Life Cycle Assessment - Aalborg Universitetpeople.plan.aau.dk/~poul/Material/Tools/TSD12.pdf ·...

12

Life Cycle Assessment

Mikkel Thrane and Jannick H Schmidt

The aim of this chapter is to introduce life cycle assessment (LCA) and its application according to the ISO 14040 (2006) and ISO 14044 (2006) stan-dards. The chapter provides the basis for carrying out an LCA and includes tables that can be used as a checklist. The level of sophistication represented by the checklist corresponds to a screening or detailed LCA.

Compared to other methods for environmental assessment, e.g. Environ-mental Impact Assessment (see chapter 19), the most important characteris-tics of LCA are that it applies the life cycle perspective and that the inputs and outputs are ‘translated’ into potential environmental impacts.

The life cycle perspective means that LCA considers the entire life cycle of a product including raw material extraction, processing, distribution (transport), use and final disposal. Apart from providing an overview of the products entire life cycle, it may contribute to avoiding potential burden shifting between different stages of product life cycle (ISO 14040 2006 p. 6).

Potential environmental impacts may include the contribution to impact categories such as global warming, ozone depletion, nutrient enrichment, and acidification etc. LCA does typically not include social and economical impacts (ISO 14040 2006 p. Vii) but methods are being developed to address these two important dimensions of sustainability as well (Weidema 2006, Norris 2006). This chapter will only address the environmental dimension, but an integrated or separate assessment of the other dimensions is relevant to avoid burden shifting in this dimension as well.

If we consider an LCA of a ‘single product’, LCA makes it possible to elucidate where the environmental impacts occur in the lifecycle, how im-portant they are, and which processes or substances they are related to. But LCA can also be used to ‘compare’ the environmental impact potential from two or more products. LCAs of single products and comparative studies are

206 · Life Cycle Assessment both relevant for development of cleaner products (and services). As an ex-ample, car designers can use LCA to compare the environmental impact from alternative design solutions such as the use of light weight (but energy intensive) materials – versus heavier (but less energy intensive) materials.

Introduction to LCA History The roots of LCA go back to the late 1960s and early 1970s where environ-mental studies applying the life cycle perspective were used to estimate the environmental burden for beverage containers by the Coca Cola Company. A British researcher, Ian Bousted, used similar approaches in the 1970s to estimate the total energy used to produce a number of different packaging materials. Still, it was not until the late 1980s that LCA gained momentum (Jensen et al. 1997).

The first guidelines for LCA were published in 1993. The guidelines were termed the ‘Code of Practice’ and developed by a working group in the Society of Environmental Toxicology and Chemistry (SETAC). The guide-line has been replaced with standards developed by the International Organi-zation for Standardization in the period 1997 to 2000 (ISO 14040-43). The standards have been revised in 2006, and reduced to two documents ISO 14040 (principles) and ISO 14044 (requirements and guidelines). Hence, the ISO standards have replaced guidelines from SETAC and are much more detailed – partly to reduce the risk of misuse – but the ‘Code of Practice’ was actually more ambitious with respect to improvement assessment, which is not included as a requirement in the ISO standards. Key concepts and definitions In short, LCA is a tool used to assess the potential environmental impacts from a product (or a service) from raw material extraction to final disposal (ISO 14040 2006 p. 3). The following definitions are based on ISO 14040 (2006). Unit processes, exchanges, and elementary flows The assessment is based on a compilation of the elementary flows related to inputs (e.g. materials, energy, transport, chemicals, and other exchanges such as land use) and outputs (e.g. emissions to air and water or solid waste) from the unit processes that compose the product system being analysed.

Thrane and Schmidt · 207

A ‘unit process’ is the smallest portion of a product system for which in-put and output data are collected (e.g. a process line or a factory).

A ‘product system’ is a collection of unit processes (with elementary and product flows) that perform one or more defined functions, that models the life cycle of a product.

’Elementary’ flows are material or energy flows from or to the environ-ment without any previous (or further) human transformation. An example of an elementary flow entering the system could be coal from a coal mine. An example of an elementary flow leaving the system could be emissions of SO2 from a coal fired power plant.

Figure 1 provides a conceptual overview of a product system including unit processes, inputs, outputs, and elementary flows. Each of the boxes (grey and white) may be composed by several unit processes.

Figure 1: Conceptual overview of a product system, unit processes, inputs and outputs and elementary flows. Inspired by ISO 14040 (2006 p. 10). Apart from inputs and outputs (exchanges) that are directly related to the immediate product chain (shaded boxes), data should be obtained for inputs and outputs for other unit processes illustrated with white boxes. This in-cludes production of various materials, energy, transport and chemicals as well as possible recycling/reuse processes and waste or wastewater treat-ment. Databases can be usefull for this purpose, but this will be elaborated later.

Extraction of raw materials

Manufacturing/Processing

Distribution

Use

Disposal

Other exchanges (e.g. radiation)

Materials

Energy and transport

Chemicals

Other(e.g. land)

Input flows(exchange)

Output flows(exchange)

Air emissions

Water emissions

Solid emissions

Waste treatment

Elementary flowTo nature

(Environmental exchange)

Elementary flowfrom nature

(Environmental exchange)

System Boundary

Reuse orrecycling

Intermediate product flows

208 · Life Cycle Assessment LCIA and the impact chain Based on the inventory of exchanges and ultimately elementary flows, it is possible to assess the potential environmental impact through calculations carried out as part of the phase ‘life cycle impact assessment’ in short LCIA. The result of the LCIA is a number of category indicator results e.g. an indi-cator for the contribution to global warming, eutrophication, and acidifica-tion. This is further elaborated in the section ‘Life cycle Impact Assessment’ later in this chapter. Hence, in ISO language LCA is: “..a compilation and evaluation of the input, outputs and the potential envi-ronmental impacts of a product system throughout its life cycle” (ISO 14040 2006 p. 2). As it appears from the definition, LCA cannot measure the absolute or pre-cise impacts, but only ‘potential’ impacts. The reason is that the impacts that eventually materialize depend on variables such as the level of exposure, and sensitivity of the receiving environment (ecosystems, humans etc.) in the area affected. In other words, there are a number of physical, chemical, and biological processes that links the results from the inventory to the category indicator or the category endpoints. This is also termed the environmental mechanism, see figure 2.

Figure 2: The environmental mechanism from elementary flows (environ-mental exchange) to damage on end-point level, inspired by Hauschild and Potting (2003 p. 19) The environmental mechanism links an environmental exchange to category indicators (midpoints) and category end-points. An example of an environ-mental mechanism where human health is involved could be the emission of CFC gases, which causes a depletion of the ozone layer in the stratosphere (mid-point). Later this will cause increased levels of radiation (also mid-point) that eventually results in a certain number of people being affected by skin diseases (end-point). The amount of CFC that enters the atmosphere depends on the fate of the substance. The number of affected people depend on their exposure to the sun, and the seriousness eventually depend on peo-ples sensitivity to ultra violet radiation (dark versus light skin colour, amount of sun block etc.).

Emission Fate Exposure Sensitivity of recie-ving environment

Mid-point End-pointEnvironmental Exchange

Damage to human health, ecosystems or resources .


LCA methodologies are being developed to include more variables, but LCA will always be a model of the world (Udo de Haes et al. 2002). In rela-tion to figure 3, it is worth mentioning that the level of uncertainty in the models tends to increase going from left to right (towards the end-point level), while the level of uncertainty in interpretation will decrease (Hauschild and Potting 2003). Tools To be able to carry out an LCA it is necessary or at least convenient to have certain tools within reach. First of all, it is necessary to have the ISO 14040:2006 and ISO 14044:2006 standards. They serve as the overall framework for carrying out the LCA, the ‘cookbook’.

Secondly, it is a good idea to have a database with information about in-puts and outputs for a large number of standard processes, materials, energy products, and chemicals.

Thirdly, it is necessary to choose a method for the LCIA, which trans-forms the input and output data to environmental impact potentials. There exist several LCIA methods that are often developed in a national context, but UNEP is now in the process of developing an international LCIA method.

Finally, it is convenient with a PC software tool to handle the calcula-tions. In PC tools such as SimaPro or Gabi, the user can choose between a variety of databases and LCIA methods, and the software can help analyzing the results. So there are four important tools – the ISO standard, a method (or several methods) for impact assessment (LCIA), a database, and a PC software tool, see figure 3. Figure 3: Four important tools to carry out an LCA.

210 · Life Cycle Assessment The LCIA calculations in chapter 13 are based on the Danish EDIP method (Environmental Design of Industrial Products). The applied PC software is the Dutch ‘Simapro 7’ and the applied databases include ETH, Buwal and LCAfood. The LCA method and the databases are both available in the PC software.

Methodological overview This section provides a quick overview of the four phases of an LCA and discusses how different levels of detail can be applied. The content of the four phases will be elaborated further in the remaining part of the chapter. Framework According to the ISO 14040 standard a LCA study includes four phases, as illustrated in figure 4 (ISO 14040 2006). Figure 4: Phases in a LCA and examples of application areas. Inspired by ISO 14040 (2006). In the goal and scope phase, the LCA-practitioner formulates and specifies the goal and scope of study in relation to the intended application. The object of study is described in terms of a functional unit (explained later). An over-view of relevant processes and the methodology applied is also described here. The system boundary, and therefore also the results, depends on the purpose of the LCA study.

1) Goal andscope

definition

3) ImpactAssessment

(LCIA)

2) Inventoryanalysis (LCI) 4) Interpretation

Life Cycle Assessment framework Application areas

Product developmentand improvement

Strategic planning

Marketing

Public policy-making

Other


The Inventory involves modelling of the product system, data collection, as well as description and verification of data. The data must be related to the functional unit defined in the goal and scope definition. Data can be pre-sented in tables and some interpretations can be made already at this stage. The results of the inventory is an LCI which provides information about all inputs and outputs in the form of elementary flow to and from the environ-ment from all the unit processes involved in the study.

The impact assessment phase is aimed at evaluating the contribution to impact categories such as global warming, acidification etc. The first step is termed characterization. Here, impact potentials are calculated based on the LCI results. The next steps are normalization and weighting, but these are both voluntary. Normalization provides a basis for comparing different types of environmental impact categories (all impacts get the same unit). Weight-ing implies assigning a weighting factor to each impact category depending on the relative importance. The methodology will be further elaborated later.

The Interpretation is basically the conclusion on the study, but besides a presentation of the key results it must also include a critical reflection about the study, uncertainty, sensitivity and methodological choices. An iterative process The double arrows in figure 4 illustrate that LCA is an iterative process, where changes in various choices at different phases occur continuously as the LCA practitioner gradually becomes wiser and more focused. For exam-ple, it may appear that the impact category ‘land use’ turns out to be much more important after the ‘impact assessment’ phase than initially assumed. This may lead to inclusion of this impact category in the ‘Goal and scope’ phase, and a reconsideration of the data collection in the ‘Inventory’ phase where new and better data for land use aspects might be required. Another example would be the appearance of new processes during the LCI, which require that the practitioner returns to the ‘Goal and scope’ phase and in-cludes these new processes in the scope. Finally, one could also imagine a situation where the LCI and the LCIA phases provide completely new knowledge about production patterns and production restrictions, in a way that undermines the whole purpose of the study, which then has to be re-considered. Application areas As indicated in the right side of figure 4, LCA has many application areas. The standard highlights product development and improvement, strategic planning, marketing, and public policy making. This seems to highlight the

212 · Life Cycle Assessment application at company and policy level, but it can also be applied at sector level, by NGOs, consumer organizations etc. Specific versus generic purposes When LCA is used at company level it is often for specific products (e.g. product documentation or development), while the application at societal level often has a more generic character (e.g. ‘criteria’ for eco-labelling or societal action plans and legislation).

The distinction between specific and generic purposes is important – for two reasons. First of all, the potential social and economical consequences increase when the purpose becomes more generic (e.g. societal action plans and legislation opposed to product development). This means that the de-mands to certainty, transparency and documentation tend to increase when the purpose has a generic character (opposite to an LCA specific product from a specific company).

Secondly, the consequences of e.g. political decisions tend to influence industrial systems many years ahead opposed to purposes of a more specific character. This means that requirements to projections and future scenarios in generic LCAs tend to increase. Documentation versus strategic purposes Another important distinction is whether the intended application is for, - documentation (e.g. hot-spot identification or product declaration) for

example as response to actual or potential demands or acusations from customers, competitors, NGOs, authorities etc, or

- for strategic purposes (strategy for product innovation, or environmental policies).

Again, the requirements to sophistication and future scenarios become larger when the purpose is strategic opposite to purposes of a more operational character such as documentation. Historical or current data are often enough for the latter, while the LCA should reflect future technologies and future markets in situations where the purpose of the LCA is strategic and has con-sequences for the next 10-20 years.

Basically, data and methods applied in the LCA should reflect the type of decision as well as the decision horizon. Conceptual, screening, and detailed LCA As mentioned, different ambition levels can be chosen when carrying an LCA. The simplest approach is a ‘conceptual LCA’, which is a qualitative assessment of the environmental aspects from cradle to grave. Besides basic

Thrane and Schmidt · 213 environmental knowledge, this method does not require knowledge about LCA methodology and can be performed in a matter of hours. Chapter 14 on EcoDesign includes several examples of assessment tools that can be used for a conceptual LCA, e.g. the ABC scheme. See also Wenzel (1998).

A screening LCA require quantitative data, and the practitioner must have knowledge about LCA methodology. It can be performed in a matter of days or months, and is often characterized by the use of existing data (e.g. from literature sources and databases). It is also possible to limit the data collec-tion to energy consumption - while the impact assessment may be limited to address only a few impact categories (e.g. only global warming).

The detailed LCA includes a comprehensive data collection, a high level of data quality and a larger number of impact categories. In addition, the detailed LCA must include an analysis of the uncertainty, sensitivity and consistency of the analysis. These definitions are based on Jerlang et al. (2001), but it is often difficult to make a clear distinction between a screen-ing and detailed LCA in practise. Considering the number of justifications and considerations that are required in the ISO 14044 standard, an LCA made according the requirements in the standard often have a character of detailed LCA, in practise.

The LCA technique can also be applied in studies that only addresses parts of the life cycle e.g. the first stages in the life cycle from raw material acquisition to processing (cradle-to-gate), a single life cycle stage such as processing (gate-to-gate) or studies which only addresses e.g. waste man-agement systems or components of a product. The ISO standard points out that such studies shouldn’t be referred to as LCAs, but instead studies that apply the LCA technique (ISO 14040 2006). Level of sophistication and purpose of study As explained in the previous section about specific versus generic purposes, there is a relationship between the intended application and the level of so-phistication. Generally, the requirements to sophistication (e.g. certainty, transparency and documentation), must be higher when the purpose is to support decision with large social and economical consequences (Wenzel 1998).

It is also worth to distinguish between situations where the LCA only is intended to be used internally and situations where the results are to be com-municated externally (to a third party). In the first case, there are no specific requirements to the level of sophistication while the requirements are equivalent to a screening or detailed LCA for studies that are to be commu-nicated externally. The latter requires a level of sophistication equivalent to a screening or detailed LCA. Specific requirements to the study report for

214 · Life Cycle Assessment LCA that are communicated externally are described in the ISO standard (ISO 14044 2006 p. 36).

A number of additional requirement apply to comparative assertion that are intended to be disclosed to the public. A comparative assertion is an en-vironmental claim about the superiority of one product versus a competing product that performs the same function (ISO 14044, 2006 p. 8). Examples of additional requirements are a detailed description of data quality require-ments, obtained data quality, critical review process, evaluation of the com-pleteness of the LCIA, and results of the uncertainty and sensitivity analysis. This level is equivalent to a detailed LCA. Further details about require-ments and content of study report are described in ISO 14044 (2006 p. 38).

Phase 1: Goal and scope The main elements of the ‘Goal and Scope’ phase are illustrated in table 1. Unless other references are specifically mentioned the main reference used throughout the following sections is ISO 14044 (2006). Phase one: Goal and scope definition Goal of the study: • Intended application • Stakeholders • Product/service alternatives

Scope: • Functional unit • System boundary • Cut-off criteria • Co-product allocation

LCIA methodology (scope): • Impact categories • Method for impact assessment • Key assumptions and exceptions

Data collection and treatment (scope): • Demands to data quality • Plans for critical review

Table 1: The main elements of the goal and scope phase according to the ISO 14044 standard. It is worth noticing that the ISO 14040 standard mainly serves to provide an overview of LCA, while ISO 14044 contains specific requirements to those carrying out an LCA. The word shall only appears one time in the ISO 14040 standard, namely in the phrase where it is stated that - when perform-ing an LCA the requirements of the ISO 14044 standard shall apply.

The different elements of the goal and scope phase will be elaborated in the following.

Thrane and Schmidt · 215 Goal of the study Intended application As mentioned earlier, LCA can be applied for documentation purposes or purposes of a more strategic character such as product development or im-provement. The need for documentation can be a respond to demands from customers or authorities, but it can also be a part of a more proactive green-marketing effort. The latter may include eco-labelling (e.g. environmental product declaration) or LCA information used to show customers that certain products or technologies represent an environmental benefit (e.g. LCA documentation of wind turbines)

LCA has the largest potential to contribute to environmental improve-ments when it is used proactively and strategically. As mentioned earlier this ideally requires that the LCA include future scenarios as strategic decisions may have consequences that materializes 5, 10 or maybe 20 years from now (Wenzel 1998, Weidema 2003). Stakeholders It is important to describe the context in which the LCA is performed. Rele-vant questions to answer are: who are the target audience? Who is the com-missioner? Who has paid for the study? What are the most important stake-holders? To whom are the LCA results to be communicated? This type of information should not be omitted as it prevents that is used in the wrong contexts or misuse in other ways. Also, it generally improves the transpar-ency and credibility of the study. Product alternatives It is possible to distinguish between two types of LCA studies, a ‘single product’ and a ‘comparative’ LCA. The first is used to perform an environ-mental assessment of a single product and is also known as a ‘hot-spot’ as-sessment. It is called a hot-spot assessment because it is used to determine where in the product’s life cycle the impact potentials are most significant. At company level this can be important knowledge in the search for eco-friendly suppliers, but also the search for improvement options, and for envi-ronmental action plans (that minimize the risk of sub-optimization or shift of burden problems).

A comparative LCA is used to compare the environmental performance of two or more products. This is especially relevant in product development, where the new and greener products often are compared to a reference prod-uct. It is difficult to determine if something is truly ‘greener’ without a refer-ence product, and comparative LCA studies are therefore often an essential part of a continuous improvement cycle. One of the advantages of compara-

216 · Life Cycle Assessment tive studies is that it is possible to disregard life cycle stages or processes that are similar for the products being compared. A number of special re-quirements should be fulfilled, if the LCA is used for a comparative asser-tion intended to be disclosed to the public. Scope Functional unit The object of the study must also be carefully described in terms of a func-tional – especially in comparative studies. A functional unit is defined as a quantified performance of a product system used as a reference unit in a LCA. The functional unit may reflect: - A quantity (amount, volume or size) - A duration period - Qualitative characteristics. If an LCA practitioner wants to compare two types of wall paint (paint A versus paint B) it is important to know how much the two buckets contain (quantity), how much time the two types of paint can last without fading or flaking off (duration period), and to which an extend they are comparable with respect to gloss, repellent qualities etc. (qualitative aspects). In certain cases it is also relevant to consider if the two products are comparable with respect to non-market relevant qualities as well. This could be the heat deliv-ered from electronic equipment, which may substitute other heat sources or which may increase the need for cooling depending on the context.

Based on the considerations of quantity, duration period and qualitative aspects, it is possible to establish the reference flow which is used as the basis for all calculations. In the paint example, the reference flow could be 1½ bucket of paint A versus 2 buckets of paint B. This reflects that paint A is a more durable paint. The focus on function ensures fair comparisons, but it may also be an eye-opener in the search for product alternatives. The pur-pose of the functional unit is to ensure a fair comparison of product alterna-tives. However, it also serves the purpose to address ‘functionality’ and in-stead of merely a physical products, which in itself may promote creative thinking and smarter ways of providing the function. System boundaries (two different approaches) Apart from describing the functional unit, the goal and scope, should address the overall approach used to establish the system boundaries.


The system boundary determines which unit processes that are included in the LCA, and must reflect the goal of the study. In recent years, two ap-proaches to system delimitation have emerged. These are often referred to as ‘consequential’ modelling and ‘attributional’ modelling.

Economic causalities versus biophysical flows: The main differences is that ‘consequential’ modelling uses a market oriented approach to identify the affected processes, while the ‘attributional’ modelling identifies proc-esses to be included by analysing the bio-physical flows in the (current) sup-ply chain. This is best illustrated by an example: In a study of the environ-mental impact from consumption of 1 litre of milk in EU, attributional mod-elling would suggest the inclusion of the producers in the current supply of milk, ending up with the agriculture stage (the cow). At first, this seems to the most logical solution.

However, quota or other production constrains exists in many product chains, and these limitations should always be considered according to the consequential approach. In EU, milk production is limited by quota. Hence, an extra demand of milk doesn’t result in additional production of milk. In practise, less milk becomes available for milk powder production. Therefore, the cow must be excluded from the product system. Instead, the production of milk powder should be included. In consequential modelling, the cow should only be included to illustrate the impact in a scenario where milk quotas are removed - or in a comparative LCA that shows the environmental consequences of choosing e.g. organic instead of conventional milk (Wei-dema 2003). This shows a close link between the ‘purpose’ of the study and the modelling of the ‘product system’.

Average versus actually affected technologies: Another difference be-tween the two approaches concerns the use of average data. In attributional modelling electricity consumption is typically modelled as a mix (average) of existing producers of electricity in proportion with their current supply. But, in consequential modelling, it is sought to model the technologies most likely to be affected by a change in demand of electricity. In the case of Denmark this is most likely coal or gas. The argument is that windmills and other renewable energy sources can be disregarded because their production is independent of small scale changes in demand. Electricity consumption is not the only example of this. Many products are traded on markets with no specific ties to producers, and while attributional modelling often suggest averages as default, consequential modelling suggest identification of the processes that are most likely to be affected. The latter requires more infor-mation about functioning of markets but Weidema (2003) provides a list of default assumption that makes this job relatively easy (Weidema 2003).

Use of system expansion: There are a few other differences between at-tributional and consequential modelling – e.g. in relation to handling of co-

218 · Life Cycle Assessment product allocation where allocation by arbitrary parameters is systematically avoided in consequential LCA, but this requires further explanations, and is therefore treated separately later in this chapter.

Choice of approach: The remaining question is what modelling approach that is most correct? The ISO 14040 standard is somewhat imprecise in its recommendations, but annex 2 stresses that:

“…the products and processes studied in an LCA are those affected by the decision that the LCA intends to support. Some applications may not appear to immediately address improvements, such as LCA to be used for education or information about the product life cycle. However, as soon as such infor-mation is applied in practice, it is used in an improvement context. There-fore, special care is necessary to ensure that the information is applicable to the context in which it is likely to be applied” (ISO 14040 2006). This formulation seems to advocate for consequential modelling, but part of the LCA community (especially in other countries than Denmark) currently interprets is as an argument for attributional LCA, as well (Christiansen 2007). In Denmark, consequential modelling has been chosen as the offi-cially preferred method (Hansen 2004), and hence the following descriptions will therefore reflect consequential modelling. It should be noted that the number of advocates for attributional modelling is decreasing, also interna-tionally (Christensen 2007). System boundaries (consequential modelling – how?) According to Weidema (2003) the fundamental rule that should be applied to all methodological choices in LCA is: “…that the data used must reflect as far as possible the ‘processes actually affected’ as a consequence of the decision that the specific life cycle assessment is intended to support” (Weidema 2003). The decision always include alternatives e.g. whether to ‘produce’ product A or B, whether to ‘buy’ product A or B, alternative ways to produce energy or alternative ways to collect and treat waste - at company or societal level. There are many possibilities but we always investigate the environmental consequences of a potential change - even in a study of a single product (a hot-spots assessment). Here, we compare a situation where we produce product A with a situation where we don’t produce product A.

The point of departure of any modelling is the decision that we try to model. In a company perspective, the affected processes could be upstream as well as down stream in the product chain. For upstream processes, it must

Thrane and Schmidt · 219 be established who are affected by a change in demand (for intermediate products, materials, energy, chemicals etc). If there are specific ties to a cer-tain supplier, and if the given supplier is able to respond to a change in de-mand (unconstrained supplier), the supplier will be affected. Eco-labelling schemes may in some cases establish such ties to certain suppliers.

However, if the supplier is constrained by quota or other restrictions, it is other suppliers on the market that will be affected. The challenge here is to identify the suppliers that are most likely to be affected on the given market. Also, for some product categories, specific ties between producers and cus-tomers, seldom exists. One example is electricity as decribed above, but it can also be food products such as coffee, sugar, soy meal etc. In this case, we have the same problem - who is affected?

Weidema (2003) presents a procedure for identification of the affected or ‘marginal’ suppliers. First it has to be established whether a local, regional or even global market is affected. However, we also have to consider which type of suppliers that are affected on these markets. For small-scale changes, which often occur at company level LCAs, the method suggests that the most competitive producers (presumably high level of technology) are af-fected on expanding markets, because they are most able to respond to a change in demand. The opposite happens if the market is in regress, because the company then affects the capacity that is being taken out of the market. Information about market trends and the regional scope of markets for a number of products is available in Weidema (2003) together with a number of default assumptions that can be used for these kinds of assessments. This may appear a little confusing, but it is a pivotal part of the LCA which should not be underestimated. Cut-off criteria As part of the system delimitation it is also necessary to consider and de-scribe the level of detail which is applied in the data collection. There should obviously be a limit to how detailed the data collection should be and to how many tiers back we should track each flows.

This can be handled through cut-off criteria. One possibility is to use mass as a criterion. All inputs could be included that cumulatively contribute to more than a certain percentage of the total mass input to the product sys-tem. Similar for energy, it is possible to define a percentage of the accumu-lated energy input to the system. However, for chemicals it is important to consider the environmental significance as well.

Apart from these considerations, the practitioner should decide whether to include capital goods (equipment) which typically include buildings, ma-chines, roads and other types of infrastructure. In an LCA of milk products capital goods could be construction of farm buildings and construction of

220 · Life Cycle Assessment machines used to pump process and store the milk, roads used for distribu-tion etc. The ISO standard suggests that capital goods are treated as an inte-grated part of the product system and that the same cut-off criteria should apply for capital goods as any other process. More recent databases e.g. Ecoinvent, include capital goods, but separate estimates should still be ob-tained for processes where databases are not used. Due to time limitations this is often omitted in practise, but some researchers argue that LCAs may give false results if capital goods are omitted (Frischknecht et al. 2007). Hence, it is a good idea to make some kind of estimates – or to make sepa-rate qualitative or semi qualitative assessments.

Generally, it should be sought to use data that reflect the same level of detail in all life cycle stages and product types investigated. PC tools such as SimaPro include a function termed ‘cut-off’ which can be adjusted at any level. If the cut-off function is set to 0.1% the software leaves out (graphi-cally) all processes that have a smaller contribution to the impact potential than 0.1%. This function can be used to avoid that a more detailed data col-lection for some products or some life cycle stages result in a biased result.

It should be mentioned that so-called IO LCA databases makes it possible to avoid that parts of the product system are excluded. Hence, by using IO LCA databases (separately or in combination with the process LCA) it is possible to avoid cut-off entirely. IO LCA databases are not the focus on the present chapter, but are briefly discussed in the section ’Inventory’. Co-product allocation Processes often yield more than one product. In other words, we typically have one (or several) determining products (main products) and one or sev-eral dependent products (co-products) leaving a unit process or a product system. The challenge is to allocate the inputs and outputs to the different products. The method used to handle this should be clearly stated and ex-plained. Phase 1 (Goal and scope) should include the overall principles, but as the actual allocation takes place in the inventory phase, this subject is further explained in the following section (phase 2: Inventory). Methodology (scope) Apart from the system boundaries and cut-off criteria, the ISO standard re-quires that the choice of impact categories and impact assessment method is described in the goal and scope phase. The following provides a brief intro-duction to some of the choice that should be made.

Thrane and Schmidt · 221 Types of impacts The ISO requirements to the choice of impact types (or categories) are that: “The selection of impact categories shall reflect a comprehensive set of en-vironmental issues related to the product system being studied, taking the goal and scope into consideration” (ISO 14044 2006). Hence, the practitioner shall make a conscious choice of relevant impact categories – not just chose a default list of impact types or a list of impact categories that promotes good or bad characteristics of a given product. The impact categories included in the EDIP 97 method (black) are listed in table 2, together with a list of examples of other impact categories that also could be considered (grey): Environmental impact Resources consumption Other related impacts

Glo

ba

• Global warming (GWP) • Ozone depletion (ODP)

• Depletion of non-renewable resources

Reg

iona

l

• Photoch. ozone formation • Acidification • Nutrient enrichment • Ecological toxicity • Human toxicity

• Depletion of renewable re-sources at regional scale

• Radiation

Loca

l

• Ecological toxicity (acute) • Human toxicity (acute) • Waste • Damage to the seabed • Land use

• As above but local scale

• Occupational H&S • Animal welfare • Noise • Odour • Accidents • Aesthetics • Radiation

Table 2: Environmental impact categories included in the EDIP 97 method (black) and examples of other relevant impact categories of which some are included in other LCIA methods. Separate qualitative (or semi-quantitative assessments) can be carried out for impacts that are not included in the standard LCIA methods, but which are considered potentially important. This could be land use, seafloor impacts, occupational health and safety, animal welfare etc. Social and economical aspects are not addressed by table 2 (apart from OH&S). Method for LCIA In practise LCA practitioners often choose a method for LCIA, which is developed in the country where the LCA is carried out. In Denmark we tend to use the Danish EDIP method, where the latest version is termed EDIP

222 · Life Cycle Assessment 2003. However, it can be an advantage to use several methods for verifica-tion purposes and to cover more impact categories.

Methods for LCIA are categorised in two groups. The first group uses a so- called ‘midpoint’ approach as these methods stop somewhere in the envi-ronmental mechanism between environmental exchanges and endpoints (see figure 3). The other group uses a so-called ‘end-point’ approach as they model the potential damage on value items such as trees etc.

The Danish EDIP method represents a mid-point approach, which models the first part of the environmental mechanism (from left to right in figure 3). The first method known as ‘EDIP 97’ considered some aspects of fate, but did not model exposure and sensitivity of the receiving environment. A more recent version ‘EDIP 2003’ does take exposure and sensitivity into account (at least to some degree) and distinguishes between emissions occurring in different geographical regions (spatial differentiation). Another method that applies the mid-point method is the Dutch CML II baseline developed at the Leiden University – see Guinée et al. (2001).

An example of a method applying the ‘end-point’ approach is the Dutch EcoIndicator 99 (Goodkoep and Spriensma 2000). This method models the influence on the end-points and goes one step further as it aggregates the end-points in three categories termed areas of protection (AoP). The three AoP areas express damage to ecosystems, human health and resources. Ecoindicator 99 uses a top-down approach. This implies that epidemiologi-cal data (such as the number of people that die or get sick from particle pol-lution per year) are used to estimate the harmfulness of various emissions. More recently methods have been developed which combines the advantages of previous mid-point and end-point methods, namely Impact 2002+ and the Stepwise 2006 (Humbert et al. 2005, Weidema et al. 2007).

The rest of the chapter focuses on the midpoint approach - in particular EDIP 97, which is also applied in the following case chapter. Key assumptions and exceptions In the goal and scope phase, it can also be a good idea to explicitly mention key assumptions and exceptions. This can be related to certain methods for co-product allocation, the system delimitation in general, exemption of cer-tain life cycle stages or impact categories, representativeness of data etc. Data quality requirements (scope) Finally, phase 1 (Goal and scope) should include a description of data types, data sources and requirements to data quality. The data quality goals can be reduced to five key parameters according to Weidema (1998). The first three are:

Thrane and Schmidt · 223 - Temporal scope - Geographical scope - Technological scope The temporal scope concerns the age of the data as well as the period for which data have been collected. Geographical scope concern the area for which data should be collected to satisfy the goal of the study, and the tech-nological scope deals with the type of technology that should be addressed. The latter may concern the level of technology (old, average or cutting edge) but also concern the question of whether the data should reflect a mix of technologies or specific technologies e.g. the marginal technology. Both the geographical and technological scope will depend on the result of the system delimitation and which processes that is included in the product system be-ing analyzed. The temporal scope strongly depends on the goal of study. Studies that concerns decisions with future implication should reflect this by using updated data and future scenarios. The two last parameters are: - Reliability - Completeness. Reliability concerns how the data have been obtained and verified. Measured data are better than estimated data and verification by mass and energy bal-ances is always a good idea to ensure high data quality.

Completeness concerns if parts of the data are missing as well as the sta-tistical representativeness of the data. Depending on the goal of scope it is important that the data cover a sufficient number of sites and for adequate time periods. It should be emphasized that the ISO standard uses some of these terms slightly differently.

The assessment of obtained data quality in the inventory should be made in the context of the initial quality requirements described in the goal and scope phase. Hence, it is only possible to assess the ‘obtained’ data quality later in the study e.g. during inventory. Data quality assessment can not be made absolute - it will always be relative to the requirements and the degree of accordance with these. Demands to verification of results The last part of the goal and scope definition is to state the requirements to verification of the LCA.

For a comparative study, it is necessary that the equivalence of the sys-tems being compared is evaluated before the interpretation. The systems being compared must have the same functional unit and similar methodo-logical considerations concerning performance, system boundaries, data

224 · Life Cycle Assessment quality, allocation procedures, and impact assessment etc. Important differ-ences should be reported (ISO 14044 2006).

Studies that are intended to be used for a comparative assertion intended to be disclosed to the public, must be evaluated through a critical review with the participation of interested parties. Under all circumstances, it should be mentioned if a critical review should be conducted and how. A critical review is basically a process intended to ensure consistency between an LCA and the recommendations and requirements in the ISO 14040 and 14044 standards (ISO 14044 2006).

Phase 2: Life Cycle Inventory The second phase ‘Life cycle inventory analysis’ (LCI) is probably the most time consuming phase. The main elements of the Inventory are data collec-tion, calculations and finally handling of co-product allocation - see table 3. Phase two: Life Cycle Inventory analysis (LCI) Data collection: • Process descriptions • Collection of quantitative and qualitative

data

Calculations and co-product allocation: • Relate data to the functional unit (or refer-

ence flow) • Data quality assessment • Presentation and discussion of results

(optional) • Handling of co-product allocation

Table 3: The main elements of the second phase ‘Life Cycle Inventory’. The different elements of this phase will be elaborated in the following. Data collection A good point of departure for the data collection is a process diagram, which shows the included unit processes and their interrelationships. The data col-lection may involve the collection of both quantitative and qualitative data. The data sources can be empirical studies, literature references, databases, expert judgement etc. In countries like Denmark, possible references are also environmental approvals and green accounts. The data can be actual meas-ured data, which is often the most accurate, but can also be based on mass and energy balances, estimates, etc.

It is always a good idea to verify the accuracy of the data (including qualitative data) by comparing different data sources. This is also termed data triangulation, and ensures a higher degree of certainty.


The present chapter focuses on so called ‘process LCA’ where data are collected for a number of specific processes that compose a specific product system. This can also be perceived as a bottom up approach to LCA. But another approach has emerged, which represent a top-down approach, namely the so-called ‘input output LCA’ or just ‘IO LCA’. Apart from a brief description, this approach will not be elaborated in this chapter, but IO LCA should be mentioned because data from IO databases can be used in process LCAs as well. This is briefly discussed in the following. Input output databases for LCA Environmental Input-Output (IO) databases are based on national economi-cal and environmental statistics. IO LCA has the advantage compared to process LCA that it covers the entire economy. It is therefore not necessary to apply cut-off in IO LCA (Weidema et al. 2005).

IO LCA data express the environmental impact for a number of product categories in specific countries or regions of the world.

This type of data is more aggregated compared to traditional ‘process’ LCA data, and even in the most detailed IO database (for US) there are only around 500 product categories. Hence, it is not possible (without modifica-tions) to make an LCA of a very specific type of product.

In the context of process LCA, IO LCA data are useful for a number of purposes e.g.: - verify LCA results, - fill out data gaps - provide an estimate of hot-spots and where to focus in data collection - provide an estimate of the significance and type of processes that are

omitted in a process LCAs IO LCA, and hybrid LCA that seeks to combine the advantages of IO and process LCA will not be further explained here, but there exist several arti-cles and books on the subject, e.g. Rebitzer et al (2004). An environmental IO study of the Danish economy is available in Weidema et al. (2005). Ex-planations are also available on the homepage www.lca-net.co Calculations After the data have been collected, they should be validated and related to the functional unit. The validation should ideally take place during the data collection and must ensure that the data quality requirements are meet. Vali-dation can be performed by mass balances, energy balances, or other types of checks such as comparisons with data from similar processes.

226 · Life Cycle Assessment

After this, the data must be related to the functional unit or the reference flow which expresses the functional unit. If the raw data are expressed as flows per year, and if the reference flow is 1 kg of product A, it is obviously necessary to divide the total exchange for the whole year with the amount of product A that is produced in the same period measured in kg. If the func-tional unit is 1 kg ‘consumed’ product at the use stage, it can be necessary to include considerations of product losses along the product chain and the result may be that the functional unit at the start of the life cycle is consid-erably larger than 1 kg (sometime you need 3 kg caught fish to product 1 kg of fish filet). Still, it is a good idea to relate the exchanges to the same prod-uct quantity e.g. 1 kg at each life cycle stage, and then compensate for prod-uct losses later. Data quality assessment The inventory could also include a data quality assessment where the ob-tained data are compared to the initial data quality requirements. Important variations from the data quality goals should be reported. In a LCA screen-ing, this will typically only include a brief description and assessment of the most problematic data sets. A detailed pedigree diagram procedure for data quality assessment for detailed LCA is available in Weidema (1998).

In the data collection, there will often be a significant number of ex-changes that are not expressed as elementary flows. An example is energy flows where the units often are kWh or MJ instead of inputs of coal and out-puts of various emissions such as CO2, SO2 etc. In most PC tools applied today there are a number of databases that can be used to determine the ele-mentary flows for a significant number of processes and products. Still, there are specific products and processes that are not available and here it is up to the LCA practitioner to collect data further back in the life cycle and express the flows as elementary flows. In cases where this is omitted for various reasons, it should be reported and discussed to which extend this omission is believed to influence the results. This can be done in the Interpretation phase.

The result of the LCI is a complete inventory of elementary flows, which is used as the input for the LCIA phase. Data which are not expressed as elementary flows (environmental exchanges) will not be treated at the LCIA step. Presentation and discussion of results at LCI level It is important to mention that it possible to make a separate presentation and discussion of the results from the inventory as a supplement to the LCIA. This has several advantages. It becomes easier to reproduce the LCIA, it shows the results on a very early stage with the least possible amount of data

Thrane and Schmidt · 227 processing and value choices, and makes comparison possible with a wider range of products – also products which are not represented by an LCA.

One way to present the results at this level is to organize the data in four groups related to Materials, Energy, Chemicals and Other aspects. In Den-mark, data collections based on these four data categories are referred to as the MECO principle (Wenzel et al. 1997). Handling of co product allocation One of the key challenges in the inventory phase is that unit processes often deliver more than one product. If we have product A, B and C from ‘one’ process - then how should we determine the exchanges for product A only, if A is the determining product that we are interested in?

That a product is determining is the same as saying that this process will be affected by a change in demand for this particular product. The determin-ing product often reflects the largest revenue of the co-products. A more detailed procedure for identification of determining and dependent co-products is available in Weidema (2001).

An example of a determining product is herring filet from a fish factory, while the dependent product in this case would be skin, head and bone that are used for animal fodder. The question is how much of the exchanges that should be ‘allocated’ to herring filet and fish waste, respectively.

The ISO standard suggests that allocation should be handled according the following stepwise procedure: 1. Divide the unit process to be allocated into two or more sub-processes

and collect the input and output data related to these sub-processes sepa-rately

2. Expand the product system to include the additional functions related to the co-products (use system expansion)

3. Divide the inputs and outputs in a way that reflects the underlying physi-cal relationship

4. Allocate the inputs and outputs in a way that that reflects other relation-ships e.g. economical relationships

Ad 1) If it is possible, a technical subdivision of the processes is the best solution, however, this presupposes that the exchanges to each of these proc-esses can be measured separately, which is often difficult.

Ad 2) If option 1 is impossible, it is suggested to use system expansion. Here we pose the question: Which activities are affected by a change in sup-ply of one or several dependent products, resulting from a change in output of the determining product? In the case of herring processing, we ask the

228 · Life Cycle Assessment question: Which products are affected by a change in the output of fish waste (skin, head and bone). In Thrane (2004) it is argued that most of the fish offal is processed into animal fodder, which substitutes soy protein. Hence, the system is expanded to include the processing of fish waste into animal fodder as well as the avoided exchanges related to the avoided production of soy protein. As for any other part of our system delimitation, we should de-termine the marginal producers for soy protein because it is traded on a global market. According to Weidema (2003) the marginal producers are the most competitive in Argentine or Brazil because the market trend is increas-ing.

When the affected activities are identified it remains to establish how much soy protein one kg of fish waste substitutes. This type of assessment can be based on the protein content. Detailed guidelines of how co-product allocation can be systematically avoided are available in Weidema (2003).

Ad 3) If system expansion is impossible, the ISO standard suggests that the inputs and outputs are partitioned according the physical relationships.

If the ratio between the determining and dependent products can be ad-justed, it is sometimes possible to estimate the exchanges related to each product by measuring the change in exchanges as a function of an increase of product A - while the production of product B is held constant (or oppo-site).

For truck transport of different insulation materials, the partitioning of in-puts and outputs should be handled according to the volume of the different products, because it is the volume of the products that determines the load capacity. However, for transport of bricks or steel, weight is more likely to express the underlying physical relationship between exchanges and prod-ucts. There are many other examples e.g. allocation according to volume for cold storing, allocation according to surface area for surface treatment proc-esses etc.

Ad 4) As the last option, the ISO standard mentions other types of alloca-tion based on ‘other relationships’ e.g. relative value (or revenue). This is also termed economical allocation. According to Jerlang et al. (2004) this is not an opening for the use of any kind of arbitrary allocation method as the ‘relationship’ should be justified. When several allocation procedures seem applicable, the ISO standard requires that a sensitivity analysis is conducted.

Allocation procedures for reuse and recycling have not been separately described in this chapter, nor have allocation in relation to multiple-input. However, the ISO 14044 standard has a special section about this subject – see ISO 14044 (2006 p. 22). The subject is also discussed in Weidema (2003).

The consequential approach to system delimitation applied in this chap-ter, has two main characteristics: it seeks to model affected or ‘marginal’

Thrane and Schmidt · 229 technologies and co-product allocation is ‘systematically’ handled by techni-cal subdivision, system expansion or physical relationship (option 1, 2 or 3). In Weidema (2003) it is argued that option 3 can be perceived as a special case of option 2. System expansion is being perceived as synonymous with consequential modelling, but in fact the ISO standards has always given a high priority to system expansion.

Phase 3: Life Cycle Impact Assessment The third phase ‘life cycle impact assessment’ (LCIA) includes characteriza-tion and valuation (normalization and weighting). This phase takes you from elementary flows to potential impacts. The most important elements in this phase are listed in table 4. Phase three: Life Cycle Impact Assessment (LCIA) Characterization: • Classification (Assignment of LCI results

to impact categories) • Characterization (Conversion of LCI re-

sults to common units and the aggregation within the impact category)

Valuation: • Normalization (Calculation of the magni-

tude of indicator results relative to refer-ence information).

• Weighting (The normalized values are multiplied with a weighting factor)

• Additional LCIA data quality analysis

Table 4: The main elements of Life Cycle Impact Assessment. The different elements of this phase will be elaborated in the following. It should be noticed that the selection of impact types is described in the sec-tion ‘Goal and Scope’. Characterization The characterization results show how much various processes, life cycle stages or entire product systems, contribute to different impact categories. In the first step of characterization, the terminated exchanges are assigned to the different impact categories in question. Each of the impact categories is represented by a category indicator, which may vary depending on the LCIA method (the characterization model). For global warming, most characteriza-tion models use CO2 as the category indicator. Still, CO2 and CH4 do not contribute equally to global warming, and are therefore multiplied with so-called equivalency factor – see figure 5.


Figure 5: Interrelationships between elementary flows (environmental ex-changes), impact category and category indicator / impact potentials. In other words, the different emissions are multiplied with an equivalence factor depending on their relative contribution to the different impact catego-ries. For non-flow related impacts, there is used the same line of thinking, but obviously we cannot talk about emissions for impact categories such as land use or seabed impacts from off shore activities or fishing.

Hence, the impact potential for each impact category is simply the aggre-gated value of the elementary flows multiplied by the respective equivalence factors.

It is important to notice that we only consider impact potentials. Whether the potentials materialize will depend on a long series of other factors such as precise fate, exposure, background concentrations, recipient sensitivity etc.

The fact that LCA only models the impact ‘potentials’ can be perceived as an inherent precautionary principle in the method, as we assume that all potential impacts materializes. On the other hand, LCA methods do not con-sider synergetic effects. For human health and safety, it is well known that the health impacts caused by the presence of two or more substances can be larger than the mathematical sum of the health impacts of each substance individually (Riisgaard 2002).

Even though there are large uncertainties, the results based on characteri-zation are probably the most reliable compared to results, which are normal-ized and weighted. Still, it can be necessary to use normalization and weight-ing. In a hot-spot analysis this could be the case if the processing stage is most important with respect to global warming, while the use stage is more important with respect to ozone depletion and eutrophication. Here, it is not possible to determine which of the two stages that represent the most signifi-cant impact potential on an aggregated level.

Category indicator kg CO2-equivalents

kg NO3-equivalents

kg SO2-equivalents

Env. exchangesCO2

CH4

NOx

SO2

Impact categoryGlobal warming

Eutrophication

Acidification

125

1.350.7

1

Characterization factor Impact potential

Thrane and Schmidt · 231 Normalization (optional) If necessary, a valuation is carried out. In the Danish EDIP method this in-cludes normalization and weighting. It should be noted that the valuation (i.e. normalization and weighting) is an optional element according to the ISO standard 14044. This is partly due to the lack of international consensus on a preferred method.

The results from the characterization can be difficult to interpret. An ex-ample of characterization results could be 2⋅104 gram CO2 equivalents (for global warming) and 156 gram SO2 equivalents (for acidification). These figures are not possible to compare and difficult to relate to.

Normalization provides a basis for comparing impact categories by divid-ing the scores to ‘something’ we can relate to - a normalization reference. In the EDIP method the applied normalization reference is an average person’s annual contribution to each impact category. It is recommended that the ref-erence reflects an average global citizen for global impacts such as global warming. For impacts of a local and regional scale such as eutrophication, it is suggested that the normalization reference should reflect the contribution from an average citizen within the area of impact (this could be approxi-mated by the country or a specific region where the emission occurs). Due to the lack of data, average Danish citizens have previously been applied as default normalization reference for non-global impact categories in the EDIP method.

In EDIP 2003 (Hauschild and Potting 2003) data are provided that make it possible to apply normalization references for specific countries and re-cipient types depending on where the emission occurs.

After the normalization step all impact categories are measured by the same unit, namely Person equivalents (PE) or micro person equivalents (mPE). If a product contributes with 5mPE to global warming, it means that the impact potential is equivalent to 0.5% of a global citizen’s average con-tribution to global warming. A comparison after the normalization phase would implicitly reflect that all impact categories are perceived as being equally important or serious. This is weighting in itself. Thus weighting can not be avoided in a valuation. Weighting (optional) The weighting step is an evaluation of the relative importance or seriousness of each impact category. The weighting factors can be established in several different ways, just as normalization. In the Danish EDIP method, the weighting step is based on the distance-to-target method, where political reduction targets are determining for the size of the weighting factors. If the

232 · Life Cycle Assessment political reduction target for green house gases are 20% over the next 10 years, the weighting factor is 1/0.8 = 1.25. The weighting factors for EDIP 97 reflect the reduction targets for the period 1990-2000. In the EDIP method, the weighted are measured in terms of weighted micro person equivalents (WmPE).

The distance-to-target method is chosen in EDIP, because it is assessed to be a method, which reflects the ‘scientific’ seriousness of the impact type in question as well as the ‘perceived’ seriousness of the impacts in the society. Also, it should be stressed that the method could be interpreted as an alterna-tive type of normalization where the normalization factor is the target for normalized person equivalents instead of the status quo situation (Wenzel et al. 1997). ISO 14044 stresses that weighting shall not be used for compara-tive assertions disclosed to the public, but as the weighting procedure in EDIP can be perceived as normalization as well, it is possible to argue that the EDIP results not are addressed by this limitation (Jerlang et al. 2001 p. 77).

The normalized and weighted results represent data that are relatively easy to understand, even for non-experts. Still, the results are influenced by a number of methodological choices and assumptions with political implica-tions. More precisely, it is possible to argue for other methodological choices during normalization and weighting, and that this could lead to significantly different results in many cases. Thus, normalized and weighted results should be interpreted with caution.

The EDIP method includes an impact category termed depletion of non-renewable resources. It is important to stress that the weighting method for this impact category is fundamentally different from other impact categories in EDIP. The normalization reference is the average resource consumption for a global citizen per year, but the weighting step is not performed accord-ing to the distance-to-target method. Instead the weighting factor reflects the remaining resources available per person. Thus, the weighted result can be obtained by dividing the resource consumption of a given material, related to the product in question, with the remaining resources available per person of the given resource.

It should also be worth mentioning that there exist a number of other weighting methods than ‘distance-to-target’ and ‘supply horizon’ for re-sources. This includes monetary weighting and panel weighting.

Monetary weighting often uses the willingness to pay (WtP) approach, and the weighting factor ideally reflects individuals or society’s willingness to pay. Still, the method is difficult to apply in practice. As an example, wood has a market value (use value), but it also has an indirect value i.e. the recreation value of the forest (indirect use value). Furthermore, we have a number of non-use values such as the value of knowing the forest is there

Thrane and Schmidt · 233 (existence value), the value of knowing that future generations can use it (bequest value) and the value of knowing that we have the opportunity to use it - option use value. (Udo de Haes et al. 2002). Obviously, estimations of all these parameters will reflect considerable uncertainties.

Panel weighting means that a group of people (environmental experts, experts from other sciences, stakeholders, lay people or a mix) discuss and reach consensus on the relative importance of the impact categories (or areas of protection) in question. (Udo de Haes et al. 2002).

A weighting method termed ‘grouping’ is also a possibility, but this is a very simple approach where the impact potential is separated in a number of groups depending on their properties. One way to perform grouping is to categorize the impacts according to the scale of impact (local, regional and global impact potentials), the area of protection (environment, humans or resources) or the other parameters. An example of grouping of impact cate-gories is given in table 2. One of the disadvantages of grouping is that the results can be difficult to interpret. Additional LCIA data quality analysis Depending on the accuracy and detail needed to fulfil the goal of the study it may be necessary use techniques to better understand the significance, un-certainty and sensitivity of the results.

A gravity analysis can be used to obtain information of which processes and substances that contribute most the different indicator results. Most LCA PC tools have a number of build in functions which can be used for this.

The uncertainty analysis is a procedure to establish the uncertainties of the data and to predict how different methodological assumption influences the results.

Uncertainties of data can be expressed as ranges or probability distribu-tions. If there is performed a data quality assessment in phase two (the In-ventory phase), this is obviously a valuable input for the assessment. Meth-odological uncertainty can be related to system delimitation (including co-product allocation), characterization, normalization and weighting.

Uncertainty can typically not be established objectively, and is often based on knowledge from experts or rules of thumb. A rule of thumb that is often applied to estimate data uncertainty ranges, is to divide with two and multiply with two. In a detailed LCA, it is a good idea to present the result of the uncertainty analysis in a table, which provides an overview of processes, uncertainty ranges, omissions, and other methodological aspects.

The sensitivity analysis is a procedure to determine how changes in data and methodological choices affect the results (ISO 14044 2006 p. 30). Some PC tools such as SimaPro, are able to make Monte-Carlo simulations, which

234 · Life Cycle Assessment is a mathematical method used to ‘add’ uncertainties. This makes it possible to provide the LCIA results with uncertainty intervals. However, it should be noticed that the intervals are fictive in the sense that great uncertainties are involved already when the basic data uncertainties are estimated. The sensi-tivity analysis is a very important part of the LCA. It can be a good idea to test how the results changes according to a number of extreme choices. If the initial analysis shows that product A is environmentally preferably compared to product B, it is a good idea to test how sensitive this conclusion is to a number of extreme choices that favour product B instead.

A number of special requirements apply LCIA intended to be used in comparative assertions intended to be disclosed to the public – see ISO 14044 (2006 p. 31).

Phase 4: Interpretation The Interpretation is the fourth phase of the LCA. This phase includes pres-entation of the most important results but it also includes a critical reflection about the study, uncertainty, sensitivity and not least the methodological choices. The most important steps in the interpretation are listed in table 5. Part four: Interpretation Identification of significant issues: • Presentation of key LCI & LCIA results

Evaluation: • Completeness-, sensitivity- and consis-

tency check

Results and conclusion: • Conclusion and recommendation based on

results as well as uncertainty related to data and methodology.

Table 5: The most important steps in the interpretation phase. The different elements of this phase will be elaborated in the following. Significant issues The objective of this phase is to structure and present the key results from the LCI and LCIA phase – in accordance with goal and scope definition. This structured information may have been carried out at earlier phases of the LCA. In this case, it is only necessary to draw the main conclusions.

The structuring approach depends on the goal and scope of the study. If the purpose was to establish which life cycle stages that represented the lar-ges impact potentials, it is obvious to make a differentiation according to the individual life cycle stages (contribution analysis). It is also possible to dif-ferentiate between different groups of processes such as transport, energy,

Thrane and Schmidt · 235 cooling etc (dominance analysis). If the goal and scope is to determine pos-sibilities for action, it would also be relevant to distinguish between different degrees of management influence (influence analysis). There are many pos-sibilities and interested readers can find more information and examples in ISO 14044 (2006 p. 44-49) Evaluation The evaluation includes a discussion or analysis of aspects of consistency, completeness and sensitivity. The evaluation element can be carried out si-multaneously with the identification of significant issues.

The completeness check should make sure that all the required informa-tion has been available. This includes information about data types, data gaps, exchanges from all relevant processes and life cycle stages etc. The ISO standard includes examples of matrixes that can be used to present this information – see ISO 14044 (2006 p. 50).

The sensitivity analysis includes an analysis of the sensitivity to data un-certainty and methodological uncertainty or choices. It is recommended to perform the sensitivity analysis in the LCIA phase (see ‘Additional LCIA data quality analysis’), but the main results and conclusions can be discussed here in the evaluation.

Finally, the consistency check has the purpose to determine whether the assumptions, methods and data are consistently applied along the life cycle or between products that are being compared. In this regard, it is essential to establish whether the methodological choices, as well as data types and qual-ity, lead to any bias with respect to the importance of the individual life cy-cle stages or the importance of product A instead of product B in a compara-tive LCA. Results and conclusion Finally, the key results should be described and discussed on the basis of the evaluation. This will lead to the conclusion which addresses the purpose of study and the answers to the key problem described in phase 1 (Goal and scope definition).

It is important to mention the assumptions under which the conclusions are made - explicitly. As for other types of conclusions it is relevant to ad-dress the most important uncertainties, and explicitly stress if other conclu-sions could be obtained by other methodological approaches. It is also a good idea to describe ‘for which purposes’ the results can be applied and explicitly mention where the study results cannot be directly applied. This will help preventing misuse of the study and the results.


Perspectives As it has been described, LCA is an ideal tool for environmental assessment of products and services. LCA can be used together with environmental management systems (EMS) described in chapter 5, as well as a tool for Ecodesign that will be described in chapter (14) – and is generally applicable for decision making at both company and societal level. Addressing the right issues The LCA practitioner must be aware that LCA is not performed in a vac-uum, but serves as a tool for decision-making and prioritisation. Therefore, it is a good idea to involve decision makers and other stakeholders before and during the study. Also, it is necessary to address the relevant products (e.g. food content instead of merely the packaging, transport systems and engine types instead of merely car parts etc).

It can be tempting to address products or environmental aspects for which the data availability is high. This can sometimes be relevant in a learning process, but the LCA practitioner should keep in mind that the overall goal is greener products and life styles, and not greater precision in calculations addressing issues of minor importance. Thus, it is necessary to distinguish between ‘need to know’ and ‘nice to know’.

Other major challenges are to address relevant impact categories and life cycle stages including the use stage, which often turns out to be important (Weidema 2000). Finally it is worth mentioning price differences. In com-parative LCA’s price differences are seldom taken into account, but it is obvious that money save on buying product A (instead of B), will provide the consumer with additional purchasing power that eventually will be spending on other goods. This will generate additional impacts which the cheaper alternative should be accounted for. The subject (marginal spending) has not been discussed in the present chapter, but a thorough discussion is available in Thiesen et al (2006) Future developments of the LCA In the future, we will probably see LCA tools that integrate considerations about socio-economical aspects. At least one example has already emerged - the ‘Stepwise 2006’ method proposed by Bo Weidema and colleagues. This method combines the elements from EDIP2003 and IMPACT2002 (partly based on previous work with Ecoindicator), but includes new impact catego-ries, such as injuries. Furthermore the method is prepared for integration of social and economic impacts. The methods models beyond the end-point level, and may provide the user with one single score (Weidema et al. 2007). Hence, it must be expected that the development of LCIA methods with

Thrane and Schmidt · 237 social and economical impact pathways will continue. This will enable more comprehensive assessments, where we ideally avoid shift of burden prob-lems where reduced environmental impacts (in one area) may happened on the expense of negative social consequences due to economical pathways in another area. It should be stressed, however, that many scientist are worried about this development – especially the integration of methods. This is partly because of the inherent uncertainties, but also because it may hide value choices and close further debates. Political (Value) choices in LCA It must be stressed that LCA studies may produce results with significant uncertainty and based on embedded political choices, especially during sys-tem delimitation, normalization and weighting. Furthermore, there are a number of ‘hidden’ assumptions that represent a kind of hidden weighting. One example is that the environmental impacts that occur in 100 or 200 years are considered equally important as the impacts that will occur imme-diately. Hence, the future (and future generations) is considered ‘as’ impor-tant as the present. In other words the LCIA methods do not suggest or apply any discounting – much in contrast to economical assessment methods such as cost benefit assessment. This subject if further elaborated in Hellweg et al (2003). Another characteristic is that impacts on humans that occur in India are considered equally important to impacts on humans in Denmark. Hence, lives are considered equally important independent of race, country, and religion. This is something that may seem evident to most – but also some-thing that is seldom reflected in political decision making in practise.

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