In the Nordic Guidelines (Lindfors et al.,1995a,b), the boundaries between the Technosphere and the Biosphere are defined similarly to the boundaries between the product system and the environment system in Heijungs et al. (1992). The authors mention that "any change in natural production rate caused by a technical activity should thus be accounted for". Furthermore, "natural biomass production as well as emissions from landfills should in principle be included in the inventory, whenever relevant". According to these authors the time horizon for emissions from landfill should be set by the temporal boundary defined in the Scope definition. The report of the Dutch Platform ‘LCA & Waste’ (Udo de Haes et al., 1997) describes the results of discussions on modeling end-of-life processes in LCA among a group of some 30 Dutch LCA experts between November 1995 and September 1996


The group of experts concluded that landfill sites could either be regarded as part of the product system or as part of the environment system. The choice depends on the degree of control at the site: a very well controlled, i.e. managed, site might be regarded as part of the product system, an uncontrolled site as part of the environment system. We conclude with respect to landfill sites that a useful distinction can be made between controlled and uncontrolled sites. The latter should be regarded as part of the environment system, with flows to them regarded entirely as emissions, while controlled sites form part of the product/function system. Actual estimation of landfill emissions is covered in Section 3.6 on data collection. Emissions from controlled landfills should be quantified to an infinite time horizon in detailed LCA studies

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


Actual estimation of landfill emissions is covered in Section 3.6 on data collection. Emissions from controlled landfills should be quantified to an infinite time horizon in detailed LCA studies. However, the actual duration of relevant emissions will be far shorter than eternity, depending on the types of wastes involved and landfill site conditions. Following an initial period, all wastes exhibit a tendency towards reduced emission activity or, conversely, towards increased immobilisation. For household wastes, for example, a time horizon of 200 years seems to cover virtually all emissions, in temperate climates, as far as organic substances are concerned. In very dry and hot regions, this time horizon may be much longer. In the very long run, as considered in steady-state modeling, leaching will remove virtually all inorganics such as heavy metals


In very dry and hot regions, this time horizon may be much longer. In the very long run, as considered in steady-state modeling, leaching will remove virtually all inorganics such as heavy metals. For organic chemicals, emissions from landfill should therefore be integrated over 200 years and for inorganic chemicals, over infinity. The latter means that each input of inorganic chemicals to landfill is regarded as an emission to soil. As with fate modeling, it is questionable whether extremely long time horizons are entirely relevant for interpretation with respect to inorganics. Hence, it may be useful to perform a sensitivity analysis on emissions over the first 200 years only (‘cut-off’) or to discount them in some, as yet unspecified, way (cf. Finnveden, 1999b). With respect to the economy-environment boundary in agriculture there have been several developments have in the past decade


Finnveden, 1999b). With respect to the economy-environment boundary in agriculture there have been several developments have in the past decade. The report ‘Application of LCA to Agricultural Products’ (Wegener Sleeswijk et al., 1996) focused on LCAs for agricultural products. One of the subjects discussed in this report was the boundary between the product system and the environment. There it was opted to include the agricultural soil in the environment system, for the main reason that damage to the soil should be regarded as an


Part 3: Scientific background environmental impact in order to differentiate between systems differing in their impact on soil quality1. Furthermore, it was opted to regard the harvested portion of the crop as an economic output of arable farming and thus as part of the economic system, with the remaining portion being regarded as part of the environment system. A consequence of this choice is that use of pesticides is regarded as an emission to the environment, except for pesticides which end up on the harvested crop. Horticultural production in which no natural soil is used for production belongs entirely to the economy, except for the soil itself, which remains part of the environment system. Another argument for defining the soil as part of the environment stems from the principle of ‘multifunctionality’


Another argument for defining the soil as part of the environment stems from the principle of ‘multifunctionality’. This principle, regularly applied in the context of public policy, implies that the quality of, say, agricultural soil should be maintained at such a level that it can also fulfil other functions, including ecological functions. If land is taken out of agricultural use, the quality of the soil should be such as to permit other types of land use. Other choices are possible here. Audsley et al. (1994), for example, opted to regard soil as part of the economy, right down to the depth of the water table, because soil is an integral part of farming systems. In specific agricultural studies, the analysis of the top layer may be of importance. In general LCA a simple system boundary excluding soil seems adequate enough. In this Guide the multifunctionality principle is followed


In general LCA a simple system boundary excluding soil seems adequate enough. In this Guide the multifunctionality principle is followed. Thus, agriculture and forestry are taken to be economic processes, agricultural and forestry soils remain part of the environment system, the harvested portion of the crop flows to other processes in the economic system while the non-harvested portion remains in the environment system. System boundary problems may also occur when pollutants are moved from one environmental compartment to another, as is the case when contaminated sediments from surface waters are transferred to adjacent land, i.e. soil (Gorree & Kleijn, 1996; Blonk & Van Ewijk, 1996) when waterways and harbours are dredged. Since no ‘new’ pollutants are added to the environment (no new emissions take place), the effects would not be visible in an LCA


Since no ‘new’ pollutants are added to the environment (no new emissions take place), the effects would not be visible in an LCA. In the report of Gorree & Kleijn, however, it is proposed that sediment removal from surface waters be treated in the same way as uptake by production forests: as a negative emission (see textbox). The distribution of this sediment on the surrounding soil is subsequently treated as a ‘positive’ emission to the terrestrial environment. Carbon dioxide, as a naturally occurring compound or an anthropogenic emission, takes part in the so-called geochemical carbon cycle. Short and long carbon cycles can be distinguished. Reforestry, for example, is a process with a short carbon cycle: during tree growth a certain amount of atmospheric is fixed, but is ultimately released (as or when the wood is landfilled, incinerated or decays naturally


The most appropriate way to treat short carbon cycles is to view them as cycles and thus, at the systems level, subtract the fixation of during tree growth from the emitted during waste treatment of discarded wood and to quantify the emitted (this balancing of carbon as an element in compounds such as and is necessary because and have different characterisation factors with respect to global warming A detailed elaboration of this kind of short carbon cycle has been developed by Virtanen and Nillson (1993) in their study on paper board. An example of a long carbon cycle is geochemical carbon fixation in fossil fuels and atmospheric release of carbon in the form of and when these fuels are burned. With long carbon cycles, carbon fixation is too slow a process and and emissions should be accounted for in their entirety, without further balancing against prior fixation. Source: Guinée (1995)


With long carbon cycles, carbon fixation is too slow a process and and emissions should be accounted for in their entirety, without further balancing against prior fixation. Source: Guinée (1995). Concluding, in agricultural LCAs a distinction between ’negative’ and ‘positive’ emissions may be relevant. In every LCA, moreover, it is very important to ensure that system boundaries are defined consistently throughout, i.e. with respect to characterisation factors and normalisation factors (cf. Section 4.6). A final issue of possible ambiguity with respect to system boundaries concerns waste water treatment plants (WWTP). Bearing in mind the definition of unit process, a WWTP should be regarded as an economic process. This implies that releases to a sewage system are not considered as an emission into the environment, but as a flow to the unit process WWTP. Only the releases of treated waste water from the WWTP to surface (fresh) water are taken to be emissions


Only the releases of treated waste water from the WWTP to surface (fresh) water are taken to be emissions. Because WWTP process data are sometimes difficult to obtain, in practice waste water treatment will sometimes be omitted from the flow diagram. In such cases this should be clearly reported and justified, however. 1 An exception here is horticultural production, where production generally takes place on an artificial substrate rather than natural soil. Such production belongs entirely to the economy.


Part 3 : Scientific background Clear and detailed reporting of choices, data and assumptions is crucial for the entire Inventory analysis. This is particularly important with respect to the definition of the economy-environment boundary, since sound decision-making demands clarity on what has been included in the environment and what not. General reporting guidance is provided by a number of documents. The general requirements of ISO 14040, clause 6, cited in Chapter 2, also apply here. In addition, ISO 14041 (1998E), clause 8 gives the following requirements for a third-party study report on Inventory analysis: “The results of an LCI study shall be fairly, completely and accurately reported to the intended audience as described by the relevant parts of clause 6 of ISO 14040 (1997E). If a third-party report is required, it shall cover all items marked with an asterisk


If a third-party report is required, it shall cover all items marked with an asterisk. All additional items should be considered.” More specifically with respect to system boundaries, we have the following recommendations by Lindfors et al. (1995a): The initial system boundaries chosen in relation to the goal and scope of the study shall be reported. The detailed system boundaries, viz. geographical, life-cycle and technosphere-biosphere boundaries, shall be described and justified in full. The SETAC-Europe Case studies Working Group (Meier et al., 1997) give the following (minimum) reporting guidelines: Systems and system boundaries should be clearly defined for all stages of the product system life cycle, including inputs, processing routes, spatial and temporal considerations. Ancillary data for the product system should be clearly defined. Exclusions should be stated and justified along with a consideration of the significance of any exclusions on the outcome of the study


Exclusions should be stated and justified along with a consideration of the significance of any exclusions on the outcome of the study. PROSPECTS If some degree of differentiation as to the time of impacts is to be included in the Impact assessment phase, this will have consequences for the Inventory analysis in terms of how the relevant time horizon is to be specified. How this is to be combined with steady-state modeling is a conceptually complex question. No other developments are foreseen for this topic


How this is to be combined with steady-state modeling is a conceptually complex question. No other developments are foreseen for this topic. CONCLUSIONS Concluding, with respect to some of the principal ambiguities regarding the boundary between the economy and the environment we recommend the following: agricultural production soil and unharvested parts are allocated to the environment forestry soil and unharvested parts are allocated to the environment natural forests growth of natural forests is not part of the economy wild fish, game, fruits, herbs growth is not part of the economy The distinction between positive and negative emissions is relevant for chemicals absorbed from the environment or consituting an emission from a human activity, such as dredging or agriculture


Dredging for specific purposes (shipping channels, clay for bricks or dykes) should be included as a negative emission (from the environment) , and as a positive emission when returned to the environment (particularly when this is to a different environmental compartment). In agriculture, and particularly in forestry (note : not natural forests), sequestering of in biomass should be considered a negative emission, while or released during waste processing of agricultural products should be taken as a positive emission. for controlled landfills, emissions of organics should be integrated over 200 years, emissions of inorganics ad infinitum; for uncontrolled landfills, all inputs of potentially hazardous chemicals should be considered as immediate emissions to the environment. As with fate modeling, it is questionable whether extremely long time horizons are entirely relevant for interpretation with respect to inorganics. Hence, it positive/negative emissions landfill emissions


Part 3: Scientific background may be useful to perform a sensitivity analysis on emissions over the first 200 years only (‘cut-off’) or to discount for them in some, as yet unspecified, way. mining tips to be treated in the same way as landfill waste water treatment plants WWTPs should be regarded as an economic process, implying that only the releases of treated waste water from the WWTP to surface (fresh) water are considered as emissions. Because WWTP process data are sometimes difficult to obtain, in practice waste water treatment will sometimes be omitted from the flow diagram. In such cases this should be clearly reported and justified, however. In the case of landfill and mining tips, when boundaries are still unclear we recommend adopting a pragmatic approach. Mining wastes disposed of in mines can be regarded as returning to the lithosphere and do not then constitute emissions (provided the mine is still as isolated from its biotic surroundings as it was before it was opened)


Regulated use of waste materials on soil substrates, as when mining wastes are applied under permit for the foundation of roads or buildings, do not constitute emissions either, although subsequent emissions from the new road structure should be duly recorded. At controlled landfill sites, only the emissions out of the site are recorded, in the way specified above. At non-regulated landfill sites, inflowing wastes are also treated in their entirety as emissions. For partially controlled sites, a mix may be applied. Finally, there is the question of how to treat short and long carbon cycles. Short carbon cycles should preferably be regarded as cycles and thus, at the systems level, the fixation of during tree growth should be subtracted from the emitted during waste treatment of discarded wood and any emissions should be quantified. For long carbon cycles, and emissions should be recorded in their entirety, without further balancing against prior fixation


For long carbon cycles, and emissions should be recorded in their entirety, without further balancing against prior fixation. RESEARCH RECOMMENDATIONS Elaborate the specification of temporal aspects in a steady-state modeling context, especially for landfill and long-cycle systems, inter alia in relation to temporal aspects of Impact assessment. Detail the boundaries between Technosphere, Biosphere and Lithosphere in a more systematic manner. 3.3 Flow diagram TOPIC The flow diagram provides an outline of all the major unit processes to be modeled, including their interrelationships. It is helpful in understanding and completing a system to describe the system using a process flow diagram. DEVELOPMENTS IN THE LAST DECADE Heijungs et al


It is helpful in understanding and completing a system to describe the system using a process flow diagram. DEVELOPMENTS IN THE LAST DECADE Heijungs et al. (1992) The life cycle consists of interlinked economic processes, each process input coming either from another process or directly from the environment and each process output flowing either to another process or to the environment, at least after allocation. Economic processes are taken to comprise resource extraction, production of materials and components, product manufacture, product use and associated waste processing, including recycling and reuse, and a variety of ancillary processes, such as transport and electricity generation. Any processes omitted should also be specified. In practice a summary flow diagram will first be compiled that includes only the key processes of resource extraction, product manufacture and associated transport


In practice a summary flow diagram will first be compiled that includes only the key processes of resource extraction, product manufacture and associated transport. The interconnected processes underlying each of these are then represented separately in partial flow diagrams, allowing the practitioner to ‘zoom in’ to the items of interest. With respect to the topic of flow diagram ISO states only that “it is helpful to describe the system using a process flow diagram showing the unit processes and their interrelationships”. In addition to this initial flow diagram, preparation of ‘specific’ flow diagrams is discussed as the first step in the paragraph on Preparing for data collection (6.2): "drawing of specific process flow diagrams that outline all unit processes to be modeled, including interrelationships".


Part 3 : Scientific background Drawing up an initial flow diagram can be regarded as a practical aid to data collection. As it will only become apparent which economic flows and delivering processes are relevant to a particular study as data are retrieved, compiling a definitive flow diagram will be a highly iterative process. Initially, the diagram will cover only the process delivering the functional unit and the adjacent processes supplying the main raw materials and treating the principal waste flows, and their respective interconnections. Even after all the relevant data have been collected, though, a truly comprehensive diagram will in practice be impossible to compile. Given the common occurrence of process loops and multi-outputs, such a diagram would simply include too many unit processes in overly complex interrelationship


Given the common occurrence of process loops and multi-outputs, such a diagram would simply include too many unit processes in overly complex interrelationship. In most cases, then, the only workable solution is to draw up a basic flow diagram showing the main constituent unit processes, from which to ‘zoom in’ to the underlying unit processes, represented separately in partial flow diagrams of their own. Wegener Sleeswijk & Huppes (1994) propose the following conventions for constructing flow diagrams. unit processes are represented as boxes; economic flows are represented as arrows between such boxes; economic flows enter a unit process at the top of the box and leave a unit process at the bottom of the box; the main direction of flow through the flow diagram is top-down, although recursive flows are in the reverse direction; all boxes contain text labels with the name of the process, e.g


‘production of sulphuric acid’; arrows are labeled only in so far as their name is required for an unambiguous understanding of the flow diagram; a flow diagram should preferably not contain more than 20 boxes; use of partial flow diagrams may be useful; for each box, it should then be clearly indicated whether it represents an undivided unit process or an aggregated unit process, possibly elaborated elsewhere in a partial flow diagram. These rules should be seen as recommendations for an optimum use of flow diagrams rather than as binding guidelines. For reporting flow diagrams Lindfors et al. (1995a) provide the following recommendations: The studied systems should be defined and reported using flow diagrams at the maximum level of detail used in the study. Sub-systems may be aggregated to higher levels if appropriate, as long as detailed descriptions are provided (e.g. in an appendix). On this issue Meier et al


Sub-systems may be aggregated to higher levels if appropriate, as long as detailed descriptions are provided (e.g. in an appendix). On this issue Meier et al. (1997) recommend that the process flow diagrams reported describe the entire system under study and include system boundaries, major inputs, products and co-products, as well as the main production sequence, ancillary materials and energy/fuel production. PROSPECTS No specific developments are foreseen for this topic


PROSPECTS No specific developments are foreseen for this topic. CONCLUSIONS Concluding, we recommend: to distinguish between an initial flow diagram, at the level of aggregated processes for each life cycle stage, and a detailed flow diagram, at the level of (possibly) unit processes; to denote (aggregated) processes by boxes and economic flows by arrows and adopt the other recommendations of Wegener Sleeswijk & Huppes (1994); to exclude environmental flows from flow diagrams, for pragmatic reasons; and to draft the flow diagram or diagrams as an iterative process during the data collection step. RESEARCH RECOMMENDATIONS No specific research is recommended.


Part 3: Scientific background 3.4 Format and data categories TOPIC A key task of the Inventory phase is the collection of process data. This usually involves large quantities of data in electronic form, retrieved in part from databases set up by others. To render these comparable and mutually consistent a standard data format must be developed. All the various data categories should be assigned a specific place in this format and a general description given of each to facilitate and guide data entry and retrieval. Ideally, the data formats used for data exchange (paper version of technical software format) and for (software) processing should be identical. However, as software packages differ in terms of underlying data model (often unspecified), no overall format for data processing can be recommended. Such a format can be drawn up for data exchange, however, reducing substantially the efforts required for processing with specific software


Such a format can be drawn up for data exchange, however, reducing substantially the efforts required for processing with specific software. DEVELOPMENTS IN THE LAST DECADE Although ISO 14041 (1998E) does not distinguish a separate ‘format’ step, clause 5.3.4 covers ‘Description of data categories’ which is clearly related. This, in Annex A4 of ISO 14041 (1998E), an example is given of a Data Sheet for a Unit Process. This Annex is provided for purely illustrative purposes. In the accompanying text it is stressed that there are no fixed rules for either the number of data categories or the amount of additional information required on the quality and uncertainty margins of the data. The format given in the example has entry spaces for material, energy and water inputs, material outputs (including products) and emissions to air, water and land as well as other releases (e.g. noise, radiation, vibration, odour, waste heat, etc.)


noise, radiation, vibration, odour, waste heat, etc.). There is also space for reporting other characteristic data on the process in question. On the topic of data categories ISO 14041 (1998E) clause 4.4 states, furthermore, that: “the major headings under which data can be classified include: energy inputs, raw material inputs, ancillary inputs, other physical inputs; products; emissions to air, emissions to water, emissions to land, other environmental aspects.


Part 3: Scientific background Other data categories for which input and output data may be collected include, for example, noise and vibration, land use, radiation, odour and waste heat.” As mentioned, ISO 14041 has little to say on data format as such. It is nonetheless a very important issue, particularly when it comes to data exchange. Streamlining of data exchange can lead to substantial savings on both the time and resources required for an LCA study. A format is characterised by: 1. 2. 3. its conceptual, i.e. primary structure; the items it comprises: the spaces to be filled in by the user; the rules for filling the spaces: ‘the cookbook’. When drawing up a format, due allowance should be made for the fact that various different kinds of process data will be employed in any given LCA. Raw process data (from a variety of sources with their own data formats) can be processed by various procedures into four basic forms of process data; 1. 2. 3. 4


Raw process data (from a variety of sources with their own data formats) can be processed by various procedures into four basic forms of process data; 1. 2. 3. 4. unallocated unit process data; allocated unit process data; (partial) system data retraceable to unit process data (partial) system data irreversibly aggregated. In principle, these various kinds of data set have different format requirements. In the case of 4. partial system data irreversibly aggregated, for example, it is necessary to indicate the allocation procedures and cut-offs employed and so on, while with unallocated unit processes these issues have no relevance. Another important point is the distinction between data and system. More particularly, the question is then whether and, if so, how the various processes (of whatever category) are interconnected within the database. A database may, for example, comprise data on a whole series of processes stored completely independently of one another


A database may, for example, comprise data on a whole series of processes stored completely independently of one another. It may also contain data on process interlinkages, however, in which case there is in fact no longer any distinction between data and system. The way in which the processes are interlinked then forms the system; cf. 3, above, “system data retraceable to process data”. In the LCA context, data and system should preferably not be combined until such time as an actual case study is to be performed. An LCA database may sometimes contain two different data sets for the same or comparable processes, for example when two literature sources report different data for the same process, or when there are two ways of producing a given material. In each specific case study one particular process, one particular literature source or one particular production method will have to be selected


In each specific case study one particular process, one particular literature source or one particular production method will have to be selected. What occurs in a case study is thus a stringing together of a series of selected processes. All this has consequences for the data format. Where a given process input goes to or where a given output comes from does not therefore belong in the process data format but in the description of the case study in question. In short, then, data and system should in principle be kept separate. This is seldom the case in current LCA databases, however. The format itself, a user data sheet with a certain structure, is in principle an entirely separate issue from how the data are stored. One of the standard methods for data storage is in a relational database (as in SPINE and in Huber, 1996)


One of the standard methods for data storage is in a relational database (as in SPINE and in Huber, 1996). The essential feature of a relational database is that all independent information is stored at a single location, which has the major advantage that updates need only be introduced at this one location. This is achieved by the data for a given process being stored in a great many different tables. This concept forms the basis of virtually all commercial database software, such as DBASE, ORACLE and MS-ACCESS, and has for years been the standard method for storing large amounts of data. Use of a relational database demands high standards of database integrity and the same holds for all the various elements of the user data sheet. Current data exchange formats like SPOLD have opted for easy readability and are not relational in design. When updating such nonrelational databases severe inconsistencies are to be expected


Current data exchange formats like SPOLD have opted for easy readability and are not relational in design. When updating such nonrelational databases severe inconsistencies are to be expected. If use is made of a relational database it would seem an obvious approach to employ it to store data not only data on individual unit processes but also on process interlinkages. As already mentioned, the latter kind of data relate to the system rather than to individual processes. As such, then, they do not belong in a discussion of the format for data on unit processes. Tables with data relating to Classification/Characteriserion and Evaluation may also be included. These data should also be independent of the Inventory data. Since 1992 a number of different data formats for LCA have been developed around the world, most of them in the form of tabular data sheets


Since 1992 a number of different data formats for LCA have been developed around the world, most of them in the form of tabular data sheets. Some, such as SPOLD and SPINE (discussed below) have been implemented in database software, while others are available as a printed form only. A number of LCA databases have also been developed that store process data in a proprietary format (e.g. ETH, SimaPro, SAEFL). Here we restrict ourselves to a discussion of SPINE and SPOLD.


Part 3 : Scientific background SPINE SPINE, the Sustainable Product Information Network for the Environment, is a joint initiative of the Swedish organisation IVL and the Chalmers University of Technology established in the context of the Nordic project on Environmentally sound Product development (NEP). The SPINE report (Steen et al., 1995), has been adopted by ISO as a “New Work Item Proposal” and is to be further elaborated. Although the proposal names SPOLD (see below) as a ‘Liaison Organisation’, the precise mutual status of these two formats is not yet entirely clear. At the time of writing of the present Guide, the outcome of the work based on this proposal was not known. SPINE is a relational database designed to permit communication between different software tools


SPINE is a relational database designed to permit communication between different software tools. The key question addressed during development of SPINE was: what kind of information is of value for LCA and how are the various data related? The key concept of the database is that of an activity, to be interpreted here as either a unit process or a (partial) system. An activity has quantifiable inputs and outputs. An important aspect of SPINE is that it stores not only actual the process data but also data on the flows between the various activities, thus creating a network-type model. The SPINE model is linear and homogeneous. The database has extensive capacity for storing qualitative information on all elements (flows, activities, substances, etc). This qualitative information is stored in separate tables, essentially independent of the quantitative data, and of little significance for the actual functioning of the database


This qualitative information is stored in separate tables, essentially independent of the quantitative data, and of little significance for the actual functioning of the database. SPINE can be used to store not only inventory data but also Impact assessment data (equivalency factors and so on), although the designers note that the latter module is still under development. (The subject of ‘Multifunctionality and allocation’ is treated in Section 3.9 of this Guide.) Processes can be aggregated to ‘mega-processes’ by means of a hierarchical structure. Data input to SPINE is by means of a screen form, similar to the aforementioned printed forms, and is indeed also available as a printed form. It is to be concluded that SPINE has a solid, intrinsically consistent format. One potential drawback of SPINE is the rigidity resulting from using the relational database concept


It is to be concluded that SPINE has a solid, intrinsically consistent format. One potential drawback of SPINE is the rigidity resulting from using the relational database concept. SPOLD SPOLD, the Society for the Promotion Of Life-cycle assessment Development, seeks as one of its missions to establish a comprehensive database of commodity materials and services. An important aspect of SPOLD’s work is the key focus on achieving consensus. In 1996 a SPOLD Status Report was published entitled Introduction into a Common Format for Life-Cycle Inventory Data’ (Singhofen, 1996; cf. Singhofen et al., 1996). In contrast to SPINE, for example, this document describes a paper format and distinguishes between a descriptive and a prescriptive approach. Given the importance attached to consensus by SPOLD, it was ultimately opted to start with a descriptive approach, providing as much scope as possible for using existing data(bases) and including all information deemed relevant


An important feature of the SPOLD format is that it has space for data on both unit processes and partial systems (aggregated unit processes). Thus the first question users are asked during data input is whether the data relates to a unit process or a partial system. Although in theory information should obviously be collected at the unit process level, in practice data are often found to relate to (partial) systems and SPOLD consequently reserves due space for the latter. The SPOLD format has five components: A. B. C. D. E. identification of data set (who, what, how?) system description, in cases involving system data (what is included, what assumptions?) graphic presentation of the system (now abandoned by SPOLD) input and output data, plus space for mass and energy balances references


Part A: Identification of data set do the data relate to a unit process or a partial system (aggregated unit processes)? who entered the data, and when? geographical, temporal and technological representativeness sources Part B: System model subsystems cut-off criteria coproduction and associated allocation rules energy, transport and waste model other assumptions


Part 3: Scientific background Part C: System structure graphic presentation of the model (abandoned by SPOLD for mainly practical reasons) Part D: Inputs en outputs Inputs from the technosphere (economy): materials/fuels, electricity/heat from nature (environment): natural resources other Outputs to the technosphere (economy): (co-)products, waste to nature (environment): air, water, land, non-material other interventions Mass and energy balances Part E: References The aim of SPOLD is to use the aforementioned descriptive format to develop a second-generation format, this time prescriptive. The new format is intended for use with a dictionary (list of definitions) and a multi-user test, leading to an electronic format that can serve as a basis for a data exchange network. In the meantime the first-generation (descriptive) SPOLD format has also been made available electronically, downloadable from Internet


In the meantime the first-generation (descriptive) SPOLD format has also been made available electronically, downloadable from Internet. Goedkoop (1998) has developed a simplified version of the original SPOLD format, the main feature of which is that a large number of specific fields have been replaced by ‘free’ text fields with no fixed structure. This is most relevant in part B of the SPOLD format: system data (as opposed to unit process data). One benefit of the simplified format is that it is more user-friendly for LCA practitioners, while still dovetailing well with the original SPOLD format. A disadvantage is that use of a greater number of free text fields means it is no longer relatively straightforward to export data to the full SPOLD format. DALCA DALCA (Van Dam et al., 1996), implemented by TNO, stands for DAta for Life Cycle Analysis


DALCA DALCA (Van Dam et al., 1996), implemented by TNO, stands for DAta for Life Cycle Analysis. DALCA is a "feasibility study on the future availability of universally accepted, reliable environmental data on processes (as ‘building blocks’ for environmentally oriented product information)". DALCA focused on products manufactured by the plastics processing industry. A data format is presented in Appendix 2 of the report. It is largely in line with that provided in the ’92 guide and consists of four tables: 1. 2. 3. 4. data identification data transport verification. The first table covers process identification, including process description, representativeness, author(s) and flow diagram. The second is for average data, with space provided for data margins and quality. The third provides dedicated space for transport data


The second is for average data, with space provided for data margins and quality. The third provides dedicated space for transport data. The fourth and last table is a proposal for presentation of process verification and includes such aspects as: comparison with external sources, completeness, internal consistency (incl. balances), reliability, status, data gaps and so on. The principal difference from the format of the ’92 guide is the specific focus on transport and verification. It is noteworthy that the verification table asks for an explicit indication of who is responsible for verification: the company, an independent institute, the subcontractor or others. EDIP (Wenzel et al., 1997) stands for Environmental Design of Industrial Products and has been developed by the Institute for Product Development, the Technological University of Denmark, five Danish industries, the Confederation of Danish Industries and the Danish EPA. Wenzel et al


Wenzel et al. 1997 briefly describe a paper format for use in LCAs. Although it is largely similar to the format of the ’92 guide, one important difference is the attention given to characterising the geographical site of the process in question and the extent to which the data are specific to this site. There is even space for including location-specific data relating to characterisation, normalisation and evaluation. The format also has space for information on working conditions. Boguski (1996) provides a review of LCA, including inventory and data format. Chapter 2 of this work gives a basic data format that is again largely similar to that of the ’92 guide, differing in the following main respects: less focus on qualitative information such as representativeness, and subcategorisation of economic inputs and outputs into transport, off-site energy purchases, autogenerated power and coproducts.


Part 3 : Scientific background As part of the Dutch MRPI project (‘Environmentally Relevant Product Information for the Construction Industry’) a format has been developed for collecting data on processes such as those occurring in the construction sector (Anonymous, 1998). MRPI is concerned with "validated information on the environmental aspects of building materials, building products or building elements, drawn up at the initiative of the producer or his representative (the trade association, for example) by means of an environmental life cycle analysis" (Anonymous, 1998). The process data required for the LCA are collected together on a printed form. This format, however, is devoted specifically to the construction sector and building materials. Although the design of this form is too specific to serve as a general LCA format, it might serve as a useful aid in drawing up such a format


Although the design of this form is too specific to serve as a general LCA format, it might serve as a useful aid in drawing up such a format. Principal differences Each of the formats discussed above provides space for quantitative information on the economic and environmental inputs and outputs of the process under study. SPINE is the only format based on the relational database concept, which is a solid guarantee of database integrity. A key feature of SPINE is that it stores not only the actual process data but also provides space for including process interlinkages, i.e. system data. It is, of course, debatable whether this format is ‘complete’ and whether all the items included really belong there. Another important respect in which various formats differ is in the space provided for qualitative information


Another important respect in which various formats differ is in the space provided for qualitative information. This varies from single-line entry (the name of the process) to an extended list of items relating to data quality and representativeness and sometimes to (partial) system data such as allocation methods used, cut-off criteria and so on. They also differ in the space provided for indicating data error margins: some formats ignore this issue entirely, while others provide scope for including upper and lower bounds or spread for all data entered. In addition, most of the formats traced are paper formats, although there are plans to convert some of them to electronic form. Finally, the formats differ in the degree to which they break down economic and environmental inputs and outputs into such categories as transport, energy and materials: some formats have many such subcategories, while others have none


PROSPECTS Although there is presently no ISO standard for data format, a Swedish application to develop a format on the basis of Spine and SPOLD was recently honoured by ISO. As the authors themselves state, the current version of the SPOLD form is based on a descriptive analysis of the formats currently used by LCA practitioners. This means that the format is not entirely homogeneous and that some of the items included are questionable. A second-generation SPOLD form is to be developed, based on a prescriptive format. With respect to data format a choice must be made from among the following options: 1. 2. 3. 4. one of the existing formats a modified version of an existing format a combination of several existing formats an entirely new format. The overriding aim in designing a data format is to guide LCA users in collecting process data, and not to establish how these data are to be stored and coupled


The overriding aim in designing a data format is to guide LCA users in collecting process data, and not to establish how these data are to be stored and coupled. The new Guide therefore includes a data sheet rather than a description of underlying data structure requirements. We would stress once again, though, that a data structure in the form of a relational database is a solid guarantee of database integrity. On the basis of the aforementioned and other criteria, a variety of forums including the SETAC Working Group on Data availability have expressed a preference for the SPOLD user form CONCLUSIONS The data sheet that is best supported by existing software is the SPOLD form. It is consequently recommended to use this form as long as there is no easy-to-implement alternative employing a relational database. It is to be noted that SPOLD and SPINE are meanwhile being combined by way of ISO/TR 14048 (in prep.)


It is to be noted that SPOLD and SPINE are meanwhile being combined by way of ISO/TR 14048 (in prep.). These developments, which had just begun when this Guide was being written, should therefore be followed closely. Once these efforts yield a practicable form(at), this should be used instead of the SPOLD format. RESEARCH RECOMMENDATIONS Research should focus on use of relational databases in LCA and on translating relational database requirements into a data format and data sheet. Relational databases appear to be a very promising tool for ensuring the integrity of the LCA database and, consequently, that of the LCA results. Especially promising in this respect is the initiative, in the ISO context, to combine SPOLD en SPINE: see ISO/TR 14048 (in prep.).


Part 3: Scientific background 3.5 Data quality TOPIC For LCA models, like any other model, it holds that ‘garbage in = garbage out’. In other words, data quality has a major influence on results and proper evaluation of data quality is therefore an important step in every LCA. Even if the quality of individual data is high, however, such data can still yield erroneous results if used to answer questions on which they have limited or no bearing. The data used in a given case study should, for instance, be representative for that particular study. Quality requirements thus refer to both the reliability and the validity of process data. As validity depends on the application in question, it is not validity requirements as such that are specified here but the data needed to assess that validity. DEVELOPMENTS IN THE LAST DECADE Heijungs et al. (1992) The '92 guide treats this topic in section 2.2.2, on the representativeness and quality of data, as part of the Inventory analysis (pp


DEVELOPMENTS IN THE LAST DECADE Heijungs et al. (1992) The '92 guide treats this topic in section 2.2.2, on the representativeness and quality of data, as part of the Inventory analysis (pp. 32–33). The following sub-steps are distinguished: the representativeness of the processes; the quality of the process data; the overall assessment of the process data. In ISO 14041 (1998E), clause 5.3.6, where ‘Data quality’ is part of the ‘Scope of the study’, the following statements are made with regard to data quality issues (see textbox): Descriptions of data quality are important for understanding the reliability of the results of a study and properly interpreting its outcome. Data quality requirements shall be defined to enable the goal and scope of the study to be met. Data quality should be characterised in both quantitative and qualitative terms and with due reference to the methods used to collect and integrate the data


Data quality should be characterised in both quantitative and qualitative terms and with due reference to the methods used to collect and integrate the data. Data quality requirements should be set for the following parameters: temporal coverage: the desired age of data (e.g. within the last five years) and the minimum period of time (e.g. annual) over which data should be collected; geographical coverage: geographical area from which data on unit processes should be collected to satisfy the goal of the study (e.g. local, regional, national, continental, global); technology coverage: technology mix (e.g. weighted average of the actual process mix, best available technology, or worst operating unit). Consideration shall also be given to additional descriptors defining the sort of data required, e.g. collected from specific sites versus data from published sources, and whether the data is to be measured, calculated or estimated


collected from specific sites versus data from published sources, and whether the data is to be measured, calculated or estimated. Data from specific sites or representative averages should be used for those unit processes contributing the majority of the mass and energy flows in the systems under study, as determined in the sensitivity analysis performed in 5.3.5. Data from specific sites should also be used for unit processes deemed to have environmentally relevant emissions. In all studies, the following additional data quality requirements shall be considered at an appropriate level of detail depending on the Goal and scope definition: precision: measure of the variability of the data values for each data category expressed (e.g


variance); completeness: percentage of locations reporting primary data relative to the potential number in existence for each data category in a unit process; representativeness: qualitative assessment of the degree to which the data set reflects the true population of interest (i.e. geographical, temporal and technology coverage); consistency: qualitative assessment of how uniformly the study methodology is applied to the various components of the analysis; reproducibility: qualitative assessment of the extent to which information about the methodology and data values allows an independent practitioner to reproduce the results reported in the study. Where a study is used to support a comparative assertion that is disclosed to the public, all data quality requirements described in this subclause shall be included in the study


Where a study is used to support a comparative assertion that is disclosed to the public, all data quality requirements described in this subclause shall be included in the study. Where a study is used to support a comparative assertion that is disclosed to the public, all data quality requirements described in this subclause shall be included in the study. Source: ISO 14041, 1998E. The aforementioned EDIP report (Wenzel et al., 1997) emphasises the importance of process characterisation. Data quality is described at two levels: the level of individual inputs and outputs and that


Part 3 : Scientific background of the whole process


According to EDIP, data characterisation should generally cover the following items: Definition of the scope of the process1 description of operations included and excluded and of inputs and outputs not linked to other economic processes specification of co-products produced and method for allocating these Data characterisation description of known data gaps description of the data source how well do the data describe the process and how representative of the average are they? description of the representativeness of the process for the objective of the study description of the assessment/calculation of the coefficients of statistical variation for the environmental inputs and outputs mass balance: calculation of the mass balance for the process technological development description of technological developments and trends in the most important inputs and outputs description of the projection of the process2 Several remarks are in order


In the assessment of statistical variation, uncertainties in economic flows may be more important than those in environmental inputs and outputs. Furthermore, it may be useful to make a clear distinction between the overall validity and reliability of data (and other data quality aspects) at the process level, independent of the application involved (as given by database requirements, for example), and their validity and reliability in a specific application. Nordic Technical Report No. 9 (Lindfors et al., 1995b) starts out by referring to data collection as the most time-consuming and thus the most expensive element of LCA. In screening product LCAs the amount of time required for this task can be reduced by using readily accessible data. However, this data may be of inferior quality. The report mentions three aspects to which due attention should be paid when selecting data for use: level of technology; age of data; site specific vs. average data. Lindfors et al


The report mentions three aspects to which due attention should be paid when selecting data for use: level of technology; age of data; site specific vs. average data. Lindfors et al. (1995b) consider all three aspects very important for the results of the study. A number of parameters are suggested for assessing the quality of databases used in LCA; correctness; reliability; integrity; usability; portability; maintainability; flexibility; testability. Building on Weidema (1994), Lindfors et al. (1995a) elaborate a detailed scheme of data quality parameters, represented as a ‘data pedigree matrix’ (Table 3.5.1)


Building on Weidema (1994), Lindfors et al. (1995a) elaborate a detailed scheme of data quality parameters, represented as a ‘data pedigree matrix’ (Table 3.5.1). They propose reporting data in the following format: 2.3 MJ - 0.1, %95 (2,2,3,1,1,1) A B C D (a,b,c,d,e,f) where A = the magnitude of the data as a numerical or descriptive expression (here: 2.3) B = the ‘unit base operation’ as a standard SI-unit and multiplier (here: Mega, C = the spread of the data as a percentage range or standard deviation (here: - 0.1) D = an assessment of the reliability of data, confidence interval or qualitative description (here: %95) 1 In the EDIP report the format is described in both table 22.5 and figure 9.1, which unfortunately use different titles and terminology. Here, the terminology from table 22.5 has been employed. 2 The term ‘projection’ in the EDIP book refers to the extrapolation of process data to the future year in which the newly developed product is to be launched on the market.


Part 3: Scientific background and a,b,c,d,e,f represent the origin (pedigree) of the data and thus its representativeness (see Table 3.5.1 below). Weidema (1998b), building on Wesnaes & Weidema (1996), presents an adapted version of the pedigree matrix set up by Lindfors et al. (1995a). According to the author "the objective of the pedigree matrix is to provide a data quality management tool, which makes it easy to survey the data quality, to point at possibilities for improvements in data quality and to trace back sources of uncertainty".


Part 3 : Scientific background It is not, as yet, feasible to reason backwards from a required level of confidence in the results of an LCA study to specification of requirements vis-à-vis the validity and reliability of the model and data to be employed. Nor is there yet a comprehensive method for establishing the overall quality of results on the basis of quantitative yardsticks of reliability and validity. Full statistical analysis, including analysis of error propagation, will not be available to LCA practitioners for a long time yet, if ever. The most one can do is trace back the factors determining the overall quality of the results, and make some semi-quantitative elements operational to aggregate the otherwise very substantial amount of data on quality aspects that accumulate in a study. A framework for this has been elaborated by Van den Berg et al. (1999) with some preliminary operationalisations. The main philosophy followed is that of Funtowicz & Ravetz (1990)


A framework for this has been elaborated by Van den Berg et al. (1999) with some preliminary operationalisations. The main philosophy followed is that of Funtowicz & Ravetz (1990). For some elements of this framework semi-quantitative approaches are available and may be applied (Weidema, 1998b; Van Oorschot, 1999; Van den Berg et al., 1999) In relation to reporting, Lindfors et al. (1995a) suggests that if risk of accident is signalled with a ‘red flag’, the criteria for such a flag shall be reported. They state, furthermore, that “data and data quality issues shall be reported giving: 1. 2. Information on the source of data used At least a qualitative declaration of data quality (specific or average, from what year, representativeness, estimate of variability (uncertainty). If not known, this should be stated.” Meier et al., 1997 give the following reporting guideline: “all data sources for the study should be clearly identified and referenced


If not known, this should be stated.” Meier et al., 1997 give the following reporting guideline: “all data sources for the study should be clearly identified and referenced. Key indicators of data quality should be reported, such that choices made are justified and transparent, including 1. 2. 3. 4. 5. 6. 7. age of data frequency of collection spatial/temporal considerations accuracy precision uncertainty etc. If data are presented in an aggregate form (i.e. for confidentiality reasons) the aggregation procedure should be fully described. A sensitivity analysis should also be performed on key data sets.” PROSPECTS Significant progress may be expected in this area in the coming years, but a full traditional error analysis will probably never be possible in LCA since 1) there is no normal (gaussian) distribution, as processes are highly non-linear; and 2) there is also no normal distribution for the inter-process connecting flows.


Part 3: Scientific background CONCLUSIONS Based on the above analysis of the state-of-the-art in the field of data quality assessment in LCA, we recommend as the best available practice for today: to follow the ISO 14041 (1998E) requirements (5.3.6) concerning data quality and optionally use a ‘pedigree matrix’ for reporting data quality assessments on individual processes; to trace back the factors determining the overall quality of the outcome by undertaking a Contribution analysis (see Section 5.4) and a Perturbation analysis (see Section 5.5), and to perform a sensitivity analysis on a number of key parameters (see Section 5.6); and (optionally) to apply a general framework for quality assessment of results (e.g. Van den Berg et al., 1999), and to apply semi-quantitative approaches, as available, for some of the elements of such a framework (e.g. Weidema, 1998b; Van Oorschot, 1999; Van den Berg et al., 1999)


Van den Berg et al., 1999), and to apply semi-quantitative approaches, as available, for some of the elements of such a framework (e.g. Weidema, 1998b; Van Oorschot, 1999; Van den Berg et al., 1999). Given this unsatisfactory state of affairs it is very important not only to present the results of the study in a transparent way (a quality aspect itself) but also to perform a quality assessment and report on it in an explicit and transparent manner, mentioning the desired as well as minimum data quality goals sought. RESEARCH RECOMMENDATIONS It is not yet possible in LCA to elaborate a standardised method for overall assessment of data quality. For the process data used in the Inventory phase this would, for example, mean having to know the probability interval of every input and output of every process involved in the life cycle. Similarly, the validity of the inventory model and the environmental effect models would have to confirmed


Similarly, the validity of the inventory model and the environmental effect models would have to confirmed. At the moment appropriate yardsticks of statistical distribution and a validation procedure to quantitatively assess overall model validity are both lacking. 3.6 Data collection and relating data to unit processes TOPIC In this step of the Inventory analysis all relevant data on the unit processes are collected and all flows connected to the unit processes quantified in accordance with the format specified above (Section 3.4). The reference flow(s) (see Figure 2.4.1) defined in the Goal and scope definition phase of the study form(s) the point of departure for data collection. The process data available to the practitioner may be structured in any number of ways. In LCA databases process data is often organised around unit processes, relating a given economic output to economic inputs and environmental inputs and outputs


In LCA databases process data is often organised around unit processes, relating a given economic output to economic inputs and environmental inputs and outputs. Process data provided by companies are often also organised around unit processes, but given in terms of inputs and outputs per unit time, e.g. emission of 5 tonnes of per year, input of 1000 tonnes of wood per year, etc. In current LCA databases process data is almost always quantified in relation to some physical (reference) flow (e.g. one kg of material or 1 MJ of electricity). In ISO 14041 (1998E) this step of relating all inputs and outputs to one reference flow is described in paragraph 6.4.3 “Relating data to unit processes”. DEVELOPMENTS IN THE LAST DECADE Heijungs et al. (1992) The step Entering theprocess data in the ‘92 guide is more or less equivalentto ISO's data collection step. The former was divided into two sub-steps (pp


(1992) The step Entering theprocess data in the ‘92 guide is more or less equivalentto ISO's data collection step. The former was divided into two sub-steps (pp. 30–35): 1 ) quantification of the inputs and outputs (including format and quantification of data) 2) representativeness and quality of the data. The second topic has been discussed here in the previous section (see section 3.5). Under the first heading several topics were considered. The central topic, data format and data categories, is discussed here in section 3.4. Other items discussed included the non-linear characteristics of most economic processes. The solution adopted for this problem there was to work with long-term marginal process data. Here, this discussion has been further refined and can be found in section 1.2.3.4 and section 2.3. Furthermore, it was recommended to use SI units whenever possible


Here, this discussion has been further refined and can be found in section 1.2.3.4 and section 2.3. Furthermore, it was recommended to use SI units whenever possible. In ISO 14041 (1998E) three steps appear to be distinguished: 1) preparing for data collection 2) data collection 3) validation of data


Part 3 : Scientific background These are mainly procedural steps. On preparing for data collection, ISO 14041 (1998E) states the following (see text box): The definition of the scope of an LCA study establishes the initial set of the unit processes and associated data categories. Since data collection may span several reporting locations and published references, several steps are helpful to ensure uniform and consistent understanding of the product systems to be modeled


Since data collection may span several reporting locations and published references, several steps are helpful to ensure uniform and consistent understanding of the product systems to be modeled. These steps should include: drawing of specific process flow diagrams that outline all unit processes to be modeled, including interrelationships; description of each unit process in detail and listing of data categories associated with each unit process; development of a list that specifies the units of measurement; description of data collection techniques and calculation techniques for each data category, to assist personnel at the reporting locations to understand what information is needed for the LCA study; and provision of instructions to reporting locations to document clearly any special cases, irregularities or other items associated with the data provided. An example of a data collection sheet is provided in annex A. Source: ISO 14041, 1998E


An example of a data collection sheet is provided in annex A. Source: ISO 14041, 1998E. With regard to data collection, ISO 14041 (1998E) states that “the procedures used for data collection vary with each unit process in the different systems modeled by an LCA study. Procedures may also vary due to the composition and qualification of the participants in the study and the need to satisfy both proprietary and confidentiality requirements. Such procedures and reasons should be documented.” In clause 5.3.3 on initial system boundaries and clause 6.3 on data collection, ISO 14041 states (see text box): Each of the unit processes should be initially described to define: where the unit process begins, in terms of the receipt of raw materials or intermediate products; the nature of the transformations and operations that occur as part of the unit process; and where the unit process ends, in terms of the destination of the intermediate or final products


Data collection requires thorough knowledge about each unit processes. To avoid double counting or gaps, the description of each unit process shall be recorded. This involves a quantitative and qualitative description of the inputs and outputs which are needed to determine where the process starts and ends, and the function of the unit process. Where the unit process has multiple inputs (e.g. multiple effluent streams to a water treatment plant) or multiple outputs, data relevant for allocation procedures shall be documented and reported. When data are collected from published literature, the source shall be specified. For those data collected from literature which are significant for the conclusions of the study, the published literature which supplies details about the relevant data collection process, about the time when data have been collected and about further data quality indicators, shall be specified


If such data do not meet the initial data quality requirements, this shall be stated. Source: ISO 14041, 1998E. On the subject of relating data to unit processes, ISO 14041 (1998E) clause 6.4.3 states: "For each unit process, an appropriate reference flow shall be determined (e.g. 1 kg of material or 1 MJ of energy). The quantitative input and output data of the unit process shall be calculated in relation to this reference flow."1 The EDIP books (Wenzel et al., 1997) explicitly mention the importance of a data check when electronic databases are used and distinguish several types of processes, based on the possible suppliers of such data: raw materials extraction and materials production: producers’ associations and private companies; product manufacturing processes and use processes: private companies; disposal processes: public authorities, research institutes; transport processes and energy systems: specialized information centres; Wenzel et al


(1997), furthermore, mention the future need for so-called data networks in contrast to current static databases. In these data networks each individual expert body must handle and update its 1 Note that this ‘reference flow’ is different from that defined and used in section 2.4 on Function, functional unit, alternatives and reference flows


Part 3: Scientific background part of the database on the product system. They mention, as s precondition for ensuring viability, that each body has an intrinsic commercial interest in participating. Another interesting item in the EDIP report is that they provide some basic guidelines for collecting data on the working environment. In this context four different impact categories are mentioned: chemical impacts; impacts of noise; impacts of monotonous repetitive work; risk of accident. Wegener Sleeswijk et al. (1996) mention several issues specific to Inventory analysis in LCAs on agricultural products. Such LCAs involve a number of problems connected with the production level to which the data to be gathered should relate. Compared with many other economic activities, agricultural production encompasses a relatively large number of production units (in this case: farms)


Compared with many other economic activities, agricultural production encompasses a relatively large number of production units (in this case: farms). The production processes - and consequently the associated environmental interventions, too - may differ markedly from farm to farm. Therefore the results of the LCA will be dependent on the type of data used: average or representative data, or data from individual farms. The type of data to be chosen depends on the Goal and scope of the study. For example, if the aim is to inform consumers about the environmental impacts of a certain product, such as a bottle of milk, one should, in principle, review the particular farm from which the milk in the given bottle originates. In practice, however, the bottle will contain milk from different farms, mixed at large production centres, and average data should therefore be used to describe the environmental impacts of the product


If a government is aiming to assess the variation in impact of current modes of milk production, data from individual operational farms should be used. If, on the other hand, the goal is to assess future scenarios based on current policy trends, use should be made of normative data (representative for modern farms). In principle this problem is not restricted to agricultural LCAs but may extend to all LCA studies. An interesting case in point is the modeling of waste management processes for long-lived products. In a study on water pipes, crash barriers and roof gutters by Kortman et al. (1996) waste management scenarios were developed for the year 2015. For communication reasons, it may be useful to make a distinction here between primary data (measured on-site) and secondary data (data from literature and databases, for example, or estimates from IOA models)


Additionally, a distinction is often made between so-called foreground systems and background systems (SETAC Clift et al., 1998; see also Anonymous, 1999). Clift et al. (1998) defined the foreground system as the set of processes whose selection or mode of operation is affected directly by decisions based on the study. The background system comprises all other processes interacting directly with the foreground system. It was stressed by Clift et al. (1998) that the distinction between foreground and background systems (or processes) has nothing to do with the environmental importance of their respective impacts; either the foreground or the background system may have the greater impact (Clift et al., 1998). For foreground processes primary data will normally be collected, while for background processes use will generally be made of secondary data sources. As Clift et al


For foreground processes primary data will normally be collected, while for background processes use will generally be made of secondary data sources. As Clift et al. (1998) state, the distinction between foreground and background will frequently be clear, for example, when the decision-maker is the operator of a set of processes constituting the natural foreground. In other cases it will not be as clear, however, for example when LCA is used as a basis for purchasing decisions. Background processes may then be defined as those processes underlying a market seen as more or less homogeneous from the perspective of the process providing the functional unit


Background processes may then be defined as those processes underlying a market seen as more or less homogeneous from the perspective of the process providing the functional unit. In order to avoid these rather theoretical discussions, a more pragmatic definition will be used in this Guide: foreground systems or processes are those systems or processes for which primary, site-specific data are used in an LCA (for whatever reason); background systems or processes are those systems or processes for which secondary data from databases, public references or estimated data based on IOA models are used. The more foreground processes a specific LCA study includes, the more ‘detailed’ the LCA will be. In LCA practice the use of generic databases is almost indispensable for background processes. There are several databases available, differing in terms of status, spatial, temporal and technical representativeness, types of processes covered and data formats employed


There are several databases available, differing in terms of status, spatial, temporal and technical representativeness, types of processes covered and data formats employed. It should be duly checked whether the unit process data in the database has already been subjected to allocation procedures. LCA practitioners should be aware that a choice for a particular database may greatly influence the ultimate results of the study (e.g. Copius Peereboom et al., 1998).


Part 3 : Scientific background Due attention should also be given to the units in which the collected data are expressed, to ensure that all the processes can be mathematically connected once all the data is available. With respect to units, there are three issues to which particular attention should be paid: conversion of Bq to kg, and vice versa; conversion of dB to and calculation of ‘land occupation’ and ‘land transformation’ as data subcategories of ‘land use’. Practical tips are provided for all three issues in Part 2b, Section 3.6. One special issue in data collection is how data relate to unit processes. As Curran (1996) states, "raw materials and energy data for production facilities are often expressed in terms of annual or monthly production”. These numbers will therefore have to be translated to units per quantity of product


These numbers will therefore have to be translated to units per quantity of product. This presupposes a separate step translating time-related data to data per quantity of product (reference flow), which then form the input for LCA databases or case studies. For unallocated databases this involves the arbitrary choice of making one of the products into a reference flow. The need for such translation is debatable: it is an extra step that can be easily avoided by using a scaling factor that includes time as a dimension (Heijungs, 1998a). One can even argue that valuable information on the magnitude of a process is lost in the process of translation. Data from a number of important data sources such as corporate environmental plans and reports, emission registration systems and statistical information systems often include the time dimension


Data from a number of important data sources such as corporate environmental plans and reports, emission registration systems and statistical information systems often include the time dimension. If these data are transferred to (a) database(s) with no prior translation, the same database(s) can be used for LCA and for other environmental tools like Substance Flow Analysis (SFA), Material Flow Accounting (MFA), Risk Assessment (RA), etc. However, LCA software tools do not always permit use of process data that include a time dimension. Omitting this dimension in LCA calculations poses no problem, however, as it is the ratio between inputs and outputs that is used here


Omitting this dimension in LCA calculations poses no problem, however, as it is the ratio between inputs and outputs that is used here. Since there are strong arguments for having access to process data that include a time dimension and because most LCA data currently consist of data related to a reference flow, it is strongly recommended to develop LCA software and databases that allow both types of process data to be used (for an example of operational software that includes this option, cf. http://www.leidenuniv.nl/interfac/cml/ssp/cmlca.html). Finally, the issue of group or sum parameters needs to be discussed. Some chemicals, such as PAHs and CFCs, are recorded in the Inventory analysis as group parameters1, although characterisation factors are only available for individual chemical species, such as anthracene and chrysene, or CFC–11 and CFC–12


Other group parameters regularly encountered in inventories for which this problem occurs include Volatile Organic Compounds (VOC), sulphur compounds, absorbable organic halogens (AOX) and hydrocarbons (CxHy). These group parameters should preferably be broken down into their individual chemical constituents and specified as such. If data on the ‘real’ constituents is not available, group parameters should be broken down into their individual chemical constituents using generic conversion factors like those published by Derwent et al. (1996) or used for Dutch Emission Registration. If these options are not feasible or do not cover the group of chemicals in question, one should take the arithmetic mean of the individual species of the group as a surrogate characterisation factor. If emission data are available for ‘hydrocarbons’ only, for example, a group POCP can be derived as the arithmetic mean of the POCPs of the individual hydrocarbons


If emission data are available for ‘hydrocarbons’ only, for example, a group POCP can be derived as the arithmetic mean of the POCPs of the individual hydrocarbons. This approach is a method of last resort and, in this example, is only of value if the POCPs of the individual hydrocarbons vary within a ‘reasonable’ range. Because of the often large number of emissions encompassed in ‘organic compounds’ or ‘AOX’, however, and the major spread in characterisation factors, use of an arithmetic mean would introduce unacceptable uncertainties. In such cases a ‘best estimate’ should be used to specify the group parameter in terms of consistuent species for the purpose of characterisation. Although incomplete, any specification is better than none and also generally superior to an ‘arithmetic mean’ approach. PROSPECTS No specific developments are foreseen for this topic


Although incomplete, any specification is better than none and also generally superior to an ‘arithmetic mean’ approach. PROSPECTS No specific developments are foreseen for this topic. CONCLUSIONS ISO 14041 requirements with respect to the description of unit processes, data categories and data collection procedures, etc. have been adopted in this Guide. Furthermore, it seems useful for communication reasons: to distinguish between primary data (measured on-site) and secondary data (from literature and databases, for example, or estimates from IOA models); and 1 Group parameters are valid parameters only if measured as such, not when calculated from measurements on individual chemicals.


Part 3: Scientific background to distinguish between: foreground systems or processes: systems or processes for which primary, site-specific data are used (for whatever reason); background systems or processes: systems or processes for which secondary data are used. The more foreground processes a specific LCA study includes, the more ‘detailed’ the LCA will be. Finally, SI-based or -derived units should be used throughout and unit process data should preferably be collected in terms of annual flows in order to allow use of these data by other environmental tools. Note 1: as discussed in Section 2.4 on ‘Function, functional unit, alternatives and reference flows‘, the issue of standard, recommended and actual performance should be dealt with here, although the discussion of developments on this topic was treated in Section 2.4


The recommendation with respect to this subject is: quantify the key parameters of the system’s reference flow, preferably based on: actual performance, and otherwise standard or recommended performance. Note 2: the issue of technology coverage has been discussed in Section 2.3 on ‘Scope definition’, where it is recommended to take current state-of-the-art technology as a starting point for data collection. RESEARCH RECOMMENDATIONS Since data availability is one of the most important problems in LCA practice, it is recommended to establish a project to construct a database comprising a set of reference processes with their corresponding interventions. Use of the Internet as a medium for sharing LCA data should also be developed further. Furthermore, LCA software should preferably allow scaling of data, including a time dimension. 3.7 Data validation TOPIC In this step the validity of the process data collected is checked


Furthermore, LCA software should preferably allow scaling of data, including a time dimension. 3.7 Data validation TOPIC In this step the validity of the process data collected is checked. Various tools are available for this purpose, including mass balances, energy balances and comparison with data from other sources (e.g. comparative analysis of emission factors). Any data found to be inadequate during the validation process should be replaced. Similarly, missing data should be identified in this step and a decision made on how these gaps are to be filled. DEVELOPMENTS IN THE LAST DECADE Heijungs et al. 1992 Validation of process data is partly discussed in section 2.2.2 of Heijungs et al. "The representativeness and quality of the data". In the discussion on "The quality of the process data" verification of the data by means of mass and energy balances is mentioned, as well as a check on data completeness


In the discussion on "The quality of the process data" verification of the data by means of mass and energy balances is mentioned, as well as a check on data completeness. With respect to ‘data validation’ ISO 14041 (1998E) states the following (see textbox): A check on data validity shall be conducted during the process of data collection. Validation may involve establishing, for example, mass balances, energy balances and/or comparative analysis of emission factors. Obvious anomalies in the data appearing from such validation procedures require alternative data values which comply with the data quality requirements as established according to 5.3.6. For each data category and for each reporting location where missing data are identified, the treatment of the missing data and data gaps should result in: a data value which is justified; a "zero" data value if justified; or a calculated value based on the reported values from unit processes employing similar technology


The treatment of missing data shall be documented. Source: ISO 14041 (1998E). Although the general topics of data quality and data quality indicators are discussed in a number of publications, including Wenzel et al. (1997) and Lindfors et al. (1995a), the topic of data validation as described here is not found in the literature except in ISO 14041 (1998E), being subsumed elsewhere under general data quality assessment.


Part 3 : Scientific background PROSPECTS No specific developments are foreseen for this topic. CONCLUSIONS Concluding, we recommend following the ISO 14041 requirement to check the validity of the process data collected by drafting mass balances and energy balances, by comparison with data from other sources (e.g. comparative analysis of emission factors). RESEARCH RECOMMENDATIONS No specific research is recommended. 3.8 Cut-off and data estimation TOPIC In principle an LCA should track all the processes in the life cycle of a given product system, from the cradle to the grave. In practice this is impossible, however, and a number of flows1 must be either roughly estimated or cut off2 and subsequently ignored. The root problem behind the cut-off issue is a lack of readily accessible data, implying disproportionate expenditure of funds and effort on data collection. Cutoff may substantially influence the outcome of an LCA study, however, and means that ‘easy’ LCAs come at a price


Cutoff may substantially influence the outcome of an LCA study, however, and means that ‘easy’ LCAs come at a price. The cut-off criteria specified in the past, such as omitting mass flows under 1 (or 5)%, lead to the fallacy of disaggregation: by splitting up processes after a more detailed review, most flows can be reduced to less than the specified percentage. If the rule is that a cut-off can be introduced if contributions to impact assessment results are below a certain percentage, there is a danger of the same fallacy occurring. In addition, it seems odd to omit data from the computations having gone to all the effort of generating them. Thus, the cut-off problem can be reformulated as a problem of having to quantitatively estimate the environmental interventions associated with flows for which no readily accessible data are available. 1 In the cut-off discussion, the term “flows” refers specifically to all economic input flows and the output flow “waste to be treated”


1 In the cut-off discussion, the term “flows” refers specifically to all economic input flows and the output flow “waste to be treated”. 2 In section 2.3, on scope definition, one situation was noted in which a cut-off may be introduced on grounds other than those discussed below: a difference analysis.


DEVELOPMENTS IN THE LAST DECADE Heijungsetal. 1992 The ’92 guide states the following with regard to the boundary between relevant and irrelevant processes: a boundary should be drawn somewhere to avoid the problem of infinite regression; it should be decided whether such items as capital goods or a staff canteen should be included in a process; the preferred criterion for excluding particular processes is a quantitative estimate of their relative contribution to environmental effects; the costs of maintenance and depreciation may provide another indicator; if these form a substantial part of the product price, the environmental intervention associated with capital goods should not be excluded a priori. In ISO 14041 (1998E) the subject of cut-off is given ample attention


In ISO 14041 (1998E) the subject of cut-off is given ample attention. In clause 5.3.3 ISO 14041 states the following with respect to the definition of initial system boundaries (see text box): The systems boundary defines the unit processes which will be included in the system to be modeled. Ideally, the product system should be modeled in such a manner that inputs and outputs at its boundary are elementary flows. In many cases there will not be sufficient time, data, or resources to conduct such a comprehensive study. Decisions shall be made regarding which unit processes will be modeled by the study and the level of detail to which these unit processes shall be studied. Resources need not be expended on the quantification of such inputs and outputs that will not significantly change the overall conclusions of the study. Decisions shall also be made regarding which releases to the environment shall be evaluated and the level of detail of this evaluation


Decisions shall also be made regarding which releases to the environment shall be evaluated and the level of detail of this evaluation. In many instances those system boundaries defined initially will subsequently be refined on the basis of the outcome of the preliminary work (see 6.4.5). The decision rules used to assist in the choice of inputs and outputs should be clearly understood and described. Further guidance on this process is provided in 5.3.5. Any decisions to omit life cycle stages, processes or data needs shall be clearly stated and The systems boundary defines the unit processes which will be included in the system to be modeled. Ideally, the product system should be modeled in such a manner that inputs and outputs at its boundary are elementary flows. In many cases there will not be sufficient time, data, or resources to conduct such a comprehensive study


In many cases there will not be sufficient time, data, or resources to conduct such a comprehensive study. Decisions shall be made regarding which unit processes will be modeled by the study and the level of detail to which these unit processes shall be studied. Resources need not be expended on the quantification of such inputs and outputs that will not significantly change the overall conclusions of the study. Decisions shall also be made regarding which releases to the environment shall be evaluated and the level of detail of this evaluation. In many instances those system boundaries defined initially will subsequently be refined on the basis of the outcome of the preliminary work (see 6.4.5). The decision rules used to assist in the choice of inputs and outputs should be clearly understood and described. Further guidance on this process is provided in 5.3.5. Any decisions to omit life cycle stages, processes or inputs/outputs shall be clearly stated and justified


Further guidance on this process is provided in 5.3.5. Any decisions to omit life cycle stages, processes or inputs/outputs shall be clearly stated and justified. The criteria used in setting the system boundaries dictate the degree of confidence in ensuring that the results of the study have not been compromised and that the goal of a given study will be met. Several life cycle stages, unit processes and flows should be taken into consideration, e.g.: inputs and outputs in the main manufacturing/processing sequence; distribution/transportation; production and use of fuels, electricity and heat; use and maintenance of products; disposal of process wastes and products; recovery of used products (including reuse, recycling and energy recovery); manufacture of ancillary materials; manufacture, maintenance and decommissioning of capital equipment; additional operations such as lighting and heating; other considerations related to Impact assessment (if any)


It is helpful to describe the system using a process flow diagram showing the unit processes and their interrelationships. Each of the unit processes should be initially described to define: where the unit process begins, in terms of the receipt of raw materials or intermediate products; the nature of the transformations and operations that occur as part of the unit process; and where the unit process ends, in terms of the destination of the intermediate or final products. It should be decided which input and output data should be traced to other product systems, including the decisions about allocation. The system should be described in sufficient detail and clarity to allow another practitioner to duplicate the Inventory analysis. Source: ISO 14041 (1998E); clause 5.3.3. Part 3: Scientific background


In clause 5.3.5 ISO 14041 (1998E) provides more detailed information on possible cut-off rules (see text box): During the scope definition, the initial set of inputs and outputs is selected for the inventory. This process recognizes that it is often not practical to model every input and output into the product system. It is an iterative process to identify the inputs and outputs which should be traced to the environment, i.e. to identify which unit processes producing the inputs or which unit processes receiving the outputs should be included in the product system under study. The initial identification is typically made using available data, and inputs and outputs should be more fully identified after additional data are collected during the course of the study, and then subjected to a sensitivity analysis (see 6.4.5). The criteria and the assumptions on which they are established shall be clearly described


The criteria and the assumptions on which they are established shall be clearly described. The potential effect of the criteria selected on the outcome of the study shall also be assessed and described in the final report. For material inputs, the analysis begins with an initial selection of inputs to be studied. This selection should be based on an identification of the inputs associated with each of the unit processes to be modeled. This effort may be undertaken with data collected from specific sites or from published sources. The goal is to identify the significant inputs associated with each of the unit processes. Several criteria are used in LCA practice to decide which inputs to be studied, including a) mass, b) energy and c) environmental relevance. Making the initial identification of inputs based on mass contribution alone may result in important inputs being omitted from the study


Making the initial identification of inputs based on mass contribution alone may result in important inputs being omitted from the study. Accordingly, energy and environmental relevance should also be used as criteria in this process: a) mass: an appropriate decision, when using mass as a criterion, would require the inclusion in the study of all inputs that cumulatively contribute more than a defined percentage to the mass input of the product system being modeled; b) energy: similarly, an appropriate decision, when using energy as a criterion, would require the inclusion in the study those inputs that cumulatively contribute more than a defined percentage of the product system’s energy inputs; c) environmental relevance: decision on environmental relevance criteria should be made to include inputs that contribute more than an additional defined percentage to the estimated quantity of each individual data category of the product system


For example, if sulfur oxides were selected as a data category, a criterion could be established to include any inputs that contribute more than a predefined percentage to the total sulfur oxide emissions for the product system. These criteria can also be used to identify which outputs should be traced to the environment, i.e. by including final waste treatment processes. Where the study is intended to support a comparative assertion made to the public, the final sensitivity analysis of the inputs and outputs data shall include the mass, energy and environmental relevance criteria, as outlined in this subclause. All of the selected inputs identified by this process should be modeled as elementary flows. Source: ISO 14041 (1998E); clause 5.3.5


All of the selected inputs identified by this process should be modeled as elementary flows. Source: ISO 14041 (1998E); clause 5.3.5. Finally, clause 6.4.5 of ISO 14041 states (see textbox): Reflecting the iterative nature of LCA, decisions regarding the data to be included shall be based on a sensitivity analysis to determine their significance, thereby verifying the initial analysis outlined in 5.3.5. The initial product system boundaries shall be revised as appropriate in accordance with the cut-off criteria established in the scope definition. The sensitivity analysis may result in: exclusion of life cycle stages or unit processes when lack of significance can be shown by the sensitivity analysis; exclusion of inputs and outputs which lack significance to the results of study; inclusion of new unit processes, inputs and outputs that are shown to be significant in the sensitivity analysis. Source: ISO 14041 (1998E); clause 6.4.5


Source: ISO 14041 (1998E); clause 6.4.5. All these requirements are designed "to limit subsequent data handling". Their practicability is limited, however, for the data to be considered for cut-off must first be collected. Further data handling subsequent to data collection rarely poses a problem, in contrast to data collection itself. From the ISO requirements it can be concluded that the cut-off problem is primarily a problem of data availability, with data collection consequently involving disproportionate use of resources. In all cases where data are available, no cut-off should be made. If data are lacking, there is a cut-off problem and this should be duly addressed. Part 3 : Scientific background


In today's LCA studies capital goods are often cut off. Although capital goods can in principle be cut off like any other inputs (or outputs), there is a definition problem, in that agreement is lacking on what exactly constitutes a capital good, under what circumstances. In an LCA on a utility building the building will be the primary focus of the study, while in other studies it will be regarded as a capital good. It would appear more consistent, however, to treat capital goods the same as any other input or output flow. This approach has the key implication that industrial plant and equipment, as capital goods should, in principle, be included within the bounds of every LCA study (i.e. the processes required to build, maintain and decommission them). We therefore recommend that the same rules be applied for the cut-off of capital goods as for any other input or output flow


the processes required to build, maintain and decommission them). We therefore recommend that the same rules be applied for the cut-off of capital goods as for any other input or output flow. In the past decade there have been a number of developments with regard to the issue of cut-off, beyond the terms of ISO. These can be divided into three categories: 1. avoiding cut-off by estimating flows using environmentally extended input-output modeling; 2. avoiding cut-off by estimating flows from similar flows for which data are known; 3. applying cut-off on the basis of predefined criteria. Methods will be discussed below for each category. 1. Avoiding cut-off by estimating flows using environmentally extended input-output modeling Economic input/output analysis (IOA) was developed by Leontief over fifty years ago. IOA proceeds from a so-called transaction table recording monetary flows between individual sectors of industry and the values added within each


IOA proceeds from a so-called transaction table recording monetary flows between individual sectors of industry and the values added within each. In particular, such tables are used to represent the annual sales of each sector (to all others), offset by that sector's overall procurements (from all others). Transaction tables are converted into a matrix of coefficients in which each element of the table is expressed as a proportion of the total monetary output of the sector in question. Each column of this matrix represents the unique input characteristics required to produce the output in question. If it is assumed that coefficients are independent of production volume, the total direct and indirect inputs required for supplying a given external demand can be calculated by solving a system of linear equations. Under this assumption of linearity, the inputs required to meet $1,000 of external demand will be ten times greater than for $ 100 of demand


Under this assumption of linearity, the inputs required to meet $1,000 of external demand will be ten times greater than for $ 100 of demand. Economic input-output models are applied in a variety of fields, including (economic) impact assessment, imputation assessment and energy analysis. Such models can be extended to include environmental data such as emissions and resource use per unit monetary output, on a sector-by-sector basis. The environmental interventions associated with satisfying a particular external demand can then be calculated by multiplying the interventions per unit sectoral output by the sum total of the direct and indirect inputs required for that demand. An environmental input-output database has been developed at Carnegie-Mellon University, using a 1992 US input-output table and 1996 Toxic Releases Inventory (TRI) data (Lave et al., 1995; Hendrickson et al., 1998). Both the data and method were available at a website