Both the data and method were available at a website. Unfortunately, several important sectors such as electricity generation, mining and solvent recycling were absent in the TRI data. Moreover, the online service is no longer available at the moment. We can only hope that this situation will be remedied soon and practitioners are encouraged to follow these developments closely. Suh has compiled an environmentally extended input-output table based on the most recent data sources available, including a 1996 US input-output table and TRI 98: Missing Inventory Estimation Tool (MIET). The data file is stored as an MS Excel spreadsheet in which all the direct and indirect environmental interventions are calculated by entering the estimated value of the flow in question. These results can be employed directly for estimating cut-off flows by adding them to the inventory. Support can be obtained online through http://www.Ieidenuniv.nl/interfac/cml/lca2/index.html


These results can be employed directly for estimating cut-off flows by adding them to the inventory. Support can be obtained online through http://www.Ieidenuniv.nl/interfac/cml/lca2/index.html. Alternatively, the hybrid model presented by Suh & Huppes (2000a) can be utilised for in-depth simulation. This hybrid model can be used to simulate full interactions between selected processes and the broader national economy embodied in the input-output table. Although IOA provides a method for estimating lacking data, it has its limitations. In particular, practitioners should be aware of the following major shortcomings (Suh & Huppes, 2000a; 2000b): Input-output models provide information in aggregated form only and it may therefore be difficult to identify appropriate sectors to represent the missing flows. An lOA-based inventory for ‘aluminium can’ will yield the same result as for ‘tin can’ or ‘iron can’, since both are classified under the same IO code, 390100: Metal cans

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


An lOA-based inventory for ‘aluminium can’ will yield the same result as for ‘tin can’ or ‘iron can’, since both are classified under the same IO code, 390100: Metal cans. Matters will be even worse if the product in question is located under aggregated classifications such as 020503: Miscellaneous crops, 110900: Other construction, or 570300: Other electronic components. Part 3: Scientific background


IOA is based on assumed proportionality within the coefficient matrix, implying that economies of scale are ignored. Two scaling effects are relevant, however: input intensity and emission intensity per unit output, both of which generally decrease with increasing plant size. IOA gives the same amount of resource use and emissions per unit output regardless of scale. The base year of the latest version of the US input-output table is 1996. Given the dynamic nature of the modern economy, the economic structure of the day may no longer match that charted several years ago. Besides innovations in actual production technology, rapid development of environmental control technology and regulation are therefore also not taken into account. Although the errors due to this time lag have been reduced somewhat by using the latest (1996) input-output table, for some sectors this might be insufficient


Although the errors due to this time lag have been reduced somewhat by using the latest (1996) input-output table, for some sectors this might be insufficient. In input-output tables, capital goods such as buildings, plant and equipment are counted as net outputs rather than inputs. That is to say, if a chemicals company builds a new facility or acquires new plant, the environmental interventions caused by those capital goods are not included in the total direct and indirect environmental emissions per unit monetary output of the overall chemical industry. The results given by an input-output based inventory model will therefore not include the environmental burden associated with capital goods as inputs. Potential errors may also be introduced through representation of data in monetary terms. Any analysis using the monetary input-output table assumes that the monetary flows in the input-output table precisely represent the actual physical flows between industries


Any analysis using the monetary input-output table assumes that the monetary flows in the input-output table precisely represent the actual physical flows between industries. This assumption implies perfect price homogeneity, which is not the case in practice. Secondly, monetary values must be converted into physical units for the purpose of LCA, which requires accurate valuation considering such matters as inflation. In addition to these shortcomings, it should be noted that environmental IOA obviously cannot provide estimates where inventory data on the consumer use and/or post-consumer phase are lacking. 2


In addition to these shortcomings, it should be noted that environmental IOA obviously cannot provide estimates where inventory data on the consumer use and/or post-consumer phase are lacking. 2. Avoiding cut-off by estimating flows from similar flows for which data are known A second option for estimating the environmental effects associated with flows for which such data are lacking is to look at similar flows for which process data are available and determine the closest ‘lookalikes’ with respect to chemical structure, chemical properties, physical properties or other comparable properties. For example, missing data on an (in)organic catalyst used in a refinery process might be estimated from the process ‘production of (in)organic chemicals’ in the ETH database (Frischknecht et al., 1993/1995/1996)


Capital goods in general might be estimated by subtracting the electricity production data of the SAEFL database excluding capital goods (SAEFL, 1998), from the same electricity production data of the ETH database but including capital goods (Frischknecht et al., 1993/1995/1996). Another method for estimating the impacts1 arising from capital goods, particularly buildings, has been developed by Lindeijer (1998). In this method a rouigh estimate is made of the potential significance of the associated environmental impacts, using just a few data such as surface area, height, annual output and building lifetime. In this way an environmental impact profile is estimated by multiplying these figures by the individual profiles of reference construction parts, as calculated by the Eco-Quantum software. These kind of estimates can obviously be performed at various levels of sophistication


These kind of estimates can obviously be performed at various levels of sophistication. For example, a practitioner could consult an expert, a chemist say, on which flow is the most reasonable ‘look-alike’ to the flow for which the data are lacking with respect to chemical structure. The capital goods method developed by Lindeijer (1998) has also been developed at three different levels of sophistication. As there is no documented method for most of the options discussed above, it is difficult to discuss this issue in general terms. In general, non-documented options are likely to involve considerable arbitrariness, as expert opinions on what is ‘reasonably similar’ may differ significantly from case to case and practitioner to practitioner. In addition, the capital goods method described provides only a rough indication of actual environmental impact


In addition, the capital goods method described provides only a rough indication of actual environmental impact. The uncertainties associated with the various input data used in this approach have been assessed; for further details, see Lindeijer (1998) and Part 2b, Section 3.7 of this Guide. Another important practical drawback of this method is that it estimates environmental impact profiles using the Impact assessment methods proposed by Heijungs et al. (1992). As new Impact assessment methods are recommended in the present Guide, this implies that the capital goods method would yield results that are incompatible with the Impact assessment results of the remainder of the LCA in question. This method can therefore not be recommended here. 1 Note that these estimates would be given as environmental profiles, in terms of indicator results for abiotic depletion, global warming, ozone depletion, etc., and not as inventory results. Part 3 : Scientific background


3. Cut-off based on predefined criteria Cut-off criteria like those mentioned in ISO 14041 (1998E) are in general use by LCA practitioners today to decide which flows will be studied and which to exclude. In the Nordic Guidelines (Lindfors et al., 1995a,b) three different principles are mentioned for introducing upstream cut-offs (i.e. those not involving waste management processes): cut-off at a predefined upstream stage of the lifecyle; cut-off at a predefined mass percentage of the input flows associated with each individual process; a slightly modified version of the second principle, with cut-off of upstream input flows only when they fall below a certain, predefined percentage of the total mass inputs of the product system (see ISO/TR 14049,1998). The authors mention that “these cut-off criteria should only be applied for common emissions for which associated environmental impacts mainly depend on mass flow and not on quality


The authors mention that “these cut-off criteria should only be applied for common emissions for which associated environmental impacts mainly depend on mass flow and not on quality. As a default approach this type of cut-off criteria may only be used for energy related emissions." For certain emissions a flagging procedure is proposed. In a report on application of LCA to agricultural products (Wegener Sleeswijk et al., 1996) discussion of the cut-off issue focused on capital goods. In the agricultural sector a variety of capital goods are employed with a relatively short service life (e.g. farming machinery) as well as capital goods requiring relatively large quantities of materials (e.g. farm tracks and roads). The authors argue that the environmental interventions associated with machinery production and maintenance as well as with farm tracks and roads should not therefore be omitted from LCAs on agricultural products


Farm buildings, on the other hand, can generally be excluded, except in the case of greenhouse horticulture and in studies where farm buildings constitute the main issue. PROSPECTS In a PhD project CML (See Suh & Huppes, 2000a) is working on a hybrid input-output model, which may be useful for estimating missing data in the LCA context. This hybrid model can be used to simulate full interactions between selected processes and the broader economy. However, the limitations of preparing inventories on the basis of input-output analysis should be clearly recognised by users and this type of estimate should be kept to a minimum, particularly if estimated flows prove to be significant. Developments on this issue will therefore need to be updated in this Guide in due course


Developments on this issue will therefore need to be updated in this Guide in due course. CONCLUSIONS If data on specific process flows are lacking, arbitrary cut-offs should be avoided wherever possible by using suitable methods of approximation: This allows an initial estimate to be made, permitting a more reasoned decision on whether to collect process-specific data for the flows in question or subsequently ignore them. Environmentally extended Input-Output Analysis (IOA) is generally recommended as an approximation method, because of its broad applicability (almost all flows) and completeness in terms of system approach. If comparison to a similar process (based on expert judgement, for example) is anticipated to provide a better estimate than IOA or if IOA is not applicable (use and waste management phases), this may be used alongside or instead of IOA


RESEARCH RECOMMENDATIONS To avoid mere cut-off with respect to the boundary between processes to be included and those to be ignored, appropriate procedures need to be developed based on estimates of the environmental interventions associated with these processes. Input-output analysis is one of the most promising avenues of research here. Since the Carnegie Mellon and the Suh and Huppes (2000a; 2000b) models are based solely on US input-output data, there is a need to develop a more general (average-world) model or at least regional (e.g. European and Asian) submodels. 3.9 Multifunctionality and allocation TOPIC Most industrial processes are multifunctional. Their output generally comprises more than a single product, and raw material inputs often include intermediates or discarded products. In other ways, too, production processes are dynamically interlinked with other processes, technologically, behaviourally, and otherwise


In other ways, too, production processes are dynamically interlinked with other processes, technologically, behaviourally, and otherwise. LCA practitioners are thus faced with the problem that the product system or systems Part 3: Scientific background


Part 3 : Scientific background Within these headings, individual data categories shall be further detailed to satisfy the goal of the study. Heijungs et al. (1992) In the ’92 guide the data categories are discussed in 2.2.1 Quantification of the inputs and outputs. It is stated that since all economic outputs of processes are connected to economic inputs of other processes these input and output categories should be fully symmetrical. For both inputs and outputs a distinction is made between goods, services, materials, energy and waste for treatment. Environmental inputs and outputs are not symmetrical. In the ’92 guide the following inputs from the environment were distinguished: abiotic resources, biotic resources, energy resources and space. The environmental outputs were as follows: emissions to air, water and soil, radiation, noise, heat, light and accidents


The environmental outputs were as follows: emissions to air, water and soil, radiation, noise, heat, light and accidents. It is also mentioned that some environmental outputs may be of a more qualitative nature, such as ecosystem fragmentation due to road-building programmes. “Negative emissions” are also mentioned, occurring mainly in processes on the boundary between the environment and the economy, e.g. the uptake (negative emission) of by forests and crops. Section 2.2.1 of the ’92 guide also introduces the term format in relation to the manner in which LCA Inventory data are to be stored and processed. A distinction is made between the conceptual format and the technical (i.e. software) format. The conceptual format relates to the main structure (see Figure 3.2.1), the technical format to the rules for filling in the process data


software) format. The conceptual format relates to the main structure (see Figure 3.2.1), the technical format to the rules for filling in the process data. The main structure indicates the input and output categories of the process: economic inputs and outputs, and environmental inputs and outputs (see Figure 3.2.1) and also Appendix A.1. of Heijungs et al., 1992). The ’92 guide does not elaborate the technical format in any further detail (although CML later drew up a such a format based on the conceptual format). Besides the economic and environmental inputs and outputs, the conceptual format also includes the following aspects: who entered the data, and when representativeness of the data (scale, dating, duration, status) quality of the data (clarity, accuracy, completeness) source of the data mass and energy balance of the process. There is also reference to the fact that unquantifiable aspects may be important for some processes


There is also reference to the fact that unquantifiable aspects may be important for some processes. The conceptual format explicitly provides space for this purpose. The background to the ’92 guide (section 2.3.1) specifies a number of general requirements the format should meet: Methodological delineation: The format will have to be particularly oriented towards those environmental interventions ultimately of importance for Classification. The format will have to be oriented toward the “potentially relevant” economic inputs and outputs. Other requirements: The format shall be geared to practical data availability. The format shall be in line with current practice wherever possible The format shall be readily comprehensible and suitable for international use. The format shall be such as to prevent LCAs being swamped by having to fill in an excessive number of process data


The format shall be such as to prevent LCAs being swamped by having to fill in an excessive number of process data. For example, under emissions to air, data categories such as carbon monoxide, carbon dioxide, sulfur oxides, nitrogen oxides, etc. can be separately identified.” In clause 5.3.4 ISO 14041 continues: “The data required for an LCA study are dependent on the goal of the study. Such data may be collected from the production sites associated with the unit processes within the systems boundaries, or they may be obtained or calculated from published sources. In practice, all data categories may include a mixture of measured, calculated or estimated data. Subclause 4.4 [see above] outlines the major headings for the inputs and outputs that are quantified for each unit process within the systems boundary. These data categories should be considered when deciding which data categories are used in the study


These data categories should be considered when deciding which data categories are used in the study. The individual data categories should be further detailed to satisfy the goal of the study. Energy inputs and outputs shall be treated as any other input or output to an LCA1. The various types of energy inputs and outputs shall include inputs and outputs relevant for the production and delivery of fuels, feedstock energy and process energy used within the system being modeled. Emissions to air, water and land represent discharges from point or diffuse sources, after passing through emissions control devices. The category should also include, when significant, fugitive emissions. Indicator parameters, e.g. biochemical oxygen demand (BOD), may also be used. 1 In many contexts there is a special interest in the extraction of energy resources


Indicator parameters, e.g. biochemical oxygen demand (BOD), may also be used. 1 In many contexts there is a special interest in the extraction of energy resources. As the depletion of energy resources is treated in the Impact assessment as part of general abiotic depletion, energy resource use may be specified in terms of the total energy content of all the abiotic energy resources involved, e.g. using the heat values as specified in the ETH database on energy transformation processes. This information does not enter the computations in the Impact assessment, however. [Addition from authors of this Guide.]


under study provide more functions than the one investigated in the functional unit of interest. An appropriate decision must therefore be made as to which of the economic flows and environmental interventions associated with the product system under study are to be allocated to (the functional unit provided by) that system. Decisions on the specifics of allocation will obviously be determined by the precise nature of the system boundaries as previously defined (see Section 3.2), for these determine which inputs and outputs are to be taken as being associated with the function of interest. An appropriate allocation procedure is thus required to partition the inputs and outputs of all relevant processes to the appropriate product systems


An appropriate allocation procedure is thus required to partition the inputs and outputs of all relevant processes to the appropriate product systems. In comparative LCAs, the problem is compounded, for any comparison of alternative product systems for fulfilling the function of interest is hampered by the fact that each of these alternatives will be associated with a variety of different additional functions, in addition to the function of interest. As an example, cadmium production from zinc ore yields zinc as a co-product, while production from phosphate ore yields phosphorus as a co-product. The aim of the allocation step is then to render the two production systems ‘equivalent’, for which two basic methods are available. Equivalence can be achieved either by subtracting those parts of the systems that function for other product systems, resulting in a system providing a single function (viz


Equivalence can be achieved either by subtracting those parts of the systems that function for other product systems, resulting in a system providing a single function (viz. the primary function), or by adding further subsystems such that the two systems provide the same set of functions. Viewed from this angle allocation is essentially a process-level problem, closely related to the definition of system boundaries. At the same time the issue of multifunctionality is far broader, however, having a bearing on the entire issue of inventory modeling at the core of LCA. In attempting to map the complexities of the production, consumption and waste management systems embedded in the dynamic real-world economy, the models employed in LCA inevitably introduce a host of simplifications


At present the chief goal is still to model basic process-technological interlinkage, using fixed input-output coefficients and steady-state inventory models and provisionally ignoring any further relations of a social, cultural and political nature. Although progress is being made on incorporating some economic mechanisms, the issue is a complex one that raises basic questions about modeling choices. In this sense, then, process multifunctionality can be regarded as a modeling problem at the systems level. In more technical terms, and assuming purely linear relationships,, there is then an imbalance between the number of equations and the number of variables in the model, to be solved by adding or subtracting equations or variables


There are thus two aspects to what is loosely referred to as allocation: an ‘allocation problem’, in the narrow sense of partitioning (the inputs and outputs of) unit processes among product systems, and a ‘multifunctionality problem’, a broader issue cutting right across LCA inventory modeling. These problem definitions are aligned with basic choices about where physical causality is to be incorporated in the LCA procedure: during modeling, or at the allocation stage. As inventory models are further refined to more broadly mirror real-world causality and hence become more complex, the problem of multifunctionality will be compounded. Then, too, Some form of allocation will therefore remain unavoidable and an appropriate, pragmatic procedure will have to be adopted. Given the additional fact that much of the work on allocation is closely allied with developments in LCA modeling, the topics of allocation and multifunctionality are treated together in this section


Below we first summarise relevant sections of ISO Standard 14041 (ISO, 1998E), which provides a stepwise allocation procedure for practitioners. Next we turn to the 1992 guide (Heijungs et al., 1992), which is oriented more towards multifunctionality as a systems (i.e. modeling) problem. In these introductory paragraphs we also outline the basic contours of the problem in its dual perspective and consider some ambiguities of terminology. There follows an analytical and strategic review of recent developments in LCA modeling and allocation. This serves as a stepping stone to a statement of the principal choices made in this Guide, which are then related to the ISO allocation framework. The recommended procedures are described in greater detail in the ‘conclusions’ of this section, where some of the main options available for sensitivity analysis are also indicated. The topic of multifunctionality and allocation is also treated separately in Appendix C


The topic of multifunctionality and allocation is also treated separately in Appendix C. DEVELOPMENTS IN THE LAST DECADE ISO The topic of allocation is treated in ISO 14041(ISO, 1998E). In clause 6.5.1 the basic terms of the issue are set out as follows: "Life cycle inventory analysis relies on being able to link unit processes within a product system by simple material and energy flows. In practice, few industrial processes yield a single output or are based on a Part 3 : Scihentific background


linearity of raw materials inputs and outputs. In fact, most industrial processes yield more than one product, and they recycle intermediate or discarded products as raw materials. Therefore, the materials and energy flows as well as associated environmental releases shall be allocated to the different products according to clearly stated procedures." In the terms of the present Guide: in the case of multifunctional processes, the associated environmental interventions are to be appropriately allocated to the product systems under study. In Section 6.5.2 of ISO 14041 several principles for allocation are then stated. In the first place, allocation procedures are to approximate fundamental input-output relationships as closely as possible. Second, the sums of the inputs or outputs of a multifunctional unit process allocated to its various goods and services are to equal the unallocated inputs or outputs of that process. This is known as the ‘100% rule’


This is known as the ‘100% rule’. Where several alternative options for allocation are available, finally, a sensitivity analysis is to be conducted to illustrate the consequences of the particular choices made. Section 6.5.3 lays down an allocation procedure comprising three steps. Step 1 does not, strictly speaking, belong to the allocation procedure, as its aim is to avoid allocation “wherever possible”, by either of two options: division of multifunctional processes into two or more monofunctional subprocesses (step 1a), or expansion of the product system to include the additional functions related to the coproducts (step 1b). The latter step essentially means redefining the functional unit and the system boundaries, and must therefore be conducted in accordance with the provisions for defining these basic parameters. Where allocation is unavoidable, ISO prescribes one of two alternatives


Where allocation is unavoidable, ISO prescribes one of two alternatives. Step 2 states that system inputs and outputs should be “partitioned on the basis of the underlying physical relationships between them”, i.e. reflect the way in which the inputs and outputs are affected by quantitative changes in the products or functions delivered by the system If this is not feasible or does not provide a full solution, step 3 of ISO 14041 is to be followed, with partitioning being based on “other relationships” between inputs and outputs, most notably relationships of economic value. Avoidance of allocation, the first step of the ISO procedure, is quite simple in variant 1a, as when truly independent processes are lumped together into one unit process. Specifying these underlying single processes then solves the problem. In variant 1b, system expansion, the solution is more problematical than it may seem at first glance


Specifying these underlying single processes then solves the problem. In variant 1b, system expansion, the solution is more problematical than it may seem at first glance. Consider the case of a comparison between product system alternatives (as opposed to a single alternative LCA). To create 'equivalence' between the functions provided by these alternatives, in principle one may simply add to each alternative all the additional functions pertinent to the other alternatives. With a large number of alternatives, and with more detailed analysis of underlying multifunctional processes, however, such 'system expansion' will lead to a new de facto functional unit comprising a vast number of extraneous functions. The system as a whole may thus become inordinately large and be quantitatively dominated by all the added processes, with all their attendant uncertainties


The system as a whole may thus become inordinately large and be quantitatively dominated by all the added processes, with all their attendant uncertainties. One partial solution to this problem is not to add functions to the various alternatives but to subtract them from those alternatives providing additional functions. This approach to ISO's system expansion is also known as the 'substitution method' or ‘avoided burden method’ and is discussed in greater detail below. Thus, in a comparison of, say, alternatives for soap production from caustic soda, the upstream monofunctional chlorine production chain is not added to all the alternative product systems, but is subtracted from the former. From an economic perspective, however, this procedure can be regarded as product substitution, the additional chlorine from soap production replacing monofunctional chlorine production elsewhere


From an economic perspective, however, this procedure can be regarded as product substitution, the additional chlorine from soap production replacing monofunctional chlorine production elsewhere. It thus involves a number of implicit assumptions on which material production is replaced, on the absence of new market demand arising through substitution (i.e. only substitution within existing markets) and on the feasibility of other multiple outputs within the system being able to be dealt with in the same way. If these assumptions are unfounded, i.e. if such substitution is unlikely to occur in reality, subtraction becomes an artificial procedure, adopted solely to yield a monofunctional system. This is indeed the case in the soap example, for in reality there is no such thing as a monofunctional chlorine process. Thus, there is considerable ambiguity between the terms of ISO's step 1b: system expansion and step 3: allocation based on "other relationships", viz. economic


Thus, there is considerable ambiguity between the terms of ISO's step 1b: system expansion and step 3: allocation based on "other relationships", viz. economic. These issues will be considered in more detail below. The requirements set by ISO on this kind of subtraction procedure are that the alternative systems to be subtracted should be “known” and that assumptions about what is actually replaced by its output be “welldocumented” (ISO 14041, 1998E; B.2). As we have seen, though, this is often problematical, for with many joint products there is no independent production process to add or subtract. Moreover, if the substitution system is itself multifunctional, the multifunctionality problem remains, in the line of reasoning of ISO step 1b, again to be tackled by subtraction, for each of the multiple flows. Even if applied only partially, in cases where realistic alternatives are indeed known, system expansion would lead to endless Part 3: Scientific background


regress, involving virtually all the world’s production processes, for the simple reason that virtually any substitution system is itself a complex multifunction system. It should be reiterated here that we shall interpret the ISO procedure within the dual framework discussed earlier: in the context of the empirical modeling of all kinds of process multifunctionality, and as a solution to the multifunctionality problem, which by definition then is not empirical modeling, although it may of course reflect empirical relationships in a looser sense. Most ISO steps may be interpreted either as modeling or as partitioning, as a solution to the multifunctionality problem. Division of multifunctional processes (ISO step 1a) means taking a closer look at empirical relationships and is clearly part of modeling. Given the implications for redefinition of the functional unit and system boundaries, system expansion (step 1b) is also treated by ISO implicitly as a modeling step


Given the implications for redefinition of the functional unit and system boundaries, system expansion (step 1b) is also treated by ISO implicitly as a modeling step. It represents the acceptance of multifunctionality. If viewed as a solution to the multifunctionality problem, expansion of the functional unit to include the functions of the co-products means that these latter functions must be incorporated as separate production chains in all the product alternatives that are not associated with these co-products. Particularly if there are several alternatives, each with several but different coproducts, the perspective on the original functional unit soon becomes clouded as the system is expanded to include ever more functions. The situation is illustrated in Table 3.9.1. System expansion renders systems comparable by adding to the respective product systems co-products that are deemed 'equivalent' (A' for A, etc.)


The situation is illustrated in Table 3.9.1. System expansion renders systems comparable by adding to the respective product systems co-products that are deemed 'equivalent' (A' for A, etc.). This yields a series of hypothetical systems providing the same, multiple functions. Since subtracting a constant factor from (the functions associated with) each alternative leaves the differences between the alternatives mathematically unaltered, one way of reducing complexity is to simply deduct from each alternative the sum total of all the ‘extra’ functions (the set A’+ B’ + C’), yielding product systems assumed to be monofunctional, with just a single co-product (system) subtracted. As already mentioned, the problem with this kind of subtraction is that it can be interpreted not as a solution to the multifunctionality problem but as a step in modeling. It is then not ‘subtraction’ but ‘substitution’, i.e. a modeling of the economic substitution of processes


It is then not ‘subtraction’ but ‘substitution’, i.e. a modeling of the economic substitution of processes. The implicit logic is that if electrical power is generated as a co-product in some waste management process this will lead to reduced power generation elsewhere. There is unjustified optimism about (system) expansion and (function) subtraction as a solution to the allocation problem. There are two main drawbacks. First, system expansion generally means adding new multi-functional processes, thus merely increasing the number of processes that need to be partitioned. Second, this procedure may often constitute an artificial solution to the multifunctionality problem, if the functions taken for expansion and subtraction are known in reality not to be the relevant ones. This is not a very elegant solution, as such imaginary solutions may introduce large and unknown uncertainties in outcomes


This is not a very elegant solution, as such imaginary solutions may introduce large and unknown uncertainties in outcomes. A practical solution is to employ system expansion as a proxy type of modeling, expanding the system using processes that are not precisely equivalent but are similar enough for them to be taken as reasonable approximations. These processes should already have been rendered monofunctional by means of some allocation procedure, in a prior study, for example. For simplified LCA studies this may indeed represent a useful mixed solution: system expansion on the basis of previous allocation. A frequently employed option for subtraction is to use the allocated cradle-to-gate (partial) LCAs in the ETH energy production database (Frischknecht et al., 1993/1995/1996)


A frequently employed option for subtraction is to use the allocated cradle-to-gate (partial) LCAs in the ETH energy production database (Frischknecht et al., 1993/1995/1996). Allocation based on physical relationships (step 2 of the ISO procedure) is part of modeling if these relationships are indeed specified, as when a waste management model specifies emissions as a function of the input of some waste. This is a cleår example of modeling in which the multifunctionality problem does not arise. (Whether this type of modeling is adequate for all or even most LCA purposes is another question.) If such empirical relationships are not modeled, some physical relationship may still be used for partitioning, as a somewhat artificial solution. The same holds for the final ISO step, partitioning on the basis of "other relationships", such as economic value. Market mechanisms may be modeled, and are then part of the modeling leading to the multifunctionality problem, or they may be ignored


Market mechanisms may be modeled, and are then part of the modeling leading to the multifunctionality problem, or they may be ignored. Table 3.9.1 : Expansion and subtraction as solutions to the multi-functionality problem. Intended FU (X) alternative system alternative system alternative system co-product A B C expanded system : multiple but equivalent functions subtracted system : one function only Part 3 : Scientific background


The point here is that if there is relevant real-world knowledge available on empirical relationships, it should be used not only for allocating multifunctional processes, but also, at a primary level, for constructing a more accurate and consistent inventory model. ‘System expansion’, in the real world, is part of a broader class of economic substitution mechanisms, involving market mechanisms, income effects, investment decisions, etc. As economic mechanisms are clearly fundamental to industrial processes, model quality would be greatly improved if market mechanisms were somehow incorporated. At the same time, though, introducing economic mechanisms would increase the complexity of LCA inventory modeling enormously and compound the problem of multifunctionality still further. It may even be questioned whether such modeling can be reconciled with certain key features of LCA, such as an arbitrary amount of the functional unit


It may even be questioned whether such modeling can be reconciled with certain key features of LCA, such as an arbitrary amount of the functional unit. The steps of the ISO allocation procedure and their interpretations in this Guide are summarised in . Table 3.9.2: ISO terminology and its interpretation in this Guide. ISO terminology: allocation problem: at process level solution Step 1 : avoiding allocation: 1 a division of unit processes 1 b system expansion Step 2: otherwise, allocation reflecting physical relationships Step 3: otherwise, allocation reflecting other relationships interpretation in this Guide: multifunctionality problem: at unit process level or at systems level modeling modeling of broader functional unit, or: modeling of economic substitution or: non-empirical, artificialsolution modeling, or: allocation as partitioning of unit processes modeling, or: allocation as partitioning of unit processes Heijungs et al


(1992) The multifunctionality problem was treated at length in the 1992 guide and background document (Heijungs et al., 1992; pp. 22-34). Several problem definitions were given and potential solutions discussed. Although the problem was defined at a systems level, solutions were discussed at the process level, in conformity with ISO. System expansion was not deemed a satisfactory solution, for incorporating ever more processes, themselves multifunctional, leads to highly unwieldy systems that are extremely difficult to compare. Individual processes were described in terms of inflows and outflows, some of which were to be allocated and others allocated to. Although "social causality" was stated to be the guiding principle, it is not always applicable, for practical or theoretical reasons


Although "social causality" was stated to be the guiding principle, it is not always applicable, for practical or theoretical reasons. Social causality refers to the aims of operators, who adjust operations to shifts in demand and more generally to supply and demand mechanisms, influencing the product mix of multifunctional processes. The 1992 guide defines the multifunctionality problem in relation to system boundary definition. Coproducts not intrinsic to the system under study need not be followed further: they cross the system boundary. However, if outputs are produced in a waste management system that can usefully be applied in other systems, as with some kinds of fly ash and sewage sludge, there is no multifunctionality problem; the flows should then be followed further in the processes where they are applied


Open-loop recycling is treated at the systems level: primary production is assigned to the first system of use, with all upgrading processes being considered part of the second system of use. Final waste processing, including landfill, is assigned to the process where it actually occurs, which is generally multifunctional. Although allocation rules for multiple waste processing were discussed in the 1 992 background document, no clear procedural guidelines were given in the guide. Compared to the 1992 guide, the current Guide has more precise rules for setting systems boundaries, in relation to the economic value of flows. It is also more explicit with respect to the allocation rules to be applied, as elaborated below. Moreover, the rules for solving the multifunctionality problem are now more uniform across the three situations distinguished in 1992: co-production, combined waste processing and open-loop recycling


Other methods A general survey of developments in the field of allocation is provided by Lindeijer and Huppes (see Appendix C) and specifically for open-loop recycling by Ekvall & Tillman (1997), Kim et al. (1997) and Klöpffer (1996). We shall not discuss all these developments individually but review the field from a more strategic and analytical perspective, for unfortunately we are here not dealing with refinements of existing positions, paving the way to ever better, more detailed solutions. In Kuhnian terms: there is still no dominant ‘normal science’ paradigm in LCA, but several competing paradigms, all rather imprecisely defined. These paradigms are not reflected or defined solely at the level of allocation. As already indicated, the multifunctionality problem is intimately related to the more fundamental issue of inventory modeling. The following analysis of developments in the last decade therefore covers both allocation-asPart 3: Scientific background


partitioning and inventory modeling. As the various approaches to the problem involve paradigmatic choices, they have one feature in common: they tend to lead to improvements in one area while creating new problems in others. A second issue is that causal relationships which have been incorporated in modeling cannot be used once more in allocation. Consequently, choosing a method for tackling the multifunctionality and allocation problem is not merely a question of single-step improvements. Analytic survey: trends This review concerns allocation and modeling. The more general subject of modeling for LCA and, still more general, for Industrial Ecology, is touched upon only in relation to the multifunctionality problem as specific to LCA. Certain broader topics relating to LCA modeling have been treated in Section 1.2.2 above


Certain broader topics relating to LCA modeling have been treated in Section 1.2.2 above. LCA inventory analysis has progressed in two main ways over the past decade: more specific methods and models have been developed, and there are now clearer ideas on how to interpret and compare their results. The basic aim of all this work has been to include more causal mechanisms of known relevance in inventory models. At the same time, though, there is a general conviction that LCA should be simplified, enabling easier and hence broader application. Up to a point, the conflicting aims of making LCA both ‘better’ and ‘simpler’ can be practically resolved by using better databases and software. These may reflect enhanced methods, on the one hand, and are easy to apply, on the other. At the methods level the conflict remains, however. For this reason, in this Guide a distinction has been introduced between ‘simplified’ and ‘detailed’ LCA


At the methods level the conflict remains, however. For this reason, in this Guide a distinction has been introduced between ‘simplified’ and ‘detailed’ LCA. Below, we first consider detailed LCA, which employs more sophisticated modeling and allocation methods. After this fairly lengthy treatment, a simplified form of analysis is derived that still retains as much realism as possible. Certain simple types of modeling may even avoid the problem entirely, as when economic processes are characterized in monetary terms only, using sectoral input-output models with environmental extensions, for example (Lave et al., 1995; Hendrickson et al., 1998). As a gross simplification, this type of modeling may be used in LCA, thus rendering the multifunctionality problem invisible. We regard the application of such IO models as useful for estimating missing data but not as a general modeling approach (see Section 3.8), nor as a solution to the allocation problem


We regard the application of such IO models as useful for estimating missing data but not as a general modeling approach (see Section 3.8), nor as a solution to the allocation problem. It is sometimes proposed to include exergy analysis in LCA inventory modeling and solve the allocation problem by means of this single measure on product flows (and even environmental flows), as seems to be advocated by Ayres (1998) and, to a lesser extent, by Cornelissen (1997). Exergy analysis may constitute a useful tool for identifying strategies for improving energy efficiency, as improved system alternatives. If such a strategy has then indeed been effective in reducing various forms of energy resource use (e.g. oil, coal, uranium) is a matter of LCA inventory modeling of the alternatives concerned, comparing these, and other, environmental interventions. In the present discussion of multifunctionality and allocation we give no further consideration to the issue of exergy analysis


In the present discussion of multifunctionality and allocation we give no further consideration to the issue of exergy analysis. Other types of model may aggravate the allocation problem substantially, as is the case with most partialequilibrium economic models. Substitution, for example, is in reality always only partial. Shifts in volumes and prices in one process will thus lead to adjustments in the volumes and prices of all related processes, and so on. If processes are defined at the level of detail required for environmental analysis, the 'full system' can never be modeled. Increased modeling complexity is therefore combined with incompleteness, apart from compounding the allocation problem. One solution to the multifunctionality problem is to simply accept the fact that systems are multifunctional and thus avoid the additional artificiality of allocation. This solution comes at a price, however


This solution comes at a price, however. It will then be harder to compare product system alternatives, as differences in a multitude of other functions will have to allowed for when interpreting the modeling results. This amounts to abandoning the functional unit: the very essence of what constitutes an LCA. Most current LCA inventory modeling relies on three basic simplifications, all more or less directly related to ease of use. First, unit processes are treated as ‘black boxes’, with constant and linear input-output coefficients, in which the actual variability of process parameters and operating conditions are thus implicitly disregarded. A second, related simplification is that no attempt is made to incorporate the actual objectives of process operators, i.e. plant owners, managers and investors. As a consequence, changes in exogenous circumstances do not lead to endogenous changes in process parameters


plant owners, managers and investors. As a consequence, changes in exogenous circumstances do not lead to endogenous changes in process parameters. Third, and again related to the previous simplifications, the inter-connectedness of processes is also modeled very mechanically and statically, in a steady-state model, ignoring all (or most) market mechanisms, as well as all other social, cultural, and political relations. These basic simplifications are obvious limitations, and overcoming them is a worthy endeavour, though by no means a straightforward one. As most recent efforts to elaborate a more sophisticated allocation Part 3 : Scientific background


method have been closely bound up with the goal of improving LCA inventory modeling, recent developments can usefully be discussed in terms of efforts to tackle the respective simplifying assumptions. We shall therefore consider, successively, efforts aimed at incorporating the following three classes of mechanisms in inventory modeling: technical relations; aims of process operators; market relations. 1. 2. 3. Technical relations Today there is a vast body of technological knowledge that can be usefully incorporated in LCA. Systems engineering models of complex systems have become increasingly accurate and a variety of sophisticated databases and software are now available, very similar to LCA software, such as Chemsys (Chemstations Inc., 1997). Generally, they comprise a mixture of physical (’natural science type’) causality with operational practice based on technical and economic aims


Generally, they comprise a mixture of physical (’natural science type’) causality with operational practice based on technical and economic aims. In the field of waste management, process models with ‘internal relations’ have been developed to determine the effects of processing an extra amount of a particular waste flow; for a review, see Sundberg et al. (1998). A number of technological models have also been developed for specific use in LCA; see, for example, Eggels & van der Ven (1995). Such models have also been incorporated in LCA software. In models permitting independent variation of useful functions, these technical specifications may help solve the multifunctionality problem or, better, avoid it altogether. A change in one of the outputs associated with a functional unit can then be related to changes in the overall process, leaving all other functional outputs constant. It is then these changes that are due to the functional unit


It is then these changes that are due to the functional unit. If the co-products cannot be varied independently, i.e. if it is a joint (rather than just combined) process, the multifunctionality problem of course remains. There is one major problem with recent developments along these lines. Most available models indicate the results of short-term changes only, computing effects on capacity utilisation while leaving installed capacity unchanged. While such models may be very useful in optimising use of existing installations, most of the questions posed in LCA relate to the medium and long term, in which, conversely, installed capacities change, with capacity use more or less given. This partial ‘solution’ to the allocation problem therefore comes at the price of an unrealistic model


This partial ‘solution’ to the allocation problem therefore comes at the price of an unrealistic model. The obvious but by no means straightforward solution is to model long-term technical relations, incorporating changes in installed capacities and in operational utilisation due to particular choices of product system. Both the short- and the long-term analysis are forms of marginal systems analysis; in the short-term analysis it is marginal changes in capacity use that are relevant, with installed capacities remaining constant; in the long-term, it is marginal changes in installed capacities, at intended capacity use, that are relevant. So long term marginal analysis specifies the changes in average functioning. See also Section 1.2.3.4. There is wide debate on how best to model causality within process relationships, treated in the context of allocation in ISO 14041 only partially under the heading ‘physical relationships’


There is wide debate on how best to model causality within process relationships, treated in the context of allocation in ISO 14041 only partially under the heading ‘physical relationships’. These relationships define the input and output coefficients of all the processes included in the LCA inventory. However, these ‘technical’ coefficients also reflect the aims of process operators, and these are mainly economic. Furthermore, ‘physical causality’ is generally in the wrong direction when applied in LCA: in a physically determined system it is the inputs and system conditions that cause the output, while LCA is concerned with how the multiple products delivered by a given process, as outputs, affect its functioning, including inputs of raw materials and intermediate products. From this perspective, the actual 'physical relationships' embodied in the technological plant are generally of no more than secondary influence


From this perspective, the actual 'physical relationships' embodied in the technological plant are generally of no more than secondary influence. There may be one exception here: where the ‘cause’ coincides with the ‘function’, as in waste processing, where the wastes imported are the precise cause of the emissions and other outflows of the plant in question. Even in this case, though, the inputs of capital and ancillary goods are not physically determined but induced by regulations and a variety of socio-economic factors. Pure physical causality can never fully explain the functioning of even waste management systems. In mixed waste treatment, in particular, the rationale for co-processing often lies in economies of scale, with consequent acceptance of inferior (because unspecialised) treatment. From this broader perspective, there is good reason to use economic allocation, i.e


From this broader perspective, there is good reason to use economic allocation, i.e. the share in the total proceeds of the waste management process represented by sum paid for processing this specific waste. As already discussed, there are thus two conflicting options here: to incorporate physical, i.e. technical causality in modeling, thereby possibly resolving the multifunctionality problem, or to resolve the problem as part of an allocation procedure. The solution proposed by Eggels & van der Ven (1995) is a mixed one. It distinguishes between product-related and process-related flows and emissions. Product-related emissions indicate what would happen if the specific waste flow were not part of the total waste flow (incremental change, with fixed installed capacity) Part 3: Scientific background


or if a unit more or less were processed (marginal change, also with fixed installed capacity). This modeling is generally done on theoretical grounds, e.g. explaining cadmium emissions to air in terms of the cadmium content of the waste products being processed. By relating all emissions to their sources in specific waste products, only part of total emissions are explained, termed by them ‘product-related emissions’. Stopping here would mean violation of the 100% rule, i.e. total allocated emissions would not equal total emissions. In a following step, then, the remaining unallocated emissions, termed ‘processrelated emissions’, are allocated on a different basis: by mass, for example. The situation becomes slightly more complex if, say, electricity is generated alongside the waste processing function. By first substituting a stand-alone electrical power plant for the co-produced energy, the same procedure can be applied


By first substituting a stand-alone electrical power plant for the co-produced energy, the same procedure can be applied. First the product-related emissions are established and then the process-related emissions, which may now be negative, and both allocated to the various waste flows being processed. One drawback of this method is that in most cases the physical-causal analysis used to establish the product-related emissions relates to the short term, with fixed capacity and variable capacity utilisation, while LCA is concerned primarily with the longer term, with fixed capacity utilization and variable installed capacity. Even if one were to accept the validity of a short-term analysis as a proxy for the long term, the ins and outs of that analysis remains unclear. How to tackle the PVC contribution to dioxins emissions from a waste incinerator in which other chlorine-containing wastes (e.g


How to tackle the PVC contribution to dioxins emissions from a waste incinerator in which other chlorine-containing wastes (e.g. kitchen wastes) are also being processed? Current models indicate that the marginal contribution is negligible, as the chlorine input required to produce all the dioxins is much lower than the actual input, from either PVC or kitchen wastes. The marginal contribution to dioxin formation, the basic method for establishing physical causality, is therefore virtually zero. Clearly, more work needs to be done before process models properly reflect the underlying internal technical relations. The task then is to elucidate both the modeling principles required to handle long-term capacity adjustment and the precise empirical relationships that need to be incorporated in the model. For the time being, realistic modeling of physical relationships within the process is an option in exceptional cases only, relating to waste management


For the time being, realistic modeling of physical relationships within the process is an option in exceptional cases only, relating to waste management. For establishing long-term relationships involving changes in installed capacities, no clear approaches to improved modeling as yet exist. Thus, the multifunctionality problem must be resolved mainly as part of the allocation procedure, rather than on the basis of physical relations. Aims of process operators Most industrial processes have many degrees of operational freedom, over the shorter term but particularly in the long term, as investments in new capacity may fundamentally change the nature and scale of operations. To incorporate the aims of process operators in LCA therefore appears to be a promising methodological extension. There is indeed a wealth of knowledge on the aims of business enterprises, which might be expressed in terms of a mixture of goals as e.g


There is indeed a wealth of knowledge on the aims of business enterprises, which might be expressed in terms of a mixture of goals as e.g. market share, cash flow, profits, shareholder value and long-term viability, as defined by the firm in question. Combined in a goal function, an optimum can be defined for the functioning of the process (or set of processes) operated by a firm, by maximising its goal function. It is process operators, usually the owners or their representatives, who decide how processes are operated. Their aims are primarily economic, viz. to minimise cost or maximise net proceeds or profits. Such types of model are sensible only if used in tandem with models having specified internal technical relations. Assuming some goal function which operationally specifies the aims, an optimum system can be developed


Assuming some goal function which operationally specifies the aims, an optimum system can be developed. The first assumption here is that there is one operator for the entire system, the second that the relations specifying the system include options for capacity adjustment, to allow for the long-term perspective relevant in LCA. Then, during optimisation, capacity use is set at the desired optimum in all installations. Adding one functional unit gives the long-term marginal effect required in LCA. For all combined processes, but not for joint processes, the multifunctionality problem is then, in principle, solved. However, there are few situations in which there is just one operator maximising his goal function, and the vast majority of processes are at least partly joint in nature. An example in a relatively simple situation is that of a mining industry


An example in a relatively simple situation is that of a mining industry. Azapagic (1996) and Azapagic & Clift (1998, 1999, 2000) internalise in the model the aims of process operators using the methods employed by economists in operations research and, more generally, in production function theory (Heijungs, 1998b). Given currently available technologies, an optimum input and output mix is chosen on the basis of the assumed goal function of the operators. Because short-term technological relationships are employed, there is real progress towards solving the multifunctionality problem, as the model specifies the effects of providing an additional amount of function. For the very same reason, however, all system responses are calculated within the constraints of installed capacities, while the question of Part 3 : Scientific background


interest in LCA usually is how the world would be changed by adjusting those capacities. There is little experience with this approach in the context of long-term studies, moreover. Unfortunately, then, this kind of optimisation model has only limited relevance for most kinds of LCA. As the variations due to extra unit production of one of the products now result from investments in real production plant through added installed capacity, the multifunctionality problem remains much the same. The short-term optimisation models now available represent an initial step towards realistic short-term modeling of dynamic markets. Having laid down technical, i.e. plant parameters and a goal function, supply functions can in principle be specified. Although a number of large companies dispose over such models, they comprise little technological detail (publicly at least)


Although a number of large companies dispose over such models, they comprise little technological detail (publicly at least). The optimisation models used for allocation assume that all decisions regarding process adjustments are made by a single operator, based on his own particular goal function. In reality, of course, product systems have a multitude of different operators, each with their own goal function, and a multi-actor model is required. As the choices of each actor are influenced by those of all the others, model complexity soon spirals as the number of actors grows. The goal function and the process conditions may cover the long term, as is generally required in LCA. As long-term models employ technological averages, however, they do little to help resolve the multifunctionality problem. First, there are vastly more aggregated processes requiring subsequent allocation


First, there are vastly more aggregated processes requiring subsequent allocation. Second, in a given situation any change in overall output will affect only a limited number of constituent processes (under the simplifying assumption of linear relations). If this assumption of a single actor is abandoned, several new problems of a game-theoretical nature arise, for the choices of any one actor are influenced by those of all the others. Although economists are developing such multi-actor models, their level of technological specification is too general for the purposes of LCA. Technology-specific, multi-actor models become feasible if, e.g. an assumption of fixed prices is introduced. Then interdependency is very much reduced. If such simplified multi-actor systems are optimised economically, e.g. in terms of minimal costs, combined (but not joint) processes can be analysed as to optimal functioning


If such simplified multi-actor systems are optimised economically, e.g. in terms of minimal costs, combined (but not joint) processes can be analysed as to optimal functioning. For each alternative for the functional unit, the model would indicate installed capacities and the optimum ratio between the various product outputs given this installed capacity. This means modeling large systems, as the co-products are then also modeled. In such models, the number of allocation problems might be reduced somewhat. For primarily joint processes there would still be no solution, as the ratio between product outputs is then more or less fixed. In the context of LCA there is no systematic work currently in progress in this direction. When adjusting installed capacities, there are usually several technological options available; see, for example, the many options for electrical power production


When adjusting installed capacities, there are usually several technological options available; see, for example, the many options for electrical power production. Endogenising such choices based on optimisation rules is an endeavour that has not yet been undertaken in LCA. There is an ongoing debate on the more limited subject of the marginal process choice. Clearly, a process type that cannot be extended, as in many places hydropower, is not a relevant option for additional capacity. Only nonconstrained processes are relevant, with state-of-the-art processes as a further focus. Such technology can be viewed as a proxy for the processes that will be functioning in the not too distant future. Processes that have been introduced on an experimental basis only, like chlorine-free polycarbonate production or fast-breeder nuclear power generation, are excluded, as are older technologies that are still operational but no longer the object of investment


A similar position is adopted by Weidema et al. (1999) in their notion of the ‘marginal process’, which likewise limits the set of potentially relevant processes to include only those that can with certainty be perpetuated in the longer term. Even though there are currently no limitations on installing hydroelectric power plant, for example, use of hydropower cannot be extended indefinitely because of the limited supply of water for this purpose. In both these approaches the choice of process is exogenous. Modeling which processes are current state-of-the-art is not yet possible, for. such information is currently only exogenously given. There is no particular reason to assume that state-of-theart processes are single function processes. On the contrary, modern processes will tend to avoid waste and produce additional products


There is no particular reason to assume that state-of-theart processes are single function processes. On the contrary, modern processes will tend to avoid waste and produce additional products. This brings modeling of the marginal process back to the position adopted in Heijungs et al., 1992, where the marginal process was defined as the current–state-of-the-art process (then named: modal modern process). Such a marginal process choice is not related to the aims of the process operators. No progress is made in resolving the allocation problem, however, for a marginal or current state-of-the-art process is as multifunctional as any other (if not more so, given the aim of industrial ecology to integrate waste flows into production processes


One special case of optimisation at the systems level is where it is not linked to the goal function of the firm or firms involved, but to a social goal function such as a ‘sustainable emission level’ (Azapagic, 1996; Frischknecht, 1998; Frischknecht, 2000), involving social, economic and environmental goals. Two interpretations are then possible. One is that the answer given specifies the socially most attractive option. This answer does not indicate what will happen, or, more low key, in which direction developments will take place. It indicates a most reasonable environmental potential of some set of Part 3: Scientific background


technological options. The second interpretation (as in Frischknecht) is that, in the longer run, public policy will be adequate and society will arrive near the optimum for any technology chosen. The model then has an empirical predictive value, taking into account politico-administrative mechanisms as yet unspecified. Again, as with optimisation based on the goal of the firm, long-term models of this kind may give some empirical insight but have little to contribute to resolving the multifunctionality problem. Finally, there is the option not to expand model mechanisms with real-life goal functions of firms or society, but to include one, simplified goal of the firm, not in empirical modeling but in the interpretative allocation procedure (Huppes, 1993). As most goals of process operators relate to sales, the share of each product in total sales of the firm indicates its share in bringing about the existence of the full (unallocated) process


As most goals of process operators relate to sales, the share of each product in total sales of the firm indicates its share in bringing about the existence of the full (unallocated) process. These shares in proceeds may be used as allocation factors, allocating all flows to each of the products/functions, proportionally to their share in overall monetary proceeds. This ‘economic’ allocation procedure does not indicate the partial effect of solely delivering some extra amount of the product under investigation; it indicates its share in overall effects. Modeling is thus concerned with establishing the overall multifunctional effect of a change in demand for the functional unit, and allocation with establishing the share of each product in that overall effect


Market relations Given the crucial role of market relations in the systems-level effects of the technological choices being analysed in LCA, use of a market model comprising inter-process market relations would represent a major step forward. There are a number of developments of interest, originating both within LCA and from the field of (ecological) economics, and these will be surveyed in turn. First, however, let us recapitulate on the extent to which market relations have already been incorporated in inventory modeling and how this relates to resolution of the allocation problem. The system expansion step in ISO can be interpreted as economic substitution, being the special case of full substitution. Economists are well aware that there is rarely full substitution, as implicitly assumed in the avoided burden method (as explained in the discussion of ISO 14041, above). Only if demand is extremely elastic will changes in supply not lead to market adjustments


Only if demand is extremely elastic will changes in supply not lead to market adjustments . And even if there is a "known alternative" available (as required by ISO), to assume full substitution is still unrealistic. It is hard to assess whether partial addition of a real mechanism (substitution in only a limited number of multifunctional situations) in a rather unrealistic manner (by assuming 100% substitution) will lead overall to more valid LCA results. As the outcome of the exercise is still virtually always a multifunctional system, some form of allocation procedure is still required. What might be said is that application of substitution to all the main processes might have the advantage of avoiding the arbitrariness of applying substitution in some cases and allocation in others. Several LCA theorists, have developed proposals for handling market relations in LCA inventory modeling more satisfactorily


Several LCA theorists, have developed proposals for handling market relations in LCA inventory modeling more satisfactorily. In particular Ekvall (1999) has developed methods for handling elasticities in LCA and Weidema (2001) has developed a substitution method which solves the problem of regress, where each 'substitution system' is itself multifunctional, requiring substitution, etc. A recent development that could no longer be included in this Guide but that deserves further assessment is the value-corrected substitution method, as described in Werner & Richter (2000) and Werner (2000). It expands the applicability of the substitution method to situations of recycling where the secondary material has a lower value than the primary. Then substitution is assumed not to be to the full amount but only a fraction, given by the ratio between secondary and primary price


Then substitution is assumed not to be to the full amount but only a fraction, given by the ratio between secondary and primary price. Ekvall, rather than unrealistically assuming full substitution (as in the avoided burden method) or zero substitution, advocates using realistic default values for elasticities of supply and demand. From a practical angle, Ekvall’s proposal brings with it the problem of massive system expansion, as ever more processes partially adjust to the changes in demand resulting from some choice introduced in the product system(s) under study. Weidema (2001), for his part, advocates adhering to ‘extreme’ elasticities which, while fairly unrealistic, can at least be handled systematically. His method assumes that there is one output from every multifunctional process, a change in demand for which will lead to full adjustment of the production volume of that process. In other words, supply of that product by the process is assumed to be fully elastic


In other words, supply of that product by the process is assumed to be fully elastic. Demand for all the co-products is then taken to be fully inelastic, so that depressed demand causes no decline in production but is offset by reduced production elsewhere in some other process delivering the same product. However, any increase in demand for the elastic product will lead to increased production of all the co-products. Combination with similar assumptions on the demand side leads to a number of extremes. If demand is fully inelastic, as is assumed for some near-waste products, extra supply will not reach the market and will become waste. If demand for the co-product is fully elastic - the other option - the extra co-product will fully replace production in some other process


If demand for the co-product is fully elastic - the other option - the extra co-product will fully replace production in some other process. On this rather unrealistic basis, Weidema defines a substitution procedure that can resolve the problem of endless regress, at least in some cases, by limiting the regress to two processes which symmetrically substitute Part 3 : Scientific background


for each other in ever smaller amounts. We hence refer to his variant of the substitution method as the ‘symmetrical substitution method’. It is not entirely clear how universally applicable this method is, nor if all the problems of multifunctionality can thus be resolved (for waste processing, for example). For a fuller description, we refer the reader to the refinery example elaborated by Weidema in Part 2b. Assuming broad applicability for the moment, however, how are these two methods to be evaluated? The key problem with the symmetrical substitution method is that the techniques employed are of the market modeling type, while the specified market responses are not in line with available empirical knowledge. However, it does solve the multifunctionality problem, at least to some extent. We therefore tend to regard Weidema's method not as part of inventory modeling, but as a type of allocation, i.e. as a procedure for resolving the partitioning problem defined by prior modeling


We therefore tend to regard Weidema's method not as part of inventory modeling, but as a type of allocation, i.e. as a procedure for resolving the partitioning problem defined by prior modeling. If a relatively simple and generally applicable allocation method is available, as we believe to be the case with economic allocation, there are no good reasons to opt for the complex and rather unrealistic method of Weidema. Ekvall attempts to be more realistic in the elasticities involved in substitution. For lack of a complete data set, however, here too simplifying assumptions must be introduced. If indeed applied broadly, an ever increasing number of processes would become part of the system analysed, still requiring a separate allocation step. Further research on how to operationally introduce a market modeling step such as that advocated by Ekvall (1999) is in itself interesting, but not for solving the multifunctionality problem


Further research on how to operationally introduce a market modeling step such as that advocated by Ekvall (1999) is in itself interesting, but not for solving the multifunctionality problem. To gain an indication of the possible effects of substitution the symmetrical substitution method of Weidema may be used as a form of sensitivity analysis. The other approach to incorporating market relations in LCA starts from general economics. As the marketing programmes of most firms show, there is a wealth of empirical economic knowledge available on market relations, backed up by increasingly sophisticated models. The general structure of these models is wholly compatible with the interests of industrial ecology, moreover, as they specify the overall adjustment at the systems level as induced by a specific technology or volume change. When it comes to their potential application in LCA, however, market models have two serious drawbacks


When it comes to their potential application in LCA, however, market models have two serious drawbacks. In the first place they require use of applied general equilibrium (AGE) models at a micro-level, while such models are still barely operational at an aggregated, meso-level. General application of market models at the level of technological detail required in LCA is consequently not yet feasible. There is a second, equally fundamental problem. Increased demand for a given product (and process) never results in a full adjustment of supply. Because of the price rise induced by extra demand, demand for other goods and services will be depressed and their functions delivered to a lesser extent. This means that any switch to an alternative product will induce a virtually endless series of small changes in all other functions. Because of the price rise induced by extra demand, demand for other processes will be reduced, as will be the production volume of these other processes


Because of the price rise induced by extra demand, demand for other processes will be reduced, as will be the production volume of these other processes. The induced shift in volumes and prices is virtually endless. In each round an ever greater circle of processes will be influenced, involving ever more product systems, albeit affected to a diminishing extent. Although perhaps more realistic, this kind of analysis is not feasible as a method for comparing alternative product systems for equivalent functions, as in LCA. So why not skip LCA, then, and simply switch to this more realistic kind of market analysis, thereby abandoning this central restriction of LCA: the functional unit as the lynchpin of inventory calculations? There is a simple reason


Contemporary market analysis does not go into the kind of detail required for tying economic activities to environmental effects, for the complex task of accurately modeling all the various kinds of processes is beyond current data gathering and modeling capacity. The systems involved in market modeling generally span the globe, as does LCA, but now at a very high level of aggregation only. The option of combining economic and environmental analysis in a non-LCA framework does, in principle, hold a certain appeal, though. If the key problem of evaluating combined changes in production and environmental effects could be solved, it might become a viable alternative to LCA. Bouman et al. (2000) have developed a stylised market model for use in LCA-type decision situations. With its rigorous simplifications, it is still a long way from being a realistic market model suitable for LCA-type questions


With its rigorous simplifications, it is still a long way from being a realistic market model suitable for LCA-type questions. Experimental application of this market model and a typical LCA model to the same simplified case showed that the two approaches yielded very different conclusions. If market models, stylised and simplified as in LCA, could be applied more easily, the combined use of the two model types, each with their strengths and weaknesses, might be a better option than shifting from LCA to the market type of modeling. As yet, market mechanisms have been incorporated only incidentally in inventory modeling, in the special case of substitution. If the assumption of fixed prices in the previous paragraph is removed considerable realism can again be added, as market processes are all around us. Including technical relations as well Part 3: Scientific background


as process operators’ aims in inventory models would provide an immediate specification of production functions. Again, for LCA purposes it is long-term technical relations, including long-term investments, that are relevant, and so it is long-term production functions that are relevant. Production functions specify the supply functions of the products involved. The combination of final demand and production functions specifies markets as combining supply and demand. Market models are widely used, but not in LCA. The models used by economists are rarely technology-specific. Supply functions are based on technologies and on past investment decisions. If concrete knowledge is available, it often is confidential. Even without technological specifications, though, models are of only limited value as the complexity of market relations spirals with the number of processes involved


Even without technological specifications, though, models are of only limited value as the complexity of market relations spirals with the number of processes involved. As a consequence, current operational models generally focus on a very restricted part of the system relevant in LCA; they are partial equilibrium models. Although economists have also developed more comprehensive Applied General Equilibrium (AGE) models, these are so aggregated that they have no part to play in the technology-specific decisions examined in LCA. In what way, then, might incorporation of market mechanisms in inventory models contribute to LCA-type of analysis in the not too distant future? To reduce the attendant complexities, application might be restricted to just the main processes. When comparing high-speed trains and aircraft as two alternative modes of transport on a 500-km route, for example, market analysis could be used to determine the long-term supply and demand elasticities of each


Additional investments in railways would lead to a reduction in air ticket prices adequate to maintain a desired utilisation of adjusted air transport capacity. An expansion of rail transport capacity, by whatever mechanism, will not therefore lead to an equal reduction in air transport volume. Roughly speaking, the reduction in the latter would be about half the increase in actually used rail capacity, a midpoint between full substitution and no substitution at all. An increase in air transport capacity will have a similar but quantitatively different knock-on effect on rail transport volume. Car and bus transport volumes would also be affected, of course. In this way LCA could provide more realistic information for decision support on traffic modalities. At the same time, though, the analysis would also become far more complex


In this way LCA could provide more realistic information for decision support on traffic modalities. At the same time, though, the analysis would also become far more complex. Assuming non-market inventory modeling to refer to single-function systems only (train or plane), the market-based analysis would show that besides a shift towards the extra train kilometres resulting from the investment decision in rail, air traffic would not be reduced by the same amount but substantially less. Overall transport would increase. After such an analysis it hardly seems sensible to allocate this extra air travel 'away' from the train system. This would effectively remove the extra information on the market mechanism. A very different option for incorporating market dynamics is partial economic modeling, to set parameters for the system analysed, as Kandelaars (1999) has done (cf. 1.2.3.2)


A very different option for incorporating market dynamics is partial economic modeling, to set parameters for the system analysed, as Kandelaars (1999) has done (cf. 1.2.3.2). She gives the example of a policyinduced market shift from zinc gutters to PVC gutters, in which a new equilibrium is attained in the housing stock once all the old zinc gutters have been replaced. This kind of dynamic substitution can be included in the system model, with integration over time leading to the “average” inventory system. This is a deviation from standard LCA, as the functional unit is being supplied by a dynamic rather than steadystate system. Beyond the realm of LCA, particularly for the purposes of energy analysis, larger models have been developed that not only incorporate technical (i.e. process) relations and the aims of process operators but also include dynamic path analysis, complex market mechanisms and/or macro-economic dynamics


process) relations and the aims of process operators but also include dynamic path analysis, complex market mechanisms and/or macro-economic dynamics. One such model is the MARKAL model developed by ECN, which has been adapted for broadbrushstroke environmental analysis (Gielen et al., 1998; Seebregts et al. 1999). In principle, this model is also suitable for the decision-support domain of LCA. Its broad coverage of mechanisms constitutes its evident strength, but also its weakness: its complexity is such that practitioners must dispose over specialised modeling knowledge, for otherwise the model soon becomes an impenetrable black box. For larger-scale decisions, on future energy supply systems for example, it might nonetheless be a good option to include MARKAL-type models in the toolbox, not to replace but to augment LCA (or vice versa). These models provide no specific solution to the allocation problem, however


These models provide no specific solution to the allocation problem, however. In highly aggregated versions in which each sector is assumed to produce just one product, the allocation problem does not arise. As such models are not particularly technology-specific either, they provide little scope for supporting the technology choices for which LCA is designed. Finally, for the purpose of environmental decision support Cost-Benefit Analysis (CBA) can be applied, especially for investment decisions. Some CBAs may be formulated in terms of a functional unit, as when considering different options for expanding electricity production in CBA. It is usual but by no means necessary to weigh environmental effects in CBA by quantifying some measure relating to consumer preference. If this is not done, LCA impact assessment can be made into a separate chapter of CBA, Part 3 : Scientific background


leaving the economic part of it very similar to the inventory phase of LCA. It is now usual in CBA to specify the environmental aspects of the In this environmental sense CBA is not a systems analysis. In principle CBA could be transformed into a systems analysis for environmental aspects as well if the upstream and downstream processes were specified at the detailed technological level then required. Market models are now used in CBA for such processes, but at an aggregate level only. Transforming CBA into an LCA-like systems analysis would involve the same problems as introducing market relations into LCA. The problems would be further compounded by another characteristic of CBA: its time-specific nature, at least for the main activities in the life cycle of the investment involved. Such a time-specific analysis cannot be incorporated in steady-state LCA as developed in this Guide. However, it is more compatible with the economic analysis for business decisions on product systems


However, it is more compatible with the economic analysis for business decisions on product systems. What can we conclude on incorporating market relations in LCA and how can this help solve allocation problems? Starting with the first question, the symmetrical substitution method of Weidema, still difficult to understand, is so unrealistic that it cannot be seen as a serious contribution to modeling, although technically it may solve the multifunctionality problem in a number of cases. The approach of Ekvall, while possibly more realistic, draws an ever larger number of processes into the system, as substitution is usually partial substitution. The use of market modeling to simulate shifts between product systems, as proposed by Kandelaars, may yield additional insight but does not touch on the multifunctionality problem itself


The use of market modeling to simulate shifts between product systems, as proposed by Kandelaars, may yield additional insight but does not touch on the multifunctionality problem itself. Larger models such as the MATTER/MARKAL models may have a role to play, but their complexity would seem to limit their general applicability to major studies on complex issues. Further work may allow market relations to be incorporated in LCA in some fashion, albeit at the cost of aggravating multifunctionality problems, which must then still be resolved in a separate allocation step. Can market models help resolve or avoid the multifunctionality problem, then? Including substitution processes in inventory modeling is sometimes regarded as a solution, as a means of avoiding the multifunctionality problem altogether. This is indeed the case when the additional function(s) delivered by a combined production process can be substituted, fully, by (a) single cradle-to-gate system(s)


This is indeed the case when the additional function(s) delivered by a combined production process can be substituted, fully, by (a) single cradle-to-gate system(s). Such cases are extremely rare, however. Growing application of the principles of industrial ecology will lead to ever greater process multifunctionality. In general, incorporating market mechanisms will cause a massive expansion of co-functions. In the example on transport, above, any capacity increase in one mode of transport to some degree influences all other modes. The system would therefore be multifunctional even if all the various functions involved were delivered entirely by monofunctional processes. As the vast majority of real-world processes are multifunctional, market modeling would increase multifunctionality to such an extent as to effectively preclude a solution through allocation. One solution would be to simply accept multifunctionality


One solution would be to simply accept multifunctionality. As market prices are available in this economic context, the additional functions at the systems level could be expressed in monetary terms and aggregated into a single figure. This solution has the beauty of simplicity, in that it covers the sum total of additional functions in a comprehensive and comprehensible fashion. Its drawback, though, is that the resultant environmental profile is that of the combined system of target function and all the additional functions. To compare alternatives having different overall amounts of additional function in monetary terms, the share of the target function in this total must be established, and the only way to do so is to value the function in monetary terms, too. Its share in the total economic value of the system is then its share in the overall environmental burden, as specified. This, effectively, is economic allocation at the systems level


Its share in the total economic value of the system is then its share in the overall environmental burden, as specified. This, effectively, is economic allocation at the systems level. As argued presently, allocation at the process level is then to be preferred. For practical and probably also theoretical reasons, then, full market modeling is not an option for the environmental decision support where LCA now is used for. Integrating real market modeling in LCA is even more difficult. Some additional market modeling, next to LCA, may be a best option, thus using a number of different models to indicate main mechanisms in the effects of choices. Review conclusions The first conclusion is that nobody advocates the formerly preferred options of allocation based on simple measures like mass or energy. The limited relevance of approaches grounded in ‘physical relationships’ is indeed explicitly stated in ISO 14041


The limited relevance of approaches grounded in ‘physical relationships’ is indeed explicitly stated in ISO 14041. At best, they can be used as a proxy for allocation based on economic value. There is also general agreement that allocation, if performed, should be done at the unit process rather than systems level. Many developments combine modeling adaptations and solutions to the multifunctionality problem. Keeping these steps apart would benefit modeling and would lead to clearer approaches to allocation. As to improved specification of technical relations within processes, in the field of combined waste processing allocation procedures have been developed that distinguish between waste-specific Part 3: Scientific background


emissions, reflecting physical causalities and hence belonging to the realm of modeling, and other process-related emissions, which cannot be related to specific inputs and which are allocated on a mass or energy basis, for example. The models employed partition some of the chemical elements in emissions to individual waste inflows according to the share of the latter in the total mass input of those elements. For the time being, and for these specific elements, this appears to be the best method available. However, it is at odds with the conventional thrust of causal modeling, which is based on marginal modeling, viz. varying the input and then seeing how the output varies. Also, this modeling approach may reflect short-term causal relations better than long-term


varying the input and then seeing how the output varies. Also, this modeling approach may reflect short-term causal relations better than long-term. For the ‘process-related emissions’ (as distinguished by Eggels & van der Ven (1995); see above), which still need to be allocated to the different waste inflows, the solution chosen is more dubious and not in line with current notions of allocation. Inclusion of more specific causal mechanisms is to be regarded as a modeling improvement, preferably in tandem with inclusion of those long-term mechanisms relevant to LCA. As to including the aims of process operators, this field is still largely open, although there is one exception. In combined (but not joint) processes, short-term optimisation modeling has been developed for systems operated by one owner. In this case it may indeed also help resolve the multifunctionality problem. However, short-term analysis is not generally that relevant to most LCA questions


In this case it may indeed also help resolve the multifunctionality problem. However, short-term analysis is not generally that relevant to most LCA questions. Incorporating the aims of process operators and technical relations over the longer term in models remains an appealing goal that should be pursued further. Although proven for the short term, such optimisation methods require conceptual and empirical adjustment before they can be applied to the longer-term issues with which LCA is generally concerned. In most systems, furthermore, there will be a multitude of process operators. Straightforward optimisation is then not feasible. Optimisation in terms of minimising social costs seems more relevant to developing alternatives in LCA than as a modeling improvement or allocation procedure. The relationship between social cost assessment methods and LCA has been touched upon but not yet elaborated


The relationship between social cost assessment methods and LCA has been touched upon but not yet elaborated. Although optimisation models may someday be useful in applications in an LCA context, like environmentally specific market models they can by no means yet be defined as state of the art. As to including market relations in LCA, attempts have not yet yielded operational methods. Starting from LCA, substitution, full or zero substitution is too far away from economic reality. It seems that introduction of such artificial and unrealistic modeling assumptions may indeed ‘solve’ some multifunctionality problems in a technical sense. As with other types of allocation, part of the multifunctional process then is subtracted from the full process. However, this should better not be seen as modeling, but as a specific means of allocation on the multifunctional model


However, this should better not be seen as modeling, but as a specific means of allocation on the multifunctional model. As the additional insight gained is at the expense of unreal assumptions, it seems better not to use such methods in general practice but only additionally, in a sensitivity analysis, especially when the assumptions are not too far from reality. If progress towards more realistic modeling in LCA is made, the problem of multifunctionality is not solved, not even partly, but increased. Thus, developments in the last decade have cleared the ground for improved modeling but not for a more elaborate solution to the multifunctionality problem. ISO options in terms of avoiding allocation and allocation based on physical causalities, when made operational, tend to become part of modeling, and do not then reduce multifunctionality. So the only remaining, more or less universally applicable option for allocating inputs and outputs among products (i.e


So the only remaining, more or less universally applicable option for allocating inputs and outputs among products (i.e. functions) is therefore to partition on the basis of their economic values, the main driving force behind economic processes. This option will be described in more detail below, and with operational detail in Part 2b of this Guide. An alternative and more refined approach to allocation would be to use market prices that have been adjusted so as to incorporate negative environmental impacts and other social, or external costs. This option requires an adequate, i.e. social optimum regulatory regime, however, and is not elaborated here. Allocation would then not reflect the motives of process operators but objectives for society at large, including environmental objectives. Such a shift would mean a substantial deviation from the set-up of LCA, where environmental aspects are specified and evaluated independently from economic aspects