In the illustrative case on window frames in Chap. 39, the method recommended for characterisation by the International Life Cycle Data system (ILCD) is chosen as life cycle impact assessment method (EC-JRC 2011), and all impact categories covered by the method are included in the study. In common LCA practice, a number of category indicators, based on specific characterisation models is combined into predefined sets or methods, often referred to as life cycle impact assessment methods or simply LCIA methods (EC-JRC 2011; Hauschild et al. 2013), available in LCA software under names such as ReCiPe, CML, TRACI, EDIP, LIME, IMPACT 2002+, etc. However, with an increasing number of LCIA methods and indicators available, the task of choosing one requires a tangible effort from the practitioner to understand the main characteristics of these methods and to keep up-to-date with the developments in the field of LCIA


A qualitative and comparative overview of the main characteristics of current LCIA methods can be found in Chap. 40 of the Annex of this book. 10.2.1.1 How to Choose an LCIA Method? A number of LCIA methods have been published since the first one appeared in 1984. Figure 10.1 shows the most common methodologies published since 2000 Life Cycle Impact Assessment

Hauschild et al., LCA: Theory and Practice


that all meet the requirements of ISO 14044. A more detailed overview of these methods can be found in Chap. 40. When selecting an LCIA method, the requirements, recommendations, external and internal factors and constraints discussed above all need to be considered. This leads to a number of questions and criteria that should be answered in order to systematically identify the most suitable one. Here is a non-exhaustive list of relevant questions to address: • Which impact categories (or environmental problems) do I need to cover and can I justify those that I am excluding? • In which region does my life cycle (or its most contributing processes) take place? • Do I need midpoint or endpoint assessment, or both? • Which elementary flows do I need to characterise? • Are there any recommendations from relevant organisations that can help me choose? • How easily can the units of the impact categories be interpreted (e.g


absolute units, equivalents, monetary terms, etc.)? • How well is the method documented? • How easily can the results (units, aggregation into specific indicator groups, etc.) be communicated? • Do I need to apply normalisation and if yes for which reference system (in most cases it is not recommendable to mix characterisation and normalisation factors LCIMPACT (EU) ILCD (EU) LIME 3.0 (Japan) EcoScarcity2013 (Switzerland) EcoScarcity2006 (Switzerland) EDIP2003 (Denmark) TRACI (USA) LUCAS (Canada) CML 2001 (IA) (Netherlands) Jepix (Japan) LIME (Japan) ReCiPe (Netherlands) LIME 2.0 (Japan) TRACI 2.0 (USA) EPS2000 (Sweden) 2002 2003 2004 2009 2010 2011 2012 Eco-Indicator 99 (Netherlands) IMPACT World+ (Canada, USA, Denmark, France, Switzerland) IMPACT 2002+ (Switzerland) Fig. 10.1 LCIA methods published since 2000 with country/region of origin in brackets. Dotted arrows represent methodology updates (Rosenbaum 2017) R.K. Rosenbaum et al.


from different LCIA methods due to the difference in characterisation modelling, units, numerical values, etc..)? • When was the method published and have there been important scientific advances in the meantime? • Do I have the resources/data availability to apply a regionalised methodology (providing more precise results)? • Do I need to quantify the uncertainty of both LCI and LCIA and does the LCIA method support that? ISO 14040/14044 by principle do not provide any recommendations about which LCIA method should be used, but some organisations do recommend the use of a specific LCIA method or parts of it. The European Commission has established specific recommendations for midpoint and endpoint impact categories by systematically comparing and evaluating all relevant existing approaches per category, leading to the recommendation of the best available approach (EC-JRC 2011)


This effort resulted in a set of characterisation factors, which is directly available in all major LCA software as the ILCD method. Some methods with a stronger national focus are recommended by national governmental bodies for use in their respective country, such as LIME in Japan, or TRACI in the US. Given the amount of LCIA methods available and the amount of time required to stay informed about them, it may be tempting to essentially stick to the method(s) that the LCA practitioner knows best or has used for a long time, or that was recommended by a colleague, or simply choosing a method requested by the client to allow comparison with results from previous studies. It is however beneficial to apply a more systematic approach to LCIA method selection that in combination with the LCIA method comparison in Chap. 40 allows to determine the relevant selection questions and criteria, thus optimising the interpretability and robustness of the results of the study


40 allows to determine the relevant selection questions and criteria, thus optimising the interpretability and robustness of the results of the study. The following properties are compared in Chap. 40 per impact category and for both midpoint and endpoint LCIA methods: • Aspects/diseases/ecosystems (which kinds of impacts) that are considered • Characterisation model used • Selected central details about fate, exposure, effect and damage modelling • Reliance on marginal or average indicator • Emission compartments considered • Time horizon considered • Geographical region modelled • Level of spatial differentiation considered • Number of elementary flows covered • Unit of the indicator Not all of these properties may be of equal relevance for choosing an LCIA method for each practitioner or study, but they are identified here as relevant and fact-based properties. Life Cycle Impact Assessment


Further details on the selection of impact categories, category indicators and characterisation models can be found in Rosenbaum (2017) and Hauschild and Huijbregts (2015). 10.2.2 Classification In this step, the elementary flows of the LCI are assigned to the impact categories to which they contribute; for example an emission of CO2 into air is assigned to climate change or the consumption of water to the water use impact category, respectively. This is not without difficulty because some of the emitted substances can have multiple impacts in two modes: • In parallel: a substance has several simultaneous impacts, such as SO2 which causes acidification and is toxic to humans when inhaled. • In series: a substance has an adverse effect which itself becomes the cause of something else, such as SO2 which causes acidification, which then may mobilise heavy metals in soil which are toxic to humans and ecosystems


This step requires considerable understanding and expert knowledge of environmental impacts and is therefore typically being handled automatically by LCA software (using expert-based, pre-programmed classification tables) and not a task that the LCA practitioner needs to undertake. 10.2.3 Characterisation In this step, all elementary flows in the LCI are assessed according to the degree to which they contribute to an impact


10.2.3 Characterisation In this step, all elementary flows in the LCI are assessed according to the degree to which they contribute to an impact. To this end, all elementary flows E, classified within a specific impact category c (representing an environmental issue of concern), are multiplied by their respective characterisation factor CF and summed over all relevant interventions i (emissions or resource extractions) resulting in an impact score IS for the environmental impact category (expressed in a specific unit equal for all elementary flows within the same impact category): ISc 1⁄4 X i CFi  Ei ð Þ ð10:1Þ For each impact category, the indicator results are summed to determine the overall results for the category. In the following sections, the general principles of how CFs are calculated and interpreted will be discussed. In order to provide a better understanding of what CFs in each impact category represent and how they are derived, Sects


In order to provide a better understanding of what CFs in each impact category represent and how they are derived, Sects. 10.6–10.16 will, for each impact category, explain the R.K. Rosenbaum et al.


corresponding (1) problem observed, (2) principal environmental mechanism, (3) main causes and (4) most widely used characterisation models. 10.2.3.1 What Is a Characterisation Factor? A characterisation factor (CF) represents the contribution per quantity of an elementary flow to a specific environmental impact (category). It is calculated using (scientifically valid and quantitative) models of the environmental mechanism representing as realistically as possible the cause–effect chain of events leading to effects (impacts) on the environment for all elementary flows which contribute to this impact. The unit of a CF is the same for all elementary flows within an impact category. It is defined by the characterisation model developers and may express the impacts directly in absolute terms (e.g. number of disease cases/unit toxic emission) or indirectly through relating them to the impact of a reference elementary flow (e.g. CO2-equivalents/unit emission of greenhouse gases)


number of disease cases/unit toxic emission) or indirectly through relating them to the impact of a reference elementary flow (e.g. CO2-equivalents/unit emission of greenhouse gases). 10.2.3.2 How Is It Calculated? The modelling of a characterisation factor involves the use of different models and parameters and is typically conducted by experts for a particular impact category and its underlying impact pathway or environmental mechanism. Various assumptions and methodological choices are involved and this may affect the output as reflected in the differences in results that may be observed for the same impact category when applying different LCIA methods. This must be considered when interpreting the result of the LCIA phase. The first step when establishing an impact category is the observation of an adverse effect of concern in the environment, leading to the conclusion that we need to consider such effects in the context of decisions towards more sustainable developments


Once accepted as an effect of concern, the focus will be on how to characterise (quantify) the observed effect in the framework of LCA. The basis and starting point of any characterisation model is always the establishment of a model for the environmental mechanism represented by a cause–effect chain. Its starting point is always the environmental intervention (represented by elementary flows), essentially distinguishing two types based on the direction of the relevant elementary flows between technosphere and ecosphere: • An emission into the environment (=elementary flow from the technosphere to the ecosphere), or • A resource extraction from the environment (=elementary flow from the ecosphere to the technosphere). Life Cycle Impact Assessment


10.2.3.3 Emission-Related Impacts For the first type, an emission into the environment, the principal cause–effect chain may be divided into the following main steps: • Emission: into air, water or soil (for some product systems also other compartments may be relevant such as groundwater, indoor air, etc.) • Fate: environmental processes causing transport, distribution and transformation of the emitted substance in the environment. Depending on the physical and chemical properties of the substance and the local conditions at the site of emission, a substance may be transferred between different environmental compartments, be transported over long distances by wind or flowing water, and be undergoing degradation and transformation into other molecules and chemical species. • Exposure: contact of the substance from the environment to a sensitive target like animals and plants, entire ecosystems (freshwater, marine, terrestrial or aerial) or humans


• Exposure: contact of the substance from the environment to a sensitive target like animals and plants, entire ecosystems (freshwater, marine, terrestrial or aerial) or humans. Exposure may involve processes like inhalation of air, ingestion of food and water or dermal contact via skin and other surfaces. • Effects: observed adverse effects in the sensitive target after exposure to the substance, e.g. increase in the number of disease cases (ranging from reversible temporary problems to irreversible permanent problems and death) per unit intake in a human population or number of species affected (e.g. by disease, behaviour, immobility, reproduction, death, etc.) after exposure of an ecosystem • Damage: distinguishing the severity of observed effects by quantifying the fraction of species potentially disappearing from an ecosystem, or for human health by giving more weight to death and irreversible permanent problems (e.g


reduced mobility or dysfunctional organs) than to reversible temporary problems (e.g. a skin rash or headache) These steps together constitute the environmental mechanism of the impact category and their specific features will vary depending on the impact category we are looking at. 10.2.3.4 Extraction-Related Impacts For the second type of elementary flow, a resource extraction from the environment, the principal cause–effect chain may comprise some or all of the following main steps (with significant simplifications possible for some resources where not all steps may be relevant, e.g. minerals): • Extraction or use: of minerals, crude oil, water or soil, etc. • Fate: (physical) changes to local conditions in the environment, e.g. soil organic carbon content, soil permeability, groundwater level, soil albedo, release of stored carbon, etc. R.K. Rosenbaum et al.


• Exposure: change in available quantity, quality or functionality of a resource and potential competition among several users (human or ecosystems, with different degrees of ability to adapt and/or compensate), e.g. habitat loss, dehydration stress, soil biotic productivity, etc. • Effects: adverse effects on directly affected users that are unable to adapt or compensate (e.g. diseases due to lower water quality, migration or death of species due to lack of water or habitat, malnutrition, etc.) and contributions to other impact pathways (e.g


global warming due to change in soil albedo or released soil carbon) • Damage: distinguishing the severity of observed effects by quantifying the reduction of biodiversity, or human health of a population affected (although not yet common practice, this may even go as far as including social effects such as war on water access) This mechanism will have specific features and may vary significantly between impact categories, but the principle remains valid for all extraction-related impact categories, currently being: • Land Use (affecting biotic productivity, aquifer recharge, carbon sequestration, albedo, erosion, mechanical and chemical filtration capacity, biodiversity, etc.) • Water use (affecting human health, aquatic ecosystems, terrestrial ecosystems) • Abiotic resource use (fossil and mineral) affecting the future availability of the non-renewable abiotic resources • Biotic resource use (e.g


fishing or wood logging) affecting the future availability of the renewable biotic resources and the ecosystems from which they are harvested. 10.2.3.5 The Impact Indicator The starting point of the environmental mechanism is set by an environmental intervention in the form of an elementary flow in the LCI, and the contribution from the LCI flow is measured by the ability to affect an indicator for the impact category which is selected along the cause–effect chain of the impact category. Apart from the feasibility of modelling the indicator, this selection should be guided by the environmental relevance of the indicator. For example, there is limited relevance in choosing human exposure to the substance as an indicator for its human health impacts, because even if a substance is taken in by a population (i.e


For example, there is limited relevance in choosing human exposure to the substance as an indicator for its human health impacts, because even if a substance is taken in by a population (i.e. exposure can be observed and quantified), it might not cause any health effect due to a low toxicity of the substance, and this would be ignored if a purely exposure-based indicator was chosen. In general, the further down the cause–effect chain an indicator is chosen, the more environmental relevance (and meaning) it will have. However, at the same time the level of model and parameter uncertainty may increase further down the cause–effect chain, while measurability decreases (and hence the possibility to evaluate and check the result against observations that can be Life Cycle Impact Assessment


directly linked to the original cause). Contrary to a frequent misconception, that does not mean that the total uncertainty (i.e. including all its sources, not just parameter and model uncertainty) of an indicator increases when going further down the cause–effect chain, because the increase in parameter and model uncertainty is compensated by an increase in environmental relevance. If the latter is low (as is the case for indicators placed early in the cause–effect chain) the relationship of an indicator to an environmental issue is assumed but not modelled and thus hypothetical and therefore uncertain. A detailed discussion on these issues can be found in Chap. 11. To select the impact indicator, developers must therefore strike a compromise between choosing an indicator of impact: 1. Early in the environmental mechanism, giving a more measurable (e.g


11. To select the impact indicator, developers must therefore strike a compromise between choosing an indicator of impact: 1. Early in the environmental mechanism, giving a more measurable (e.g. in the lab) result but with less environmental relevance and more remote from the concerns directly observable in the environment Versus 2. 2. Downstream in the environmental mechanism, giving more relevant but hardly verifiable information (e.g. degraded ecosystems, affected human lifetime) This has led to the establishment of two different types of impact categories, applying indicators on two different levels of the environmental mechanism: midpoint impact indicators (representing option 1 from above) and endpoint impact indicators (representing option 2)


10.2.3.6 Midpoint Impact Indicators When the impact assessment is based on midpoint impact indicators, the classification gathers the inventory results into groups of substance flows that have the ability to contribute to the same environmental effect in preparation for a more detailed assessment of potential impacts of the environmental interventions, applying the characterisation factors that have been developed for the concerned impact category. For example, all elementary flows of substances that may have a carcinogenic effect on humans will be classified in the same midpoint category called “toxic carcinogen” and the characterisation will calculate their contribution to this impact


Typical (and emerging) midpoint categories (including respective sub-categories/impact pathways) are: • Climate change • Stratospheric ozone depletion • Acidification (terrestrial, freshwater) • Eutrophication (terrestrial, freshwater, marine) • Photochemical ozone formation • Ecotoxicity (terrestrial, freshwater, marine) • Human toxicity (cancer, non-cancer) R.K. Rosenbaum et al.


• Particulate matter formation • Ionising radiation (human health, aquatic and terrestrial ecosystems) • Land Use (biotic productivity, aquifer recharge, carbon sequestration, albedo, erosion, mechanical and chemical filtration capacity, biodiversity) • Water use (human health, aquatic ecosystems, terrestrial ecosystems, ecosystem services) • Abiotic resource use (fossil and mineral) • Biotic resource use (e.g. fishing or wood logging) • Noise • Pathogens The characterisation at midpoint level of the elementary flows in the life cycle inventory results in a collection of midpoint impact indicator scores, jointly referred to as the characterised impact profile of the product system at midpoint level. This profile may be reported as the result of the life cycle impact assessment, and it may also serve as preparation for the characterisation of impacts at endpoint level


This profile may be reported as the result of the life cycle impact assessment, and it may also serve as preparation for the characterisation of impacts at endpoint level. 10.2.3.7 Endpoint Impact Indicators Additional modelling elements are used to expand or link midpoint indicators to one or more endpoint indicator (sometimes also referred to as damage or severity). These endpoint indicators are representative of different topics or “Areas of Protection” (AoP) that “defend” our interests as a society with regards to human health, ecosystems or planetary life support functions including ecosystem services and resources, for example. As discussed, endpoint indicators are chosen further down the cause–effect chain of the environmental mechanism closer to or at the very endpoint of the chains—the Areas of Protection. The numerous different midpoint indicators therefore all contribute to a relatively small set of endpoint indicators as can be observed in Fig. 10.2


The numerous different midpoint indicators therefore all contribute to a relatively small set of endpoint indicators as can be observed in Fig. 10.2. Although, different distinctions are possible and exist, typical endpoint indicators are: • Human health • Ecosystem quality or natural environment • Natural resources and ecosystem services Therefore, the same list of impact categories as for midpoint indicators (see above) applies to endpoint indicators but with a further distinction regarding which of the three AoPs are affected (e.g. climate change usually has one midpoint indicator, but two endpoint indicators, one for human health and one for ecosystem quality—see Fig. 10.2). All endpoint indicators for the same AoP have a common unit and can be summed up to an aggregated impact score per AoP (assuming equal or different weighting of each endpoint indicator)


10.2). All endpoint indicators for the same AoP have a common unit and can be summed up to an aggregated impact score per AoP (assuming equal or different weighting of each endpoint indicator). Before aggregation, however, an environmental profile on endpoint level is as detailed as on midpoint level and allows for a contribution analysis of impact categories per AoP (e.g. which impact category contributes the most to human health impacts). On midpoint level, Life Cycle Impact Assessment


aggregation and contribution analysis of multiple impact categories are only possible after applying normalisation and weighting. There are three frequent misconceptions related to that: 1. Misconception: Applying normalisation, weighting and aggregation to midpoint indicator results is the same as calculating endpoint indicator results. Or in other words, midpoint indicator results that are normalised, weighted and aggregated into one impact score per AoP have the same unit as endpoint indicator results aggregated into one impact score per AoP. Therefore, both results are identical. Fact: Even though the unit of both aggregated indicators is the same, their numerical value and their physical meaning are completely different. They are not identical and cannot be interpreted in the same way. 2. Misconception: Changing from midpoint to endpoint characterisation implies a loss of information due to aggregation from about 15 midpoints into only three endpoint indicators


2. Misconception: Changing from midpoint to endpoint characterisation implies a loss of information due to aggregation from about 15 midpoints into only three endpoint indicators. Fact: Before aggregation is applied, endpoint indicators are constituted for the same amount of impact categories as on midpoint level, but not every impact category contributes to each AoP (e.g. mineral resource depletion does not contribute to human health impacts). Therefore, the same analysis of contribution Elementary flows Climate change Human health Inventory results Midpoint Endpoint Area of protection Stratospheric ozone depletion Human toxicity (cancer or noncancer) Particulate matter formation Ionising radiation (humans and ecosystems) Photochemical ozone formation Acidification (terrestrial, freshwater) Eutrophication (terrestrial, freshwater, marine) Ecotoxicity (terrestrial, freshwater, marine) Land use Water use Natural Environment Natural resources Resource use (mineral, fossil, biotic) Fig


10.2 Framework of the ILCD characterisation linking elementary flows from the inventory results to indicator results at midpoint level and endpoint level for 15 midpoint impact categories and 3 areas of protection [adapted from EC-JRC (2010b)] R.K. Rosenbaum et al.


per impact category is possible as for normalised and weighted midpoint indicators while avoiding the need for normalisation and weighting and the associated increased uncertainty and change in meaning. 3. Misconception: Endpoint characterisation is more uncertain than midpoint characterisation. Fact: This may be the case when looking at a limited set of sources of uncertainty and how they contribute to the uncertainty of the value of the indicator. However, when considering all relevant sources of uncertainty and the relevance of the indicator for the decision at hand, the choice of indicator has no influence on the uncertainty of the consequences of the decision. This is discussed in detail in Chap. 11


This is discussed in detail in Chap. 11. To go from midpoint to endpoint indicator scores, additional midpoint-toendpoint characterisation factors (sometimes also referred to as severity or damage characterisation factors) are needed, expressing the ability of a change in the midpoint indicator to affect the endpoint indicator. In contrast to the midpoint characterisation factors which reflect the properties of the elementary flow and hence are elementary flow-specific, the midpoint-to-endpoint characterisation factors reflect the properties of the midpoint indicator and there is hence only one per midpoint impact category. Some LCIA methods only support endpoint characterisation and here the midpoint and midpoint-to-endpoint characterisation is combined in one characterisation factor. 10.2.3.8 Midpoint or Endpoint Assessment? Next to the relationship between environmental relevance and various sources of uncertainty discussed above (and in more detail in Chap


10.2.3.8 Midpoint or Endpoint Assessment? Next to the relationship between environmental relevance and various sources of uncertainty discussed above (and in more detail in Chap. 11), the possibility to aggregate information from midpoint to endpoint level while avoiding normalisation has the advantage of providing more condensed information (fewer indicator results) to consider for a decision, while still being transparent as to which impact pathway(s) are the main causes of these damages. Instead of perceiving midpoint and endpoint characterisation as two alternatives to choose from, it is recommended to conduct an LCIA on both midpoint and endpoint level (using an LCIA method that provides both) to support the interpretation of the results obtained and which complement each other respectively


10.2.3.9 Time Horizons and Temporal Variability? Environmental impacts caused by an intervention will require different amounts of time to occur, depending on the environmental mechanism and the speed at which its processes take place. This means that next to the fact that the numerous elementary flows of an LCI may occur at different moments in time during the life cycle of the product or service assessed (which may be long for certain products Life Cycle Impact Assessment


like buildings for example), there is also a difference in the lag until their impacts occur. However, the way LCA is currently conducted, potential impacts are assessed as if interventions and potential impacts were happening instantly, aggregating them over time and over the entire life cycle. This means that these potential impacts need to be interpreted as a “backpack” of potential impacts attributable to the product or service assessed. Next to such temporal variability, another potential source of time-related inconsistency in LCA is the problem of applying different time horizons for different impact categories. These time horizons are sometimes explicit (e.g. the 20 and 100 years’ time horizons for global warming potentials), but in most cases implicit in the way the environmental mechanism has been modelled (e.g. over what time horizon the impact has been integrated)


over what time horizon the impact has been integrated). This may result in a mixing of different time horizons for different impacts in the same LCIA, which may have implications for the interpretation of LCA results. For example, methane has a lifetime much shorter than CO2. Therefore, depending on the time horizon chosen, the characterisation of methane will change. This is directly connected to the question of how to consider potential impacts affecting current and immediate future generations versus those affecting generations in a more distant future. Another issue concerns the temporal course of the emission and its resulting impacts. While some impacts may be immediately (i.e. within a few years) tangible and directly affecting a larger number of individuals (human or not), some impacts may be very small at any given moment in time, but permanently occurring for tens to hundreds of thousands of years (e.g. impacts from heavy metal emissions from landfills or mine tailings)


impacts from heavy metal emissions from landfills or mine tailings). Between these two illustrative extremes, lies any possible combination of duration versus severity. 10.2.3.10 Spatial Variability and Regionalisation? Some impacts are described as global because their environmental mechanism is the same regardless where in the world the emission occurs. Global warming and stratospheric ozone depletion are two examples. Other impacts, such as acidification, eutrophication or toxicity may be classified as regional, affecting a (sub-)continent or a smaller region surrounding the point of emission only. Impacts affecting a small area are designated as local impacts, water or direct land-use impacts on biodiversity for example


Impacts affecting a small area are designated as local impacts, water or direct land-use impacts on biodiversity for example. Whereas for global impact categories the site where the intervention takes place has no considerable influence on the type and magnitude of its related potential impact(s), for regional or local impacts this may influence the magnitude of the potential impact(s) up to several orders of magnitude (e.g. a toxic emission taking place in a very large and densely populated city or habitat versus somewhere remote in a large desert). This spatial variability can be dealt with in two ways: • Identification and modelling of archetypal emission/extraction situations and their potential impacts (e.g. toxic emission into urban air, rural air or remote air) R.K. Rosenbaum et al.


or spatialized archetypes (e.g. city-specific emissions, formation and background concentrations of particulate matter and related mortality rates) Or • Modelling impacts with a certain degree of spatial resolution (e.g. sub-continental, country-level, sub-water-shed level or GPS grid-based), allowing for a characterisation which can be specific to any given place of emission or extraction Both solutions require that the place of emission/extraction is known for each flow in the inventory—either explicitly (e.g. by country or geographical coordinates such as latitude and longitude) or regarding the most representative archetype. In order to support a spatially differentiated impact assessment, the life cycle inventory must thus not be aggregated to present one total intervention per elementary flow since this will lose the information about location of the interventions which is needed to select the right CF


Otherwise, generic global average CFs need to be used, leading to a higher uncertainty due to the spatial variability not considered in the characterisation. In contrast to the site-generic LCIA method, which provides one CF per combination of elementary flow and intervention/emission compartment, the spatially differentiated characterisation method provides one CF per combination of elementary flow, intervention/emission compartment and spatial unit. For grid-based methods, this may amount to thousands of CFs for each contributing elementary flow. It depends on the impact category and emission situation to evaluate whether a spatial or archetypal setup will give the more accurate solution (e.g


It depends on the impact category and emission situation to evaluate whether a spatial or archetypal setup will give the more accurate solution (e.g. urban/rural differences in particulate matter-related health effects might not be captured by spatial models with typical resolutions lower than 10  10 km2 at the global scale, whereas an archetypal model distinguishing between urban and rural emission situations would capture such differences). It should be noted that country-based characterisation is not meaningful from a scientific point of view, as most impacts are not influenced by political borders, although from a practical data-availability point of view this currently not unusual practice is understandable and normally an improvement to not considering the spatial variation at all. It should furthermore be noted that most currently available LCA software fails to support spatially differentiated characterisation, and therefore most LCAs are performed using the site-generic CFs


It should furthermore be noted that most currently available LCA software fails to support spatially differentiated characterisation, and therefore most LCAs are performed using the site-generic CFs. 10.2.3.11 The Units? The unit of CFs for midpoint impact categories is specific for each category and LCIA method chosen, and therefore discussed in detail in the corresponding section dedicated in detail to each LCIA method in Chap. 40. However, two different approaches can be identified—expression in absolute form as the modelled indicator result (e.g. area of ecosystem exposed above its carrying capacity per kg of substance emitted for acidification) or expression in a relative form as that emission Life Cycle Impact Assessment


of a reference substance for the impact category which would lead to the same level of impact (e.g. kg CO2-equivalents/kg of substance emitted for climate change). In contrast, endpoint CFs are typically expressed in absolute units and the units are relatively common between those LCIA methods that cover endpoint modelling: Human health: [years] expressed as DALY (Disability-Adjusted Life Years). This unit is based on a concept proposed by Murray and Lopez (1996) and used by the World Health Organisation. It considers different severity contributions defined as “Years of Life Lost per affected Person” YLLp [year/disease case] and “Years of Life lived with a Disability per affected Person” YLDp [years/disease case]. These statistical values are calculated on the basis of number and age of deaths (YLL) and disabilities (YLD) for a given disease


These statistical values are calculated on the basis of number and age of deaths (YLL) and disabilities (YLD) for a given disease. This information can be combined into a single indicator using disability weights for each type of disability to yield the “Disability Adjusted Life Years per affected Person” DALYp [year/person]. Ecosystem quality or Natural environment: [m2 year] or [m3 year] expressed as Potentially Disappeared Fraction (PDF). It can be interpreted as the time and area (or volume) integrated increase in the disappeared fraction of species in an ecosystem [dimensionless] per unit of midpoint impact indicator increase. It essentially quantifies the fraction of all species present in an ecosystem that potentially disappears (regardless whether due to death, reduced reproduction or immigration) over a certain area or volume and during a certain length of time


Different ecosystems have different numbers of species that can be affected by the impact and it is necessary to correct for such differences when aggregating the potentially disappeared fractions of species across the different impact categories at endpoint (i.e. a PAF of 0.5 for 10 species represents 5 species potentially lost, whereas the same PAF for 1000 species represents 500 species potentially lost). Resource depletion and ecosystem services: Different approaches exist and since there is still no common perception of what the area of protection for resources is (Hauschild et al. 2013), there is also no consensus forming on how to model damage in the form of resource depletion. Some proposals focus on the future costs for extraction of the resource as a consequence of current depletion, and these divide into costs in the form of energy or exergy use for future extraction (measured in MJ) or monetary costs (measured in current currency like USD, Yen or Euro)


10.2.3.12 Uncertainties? Uncertainties can be important in LCIA and contribute substantially to overall uncertainty of an LCA result. For some impact categories, this contribution may be much larger than that of the LCI. At the same time, it is also crucial to be aware that large uncertainty is by no means a valid reason to exclude an impact category from the assessment. One of the more uncertain impact categories is human toxicity and it has to be capable of dealing with hundreds to thousands of different elementary flows, which may differ by more than 20 orders of magnitude in their impact R.K. Rosenbaum et al.


potential, due to the sheer number of substances (i.e. elementary flows) that may be assigned to this category and the variation in their environmental persistence and potential toxicity. It is much more certain to consistently characterise an impact category to which only a handful of elementary flows are assigned showing impact potentials that range only three or four orders of magnitude from the least to the most impacting elementary flow (e.g. eutrophication, acidification or global warming). With the exception of photochemical ozone formation, there is no other impact category that covers even 100 different elementary flows. In this respect, there is hence a factor of >1000 between other impact categories and the toxicity categories (human health and ecotoxicity)


In this respect, there is hence a factor of >1000 between other impact categories and the toxicity categories (human health and ecotoxicity). This means that due to the large variety of substances with a toxicity potential, there will always be a very large uncertainty inherent in these categories, although developers will eventually be able to lower some of the model and parameter uncertainties currently observed. Excluding them from the assessment because of their uncertainty would therefore mean that toxicity would never be considered in LCA, which clearly risks violating the goal of LCA to avoid problem-shifting from one impact category to another. Besides, the uncertainty of assigning a zero-impact to a potentially toxic elementary flow by neglecting the toxicity impact categories is certainly higher than the inherent uncertainty of the related characterisation factors


The solution rather lies in the way we interpret such inherently uncertain impact potentials, whereas a more certain impact indicator may allow for identifying the exact contribution of each elementary flow to the total impact in this category, toxicity indicators allow for identifying the (usually 5–20) largest contributing elementary flows, which will constitute >95% of the total impact. A further distinction between these will not be possible due to their uncertainty. Assuming that an average and complete LCI may contain several hundreds of potentially toxic elementary flows, one can then disregard all the remaining (several hundred) flows due to their low contribution to total toxicity. A further discussion and recommendations can be found in Rosenbaum et al. (2008). Overall uncertainty in LCA is comprised of many different types of uncertainty as further discussed in Chap. 11. Variability (e.g


(2008). Overall uncertainty in LCA is comprised of many different types of uncertainty as further discussed in Chap. 11. Variability (e.g. spatial or temporal/seasonal) may also be an important contributor, which should by principle be considered separately, as its contribution can be reduced to a large extent by accounting for it in the characterisation as discussed above for spatial variability and regionalised LCI and LCIA. Uncertainty in LCIA can only be reduced by improved data or model quality, essentially coming from updated LCIA methods, which is a good reason for a practitioner to keep up with the latest developments in LCIA, which may well lead to less uncertain results than the method one has been using for ten years. Most existing LCIA methods do not present information about the uncertainty of the characterisation factors


Most existing LCIA methods do not present information about the uncertainty of the characterisation factors. 10.2.3.13 What Are the Main Assumptions? In current LCIA methods, some assumptions are considered as a basic requirement in the context of LCA: Life Cycle Impact Assessment


• Steady-state: Although exceptions exist, LCIA models are usually not dynamic (i.e. representing the variation of an environmental system’s state over time and for specific time steps), but represent the environment as a system in steady state, i.e. all parameters which define its behaviour are not changing over time. • Linearity: As life cycle inventory (LCI) data are typically not spatially and/or temporally differentiated, integration of the impact over time and space is required. In LCIA, this leads to the use of characterisation models assuming steady-state conditions, which implies a linear relationship between the increase in an elementary flow and the consequent increase in its potential environmental impact. In other words, e.g. doubling the amount of an elementary flow doubles its potential impact


In other words, e.g. doubling the amount of an elementary flow doubles its potential impact. • Marginal versus average modelling: These terms are used in different ways and meanings in the LCA context; here they describe two different impact modelling principles or choices: a marginal impact modelling approach represents the additional impact per additional unit emission/resource extraction caused by the product system on top of the existing background impact (which is not caused by the modelled product system). This allows, e.g. considering nonlinearity of impacts depending on local conditions like high or low background concentrations to which the product systems adds an additional emission). An average impact modelling approach is strictly linear and represents an average impact independent from existing background impacts, which is similar to dividing the overall impact by the overall emissions. This is further discussed by Huijbregts et al. (2011)


This is further discussed by Huijbregts et al. (2011). Note that marginal and average modelling are both suitable for small-scale interventions such as those related to a product or service. However, when medium-scale or large-scale interventions (or consequences) are to be assessed, the characterisation factors should represent non-marginal potential impacts and may also have to consider nonlinearity. • Potential impacts: LCIA results are not actual or predicted impacts, nor exceedance of thresholds or safety margins, or risk. They are relative expressions of impacts associated with the life cycle of a reference unit of function (=functional unit), based on inventory data which are integrated over space and time, representing different locations and time horizons and based on impact assessment data which lack information about the specific conditions of the exposed environment


• Conservation of mass/energy and mass/energy balance: Mass/energy cannot be created or disappear, it can only be transferred. Following this principle, processes of transport or transformation of mass or energy are (or at least should be) modelled assuming that the mass/energy balance is conserved at all times. • Parsimony: This refers to the basic modelling principle of “as simple as possible and as complex as necessary”, an ideal balance that applies to LCIA characterisation models as well as to the entire LCA approach. • Relativity: LCA results are relative expressions of impacts that relate to a functional unit and can be compared between different alternatives providing the same function (e.g. option A is more environmentally friendly than option B). R.K. Rosenbaum et al.


An absolute interpretation of LCA results (e.g. option A is sustainable, option B is not) is not advisable as it requires a lot of additional assumptions. • Best estimates: A fundamental value choice in LCA is not to be conservative, precautionary or protective, but to focus on avoiding any bias between compared scenarios by assuming average conditions, also referred to as best estimates. Products or services assessed in LCA are typically not representing one specific example (e.g. with a serial number or from a specific date), but an average, often disregarding whether a specific life cycle process took place in summer or winter, during the day or night, etc. As discussed by Pennington et al. (2004), LCA is a comparative assessment methodology. Direct adoption of conservative regulatory methodology and data is often not appropriate, and should be avoided in LCIA in order not to bias comparison between impact categories where different levels of precaution may be applied


10.3 Optional Steps According to ISO 14040/14044 10.3.1 Normalisation The indicator scores for the different midpoint indicators are expressed in units that vary between impact categories and this makes it unfeasible to relate them to each other and to decide which of them are large and which small. To support such comparisons, it is necessary to put them into perspective, and this is the purpose of the normalisation step, where the product system’s potential impacts are compared to those of a reference system like a country, the world or an industrial sector. By relating the different impact potentials to a common scale they can be expressed in common units, which provide an impression of which of the environmental impact potentials are large and which are small, relative to the reference system


Normalisation can be useful for: • Providing an impression of the relative magnitudes of the environmental impact potentials • Presenting the results in a form suitable for a subsequent weighting • Controlling consistency and reliability • Communicating results Typical references are total impacts per impact category per: • Geographical zone which can be global, continental, national, regional or local • Inhabitant of a geographical zone (e.g. expressing the “environmental space” occupied per average person) • Industrial sector of a geographical zone (e.g. expressing the “environmental space” occupied by this product system relative to similar industrial activities) Life Cycle Impact Assessment


• Baseline reference scenario, such as another product system (e.g. expressing the “environmental space” occupied by this product system relative to a similar reference system using best available technology) Using one of the first three reference systems listed above is also referred to as external normalisation. Using the last reference system in the list is also called internal normalisation when the reference scenario is one of the compared alternatives, such as the best or worse of all compared options or the baseline scenario representing, e.g. a current situation that is intended to be improved or a virtual or ideal scenario representing a goal to be reached. Normalised impact scores, when using internal normalisation, are often communicated as percentages relative to the reference system. In the illustrative case on window frames in Chap


Normalised impact scores, when using internal normalisation, are often communicated as percentages relative to the reference system. In the illustrative case on window frames in Chap. 39 an internal normalisation is applied using the wooden frame window as reference (indexing it to 100%) to reveal how the studied alternatives compare to this baseline choice. The study also applies external normalisation in order to compare the size of the different midpoint impact scores with the European person equivalent impact scores that is provided as default normalisation references for the LCIA method applied in the study (the ILCD method). In practice, an LCIA method generally provides normalisation factors for use with its characterisation factors. The normalisation factors should be calculated using the same characterisation factors for the reference inventory as used for the inventory of the product system


The normalisation factors should be calculated using the same characterisation factors for the reference inventory as used for the inventory of the product system. Normalisation factors from different LCIA methods thus cannot be mixed or combined with characterisation factors from another LCIA method. This means that as an LCA practitioner you are usually limited to the reference system chosen by the LCIA method developers. Normalisation is applied using normalisation factors (NF). These are essentially calculated per impact category by conducting an LCI and LCIA on the reference system, i.e. quantifying all environmental interventions E for all elementary flows i for the reference system and applying the characterisation factors CF per elementary flow i, respectively, for each impact category c


Although not obligatory, the normalisation reference is typically divided by the population P of the reference region r, in order to express the NF per average inhabitant of the reference region (per capita impacts or “person equivalents”). This way, a total impact of the reference system per impact category is calculated, resulting in one NF per impact category c: NFc 1⁄4 P i CFi  Ei ð Þ Pr  1 ð10:2Þ Ensuring consistency, the LCI data used to calculate a NF need to represent a common reference year and duration of activity (typically one year, being the reference year) for all impact categories. This results in NF having a unit expressing an impact per person and year, also referred to as person equivalent. A normalised impact score NS for a product system is calculated by multiplying the calculated impact score IS for the product system by the relevant NF per impact category c: R.K. Rosenbaum et al.


NSc 1⁄4 ISc  NFc ð10:3Þ Two different approaches exist for collection of inventory data for the calculation of NFs (with the exception for global NFs, where both approaches give equal results): • Production-based (or top-down), representing the interventions taking place in the reference region as result of the total activities in the region • Consumption-based (or bottom-up), representing the interventions that are caused somewhere in the world as consequence of the consumption taking place in the reference region (and thus representing the demand for industrial and other activities within and outside the reference region) Other ways to derive NF (although somewhat bordering to weighting already) are to base them on a conceptual “available environmental space”. This can be determined using, e.g. political targets for limits of environmental interventions or impacts for a given duration and reference year (i.e


This can be determined using, e.g. political targets for limits of environmental interventions or impacts for a given duration and reference year (i.e. “politically determined environmental space” being the average environmental impact per inhabitant if the political reduction targets are to be met), or a region’s or the planet’s carrying capacity (i.e. “environmental space” being the amount of environmental interventions or impacts that the region or planet can buffer without suffering changes to its environmental equilibrium within each impact category). The latter would require knowing the amount of impact that a region or the planet can take before suffering permanent damage, which is a concept associated with much ambiguity and hence very uncertain to quantify


There is increasing focus on science-based targets in the environmental regulation with the 2 °C ceiling for climate change as the most prominent example, and this may lead to future consensus building on science-based targets also for some of the other impacts that are modelled in LCIA. Political targets are often determined at different times and apply to different periods of time. In order to ensure a consistent treatment of each impact category, it is necessary to harmonise the target values available so that all targets for any given intervention are converted to apply to the same period and reference year. The targets can be harmonised by interpolating or extrapolating to a reduction target for a common target year, computed relative to interventions in the reference year. More details can be found in Hauschild and Wenzel (1998)


More details can be found in Hauschild and Wenzel (1998). Caution is required when interpreting normalised LCA results! Applying normalisation harmonises the metrics for the different impact potentials and brings them on a common scale, but it also changes the results of the LCA and consequently may change the conclusions drawn from these. Since there is no one objectively correct choice of reference systems for normalisation, the interpretation of normalised LCA results must therefore always be done with due consideration of this choice of normalisation reference. A few main issues that need to be considered when interpreting normalised LCA results are: • Depending on the size of and activities reflected in the reference system, different biases may be introduced in the comparison of the impact scores of a Life Cycle Impact Assessment


product system. As a general principle, the larger the reference system, the lesser the risk of such bias when normalising against the background activities of society. • While supporting comparison of results across impact categories, normalised LCA results cannot be interpreted as reflecting a weight or importance of one impact category relative to others. Normalisation helps to identify the impacts from the product system that are large compared to the chosen reference system, but large is not necessary the same as important. It is therefore not suitable as the only basis for identification of key issues/impacts in a product system, unless explicitly required by the goal and scope definition (e.g. evaluating the environmental impact contribution of a product system to a reference system which it is part of)


evaluating the environmental impact contribution of a product system to a reference system which it is part of). • Unless (a) the reference system is global or (b) all environmental interventions of the product system assessed take place in the same region as those of the reference system, the direct interpretation of normalised impacts as contributions to or fractions of the reference system is misleading because parts of the life cycle of the product or service take place in different regions of the world, including outside the reference system. By expressing the different impact scores on a common scale, normalisation can also help checking for potential errors in the modelling of the product system. If the results are expressed in person equivalents, it is possible to spot modelling errors leading to extremely high or low impacts in some of the impact categories—like frequent unit errors when emissions are expressed in kg instead of g


Looking across the impact category results in a normalised impact profile, it is also possible for the more experienced LCA practitioner to check whether they follow the pattern that would be expected for this type of product or service. Although characterisation and aggregation at endpoint level leads to fewer impact scores (typically three), normalisation may still be useful with the same purposes as normalisation at midpoint level. The calculation and application of the endpoint normalisation references follows the same procedure as for midpoint normalisation, just applying combined midpoint and endpoint characterisation factors in Eq. 10.2. 10.3.2 Weighting (and Aggregation) Weighting can be used to determine which impacts are most important and how important they are. This step can only be applied after the normalisation step and allows the prioritisation of impact categories by applying different or equal weights to each category indicator


This step can only be applied after the normalisation step and allows the prioritisation of impact categories by applying different or equal weights to each category indicator. It is important to note that there is no scientific or objective basis for this step. This means that, no matter which weighting method or scheme is applied, it will always be based on the subjective choices of one person or a group of individuals. Weighting can be useful for: R.K. Rosenbaum et al.


• Aggregating impact scores into several or one single indicator (note that according to ISO 14040/14044 there is no scientific basis on which to reduce the results of an LCA to a single result or score because of the underlying ethical value-choices) • Comparing across impact categories • Communicating results applying an underlying prioritisation of ethical values Note that in all of these cases weighting is applied, either implicitly or explicitly! Even when applying no explicit weighting factors in the aggregation by simply summing up impact scores, there is always an implicit equal weighting (all weighting factors = 1) inherently applied when doing any of the above. According to ISO 14044, weighting is not permitted in a comparative assertion disclosed to the public and weighted results should always be reported together with the non-weighted ones in order to maintain transparency. The weighting scheme used in an LCA needs to be in accordance with the goal and scope definition


The weighting scheme used in an LCA needs to be in accordance with the goal and scope definition. This implies that the target group including their preferences and the decisions intended to be supported by the study need to be considered, making shared values crucial for the acceptance of the results of the LCA. This can pose important problems due to the variety of possible values among stakeholders, including: • Shareholders • Customers • Employees • Retailers • Authorities • Neighbours • Insurance companies • NGOs (opinion leaders) • ... It may not be possible to arrive at weighting factors that will reflect the values of all stakeholders so focus will typically have to be on the most important stakeholders, but is it possible to develop one set of weighting factors that they will all agree on? If this is not the case, several sets of weighting factors may have to be applied, representing the preferences of the most important stakeholder groups


Sometimes the use of the different sets will lead to the same final recommendations which may then satisfy all the main stakeholders. When this is not the case, a further prioritisation of the stakeholders is needed, or the analysed product system (s) must be altered in a way that allows an unambiguous recommendation across the applied weighting sets. The weighting of midpoint indicators should not be purely value-based. More, to some extent, science-based criteria for importance of environmental impacts may be: • Probability of the modelled consequences, how certain are we on the modelled cause–effect relations? • What is the resilience of the affected systems? Life Cycle Impact Assessment


• Existence of impact thresholds—in the characterisation modelling we typically assume linear cause–effect relationships for the small interventions in the product system, but in the full environmental scale, there may be impact levels that represent tipping points beyond which much more problematic effects occur. • If so, then how far are we from such critical impact levels—is this an important concern in the near future? • Severity of effect and gravity of consequences—disability, death, local extinction, global extinction • Geographical scale • Population density is essential for the impacts on human health


• Possibility to compensate/adapt to impact • Temporal aspects of consequences—when will we feel the consequences, and for how long? • Is the mechanism reversible, can we return to current conditions if we stop the impacts? Indeed, many of these science-based criteria are attempted to be included in the environmental modelling linking midpoint indicators to endpoint indicators, and midpoint-to-endpoint characterisation factors may thus be seen as science-based weighting factors for the midpoint impact categories. Different principles applied to derive weighting factors are: • Social assessment of the damages (expressed in financial terms like willingness to pay), e.g. impact on human health based on the cost that society is prepared to pay for healthcare (e.g. used in EPS and LIME LCIA methods) • Prevention costs (to prevent or remedy the impact through technical means), e.g


used in EPS and LIME LCIA methods) • Prevention costs (to prevent or remedy the impact through technical means), e.g. the higher the costs, the higher the weighting of the impact • Energy consumption (to prevent or remedy the impact through technical means), e.g. the higher the energy consumption, the higher the weighting of the impact • Expert panel or stakeholder assessment, e.g. weight attributed based on the relative significance, from a scientific perspective (subjective to each expert), of the different impact categories • Distance-to-target (politically or scientifically defined): degree at which the targeted impact level is reached (distance from the target value), the greater the distance, the more weight is assigned to the impact (e.g. used in EDIP, Ecopoints and Swiss Ecoscarcity LCIA methods)


used in EDIP, Ecopoints and Swiss Ecoscarcity LCIA methods). • Social science-based perspectives, not representing the choices of a specific individual, but regrouping typical combinations of ethical values and preferences present in society into a few, internally consistent profiles (e.g. used in ReCiPe and Ecoindicator99 LCIA methods). The latter approach is relatively widely used and applies three cultural perspectives, the Hierarchist, the Individualist and the Egalitarian (a forth perspective, the Fatalist is not developed for use in LCA since the fatalist is expected not to be represented among decision-makers, targeted by an LCA). For each cultural perspective coherent choices are described in Table 10.2 for some of the central R.K. Rosenbaum et al.


assumptions made in the characterisation modelling and in the development of a set of consistent weighting factors for each archetype. The different archetypal views on nature and the related risk perceptions are illustrated in Fig. 10.3. The dot represents the state of nature as a rolling ball, shifted by human activities along the curve representing nature’s reaction to a shift. Its position in the figures indicates the state of harmony between humans and nature according to the four archetypal views


Its position in the figures indicates the state of harmony between humans and nature according to the four archetypal views. 10.3.3 Grouping This step consists in placing the impact categories in one or several groups or clusters (as defined in goal and scope) and can involve sorting or ranking, applying one of two possible methods: Table 10.2 Cultural perspectives represented by preference with coherent choices (Hofstetter 1998) Time perspective Manageability Required level of evidence H (Hierarchist) Balance between short and long term Proper policy can avoid many problems Inclusion based on consensus I (Individualist) Short term Technology can avoid many problems Only proven effects E (Egalitarian) Very long term Problems can lead to catastrophe All possible effects Nature capricious (Fatalist’s View) Nature Perverse/Tolerant (Hierarchist’s View) Nature Benign (Individualist’s View) Nature Ephemeral (Egalitarian’s View) Fig


10.3 Different archetypal perceptions of nature [adapted from Thompson (1990)] Life Cycle Impact Assessment


• Sorting and clustering midpoint impact categories on a nominal basis (e.g.: by characteristics such as emission-related and resource-related, or global, regional or local spatial scales) • Ranking the impact categories according to a set (subjective—based on ethical value-choices) hierarchy (e.g.: high, medium or low priority) 10.4 Footprints Versus LCA “I was exceedingly surprised with the print of a man’s naked foot on the shore, which was very plain to be seen in the sand.” (Daniel Defoe, Robinson Crusoe, 1719). The meaning of the term “footprint” has largely evolved since Daniel Defoe’s famous novel and is currently used in several contexts (Safire 2008). Its appearance in the environmental field can be tracked back to 1992 when William Rees published the first academic article on the thus-termed “ecological footprint” (Rees 1992), which was further developed by him and Mathis Wackernagel in the following years


Its aim is to quantify the mark left by human activities on natural environment. Since then, the mental images created by the word have contributed to its use as an effective way of communicating on different environmental issues and raising environmental awareness within the scientific community as well as among policy communities and the general public. Since the early 2000s, several footprints have thus emerged within the environmental field with different definitions and meanings, ranging from improved ecological footprint methodologies to the representation of specific impacts of human activities on ecosystems or human health to a measure of a specific resource use


Prominent examples are: • Ecological footprint focusing on land use (http://www.footprintnetwork.org) • Cumulative Energy Demand (CED) focusing on non-renewable energy • Material Input Per unit of Service (MIPS) focusing on material use • Water footprint focusing on water use volumetric accounting (http:// waterfootprint.org) • Water footprint focusing on water use impacts including pollution (ISO 14046) • Carbon footprint focusing on climate change (ISO 14064, ISO/TS 14067, WRI/WBCSD GHG protocol, PAS 2050) Later developments focused on the introduction of new environmental concerns or enlarging the scope of footprints. Examples for such emerging footprints are: • Chemical footprint focusing on toxicity impacts • Phosphorus depletion footprint As illustrated in Fig. 10.4, all footprints are fundamentally based on the life cycle perspective and most of them focus on one environmental issue or area of concern. R.K. Rosenbaum et al.


They can be applied to a large variety of assessment targets like products, services, organisations, persons and populations, sites and regions, even countries or the entire world. Their success in the last decades lies in their particular strengths: • Easily accessible and intuitive concept • Easy to communicate about specific environmental issues or achievements with non-environmental experts (policy and decision-making communities, general public) • Availability of data • Easy to perform • Wide range of assessment targets can easily be assessed These strengths, however, also come with a number of important limitations: • Their focus on one environmental issue does not inform about a potential burden-shifting from one environmental issue (e.g. climate change) to another (e.g. water availability)


climate change) to another (e.g. water availability). Therefore, while they allow for identification of the best option for one environmental problem, they are not suitable to support decisions regarding environmental sustainability, which need to consider all potential environmental problems. • Some footprints only assess the quantity of a resource used (e.g. ecological footprint, CED, MIPS and volumetric water footprint), which is comparable to the accounting of quantities used or emitted in the life cycle inventory (see Chap. 9). Such footprints therefore do not inform about the associated environmental consequences of the resources used or emissions accounted, and they do not quantify potential impacts on a given area of protection. Among other, this limitation compromises the comparability of footprints for different options to choose from. • Impact-based footprints (e.g


Among other, this limitation compromises the comparability of footprints for different options to choose from. • Impact-based footprints (e.g. carbon footprint), at least historically, assess impacts on midpoint level and hence do not reflect damages, which has implications on their environmental relevance. However, with an increasing Fig. 10.4 The fundamental difference in scope and completeness between LCA and footprints while both apply the life cycle perspective Life Cycle Impact Assessment


range of endpoint impact indicators available, this may be solved with science advancing further. • Different footprints can usually not be combined to enlarge their environmental scope because their system boundaries (see Chaps. 8 and 9) are not aligned and double counting of impacts becomes likely, which increases the risk of bias to the comparison, the same way the omission of impacts does. As mentioned above, the focus on single environmental problems has important implications regarding the risks of using footprints in decision-making processes. A study by Huijbregts et al. (2008) calculated 2630 product-specific ecological footprints of products and services (e.g. energy, materials, transport, waste treatment, etc.). They concluded that “Ecological footprints may [...] serve as a screening indicator for environmental performance..


energy, materials, transport, waste treatment, etc.). They concluded that “Ecological footprints may [...] serve as a screening indicator for environmental performance... [and provide] a more complete picture of environmental pressure compared to non-renewable CED [Cumulative Energy Demand]”, while also observing that “There are cases that may [...] not be assessed in an adequate way in terms of environmental impact. For example, a farmer switching from organic to intensive farming would benefit by a smaller footprint for using less land, while the environmental burdens from applying more chemicals [i.e. pesticides and fertilisers] would be neglected”. Thus, the usefulness of the ecological footprint as a stand-alone indicator may often be limited (Huijbregts et al. 2008). The limitations of carbon footprints (i.e. the climate change impact indicator in LCA) as environmental sustainability indicators was investigated by a study from Laurent et al


2008). The limitations of carbon footprints (i.e. the climate change impact indicator in LCA) as environmental sustainability indicators was investigated by a study from Laurent et al. (2012), who assessed the carbon footprint and 13 other impact scores from 4000 different products, technologies and services (e.g. energy generation, transportation, material production, infrastructure, waste management). They found “that some environmental impacts, notably those related to emissions of toxic substances, often do not covary with climate change impacts. In such situations, carbon footprint is a poor representative of the environmental burden of products, and environmental management focused exclusively on [carbon footprint] runs the risk of inadvertently shifting the problem to other environmental impacts when products are optimised to become more “green”. These findings call for the use of more broadly encompassing tools to assess and manage environmental sustainability” (Laurent et al


These findings call for the use of more broadly encompassing tools to assess and manage environmental sustainability” (Laurent et al. 2012). This problem is demonstrated in Fig. 10.5, which shows the carbon footprint, ecological footprint, volumetric water footprint and the LCA results for an illustrative comparison of two products A and B. If one had to choose between option A and B, the decision would be different and thus depending on, which footprint was considered, whereas LCA results provide the full range of potential impacts to consider in the decision. The large variety in footprints and their definitions and methodological basis in combination with their wide use in environmental communication and marketing claims, has resulted in confusing and often contradictory messages to buyers. This ultimately limited the development and functioning of a market for green products (Ridoutt et al. 2015, 2016). In response, a group of experts established under the R.K. Rosenbaum et al.


auspices of the UNEP/SETAC Life Cycle Initiative defined footprint as “Metric used to report life cycle assessment results addressing an area of concern [the latter specified as an] Environmental topic defined by the interest of society” (Ridoutt et al. 2016). This definition underpins a footprint’s focus on environmental issues particularly perceived by society (e.g. climate change or water scarcity) and allows for a clear distinction to LCA, which is primarily oriented “toward stakeholders interested in comprehensive evaluation of overall environmental performance and trade-offs among impact categories” (Ridoutt et al. 2016) and related areas of protection. This definition also recognises the inherent complexity of an environmental performance profile resulting from an LCA study, which requires a certain expertise to be correctly interpreted. In conclusion, footprints are life cycle-based, narrow-scoped, environmental metrics focusing on an area of concern


In conclusion, footprints are life cycle-based, narrow-scoped, environmental metrics focusing on an area of concern. They are widely and easily applicable, as well as easily understood by non-environmental experts and therefore straightforward to communicate. They are particularly useful for communication of environmental problems or achieved improvements, as long as their use is restrained to their coverage of environmental concerns and care is taken when interpreting them (burden-shifting), particularly when results are disclosed to non-expert audiences (e.g. public opinion). A footprint’s life cycle perspective can be an inspiring first contact with the concept of life cycle thinking for the general public, and for policy and decision-makers it often serves as an entry-door into the concept and Fig


10.5 Comparing two products, which alternative would you choose? Examples of footprints are indicated in green shading; impact categories commonly assessed in LCA are indicated in blue shading Life Cycle Impact Assessment


methodology of LCA. Footprints have the ability to raise environmental awareness and therefore are springboards towards the use of more-encompassing assessment tools such as LCA. They can constitute a first step for organisations or companies, who can already implement procedures as a preparation for full environmental assessments. However, due to a footprint’s narrow scope and limited representativeness for a comprehensive set of environmental indicators, they are not suitable for decision-support of any kind including product labels, ecodesign, policy-support and the like. 10.5 Detailed Description of Impact Categories Currently Assessed in LCA The following sections document how the most commonly considered environmental problems (i.e. impact categories) are handled in life cycle impact assessment


impact categories) are handled in life cycle impact assessment. Ionising radiation is also a commonly addressed impact category in LCA, but was not included in the detailed overview here due to its specificity to a limited number of processes in the LCI. The impact categories are dealt with in sequence going from global over regional towards local and addressing first the emission-related and then the extraction-related categories. The common structure of the sections is: • What is the problem? • What is the underlying environmental mechanism and how is it modelled in LCIA? • What are the human activities and elementary flows contributing most to the problem? (emission-based categories only) • What are the most widely used, existing LCIA characterisation models? Beyond the classic list of impact categories discussed hereafter, there is a number of emerging categories currently in the stage of research and development


Though potentially relevant they have not yet reached sufficient methodological maturity to be operational for the majority of practitioners and no or only few LCIA methods have included them in their indicator set. Some examples are: • Biotic resources such as fish or wood • Noise • Pathogens • Salinization • Accidents • Impacts of Genetically Modified Organisms (GMO) A profound comparison of existing LCIA methods was performed by Hauschild et al. (2013) for the establishment of recommended LCIA models for the European context. Taking Hauschild et al.’s work as a starting point, the tables in Chap. 40 R.K. Rosenbaum et al.


provide a complete and updated qualitative comparison of widely used LCIA methods available in current LCA software. 10.6 Climate Change 10.6.1 Problem The greenhouse effect of our atmosphere, discovered and explored from the early 19th century, is vital to life on our planet and has always existed since the dawn of life on Earth. Without it the global average temperature of our atmosphere near the ground would be −18 °C instead of currently 15 °C. Hence, there are natural drivers and sources keeping it in balance (with periodical imbalances leading to natural events such as ice ages). In addition to those, anthropogenic activities also contribute to this effect increasing its intensity and creating global warming, which refers to the phenomenon of rising surface temperature across the planet averaged over longer periods of time. The Intergovernmental Panel on Climate Change (2014a) (IPCC) defines climate change as “a change in the state of the climate that can be identified (e.g


The Intergovernmental Panel on Climate Change (2014a) (IPCC) defines climate change as “a change in the state of the climate that can be identified (e.g. using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer”. IPCC observed an acceleration of the rise in planetary surface temperature in the last five to six decades, with the highest rates at the very northern latitudes of the Arctic. Ocean temperatures are also on the rise down to a depth of at least 3000 m and have so far absorbed most of the heat trapped in the atmosphere. Tropospheric temperatures are following similar trends as the surface. Although, still debated by few sceptics, most scientists agree on the presence of this effect with anthropogenic activities as the main cause. These are also the focal point of LCIA methodology and hence of this chapter


These are also the focal point of LCIA methodology and hence of this chapter. Effects observed by IPCC with varying degrees of confidence based on statistical measures (IPCC 2014a): • Rise of atmospheric temperature with the last three decades from 1983 to 2012 being very likely the warmest 30-year period of the last 800 years in the Northern Hemisphere and likely the warmest 30-year period of the last 1400 years • Rise of ocean temperature in the upper 75 m by a global average of 0.11 °C per decade from 1971 to 2010 • Melting of glaciers, snow and ice caps, polar sea ice and ice packs and sheets (61⁄4polar sea ice) and permafrost soils • Rise in global mean sea levels by 0.19 m over the period 1901–2010 (due to thermal expansion and additional water from melting ice) • Increase in frequency and intensity of weather-based natural disasters, essentially due to increased atmospheric humidity and consequent changes in Life Cycle Impact Assessment


atmospheric thermodynamics (i.e


energy absorption via evaporation and condensation) and cloud formation • Intense tropical cyclone activity increased in the North Atlantic since 1970 • Heavy precipitation and consequent flooding (North America and Europe) • Droughts • Wildfires • Heat waves (Europe, Asia and Australia) • Alteration of hydrological systems affecting quantity and quality of water resources • Negative impacts of climate change on agricultural crop yields more common than positive impacts • Shifting of geographic ranges, seasonal activities, migration patterns, abundances and species interactions (including in biodiversity) by many terrestrial, freshwater and marine species • Changes in infectious disease vectors The continuation and intensification of already observed effects as well as those not yet observed (but predicted by models as potential consequences offurther global warming) depend on the future increase in surface temperature which is predicted using atmospheric climate models and a variety


(but predicted by models as potential consequences offurther global warming) depend on the future increase in surface temperature which is predicted using atmospheric climate models and a variety of forecasted emission scenarios ranging from conservative to optimistic


Given the inertia of atmospheric and oceanic processes and the global climate, it is expected that global warming will continue over the next century. Even if emissions of GHGs would stop immediately, global warming would continue and only slow down over many decades. The following effects are not yet observed and highly debated in the scientific community; hence consensus or general agreement regarding their likelihood is not established. Nevertheless, they are possible impacts and should be seen as part of the possible effects of global warming, especially when considering longer time horizons. • Slowing down of the thermohaline circulation of cold and salt water to the ocean floor at high latitudes of the northern hemisphere (e.g. Gulf stream), among other things responsible for global heat distribution, oceanic nutrient transport, the renewal of deep ocean water, and the relative mildness of the European climate. This circulation as shown in Fig


This circulation as shown in Fig. 10.6 is driven by differences in the density of water due to varying salinity and differences in water temperature, and might be affected by freshwater inflow from melting ice, decreasing sea water salinity and consequently reducing its density and the density gradient between different oceanic zones. • Increasing frequency and intensity of “El Niño” events while decreasing that of its counterpart “La Niña” might be possible, although it is unclear to what extent this is influenced by global warming. One possibility is that this effect only occurs in the initial phase of global warming, while weakening again later when the deeper layers of the ocean get warmer as well. Dramatic changes cannot be fully excluded based on current evidence; therefore, this effect is considered a potential tipping element in our climate. R.K. Rosenbaum et al.


• Mobilisation and release of oceanic methane hydrate (water ice containing large amounts of methane in its crystal structure) present in deep ocean sediments and permafrost, could lead to further global warming and significantly affect the atmospheric oxygen content. There is large uncertainty regarding the amounts and size of reserves found under sediments on the ocean floors, but a relatively sudden release of large amounts of methane hydrate deposits is believed to be a main factor in the global warming of 6 °C during the end-Permian extinction event (Benton and Twitchet 2003) when 96% of all marine species became extinct 251 million years ago. • Effects on Earth’s primary “lung”: phytoplankton which produces 80% of terrestrial oxygen and absorbs a significant share of CO2


• Effects on Earth’s primary “lung”: phytoplankton which produces 80% of terrestrial oxygen and absorbs a significant share of CO2. • In addition to the environmental effects discussed above, the human population is likely to be affected by further severe consequences should other adaptation strategies prove inefficient: disease, malnutrition and starvation, dehydration, environmental refugees, wars and ultimately death. • Nonlinearity of cause–effect chains, feedback and irreversible tipping points: Although, in LCIA models, linearity of cause–effect chains is assumed, the above discussed effects present several examples of mechanisms that are unlikely to depend linearly on the temperature increase, i.e. they will not change proportionally in frequency and/or intensity per degree of change in global temperature. Furthermore, they are likely to directly or indirectly influence each other, causing feedback reactions adding further nonlinearity


Furthermore, they are likely to directly or indirectly influence each other, causing feedback reactions adding further nonlinearity. Additionally, some of these effects will be irreversible, changing the climate from one stable state to another. This phenomenon is referred to as tipping points, and the above-mentioned release of methane from methane hydrates and the alteration of the Gulf stream are examples. Lenton et al. (2008) discuss a number of additional potential tipping points. Fig. 10.6 “The big loop” takes 1500 years to circumnavigate the globe (NASA/JPL 2010, public domain, http://www.jpl.nasa.gov/news/news.php?release=2010-101) Life Cycle Impact Assessment


• Forest dieback (Boreal forest, Amazon rainforest). • Area encompassed by monsoon systems will increase with intensified precipitation. 10.6.2 Environmental Mechanism In principle, the energy reaching the Earth’s atmosphere from solar radiation and leaving it again (e.g. via reflection and infrared radiation) is in balance, creating a stable temperature regime in our atmosphere. As shown in Fig. 10.7, from the sunlight reaching the Earth’s atmosphere, one fraction (*28%) is directly reflected back into space by air molecules, clouds and the surface of the earth (particularly oceans and icy regions such as the Arctic and Antarctic): this effect is called albedo. The remainder is absorbed in the atmosphere by greenhouse gases (GHG) (21%) and the Earth’s surface (50%). The latter heats up the planetary surface and is released back into the atmosphere as infrared radiation (black body radiation) with a longer wave length than the absorbed radiation


The latter heats up the planetary surface and is released back into the atmosphere as infrared radiation (black body radiation) with a longer wave length than the absorbed radiation. This infrared radiation is partially absorbed by GHGs and therefore kept in the atmosphere instead of being released into space, explaining why the temperature of the atmosphere increases with its content of GHGs. Fig. 10.7 The greenhouse effect (©User: ZooFari/Wikimedia Commons/CC-BY-SA-3.0) R.K. Rosenbaum et al.


A cause–effect chain for climate change is shown in Fig. 10.8 and can be summarised as follows: 1. GHG emissions 2. Transport, transformation and distribution of GHG in the atmosphere 3. Disturbance of the radiation balance—radiative forcing (primary effect, midpoint) 4. Increase in global temperatures of atmosphere and surface 5. Increase in sea level due to heat expansion and the melting of land-based ice 6. Increased water vapour content of the atmosphere causing more extreme weather 7


Increase in sea level due to heat expansion and the melting of land-based ice 6. Increased water vapour content of the atmosphere causing more extreme weather 7. Negative effects on the ecosystems and human health (endpoint) Until now the unanimously used climate change indicator on midpoint level in LCA has been the Global Warming Potential, an emission metric first introduced in the IPCC First Assessment Report (IPCC 1990) and continuously updated by IPCC GHG emissions (CO2, CH4, N20, CFCs...) Increased atmospheric concentrations Increased radiative forcing Increased atmospheric temperature Extreme weather events Damage to ecosystems Damage to human health Melting of land ice Flooding Droughts Sea level rise Change in Earth’s cover Increased albedo Soot and aerosol emissions Fig. 10.8 Impact pathway for climate change Life Cycle Impact Assessment


since then with the latest version in the Fifth Assessment Report (IPCC 2013). Global warming potentials are calculated for each GHG according to: GWPi 1⁄4 R T 0 ai  CiðtÞdt R T 0 aCO2  CCO2ðtÞdt ð10:4Þ where • ai: thermal radiation absorption (instant radiative forcing) following an increase of one unit in the concentration of gas i • Ci(t): Concentration of gas i remaining at time t after emission • T: number of years for which the integration is carried out (e.g. 20 or 100 years) GWP100-year is directly used in LCIA as the characterisation factor. As shown above, it is the ratio of the cumulated radiative forcing over 100 years of a given GHG and that of CO2, with the unit of kg CO2-eq/kg GHG. Therefore, GWP for CO2 is always 1 and a GWP100 for methane of 28 kg CO2-eq/kg methane (see Table 10.3) means that methane has 28 times the cumulated radiative forcing of CO2 when integrating over 100 years


The difference in GWP20 and GWP100 for methane shown in Table 10.3 is due to the fact that methane has a relatively short atmospheric lifetime of 12 years compared to CO2’s lifetime which is at least one order of magnitude higher, which means that methane’s GWP gets lower the longer the time horizon over which it is integrated (i.e. sort of a ‘dilution’ of its effect over a longer time). On the other hand a more persistent GHG such as nitrous oxide with 120 years lifetime has a similar value when integrating over 20 and 100 years and the ‘time-dilution’ effect would only become visible when integrating over time periods significantly longer than 120 years. 10.6.3 Emissions and Main Sources Many greenhouse gases are naturally present in the atmosphere and contribute to the natural greenhouse effect


10.6.3 Emissions and Main Sources Many greenhouse gases are naturally present in the atmosphere and contribute to the natural greenhouse effect. Estimated main contributors to the natural greenhouse effect are: Table 10.3 Excerpt from the list of GWP (IPCC 2014a) Substance Molecule Atmospheric lifetime (years) Radiative efficiency (W/ (m2 ppb)) GWP (kg CO2-eq/kg GHG) 20 years 100 years Carbon dioxide CO2 1.37E−05 Methane CH4 3.63E−04 Nitrous oxide N2O 3.00E−03 R.K. Rosenbaum et al.


• Water vapour: *55% • Carbon dioxide (CO2): 39% • Ozone (O3): 2% • Methane (CH4): 2% • Nitrous oxide (N2O): 2% Anthropogenic water vapour emissions do not contribute to climate change as the presence of water vapour is a function of atmospheric temperature and evaporation surfaces. For the other constituents however, anthropogenic sources for CO2, CH4 and N2O do contribute to increasing the greenhouse effect beyond its natural state. Further relevant GHG emissions also include industrial volatile and persistent halocarbons (chlorinated fluorocarbons including CFCs (“freons”), HCFCs and perfluoromethane) and sulphur hexafluoride (SF6). GHG emissions are attributable to almost any human activity. The most important contributing activities are: burning of fossil fuels and deforestation (including releasing carbon from soil and change in albedo). Figure 10.9 shows the global contributions to GWP from five major economic sectors for the year 2010


Figure 10.9 shows the global contributions to GWP from five major economic sectors for the year 2010. Industry, agriculture, housing and transport are the dominating contributors to GHG emissions. In addition to the greenhouse gases which all exert their radiative forcing in the atmosphere over timespans of years to centuries, there are also more short-lived radiative forcing agents that are important for the atmospheric temperature in a more short-term perspective. These include: • Sulphate aerosols (particulate air pollution caused by the emission of sulphur oxides from combustion processes) that reduce the incoming radiation from the sun and thus have a negative contribution to climate change • Nitrogen oxides NO and NO2 (jointly called NOx) and VOC from combustion processes, that contribute to photochemical formation of ozone (see Sect


10.10) which is a strong but short-lived radiative forcing gas The radiative forcing impact of short-lived agents like these is very uncertain to model on a global scale, and their contribution to climate change is therefore not currently included in LCIA. 10.6.4 Existing Characterisation Models All existing LCIA methods use the GWP (Eq. 10.4) for midpoint characterisation. In terms of time horizon most use 100 years, which has been recommended by IPCC as the best basis for comparison of GHGs, while some methods use a 500 year time horizon to better incorporate the full contribution from the GHGs. As mentioned, the longer time perspective puts a higher weight on long-lived GHGs like nitrous oxide, CFCs and SF6 and a lower weight on short-lived GHGs like methane. Life Cycle Impact Assessment


So far radiative forcing agents with shorter atmospheric lifetime than methane are not considered in LCIA even though they also contribute to changing temperatures. However, a UNEP-SETAC expert workshop in 2016 recommended that climate change assessment at midpoint should be split into two sub-categories, respectively, focusing on the long-term climate change contributions and on the rate by which temperature changes occur. The two would be expressed in different metrics and not aggregated at midpoint level. It is expected that the distinction into two midpoint categories will cater better for the damage modelling since both rate of change and magnitude of the long-term temperature increase are important. Endpoint characterisation of climate change is a challenge due to the complexity of the underlying environmental mechanisms with multiple feedback loops of which many are probably unknown, the global scale and the very long time perspective


In particular damages to human health are also strongly affected by local and regional differences in vulnerability and ability of societies to adapt to changing Fig. 10.9 Direct GHG emission shares (% of total anthropogenic GHG emissions) of five major economic sectors in the world in 2010. The pull-out shows how indirect CO2 emission shares (in % of total anthropogenic GHG emissions) from electricity and heat production are attributed to sectors of final energy use. ‘Other Energy’ refers to all GHG emission sources in the energy sector other than electricity and heat production. ‘AFOLU’ stands for Agriculture, Forestry, and Other Land Use [taken from IPCC (2014b)] R.K. Rosenbaum et al.


climate conditions. Some endpoint methods have proposed endpoint characterisation factors (e.g. Ecoindicator99, ReCiPe, LIME, IMPACT World+ and LC-IMPACT), but due to the state of current climate damage models, they inevitably miss many damage pathways and are accompanied by very large uncertainties, where even the size of these uncertainties is difficult to assess. This is why other endpoint methods (e.g. IMPACT 2002+) refrain from endpoint modelling for this impact category and present the midpoint results for climate change together with the endpoint results for the rest of the impact categories. In any case, endpoint results for climate change must be taken with the greatest caution in the interpretation of results. For further details see Chap. 40 and Hauschild and Huijbregts (2015)


In any case, endpoint results for climate change must be taken with the greatest caution in the interpretation of results. For further details see Chap. 40 and Hauschild and Huijbregts (2015). 10.7 Stratospheric Ozone Depletion 10.7.1 Problem Ozone (O3) is a highly reactive and unstable molecule consisting of three oxygen atoms and forms a bluish gas at normal ambient temperature with a distinct somewhat sharp odour. This molecule is present in lower atmospheric layers (tropospheric ozone as a consequence of photochemical ozone formation) and in larger concentrations (about 8 ppmv) also in higher altitudes between 15 and 40 km above ground (stratospheric ozone). Tropospheric, ground-level ozone is considered a pollutant due to its many harmful effects there on humans, animals, plants and materials (see Sect. 10.10)


Tropospheric, ground-level ozone is considered a pollutant due to its many harmful effects there on humans, animals, plants and materials (see Sect. 10.10). However, as a component of stratospheric atmospheric layers, it is vital to life on planet Earth, due to its capacity to absorb energy-rich UV radiation, thus preventing destructive amounts of it from reaching life on the planet’s surface. Stratospheric ozone depletion refers to the declining concentrations of stratospheric ozone observed since the late 1970s, which are observed in various ways: (1) As the ‘ozone depletion area’ or ‘ozone hole’ (an ambiguous term often used in public media referring to an area of critically low stratospheric ozone concentration), a recurring annual cycle of relatively extreme drops in O3 concentrations over the poles which start to manifest annually in the late winter/early spring of each hemisphere (i.e


from around September/October over the South pole and March/April over the North pole) before concentrations recover again with increasing stratospheric temperatures towards the summer. ‘Ozone holes’ have been observed over Antarctic since the early 1980s as shown in Fig. 10.10. (2) A general decline of several percent per decade in O3 concentrations in the entire stratosphere. Ozone concentration is considered as critically low when the value of the integrated ozone column falls below 220 Dobson units (a normal value being about 300 Dobson units). Dobson Units express the whole of ozone in a column from the ground passing through the atmosphere. Life Cycle Impact Assessment