series was found to be integration over the entire period in question. We can carry this compromise over to the spatial context. A complete integration of life-cycle-related emissions over the entire globe provides a workable approach that is compatible with the idea of following a life cycle. Mathematically: The threefold integration over the three spatial dimensions means that a possible unit for is In the integration, we have ignored the possibility of integrating only partially over space to exclude, say, Antarctica, the oceans or countries that are not signatories to certain environmental protocols. This option is technically analogous to setting the time horizon to 100 years instead of infinity. We also have ignored the possibility of ‘space discounting’, because we do not consider it a meaningful option in current LCA


We also have ignored the possibility of ‘space discounting’, because we do not consider it a meaningful option in current LCA. The implicit value judgement made in the spatial integration step is that similar effects in different locations are valued the same in the ultimate evaluation of impacts. Combination of the compromises with respect to temporal and spatial integration yields the formula: Of course, this formula is of little practical use and we shall not employ it in the remainder of the text. However, it serves to emphasise the basic idea that full temporal and spatial integration of emissions of pollutants and extractions of resources is a principle that is in good agreement with the stated purpose of LCA. Continuing the discussions on model simplifications, the following choices have been made in the baseline LCA model presented in this Guide: Near-complete omission of spatial detail, by not distinguishing between emissions near different kinds of ecosystem, for example


Note that this does not mean that the distances between unit processes is set at zero: transport is merely taken into account. Neither does it mean that all unit processes are assumed to operate according to the technological state of the art that is representative for a given region. We can still distinguish between the emission characteristics of electricity production at different locations, but merely refrain from specifying where the emission occurs. The only default spatial details that are retained are those specified by a short list of environmental media: air, surface water, soil, sea and sediment. Complete omission of temporal detail. Among other things, this means that emissions are specified as total (infinite) time-integrated emissions. This does not mean that operations like storage1 are left out. Neither does it mean that all unit processes are assumed to operate according to the technological state of the art at a given point in time


Neither does it mean that all unit processes are assumed to operate according to the technological state of the art at a given point in time. We can still distinguish between the emission characteristics of electricity production needed for constructing factory buildings now and that for recycling them 50 years hence. In fact, we recommend doing so. Complete omission of non-linearities.2 This means, for example, that if the production of 1 kg steel is associated with an emission of 5 kg of a substance, the production of 2 kg steel is assumed to be associated with an emission of 10 kg of that substance (hence, fixed input-output coefficients). Omission or extreme simplification of most economic, socio-cultural and technological mechanisms influencing operation of the processes considered in the Inventory analysis


Omission or extreme simplification of most economic, socio-cultural and technological mechanisms influencing operation of the processes considered in the Inventory analysis. For example, the most common economic response to a rise in demand for a product brought about by adopting a certain option for supply of a functional unit is a rise in price and certain buyers of that product leaving the market. This market mechanism is virtually ignored. Income effects are likewise ignored. For instance, if product A is more expensive than product B, switching to B will mean the consumer has 1 To continue the analogy with space: transport is the process that carries an item from location A to location B, while storage is the process that ‘carries’ an item from day A to day B


Both transport and storage are economic processes which may need fuel, electricity, cleaning and so on, which may emit pollutants and which sometimes lead to a change in the quality (or even quantity) of the product concerned, for either better (wine) or worse (fresh flowers). 2 In this context, neglecting non-linearities means that the model structure is linear, in the sense of twice as much product requiring twice the amount of materials, leading to twice the emissions and to twice the environmental impact. It may still be the case that the simple coefficients used in the model are derived from highly non-linear models, as with global warming potential, for example.


Part 3 : Scientific background more money to spend on other (polluting) activities. This shift is not generally taken into account.1 The same holds for the phenomenon of people leaving more lamps on if light bulbs are energyefficient and hence less costly to burn. There are numerous examples of such forms of simplifications. Certain mechanisms are simply ignored (like the two above), while others are taken into limited account. An example in the latter category is given by economic substitution of certain materials by coproduced materials, where a sophisticated economic model would apply crosselasticities, while some LCA analysts assume full, or zero, substitution, or use this substitution as an artificial procedure for solving the multifunctionality problem, as a kind of allocation procedure. As a baseline, we here ignore all behavioural mechanisms except those related to changes in volume and (see above) simplify the latter mechanisms to a linear approximation


As a baseline, we here ignore all behavioural mechanisms except those related to changes in volume and (see above) simplify the latter mechanisms to a linear approximation. This has, Inter alia, important consequences for allocation; see Section 3.9. Figure 1.2.2.3.1 shows the model that is assumed to reflect the reality depicted in Figure 1.2.2.1.1 under some of the simplifications discussed above. Note that some of the complexities of economy-environment relationships are maintained. For instance, there is usually no aggregation of pollutants into substance groups (like ‘heavy metals’ or ‘organic compounds’). Similarly, economic activities are analysed at the micro-level, with ‘steel rolling’ or ‘paper bleaching’ being treated as separate activities. It is, of course, perfectly permissible to introduce further simplifications along these dimensions. They facilitate computation, but at the price of a less relevant answer


It is, of course, perfectly permissible to introduce further simplifications along these dimensions. They facilitate computation, but at the price of a less relevant answer. In our opinion the baseline simplifications proposed in this Guide represent a good compromise between quality and feasibility for structural, change-oriented LCAs. 1.2.3 Practical modeling aspects 1.2.3.1 Potential versus actual impacts There is a long tradition of debate on what types of impacts are accessible and/or predictable in LCA, and in particular in life cycle Impact assessment (LCIA). Emissions of hazardous chemicals take place at many different locations and at many different times. The default simplification principle of full space and time integration means that there is no information on spatial and temporal detail in the model. One just specifies the total life cycle loadings, in the form of aggregated releases: 5 mg mercury to water, 12 kg sulphur dioxide to air, and so on


One just specifies the total life cycle loadings, in the form of aggregated releases: 5 mg mercury to water, 12 kg sulphur dioxide to air, and so on. Hence, any attempt to interpret the contributions of these substances to environmental impact categories, such as ecotoxicity and acidification, can only be made without incorporation of spatial and temporal information, even if in some instances such details are available. Examples of items that may differ significantly between locations and times are background concentrations, presence of vulnerable ecosystems, environmental properties like soil composition, temperature and precipitation characteristics, population density and consumption patterns, and many 1 Unless, for example, the functional unit is expressed in monetary units.


Part 3: Scientific background more. Absence of such knowledge, or the lack of scope for including such knowledge, means that the impacts of releases can be estimated in very generic terms only. As an example, consider the following. If, under average conditions, substance x is 20 times more persistent and 5 times more toxic than substance y, a release of one unit of substance x is considered to be equally hazardous to a release of 100 units of substance y. This rule is also applied in non-average situations. In fact, the question whether the situation is average or non-average, and any deviation from the average, is entirely ignored in assessing potential impacts


In fact, the question whether the situation is average or non-average, and any deviation from the average, is entirely ignored in assessing potential impacts. Examples of questions that are ignored are: are background concentrations below or above supposed threshold levels? do the releases take place in a densely or sparsely populated area? do the releases take place in a vulnerable or invulnerable ecosystem? is the fish in this lake actually used for consumption or not? is the prevailing wind direction around this chimney towards or away from the sea? Assessment in terms of potential impacts is sometimes understood to imply a worst-case analysis. This is certainly not an accurate description of the main idea behind ‘generic conditions’. However, in a certain sense it points in the right direction, as it is the capacity for causing harmful effects that forms the basis of the assessment, and not so much the extent to which this capacity becomes effective


For instance, one aspect of pollution abatement is to release pollutants at the right place and the right time. Focusing on potential impacts means that such measures are ignored. Even if some further spatial detail were to be added to the models, on top of the seven media already considered, differentiating between densely and sparsely populated areas, for example, the ‘potential’ nature of the impact analysis would remain largely unchanged, as it still does not take all relevant characteristics into account. Actual impacts are sometimes contrasted to potential impacts, and their exact nature is perhaps even more confusing.1 If we move away from full space and time integration to add more and more details with respect to the spatial and temporal characteristics of release and receiving environment, we might say that we are moving into the area of actual impacts


There is a huge difference between full integration and full differentiation, however, and there is hence a broad continuum between potential and actual impacts. Understood in this sense, potential and actual impacts are not so much a pair of opposites. Rather, one may speak of a degree of actuality that is taken into account by the Impact assessment method. It is clear that the amount of information required for assessing releases will grow as the required degree of actuality rises. For assessment in entirely potential terms, it suffices to know a handful of persistency and toxicity characteristics along with some overall parameters like average temperature, average soil pH, average intake of drinking water, and so on. For an assessment in more actual terms, more parameters are needed: temperature of the release region, population density of the receiving region, and so on. An assessment in extremely actual terms is an ideal to be approached but never reached


An assessment in extremely actual terms is an ideal to be approached but never reached. It would include questions like: what is the body weight of the person walking over there at the moment of release? Is she pregnant? Is she a smoker or non-smoker? An assessment of impacts in extremely actual terms is very complicated, and it is doubtful whether there is any sense to it at all, even outside the realm of LCA. The inclusion of certain aspects of actuality is certainly important in tools like Risk Assessment (RA) and Environmental Impact Assessment (EIA), which are geared towards operating permits and other location-related decisions. For a tool that has comprehensiveness as its prime characteristic, in a first approximation it seems natural for LCA to ignore actuality-determining factors2. There is no fundamental reason for making LCA so abstract and general, merely reasons of practicality. Our objective is to construct an operational form of LCA, which implies a simple model


There is no fundamental reason for making LCA so abstract and general, merely reasons of practicality. Our objective is to construct an operational form of LCA, which implies a simple model. As better types of model and data sets become available in due course, with results amenable to interpretation, the reference method for the detailed LCA model, as well as for the simplified LCA model, can be improved. If a deeper analysis is required, and if the resources to do so are available, there is no reason to stick to detailed LCA. More extended options might be examined and in many situations nonLCA types of analysis may also be relevant. On the other hand, a detailed LCA may be more demanding than is necessary for some more simple types of decisions. 1 The juxtaposition of ‘actual’ and ‘potential’ impacts remains ambiguous. Potential impacts sometimes denote modeled impacts, and are thus opposed to measured impacts


1 The juxtaposition of ‘actual’ and ‘potential’ impacts remains ambiguous. Potential impacts sometimes denote modeled impacts, and are thus opposed to measured impacts. Sometimes the term is used to indicate the impact of below-threshold emissions, which, while not an impact in the sense of actual damage to ecosystems, might be described as occupying environmental utilisation space. 2 See appendix B for a more comprehensive discussion of the relationship between tools and applications.


Part 3 : Scientific background 1.2.3.2 Beyond the default simplifications As stated above, the proposed simplifications provide a crude reference model structure that can always be improved in a wide variety of respects as circumstances dictate. The aim of this section is to discuss some of the ideas that may be introduced for improving on the reference model quality, as specified for detailed LCA. There is one particular direction for further model detailing that has received considerable attention in the literature: spatial differentiation. This is acknowledged by a separate section being devoted to this topic. The other directions in which attempts to escape from the simplifications have been made are described in this section. So far, little attention has been paid in the literature to temporal differentiation in LCA. Owens (1996, 1997a and 1997b) criticises the lack of “spatial and temporal considerations” in LCA


So far, little attention has been paid in the literature to temporal differentiation in LCA. Owens (1996, 1997a and 1997b) criticises the lack of “spatial and temporal considerations” in LCA. He mentions rates of emissions, duration and frequency of exposure, and seasonal influences of temperature and sunlight as missing time characteristics. He gives no suggestions, however, as to how these characteristics might be included operationally. Pleijel et al. (1999) studied the influences of climate, source strength and time of day of NOx and VOC releases on tropospheric ozone formation in the context of LCA. They found considerable variation. If and how this can be included in general LCA methodology is a question yet to be answered. Finally, Potting (2000) very briefly raises the same issue in a thesis otherwise devoted to the issue of spatial differentiation


Finally, Potting (2000) very briefly raises the same issue in a thesis otherwise devoted to the issue of spatial differentiation. With respect to the inclusion of non-linearities, there is some literature relating to operations research and equilibrium models. Kandelaars (1998) discusses the materials-product chain, a concept that more or less coincides with LCA. The technical elaboration is quite different, however. Some unit processes are described within a framework of micro-economic production functions. Their concaveness contrasts with the linear, homogeneous, Leontief-type model that lies at the heart of LCA. It follows that solutions to the inventory problem cannot be found by normal matrix inversion (see Section 3.10), but that an optimisation algorithm is needed. Among other things this requires a quantity to be optimised, which is generally of a financial nature (e.g. cost minimisation or profit maximisation). Kandelaars shows that such models are feasible


Among other things this requires a quantity to be optimised, which is generally of a financial nature (e.g. cost minimisation or profit maximisation). Kandelaars shows that such models are feasible. However, all the examples she elaborates are very small product systems, comprising no more than 20 unit processes. It follows that the realism gained by including non-linearities is accompanied by a loss of realism due to a fairly drastic cut-off of ignored flows. It is difficult to say how gains and losses balance out in this and other cases. It remains the normal practice of LCA to place emphasis on completeness rather than elaborateness of mechanisms. Similar cases are provided by Gelderman (1999) and Freire etal. (2000), among others. The issue of including socio-economic, technological and other mechanisms has been discussed mainly in relation to the choices of product alternatives and functional unit and in relation to allocation based on the substitution method


However, the aforementioned use of concave production functions with an economic optimisation objective is obviously also an example of how realism vis-à-vis economic mechanisms can be improved. Related to the goal definition is the question of which product alternatives are to be deemed comparable and what amounts of these product alternatives are assumed to function as substitutes. Fluorescent lamps are not always appropriate substitutes for incandescent lamps and when they are it is not always 100 hours that substitute for 100 hours, because people tend to keep fluorescent lamps switched on when they leave the room for a while. A strong plea for incorporating real behavioural mechanisms in such situations is made by Wegener Sleeswijk et al. (1995) and by the Groupe des Sages (Udo de Haes et al., 1996). In concrete cases, empirical usage data on the various product alternatives have sometimes been incorporated (Potting et al., 1995)


(1995) and by the Groupe des Sages (Udo de Haes et al., 1996). In concrete cases, empirical usage data on the various product alternatives have sometimes been incorporated (Potting et al., 1995). See Section 2.4 for a more extensive discussion of this topic. With respect to the allocation issue (see Section 3.9), it should be emphasised first that the substitution method implies incorporation of socio-economic mechanisms in an analysis that at most points ignores such mechanisms. The assumption of the substitution method is that if the product system under study also delivers a coproduct this will obviate the need to produce a product with more or less identical features. In its crudest form, the substitution method implicitly assumes that supply of the coproduct is fully inelastic and supply of the substituted product fully elastic. In general, neither of these assumptions will hold


In general, neither of these assumptions will hold. To reflect relevant socio-economic causalities more accurately, Ekvall (1999) advocates using realistic default values for elasticities of supply and demand. A more extensive discussion of elasticities in relation to the multifunctionality problem is to be found in Section 3.9.


Part 3: Scientific background 1.2.3.3 Spatial differentiation Introduction The reliability and validity of LCA results can be substantially improved by introducing spatial differentiation. Although location-specific data will rarely be available for all processes within a product life cycle, spatially differentiated assessment may be preferable for those processes for which the required information is available. Especially for processes that appear to predominate in the overall impact of a product life cycle, additional effort to gather location-specific information is advisable. In recent years the issue of spatial differentiation has been gaining growing attention (cf. Potting & Blok, 1994; Pujol & Boidot Forget, 1994; Potting & Hauschild, 1997b; Tolle, 1997; Krewitt et al., 1998, 1999; Matsuno et al., 1998, 1999; Moriguchi & Kondo, 1998; Nigge, 1998; Potting et al., 1998, 1999; Schulze, 1999; Tørsløv et al., 1999; Potting, 2000)


No general, comprehensive approach has yet been developed, however. To render spatial differentiation generally applicable to any process in any product life cycle, spatially specific equivalency factors are needed. Potting (2000) has made a start on developing such factors for the impact categories acidification and human toxicity. Her approach is geographically restricted to Europe, however, and the human toxicity model has been elaborated for two illustrative substances only. Moreover, the approaches adopted for the two impact categories differ with respect to how the ultimate effects are defined. The values of these exercises should not be underestimated, though. In the first place, they demonstrate that spatial differentiation is a worthwhile exercise, as there may be major variation among regions. In the second place, they form a good example of how spatial differentiation can be practically implemented in LCA


In the second place, they form a good example of how spatial differentiation can be practically implemented in LCA. A necessary first step towards the ultimate goal of constructing worldwide, mutually consistent equivalency factors for all impact categories is a systematic analysis and description of how such factors might be constructed. Spatial differentiation in relation to the structure of Impact assessment Heijungs & Wegener Sleeswijk (1999) distinguish three dimensions in life cycle characterisation of toxic (and other) substances: fate, exposure (or intake) and effect. The numerical impact of an emission can be considered to be composed of three independent variables, representing these dimensions. Spatial differentiation may be independently performed for each of these variables


Spatial differentiation may be independently performed for each of these variables. One of the most important starting points for Impact assessment modeling in LCA is a definition of the distinct character of emissions in the usual steady-state modeling type of LCA elaborated here, and a short analysis of its consequences. In normal LCA practice emissions are not considered as continuous fluxes but as discrete pulses, since they are linked to single amounts of product function, rather than to (production) processes as such. These discrete emission pulses cause discrete ‘concentration pulses’. While continuous concentrations are characterised by concentration value and spatial magnitude or concentration pulses are additionally characterised by their temporal value. To handle large numbers of such concentration pulses, it is convenient to integrate them over both space and time. Integration over space (in yields amounts. The double integration therefore yields time-Integrated amounts


Integration over space (in yields amounts. The double integration therefore yields time-Integrated amounts. The advantage of space and time integration of emission pulses is that each pulse is thus characterised by a single value. This permits comparison and assessment of pulses having different spatial and temporal characteristics. The pros and cons of such integration, including the scope for making due allowance for the exceeding of thresholds, are discussed by Wegener Sleeswijk (in prep.). Spatial differentiation of fate-related aspects Multimedia fate models in Risk Assessment relate continuous emission fluxes to environmental concentrations (Mackay, 1991; Cowan et al., 1995). These same fate models can be used in LCA to relate discrete emission pulses to time-integrated environmental amounts


These same fate models can be used in LCA to relate discrete emission pulses to time-integrated environmental amounts. For a given substance, emission compartment and emission magnitude, the magnitudes of these time-integrated amounts depend on degradation, immobilisation and multimedia transport: the three main aspects of fate. Since multimedia fate models depend on geographical and climatological parameters, it is almost impossible to use them without applying some form of spatial differentiation. Parameters such as temperature, rainfall, soil composition and land/water surface ratio of continents may have a major influence on the multimedia distribution and/or degradation time of substances. The use of single values (point estimates, such as averages or default values) for such parameters can lead to major deviations


Part 3 : Scientific background between the modeling results and the real world. By distinguishing between geographical areas, the validity of the model can be improved, although this will lead to greater model complexity and additional data requirements. Most existing multimedia models distinguish between at least three emission compartments: air, soil and water, and between at least four transport compartments: air, soil, water and sediment. Spatial differentiation with respect to substance fate amounts to further division of each of these compartments . Together, the compartments make up the so-called unit world. While the spatially non-differentiated unit world consists of a (small) number of homogeneously mixed (sub-)compartments, the spatially differentiated unit world consists of a (larger) number of homogeneously mixed (sub-)compartments


A fate factor is a parameter that connects a standard emission of a certain substance into a certain compartment to the resulting (time-integrated) amount of this substance in one of the compartments of the unit world. Since one emission to one particular compartment may be distributed over many other compartments as well, most emissions are linked to a large number of fate factors. Spatial differentiation of exposure-related aspects An exposure (or intake) factor is a parameter that relates a standard (time-integrated) amount of a substance in a single environmental medium or compartment to the relative amount of this substance that eventually becomes bioavailable for (‘target’) organisms via this medium or compartment. ‘Exposure’ in LCA terminology differs from the Risk Assessment concept ‘dose’ in two ways. In the first place, exposure refers to a discrete – rather than continuous – event. This is caused by the discrete (‘mass loading’) character of emissions in LCA


In the first place, exposure refers to a discrete – rather than continuous – event. This is caused by the discrete (‘mass loading’) character of emissions in LCA. In the second place, exposure is a collective – rather than individual – measure: if the population exposed to a certain dose is doubled, the exposure itself is considered to be doubled as well. Here, LCA differs in terminology from Risk Assessment, where ‘exposure’ and ‘concentration’ are often used almost synonymously. In toxicity analysis, separate exposure modeling is always necessary for species that are exposed to substances via different environmental media. For species that are (mainly) exposed via a single compartment, effect can be linked directly to the (time-integrated) amount of substance in this particular compartment by means of fate modeling. This is the case for water and soil ecosystems in models without spatial differentiation


This is the case for water and soil ecosystems in models without spatial differentiation. In these models, the water medium and the soil medium each correspond to one compartment only. In the case of spatial differentiation, the sizes of the compartments into which each medium has been divided should be taken into account. For assessment with reference to single species – and especially for man – it will often be desirable to include population densities as well. For assessment vis-à-vis medium-wide ecosystems, it is difficult to say whether the volume of the compartment as such or a combination of volume and some sort of ‘ecosystem density’ measure is the best criterion for exposure assessment. For the time being, however, volume as such seems to be the most obvious criterion. For man, the total exposure to a substance is often the resultant of exposure via different routes: inhalation of air, intake through the skin and ingestion of drinking water and food


For man, the total exposure to a substance is often the resultant of exposure via different routes: inhalation of air, intake through the skin and ingestion of drinking water and food. Different exposure factors are required to link the (time-integrated) amounts of the substance in different media and compartments to the eventual exposure. Air is inhaled directly and (usually) unpurified. The air route is therefore relatively simple to model. Substances in water may reach us either via drinking water or via the consumption of fish and other sea foods. Exposure rates depend not only on the level of drinking water consumption, but also on fish consumption, on drinking water purification and on bioconcentration and biomagnification in fish. Substances in soil will seldom give rise to significant direct human exposure, with a few exceptions like inhaled dust particles from asbestos-reinforced roads


Substances in soil will seldom give rise to significant direct human exposure, with a few exceptions like inhaled dust particles from asbestos-reinforced roads. Indirect exposure via all manner of foodstuffs is a very significant exposure route for many substances, however. Exposure by this route depends on foodstuff consumption as such, but also on bioconcentration and biomagnification in all these foodstuffs, which are derived directly or indirectly from plants, including meat and dairy. Because of the enormous variety of foodstuffs we consume, the soil route is by far the most complex route for exposure modeling. The most obvious aspect of spatial differentiation that needs to be elaborated in the context of exposure modeling concerns differences in consumption patterns. This applies not only to food but also to drinking water, which may be either groundwater or surface water, and either unpurified or purified in some treatment system


This applies not only to food but also to drinking water, which may be either groundwater or surface water, and either unpurified or purified in some treatment system. Consumption patterns should, if possible, be combined with the magnitudes of the populations to which these consumption patterns apply, in order to calculate overall worldwide exposure to a substance from each medium or compartment.


Part 3: Scientific background So far, spatial differentiation in exposure modeling has been fairly straightforward. It is further complicated, however, by the consequences of spatial differentiation in fate modeling. If media are divided into different compartments, it is not only the consumption pattern as such, but also the origin of foodstuffs, drinking water and even air consumed that determine eventual exposure. Especially for foodstuffs, this is a complicating factor in the extreme, since large numbers of people no longer obtain their food from their immediate surroundings but from all over the world. Spatial differentiation of effect-related aspects An effect factor is a parameter that relates a standard exposure level tor a species or ecosystem to a certain effect level. Sensitivity as such, the presence and abundance of sensitive species and background concentrations are effect-related aspects. The concept of sensitivity is intimately related to the dose-response relationship


The concept of sensitivity is intimately related to the dose-response relationship. A numerical representation of sensitivity may be based either on the exposure value at which a species starts to show adverse effects to a substance (NOEC or NOAEL in toxicity assessment) or on the relative magnitude of the response to a standard increase of dose in the response area (the slope of the dose-response curve at the prevailing dose level). For doses in the area of the curve where the slope is greater than zero, the second possibility seems to be by far the best representation of sensitivity. For doses below no-effect levels, however, it is hard to say whether the relative distance to the point where adverse effects start to occur, or the (average) strength of the response to an increase of dose in the response area is the greater determining factor with respect to the latent, or ‘potential’ effect


For some substances, no-effect toxicity levels are lacking, for carcinogenic organics and for radioactive particles, for example. The doseresponse curve of such substances has an F-shape, instead of the familiar S-shape. Similar doseresponse relationships may hold for non-toxicity-related impact categories. For toxicity, it is common practice in LCA to work with no-effect levels, rather than slopes, as measures of toxicity. For substances lacking real no-effect levels, threshold values may instead be used. For other impact categories, it is more common to work with values more or less representing slopes, although real dose-response curves are seldom used in these contexts. Whenever dose-response curves are used, it should be borne in mind that the relationship between the qualitative concept of ‘response’ and the numerical values representing this concept should be explicitly defined. Both the no-effect level and the slope of the dose-effect curve vary per species and per substance


Both the no-effect level and the slope of the dose-effect curve vary per species and per substance. For aquatic and terrestrial ecosystems, no-effect levels have been defined by combining no-effect levels for various subgroups of species in the respective ecosystems. Although different tests often show major variations in sensitivity among representatives of a single species, these variations can seldom be attributed unambiguously to location-specific conditions. Contrary to the sensitivity of single species, the sensitivity of ecosystems may show important spatial dependencies, however. This is not due to sensitivity as such, but to the presence and abundance of sensitive species in given regional ecosystems: in areas lacking sensitive species, the effects of an emission will be less readily observable than in areas where such species are abundant. This phenomenon constitutes an important assumption in the critical load concepts for acidification and eutrophication


This phenomenon constitutes an important assumption in the critical load concepts for acidification and eutrophication. In the case of human toxicity it is included by taking population density into account. In the toxicity model used for calculating ecosystem no-effect levels, however, the regional presence and abundance of sensitive species has not yet been taken into account. Since dose-response relationships are seldom linear and homogeneous, the dose-response ratio is not independent of background concentrations. In regions with low background concentrations, effects may not occur, despite the presence of sensitive species. It is a matter of choice whether the purely potential effects in such ‘below-threshold’ areas are taken into account. Especially for naturally occurring substances such as minerals, which may even be benevolent in low concentrations, this choice will not always be easy to make


Especially for naturally occurring substances such as minerals, which may even be benevolent in low concentrations, this choice will not always be easy to make. In ‘above-threshold’ areas, background concentrations determine the prevailing slope of the dose-response curve. In more refined models, this may be taken into account. The relevant degree of spatial differentiation must then also be incorporated in inventory modeling and fate modeling. 1.2.3.4 Average and marginal processes and average and marginal process data Basic concepts The basic mathematical meaning of the terms ‘average’ and ‘marginal’ is clear and is summarised below. Still, application of these inherently clear concepts may give rise to difficulties. Implied in an LCA-relevant


Part 3 : Scientific background choice is the (exogenous) change in the volume of some product, as produced by some unit process (plant, facility, etc.). How, then, can we describe the resultant changes in the other inputs and outputs of that process? Let us simplify the situation to a process producing one functional output (x, say, electricity) and that has one other flow (y, say, input of fuel). A change in the demand for electricity will induce a change in the production volume of the generation process.1 This can be considered as a shift from the reference value of x (which we denote by to the value of x implied in the choice for alternative 1 (for which we use We shall call this an incremental change


The associated incremental change in fuel demand (y) is from to The relationship between x and y is known in economics and engineering as the production function.2 It is a result of the interplay between physics, chemistry, technology and economics.3 The relationship can be symbolised as a function f which maps any value of x onto a value of y (or the other way around). The production function is thus In general it is a non-linear function, involving many variables. We here treat the case of two variables with known relations. In the case of an incremental change of electricity demand (from to we need to calculate (from to This means we have to calculate Our general ignorance regarding the production function f already makes this calculation problematic. A second problem is that we cannot simply rewrite this equation as a function of the change in x, as say, even if f(x) is known4. There are, however, two situations which permit important simplifications to be introduced


There are, however, two situations which permit important simplifications to be introduced. One is referred to as a marginal change, the other as a change for which average5 data may be appropriate. See Figure 1.2.3.4.1. If the change is fairly small (as when drawing 40 W more from an existing, large power plant), we may make a linear approximation of the non-linear production function. In that case, we use where MF is the marginal factor (the ‘slope’) for the input of fuel.6 This formulation is known as using marginal data, because the data apply to a marginal change. It has the major advantage that one value, 1 For simplicity, we concentrate here on the direct (short-term) change. In the longer term such a change will lead to changes in maintenance, replacement, investments and so on. This issue will be expanded on in a later section. 2 In line with the previous note, there exist short-term and long-term production functions


This issue will be expanded on in a later section. 2 In line with the previous note, there exist short-term and long-term production functions. 3 Supply relations for inputs and demand relations for outputs are omitted from the analysis here. In combination with a given production function, these will determine the output change due to an exogenous change. 4 Only if the function f is linear through the origin or, more generally, linear and homogeneous to degree 1, can this equation be used. 5 The terminology is confusing because it differs depending on context. In economics, the relevant context here, long-term marginal cost equals the short-term average for a given technology. 6 More precisely: it is the first derivative of f with respect to x, evaluated at There is one formal requirement for this approximation: f must be differentiable, which means that it must be continuous. In practice, MF may be recorded in a database, even without full knowledge of the function f


In practice, MF may be recorded in a database, even without full knowledge of the function f. MF is then obtained through a small but nonzero change around the typical working point of the process.


Part 3: Scientific background MF, suffices to calculate the change in fuel input for any change in electricity production, as long as that change is small enough to justify linearisation. If, on the other hand, the change is ‘revolutionary’ in the sense of resulting in a complete shut-down of a facility ora full newstart-up a different approximation may be used: where AF is the average factor for the input of fuel.1 This approach is known as using average data. Here, there is no such thing as a corresponding average change. Its name derives from the fact that at the non-closed working point, the average fuel required per unit electricity generated coincides with the average factor. Using the average factor again has the major advantage that one value, AF, suffices to calculate the change in the fuel input for any change in power production, as long as that change is ‘revolutionary’ enough to justify complete start-up or shut-down


Different meanings of ‘average’ In the above, we introduced the term ‘average’ as a means of dealing with process data in ‘revolutionary’ changes. We did not mention it with respect to incremental or marginal changes. However, it may be argued that using marginal data for ascribing interventions to activities may introduce an element of unfairness. If a train is carrying 100 passengers, one can calculate the average power consumption per passenger. If one extra passenger enters the train, the train’s power consumption will increase marginally. There are, now, two alternative principles that can be adopted for assigning power consumption to this extra passenger: A. B. the ‘factual’ approach: the marginal change in power consumption is assigned to the extra passenger, or the ‘fair’ approach: the additional power consumption is distributed evenly among all 101 passengers


The second approach has the advantage that historical facts (such as who was the last passenger) are irrelevant: every passenger is treated the same way and is ‘responsible’ for a proportional2 amount of electricity use and associated environmental interventions. This also means that this approach is not susceptible to ‘strategic abuse’, as when someone argues that the aircraft was flying anyhow, forgetting that structural changes may be induced by occasional choices taken by many individual people.3 It also has its disadvantages, however. One problem of a practical nature is that one can envisage different methods for establishing partitioning over the one hundred passengers: based on number (every passenger on mass (with or without luggage), on share in sales (first class versus second class passengers), etc. There is a more fundamental problem, though


There is a more fundamental problem, though. To some extent, the use of averages seems to be at variance with the original idea that LCA is supposed to provide a model of what is actually happening. The short-term marginal indicates what is happening first, while the long-term marginal indicates what will ultimately happen in a steady state (where the marginal is equal to the average for a given technology). Integrating a dynamic model would cover the long and the short term, but is beyond practical operational feasibility. We choose the long-term average as the most fitting approach for structural decisions, neglecting short-term mechanisms in the detailed LCA. Such elements may be introduced in the extensions to detailed LCA, however. Whichever of the two approaches is preferred, an important lesson here is that even marginal data may be used for average (= proportional) assignment


Whichever of the two approaches is preferred, an important lesson here is that even marginal data may be used for average (= proportional) assignment. Fortunately, the dichotomy between factual and fair partitioning is not important for the structural type of modeling developed here; see our recommendations at the end of this chapter. Different meanings of ‘marginal’ Next, there is the problem what exactly is on the axes. First, let it be clear that a production function is normally more than a relation between one input and one output. It may be a whole vector of inputs that 1 More precisely, in the case of start-up it is the ratio and in the case of shut-down the ratio When the non-closed working point of the production characteristic in Figure 4 is the typical working point of the unit process, the two cases start-up and shut-down will yield the same average factor


There is one formal requirement here: the closed working point should have coordinates (0, 0), which means real closure. A boiler that is switched on but not used still requires some energy, but a boiler that is switched off does not. We do not preclude any choice in the treatment of capital goods here. 2 This is one reason for preferring the term ‘proportional’ to ‘average’. 3 One can also avoid ethical-sounding terms like fair by stating the problem slightly differently, as a situation A with 100 passengers, with part of this total power consumption allocated to each passenger on average, and a situation B with 101 passengers, with part of total power consumption as the average allocation. However, it should be remembered that, for structural decisions like most of those examined here, installed capacity is adjusted to a chanqe in output while intended capacity use (or operating point) remains the same


The extra passenger then requires of an extra train and the average remains the same and only situation A is relevant.


Part 3 : Scientific background is related to a whole vector of outputs. Some pairs of inputs or outputs may be directly coupled (as in the case of burned fuel and generated electricity), while other pairs of inputs and outputs may be related only in a longer time perspective (as in the case of replaced generators and generated electricity). A marginal change, such as the effect resulting from changing one unit, may thus be modeled with different time frames in mind. In economics, for example, the short-term marginal effect of increasing production equals the change in variable costs, as in the case of the extra petrol needed to get an extra passenger airborne each flight. In the somewhat longer run, an extra passenger will lead to extra food on board, extra cabin maintenance, etc., so the marginal costs will come to exceed the variable costs. In the still longer run, the number of flights will rise, as scheduled flights are related to percentage occupancy


In the still longer run, the number of flights will rise, as scheduled flights are related to percentage occupancy. Ultimately, extra aircraft will be built. Therefore, the marginal costs will now include those of the extra aircraft built and operated to adapt to the increased demand. For a given type of aircraft, the long-term marginal costs hence equal the short-term average costs. In the further chains, similar sequences result, leading to the opening of extra bauxite mines, extra cargo ships, etc. These long-term marginal costs, as required in LCA for structural decisions, are effectively equal to the average costs, as these reflect the costs of all fixed overheads. The lesson here is to be precise in the uniqueness of the decisions studied. In the aircraft costs example, there was a subtle change from one passenger extra once, to short-term effects of one passenger extra on each flight, to longer-term effects of one passenger extra on each flight


Each shift means that a different operational meaning of the adjective ‘marginal’ is to be understood. Finally, we must distinguish between the marginal process and the marginal data of a process. There may be one (or more) unconstrained process(es) that can be identified as the process(es) that will be used to satisfy the extra demand for a certain product. This process is the marginal process in a process mix. Next, it can be determined how emissions, fuel requirements, etc. will change when one marginal unit of output is needed. These changes then reflect the marginal process data, in the short, medium or long term. It is perfectly possible (or it may even be necessary) to treat the marginal process using longterm average data, so the two concepts should be clearly distinguished. In change-oriented LCA, the analysis is concerned with incremental changes rather than averages


In change-oriented LCA, the analysis is concerned with incremental changes rather than averages. However, incremental changes, like marginal changes, may coincide with average effects under the assumptions stated above. These assumptions apply in long-term modeling, as required for the analysis of structural changes. In situations where a really simple model is used, these analytic differences may disappear. With linear relations not passing through the origin, marginal and incremental effects coincide, but differ from average effects. With linear relations through the origin, marginal, incremental and average effects are all equal. Thus, in very simple models the discussion on average and marginal data may be avoided, but not the discussion on the marginal process. 1.2.3.5 Quantitative and qualitative information and notation In principle, LCA is interpreted in this report as quantitative life cycle assessment


1.2.3.5 Quantitative and qualitative information and notation In principle, LCA is interpreted in this report as quantitative life cycle assessment. The model discussed so far concerns quantifiable flows of products, materials and energy, quantifiable emissions and resource extractions, and quantifiable contributions to environmental problems. It may seem unnecessary to discuss exactly we mean by ‘quantitative information’. However, it may be rewarding to be somewhat more explicit and also to include a few words on the scope for including qualitative information. First, an example of a purely qualitative LCA. Suppose that we adhere to a dogma that says: no tropical hardwood. This would mean that tropical hardwood window frames are bad. In a life cycle context, however, it would mean that aluminium window frames are equally bad, since there will be tropical hardwood somewhere in this life cycle as well


In a life cycle context, however, it would mean that aluminium window frames are equally bad, since there will be tropical hardwood somewhere in this life cycle as well. Perhaps the office of the accountant of the aluminium smelter has tropical hardwood window frames. Of course, the amount of tropical hardwood per functional unit of window frame is much lower for aluminium window frames than it is for those made of tropical hardwood. If we wish to take this into account – and obviously we do – we must enter the domain of quantitative analysis. We conclude that qualitative information like ‘contains hardwood’ is difficult to employ in a comparative analysis covering more than a single aspect. A more sophisticated case is the desire to include qualitative attributes of the quantified information. An example is given by comparison of tropical hardwood window frames with and without an FSC certificate


An example is given by comparison of tropical hardwood window frames with and without an FSC certificate. One could argue that this calls for a method that is capable of handling qualitative information. Better still though, we could also recognise a parallel with distinguishing between emissions of copper from mercury, and distinguish extractions of FSC-certified wood from non-certified wood. In other words, the primacy of quantitative information does not imply that we stop using language to indicate the identity of the flows modeled. Technically, every distinction within a class of flows means that these flows are regarded as different. Thus, if we distinguish certified from non-certified tropical hardwood, we actually


Part 3: Scientific background have two different flows for hardwood. Likewise, distinguishing atmospheric and aquatic releases of lead effectively yields two different flows that have the term ‘lead’ in common. And the same applies to every form of spatial and temporal differentiation. It is a matter of personal preference whether we mentally consider Iberian and Scandinavian as two different flows or as one flow that comes in two varieties. In terms of modeling, however, they are two different flows, with separate entries in data tables. The general structure for indicating quantifiable entities in any science is a tripartite one (see, for example, the Handbook of Chemistry and Physics): ‘quantity’ = ‘value’ × ‘unit’ (not to be read as an equation), as in the temperature is 23 degrees Celsius


We thus have the following elements: quantity: the quantifiable entity of interest, like temperature; value: the numerical magnitude, like 23; unit1: the yardstick used to express the value, like degrees Celsius. This same general structure is, in principle, applicable to the LCA model, including its input and output data. However, several remarks are in order. ‘Quantity’ is a confusing word, because it also indicates ‘amount’ (‘quantity of product required for the functional unit’). Other terms are ‘variable’, ‘parameter’ and ‘flow type’, and, in specific contexts, ‘observable’, ‘data’, and ‘coefficient’. All these terms also have disadvantages. In the context of LCA modeling the term ‘variable’ seems to be the most appropriate. Observe that the quantity may be a simple one, like temperature or emission of but it may also be more complex, like sea temperature or emission of in Sweden


Observe that the quantity may be a simple one, like temperature or emission of but it may also be more complex, like sea temperature or emission of in Sweden. ‘Value’ is probably an even more confusing term than ‘quantity’, because it may easily suggest a focus on economics, welfare or utility. The term ‘numerical value’ is a rather clumsy solution. ‘Magnitude’ may also indicate the type of variable rather than its size. ‘Size’ and ‘amount’ are probably more unequivocal and neutral terms. Even the term ‘unit’ is not free of contextual differentiation, as in, for instance, ‘unit processes’ and ‘functional unit’. Furthermore, ‘units’ in LCA are often used loosely. The Système International des Unités lists basic and derived units, like kg, m and m/s


Furthermore, ‘units’ in LCA are often used loosely. The Système International des Unités lists basic and derived units, like kg, m and m/s. In published LCAs, guidebooks and software, one often sees unconventional units, like ‘kg/functional unit’, ‘GWP’ and ‘mPts’, and units may even be lacking altogether.2 As one terminology for all the disciplines involved in LCA seems too much to ask for, we here simply state our first preference, with what appear to be relevant synonyms: Variable: Value: Unit : parameter, quantity, entity, flow type, flow amount, size (possibly magnitude) [no synonyms] Finally, variables are often abbreviated to user-defined symbols, while units are abbreviated using conventional symbols. Speed, a variable, is written as v, u, s, or some other symbol. Attributes may be indicated using other symbols, subscripts or diacritical symbols, like v versus u, versus or v versus v. It is conventional practice to italicise symbols


Attributes may be indicated using other symbols, subscripts or diacritical symbols, like v versus u, versus or v versus v. It is conventional practice to italicise symbols. A possible unit for speed is metre per second, abbreviated to non-italic m/s. Values are not abbreviated, although the unit may be subdivided or expanded using standard prefixes, as when 1200 m is written as 1.2 km. Given the prominent usage of 1 The terms ‘unit’ and ‘dimension’ are sometimes used incorrectly as synonyms. Every quantity has a unique dimension. Speed, for instance, has the dimension length over time. Dimensions therefore provide no yardstick. Units are specific cases of yardsticks. The dimension ‘length’ can be expressed by the units meter, inch, mile and so on. Moreover, different quantities may share the same dimension. For instance, length, distance, height and thickness all have the dimension length. Finally, certain quantities can have a composite dimension with a noncomposite unit


For instance, length, distance, height and thickness all have the dimension length. Finally, certain quantities can have a composite dimension with a noncomposite unit. Energy has the dimension mass times length squared over time squared, and is usually expressed in joule (J), although the composite unit kg times square metre over square second is identical with joule. 2 What units would we recommend in these cases? “Kg/functional unit” is sometimes seen in inventory tables for the aggregated emissions of one functional unit. Almost all calculations are about a functional unit, so adding this is superfluous, just as it is superfluous to say that a can of paint contains 0.75 litre per can. A mere “kg” suffices here. In the “GWP” example more is involved. The GWP (global warming potential; see section 4.3.5) is a quantity (or variable) and it is an inherent (or intensive) property of a substance that does not depend on the amount of substance