The figure should be read as follows, giving an example of the depicted impact pathways: land occupation leads to physical changes to soil, which leads to an altered soil function and affects habitats and net primary production which eventually leads to damage on ecosystem quality. The picture provides a good display of the complexity involved in land-use modelling. For some of the presented impacts, such as warming effect due to albedo change or landscape impairment, characterisation models have yet to be developed. The same type of human activity may cause different land-use related impacts depending on the region of the world where the activity takes place. This variation is due to the strong influence of climate, soil quality, topography and ecological quality on the magnitude of the impact. For example, deforestation of a forest area LIFE ON EARTH –BIODIVERSITY Provisioning • Food • Fresh water • Wood and fiber • Fuel • ..


For example, deforestation of a forest area LIFE ON EARTH –BIODIVERSITY Provisioning • Food • Fresh water • Wood and fiber • Fuel • ... Regulating • Climate regulation • Flood regulation • Disease regulation • Water purification • ... Cultural • Aesthetic • Spiritual • Educational • Recreational • ... Supporting • Nutrient cycling • Soil formation • Primary production • ... ECOSYSTEM SERVICES Fig. 10.23 The land use impact category focuses on damage to biodiversity— which represents the foundation of ecosystems—as well as on the provision of ecosystem services, due to land conversion and land use [adapted from Millenium Ecosystem Assessment (2005)] Life Cycle Impact Assessment

Hauschild et al., LCA: Theory and Practice


for use in agriculture in the Brazilian Amazon has a greater impact in terms of number of species affected than forest clearing in an ecologically poorer European region. Because land use impacts depend on-site-specific conditions, land use is Land use Land transformation Physical changes to soil surface Terr. eutrophication Use of pesticide Physical changes to soil Physical changes to flora and fauna Chemical changes to flora and fauna Changes in water logged conditions Altered species composition Soil erosion Reduction of habitat size elsewhere Albedo change Altered soil function (SOM) Climate change Removal of unwanted substances Regulation of freshwater/ nutrient conc


Change in net primary production Unique landscapes and archaeological sites Topsoil formation / Preservation Affected biodiversity Desertification Irrigation Life cycle inventory (LCI) Possible midpoint LCIA indicators Endpoint LCIA indicators/ Areas of protection AoP human health Ecosystem quality (AoP natural environment) Resource depletion (AoP natural resources) Downstream sediment deposition Changes in CO2, N2O and CH4 release Land occupationa Fig. 10.24 Impact pathway for land use impacts; dashed arrows indicate impacts covered by emission-related impact categories and by water use in the case of irrigation [adapted from EC-JRC (2011)]. aLand occupation will not cause changes but will contribute to prolong the changed conditions R.K. Rosenbaum et al.


considered a local impact category in LCA, in opposition to other impact categories of global geographic scope such as climate change, whose environmental effects (in terms of radiative forcing) are independent of the location of the emission. As a consequence of the above explanation, methods that focus on land-use impacts should include geospatial data both in the LCI and the LCIA phases. The inventory must contain information on the geographic location of the human intervention, with a level of detail that may vary from the exact coordinates to coarser scales (e.g. biome, country, continent), depending on the goal and scope of the study and if the inventory refers to the foreground or to the background system (see Chap. 9). In the LCIA, characterisation factors for a given impact indicator must capture the sensitivity of the habitat to the impact modelled


9). In the LCIA, characterisation factors for a given impact indicator must capture the sensitivity of the habitat to the impact modelled. For example, characterisation factors for soil erosion may include information on the soil depth in the specific location of the activity under evaluation, as the impact of soil loss will depend on the soil stock size, i.e. thinner soils are more vulnerable than thicker soils (Núñez et al. 2013). Every geographic unit of regionalised impact assessment methods has its own characterisation factor. Within the boundary of such a unit, it is assumed that an activity triggers the same impacts on land. 10.14.3 Existing Characterisation Models Characterisation of land use in LCA has been extensively discussed over the last decades but is far from being settled, because the first operational methods have only been available since 2010


Until then, land use was only an inventory flowcounted in units of surface occupied and time of occupation (m2 and years) and surface transformed (m2), without any associated impact. The main reason for this “late development” is that land-use related impacts rely on spatial and temporal conditions where the evaluated activity takes place, whereas traditional LCA is site-generic. During the last few years, the release of geographical information system (GIS) software and data sets have brought new opportunities in LCA to model land-use impacts and in general, any other spatially dependent impact category. Today, there are LCIA methods to evaluate impacts on biodiversity and impacts on several ecosystem services. From the long list of services provided by terrestrial ecosystems (24 acknowledged in the Millennium Ecosystem Assessment international work programme (2005), LCA focuses on those which are recognised as being more environmentally relevant (i.e


educational and spiritual values are excluded). A non-exhaustive list of methods is provided below. For completeness, see Milà i Canals and de Baan (2015): • Impacts on biodiversity: Biodiversity should be preserved because of its intrinsic value. The most commonly applied indicator is based on species richness, given the availability of data (Scholz 2007; Koellner and Scholz 2008; de Baan et al. 2013a, b). Damage on biodiversity is commonly expressed in Life Cycle Impact Assessment


quantity of species biodiversity loss, either in relative terms (potentially disappeared fraction of species times surface, PDF.m2) or in absolute species loss. Existing indicators for biodiversity are at the endpoint level (in Fig. 10.24, Ecosystem quality-AoP natural environment box in the lower row). The UNEP-SETAC Life Cycle Initiative project on global guidance for LCIA indicators and methods provisionally recommended characterisation factors from Chaudhary et al. (2015) representing global potential species loss from land use to assess impacts on biodiversity due to land use and land-use change as hotspot analysis in LCA only (not for comparative assertions nor eco-labelling). Further testing of the CFs as well as the development of CFs for further land-use types are required to provide full recommendation. • Impacts on ecosystem services: Includes a range of indicators for life support functions that ecosystems provide


• Impacts on ecosystem services: Includes a range of indicators for life support functions that ecosystems provide. Ecosystem services are hardly covered in LCIA and proposals are still incipient. All available methods are on the midpoint level (in Fig. 10.24, boxes between the LCI and the endpoint), which means that comparison or aggregation with damages on biodiversity is not possible so far. The recent draft review of land-use characterisation models for use in Product and Organisation Environmental Footprint (PEF/OEF) provisionally (i.e. “apply with caution”) recommended characterisation factors from LANCA (Bos et al. 2016) to assess impacts on ecosystem services (EC-JRC 2016). Currently, there are LCA methods for the following ecosystem services: • Biotic production potential: capacity of ecosystems to produce and sustain biomass on the long term


Currently, there are LCA methods for the following ecosystem services: • Biotic production potential: capacity of ecosystems to produce and sustain biomass on the long term. Available indicators are based on the soil organic matter (or carbon) content (Brandão and Milà i Canals 2013), the biotic production (Bos et al. 2016) and the human appropriation of the biotic production (Alvarenga et al. 2015) • Carbon sequestration potential: capacity of ecosystems to regulate climate by carbon uptake from the air. The size of the climatic impact is determined by the amountofCO2transfersbetweenvegetation/soilandtheatmosphereinthecourse of terrestrial release and re-storage of carbon (Müller-Wenk and Brandão 2010) • Freshwater regulation potential: capacity of ecosystems to regulate peak flow and base flow of surface water


Available indicators refer to the way a land-use system affects average water availability, flood and drought risks, based on the partition of precipitation between evapotranspiration, groundwater infiltration and surface runoff (Saad et al. 2013; Bos et al. 2016) • Water purification potential: mechanical, physical and chemical capacity of ecosystems to absorb, bind or remove pollutants from water. Site-specific soil properties such as texture, porosity and cation exchange capacity are used as the basis for the assessment (Saad et al. 2013) • Erosion regulation potential: capacity of ecosystems to stabilise soils and to prevent sediment accumulation downstream. The soil performance is determined by the amount of soil loss (Saad et al. 2013; Bos et al. 2016) and how this soil loss reduces the on-site soil reserves and the biotic production (Núñez et al. 2013) R.K. Rosenbaum et al.


• Desertification regulation potential: capacity of dry lands to resist irreversible degradation on the human time-frame. A multi-indicator system of four variables, namely climate aridity, soil erosion, aquifer exploitation and fire risk, determines the desertification ecosystem vulnerability (Núñez et al. 2010) The land-use impact category is likely the LCA category most affected by potential problems of double counting. This is because methods for emissions and methods for land use have been developed under two different, incompatible approaches. Emission models are bottom-up: the starting point is the elementary flowin the LCI and the impact model describes stepwise all the mechanisms that link the cause (the LCI) to the consequence (midpoint or endpoint impact). Land-use models, in contrast, are top-down


Land-use models, in contrast, are top-down. This means that they are based on empirical observations of the state of the environment, but there is no evidence of the connection between the consequence and the (supposed) cause. For example, methods to evaluate biodiversity damage are based on databases of the species present under different land-use types. The reduction in species richness from e.g. a forest to an arable intensive agricultural land is driven by many reasons that partially add to each other: cut down of trees and replacement for crops, use of tractor and other agricultural machinery, emission of pesticides and fertilisers, etc. However, how and how much each of the reasons above contributes to the actual biodiversity loss observed in the agricultural land is not known. The development of mechanistic models such as the ones used to characterise emissions, have the potential to resolve the issue of double counting. For further details see Chap


The development of mechanistic models such as the ones used to characterise emissions, have the potential to resolve the issue of double counting. For further details see Chap. 40 and Hauschild and Huijbregts (2015). 10.15 Water Use 10.15.1 Problem Water is a renewable resource which, thanks to the water cycle, does not disappear. It is a resource different from any other for two main reasons: (1) it is essential for human andecosystem life and (2) its functions are directly linked to its geographic and seasonal availability, since transporting it (and to a lesser extent, storing it) is often impractical and costly. There is sufficient water on our planet to meet current needs of ecosystems and humans. About 119,000 km3 are received every year on land in different forms of precipitation, out of which 62% are sent back directly to the atmosphere via evaporation and plant transpiration


About 119,000 km3 are received every year on land in different forms of precipitation, out of which 62% are sent back directly to the atmosphere via evaporation and plant transpiration. Out of the 38% remaining, humans use only about 3%, out of which 2.1% for agriculture, 0.6% for industrial uses and 0.3% for domestic uses. However, despite these small fractions, there are still important issues associated with water availability. Many important rivers are running dry from overuse (including the Colorado, Yellow and Indus), greatly affecting local aquatic and terrestrial ecosystems. Humans compete for the use of water in some regions, sometimes leading to the exchange of water rights on the Life Cycle Impact Assessment


market or to the exacerbation of tensions between nations. The World Water Council described the problem well by stating: “There is a water crisis today. But the crisis is not about having too little water to satisfy our needs. It is a crisis of managing water so badly that billions of people—and the environment—suffer badly”. In addition to the current mismanagement of the water, which is strongly linked to a competing demand for human uses and ecosystems for a limited renewable resource, the human demand is only increasing, namely due to a growing population and changing diets (with increasing meat consumption). Water availability is also changing due to climate change, aggravating droughts and flooding and hence further increasing the gap between the demand and availability in many highly populated regions around the world


Since the problems associated with water are dependent on where and when water is available, as well as in which quality, it is these aspects that also need to be considered when we assess potential impacts of human freshwater use on the environment (including human health) in LCA. 10.15.2 Environmental Mechanism Before diving into the assessment of potential impacts associated with water, some concepts are important to establish first. • Types of water use: Water can be used in many different manners and the term water use represents a generic term encompassing any type of use


• Types of water use: Water can be used in many different manners and the term water use represents a generic term encompassing any type of use. Consumptive and degradative use are the two main types of use and all other types of use (borrowing, turbinated, cooling, etc.) can generally be defined by one or a combination of the following three terms: – Water withdrawal: “anthropogenic removal of water from any water body or from any drainage basin either permanently or temporarily” (ISO 2014) – Consumptive use/water consumption: water use where water is evaporated, integrated in a product or released in a different location then the source – Degradative use/water degradation: Water that is withdrawn and released in the same location, but with a degraded quality. This includes all forms of pollution: organic, inorganic, thermal, etc. (ISO 2014) • Sources of water: Different sources of water should be distinguished as impacts from using them will often differ


This includes all forms of pollution: organic, inorganic, thermal, etc. (ISO 2014) • Sources of water: Different sources of water should be distinguished as impacts from using them will often differ. In general, the following main sources are differentiated: surface water, groundwater, rainwater, wastewater and sea water. Some more specific descriptions can include brackish water (saline water with lower salinity than sea water, generally between 1000 and 10,000 mg/l) or fossil water (non-renewable groundwater) • Water availability: when used as an indicator, this describes the “extent to which humans and ecosystems have sufficient water resources for their needs”, with a note that “Water quality can also influence availability, e.g. if quality is not R.K. Rosenbaum et al.


sufficient to meet users’ needs. If water availability only considers water quantity, it is called water scarcity”. (ISO 2014). However, this term (water availability) is also used to refer to the renewable water volume that is available in a specific area during a specific time, most typically annually or monthly over a watershed (m3/year or m3/month) • Water Scarcity: Different definitions exist for water scarcity, but in LCA the following standardised one is retained: “extent to which demand for water compares to the replenishment of water in an area, e.g. a drainage basin, without taking into account the water quality” (ISO 2014) • Watershed (also called drainage basin): “Area from which direct surface runoff from precipitation drains by gravity into a stream or other water body” (ISO 2014)


In general the main watershed is taken as the reference geographical area to define the same location, as countries are often too large to represent local water issues and smaller areas would lack data and relevance As mentioned above, freshwater is received from precipitation and a fraction of it (about 38%) is made available as “blue water”, or flowing water which can be used by humans and ecosystems via lakes, rivers or groundwater. Some freshwater is also present in deep fossil aquifers, which are not renewable (not recharged by precipitation), and can be used by humans if pumped out. Groundwater aquifers can recharge lakes and rivers, and vice versa, depending on the topology, soil porosity, etc. Surface water is used by humans, aquatic ecosystems and terrestrial ecosystems, whereas groundwater can be used by some terrestrial ecosystems and humans. Water use impact assessment at midpoint level typically focuses on water deprivation


Water use impact assessment at midpoint level typically focuses on water deprivation. Although water is renewed, there is a limited amount available in an area at any point in time, and different users must share, or compete for, the resource. Consuming a certain volume of water will lower its availability for users downstream and may also affect groundwater recharge for example. Users depending on this water may be deprived and suffer consequences. The extent to which they will be deprived will depend on the water scarcity in a region (Fig. 10.25). The higher the demand in comparison to the availability, the more likely a user will be deprived. This user can be (1) humans (present and future Fig. 10.25 The potential deprivation caused by an additional water consumption in a region is assessed by multiplying this water consumption with a local water scarcity factor. The result is also called a water scarcity footprint Life Cycle Impact Assessment


generations) and (2) ecosystems (Bayart et al. 2010). Quantifying “the potential of a user (humans or ecosystems) to be deprived when water is consumed in a region” (Boulay et al. 2017) is the question normally answered at the midpoint level using for example a scarcity indicator (or user-specific deprivation potential if they exist), whereas assessing the potential damages from this deprivation on human health and ecosystem quality is an endpoint assessment. At the endpoint level, water use impact assessment is focused on the consequences of the water deprivation for humans and ecosystems. The higher the scarcity (and competition between human users), the larger the fraction of an additional water consumption that will deprive another user. Which human user is affected will depend on the share of each water user in a region, as well as their ability to adapt to water deprivation


Which human user is affected will depend on the share of each water user in a region, as well as their ability to adapt to water deprivation. If the deprived users have access to sufficient socio-economic resources, they may adapt and turn towards a backup technology like desalinisation of seawater or freshwater import to meet their needs. Impacts from human deprivation are then shifted from being solely on human health to all impact categories that are affected by the use of this backup technology. However, if socio-economic means are not sufficient to adapt to lower water and/or food availability, deprivation may occur. Since the potential impacts associated with water deprivation for humans assessed in LCA are on human health, deprivation of water for domestic use, agriculture and aquaculture/fisheries are relevant


Domestic users which already compete for water and have no means to compensate lower water availability via purchasing or technological means will suffer from freshwater deprivation, which is associated to water-related diseases caused by the use of improper water sources and change of behaviour. Agricultural users that are deprived of water for irrigation may produce less, which in turn will lead to lower food availability, either locally or internationally through trade, which may increase health damages associated with malnutrition. Similarly, lower freshwater availability for aquaculture or fisheries could lower fish supply and also contribute to malnutrition impacts, although this was shown to be negligible in comparison to other users’ deprivation. This impact pathway, leading to damages on human health, is shown in Fig. 10.26. Consuming water can also affect water availability for aquatic and terrestrial ecosystems


This impact pathway, leading to damages on human health, is shown in Fig. 10.26. Consuming water can also affect water availability for aquatic and terrestrial ecosystems. If the flow of the river is altered, or the volume of the lake is reduced, aquatic ecosystems have less habitat space and may either have to adapt or suffer a change in species density. Since water compartments are strongly interconnected, consuming water in a lake can affect the groundwater availability and vice versa, and each change in availability can lead to a loss of species. Consuming water can also alter the quality by reducing the depth of the water body for example, increasing temperature or concentrating contaminants. Aquatic ecosystems are dependent not only on a minimum volume for their habitat, but also on the flow variations which are naturally influenced by seasons. Human interference with this flow variation can also cause potential species loss


Human interference with this flow variation can also cause potential species loss. The groundwater table in some regions directly feeds the roots of the vegetation and lowering the aquifer’s level can mean that shorter roots species no longer reach their source of water. The relevant mechanisms are summarised in Fig. 10.27. These impact pathways appear R.K. Rosenbaum et al.


to be complementary, however more research is needed to determine how they should be used together and to provide one harmonised methodology. 10.15.3 Existing Characterisation Models A stress/scarcity index (here used interchangeably) is the most commonly used midpoint, even if it does not necessarily represent an actual point on the impact pathway of all endpoint categories. A scarcity index is based on the comparison between water used and renewable water available, and represents the level of competition present between the different users (ideally human users and ecosystems). Early indicators (Frischknecht et al. 2008; Pfister et al. 2009) are based on withdrawal-to-availability (WTA) ratios as these were the data available at the time


Early indicators (Frischknecht et al. 2008; Pfister et al. 2009) are based on withdrawal-to-availability (WTA) ratios as these were the data available at the time. Since water that is withdrawn but released into the same watershed (within a reasonable time-frame) does not contribute to scarcity, indicators emerged which were based on consumption-to-availability (CTA) ratios instead of withdrawals, when the needed data became available (Boulay et al. 2011; Hoekstra et al. 2012; m3 unavailable to other users Water consumption scarcity distribution of affected user(s) m3 deprived for fisheries m3 deprived for agriculture m3 deprived for domestic m3 deprived causing health damages Damage to human health socio-economic parameter effect factors for domestic, agricultural and fisheries deprivation Fig


10.26 Impact pathway from water consumption to water deprivation for human users leading to potential impacts on human health in Disability Adjusted Life Years (DALY) [adapted from Boulay et al. (2015)] Life Cycle Impact Assessment


Watershed or downstream surface water flow alteration regime Saline intrusion Ground water table change Soil moisture change of exotic species Thermic pollution of water Suspended solids delivered to rivers Groundwater consumption Surface water consumption Water fragmentation Upstream surface water flow regime alteration Biological water quality alteration Surface water temperature Water quality alteration Mortification of average flow rate, volume, area or depth of a water body Alteration of flow variability, and drought or flood intensity or occurence Changes in species interaction Artificial barrier to migration Habitat modifications Constrained water availability Damage to aquatic ecosystems (plants, fishes, etc...) Damage to terrestrial ecosystems (plants, worms, mammals, etc...) Degradative use of water Water infrastructure (dams, reservoir, canals, etc.) Consumptive use of water Fig. 10.27 Impact pathways affecting ecosystem quality [adapted from Núñez et al. (2016)] R.K


10.27 Impact pathways affecting ecosystem quality [adapted from Núñez et al. (2016)] R.K. Rosenbaum et al.


Berger et al. 2014). Further development led to the inclusion of environmental water requirements as part of the water demand in order to better represent the total water demand from all users, including ecosystems, and resulted in a ratio based on demand-to-availability (DTA) being proposed (Boulay et al. 2014). However, one important information was lost in all these indicators: the absolute availability. A ratio of 0.5 may indicate that half of the available water is currently withdrawn, consumed or demanded, but it does not inform on the magnitude of this water volume (i.e. is it 1 or 1000 m3?). Regions differ largely in terms of absolute water availability (or aridity) and this information should not be discarded by only looking at the fraction of available water that is being used


Regions differ largely in terms of absolute water availability (or aridity) and this information should not be discarded by only looking at the fraction of available water that is being used. In 2016, the WULCA group (see below) proposed the area-specific Available Water Remaining indicator (based on availability minus demand), AWARE, inverted and normalised with the world average (Boulay et al. 2017). Ranging between 0.1 and 100, this index assesses the potential to deprive another user (human or ecosystem) of water, based on the relative amount, comparing to the world average, of water remaining per area once the demand has been met. The more water remaining compared to the average, the lower the potential to deprive another user, and vice versa. It should be noted that some midpoints also propose to include quality aspects, allowing the quantification of lower availability being caused by both consumptive and degradative use


It should be noted that some midpoints also propose to include quality aspects, allowing the quantification of lower availability being caused by both consumptive and degradative use. This is either done through the use of water quality categories and the assessment of their individual scarcity (Boulay et al. 2011), or through a distance-to-target approach, or dilution volume equivalent, in relation to a reference standard (Ridoutt and Pfister 2010; Bayart et al. 2014). As mentioned above, human water deprivation can cause health damage by depriving three users: domestic, agriculture or aquaculture/fisheries. Domestic deprivation has been assessed in two methods (Motoshita et al. 2011; Boulay et al. 2011) which quantify the impact pathways described above, either mechanistically or statistically. Both provide characterisation factors in DALY/m3 consumed and the details of the differences between the methods are described in Boulay et al. (2015)


Both provide characterisation factors in DALY/m3 consumed and the details of the differences between the methods are described in Boulay et al. (2015). Agricultural deprivation has been assessed in three methods (Pfister et al. 2009; Boulay et al. 2011; Motoshita et al. 2014). Differences are based on the user competition factor (scarcity) used, the underlying sources of data, the parameter upon which to base the capacity of users to adapt to water deprivation or not, the calculation of the effect factor and, most importantly, the inclusion or not of the trade effect, i.e. the ripple effect of lower food production to lower income and importing countries. Analysis of these methods and modelling choices is provided in Boulay et al. (2015) and at time of writing a consensus was built based on these three models and is described in the Pellston Workshop report from Valencia, 2016


(2015) and at time of writing a consensus was built based on these three models and is described in the Pellston Workshop report from Valencia, 2016. For the damage that water use may cause on ecosystems, several methods exist that attempt to quantify a part of the complex impact pathways between water consumption and loss of species, i.e. ecosystem quality impacts. An overview of these methods was prepared by Núñez et al. (2016) who analysed in details the existing models, assumptions and consistency. The large majority of them have not yet found their way into LCA practice. None of these endpoint models use water Life Cycle Impact Assessment


scarcity as a modelling parameter, and hence scarcity does not represent a “true midpoint” for ecosystem quality. The assessment of impacts on the impact category resources, or ecosystem services and resources, is still subject to debate and development. The main question pending being “what exactly are we trying to quantify?”. For the case of water, this can be answered in different ways: future generation deprivation, resource-equivalent approach or monetarisation, but these still require further development. The use of non-renewable sources of water fromfossil aquifers would fall in this category. For further details see Chap. 40 and Hauschild and Huijbregts (2015). Water is a precious resource for humans and ecosystems and our attempts to protect it come in different forms and from different angles. Numerous initiatives exist and indicators of all kinds are emerging regularly and, for the time being, continuously evolving


Numerous initiatives exist and indicators of all kinds are emerging regularly and, for the time being, continuously evolving. This should not be perceived as a problem or a sign of lesser value for these indicators; it simply reflects the fact that potential issues associated with water are diverse and so are the approaches to quantify and minimise them. The LCA approach aims to quantify potential impacts associated with human activities (a product, a service or an organisation) on specific areas of protection. Water-related indicators developed within the LCA framework are aligned with this goal, and efforts have been made to build consensus on these methodologies. The WULCA (water use in LCA) expert working group of the UNEP-SETAC Life Cycle Initiative has fostered the development and global harmonisation through international consensus of the water-related impact assessment methods in LCA


For further information on the existing methods, the reader is encouraged to explore the website: www.wulca-waterlca.org. 10.16 Abiotic Resource Use 10.16.1 Problem Natural resources constitute the material foundation of our societies and economies and, paraphrasing the definition of sustainability by the United Nation’s Commission on Environment and Development (the Brundtland Commission), they are as such fundamental for our abilities to fulfil our needs as well as for future generations’ possibilities to fulfil their own needs. Since we don’t know with any certitude what the needs of future generations for specific resources will be, and in order to respect the principle of sustainability, we have to ensure that the future resource availability is as good as possible compared to the current generation’s situation, i.e. we have to consider the future availability for all resources that we know and dispose of today


we have to consider the future availability for all resources that we know and dispose of today. The definition of natural resources has an anthropocentric starting point. What humans need from nature in order to sustain their livelihood and activities is a R.K. Rosenbaum et al.


resource. For the context of LCA, Udo de Haes et al. (1999) thus define natural resources as: “... those elements that are extracted for human use. They comprise both abiotic resources, such as fossil fuels and mineral ores, and biotic resources, such as wood and fish. They have predominantly a functional value for society.” Although water and land are also resources, their use causes direct impacts on the environment. In this respect they differ from the other resources and they are therefore treated as individual impact categories and described in separate sections. Currently, the resource use impact category covers mostly fossil fuels, minerals and metals so this will also be the focus here. In terms of future availability of a resource the issue is not the current extraction and use of the resource per se but the depletion or dissipation of the resource. Similar to the use of land, the use of resources can be viewed from an occupation perspective and a transformation perspective


Similar to the use of land, the use of resources can be viewed from an occupation perspective and a transformation perspective. While a resource is used for one purpose it is not available for other purposes, and there is thus a competition situation. When resources are used in a way that caters to their easy reuse at the end of the product life, they are still occupied and not immediately available to other use, but they are in principle available to future use for other purposes. This is the case for many uses of metals today. The occupation perspective is normally not addressed in LCIA of resources today [with the exception of Schneider et al. (2011)]. Rather than resource use the focus of the impact assessment is usually on the resource loss that occurs throughout the life cycle. Resource loss occurs through transformation of the resource when the use is either consumptive or dispersive


Resource loss occurs through transformation of the resource when the use is either consumptive or dispersive. Consumptive resource use converts the resource in a way so that it no longer serves as the resource it was. An example is the use of fossil resources as fuels, converting them in the combustion process into CO2 and water. The transformation occurring in dispersive resource use does not lose the resource but uses it in a way that leads to its dispersal in the technosphere or ecosphere in forms that are less accessible to human use than the original resource was. Dispersive use occurs for most of the metals. There is still much debate about what the issue of concern of natural resources is and about how this should be addressed in LCIA (Hauschild et al. 2013)


There is still much debate about what the issue of concern of natural resources is and about how this should be addressed in LCIA (Hauschild et al. 2013). This may be explained by the difference in functional values of natural resources on the one hand, and intrinsic or existence values of other impact categories, assessing impacts on human health and ecosystem quality, on the other hand. Steen (2006) summarised different perceptions of the problem with abiotic resources in LCIA as: “..


Steen (2006) summarised different perceptions of the problem with abiotic resources in LCIA as: “... (1) assuming that mining cost will be a limiting factor, (2) assuming that collecting metals or other substances from low-grade sources is mainly an issue of energy, (3) assuming that scarcity is a major threat and (4) assuming that environmental impacts from mining and processing of mineral resources are the main problem.” The extraction of resources and their conversion into materials that are used in product systems are accompanied by energy use and direct emissions that make the raw material extraction sector an important contributor to environmental impacts and damages in many parts of the world. These impacts are addressed by the other impact categories which are considered in LCA, and hence not treated under the resource depletion impact category. Life Cycle Impact Assessment


10.16.2 Environmental Mechanism With a focus on resource availability for current and future generations, the environmental mechanism may look as shown in Fig. 10.28. It is assumed that resources with easy and/or cheap access and with high concentration or quality are extracted first. Consequently, today’s resource extraction will lead future generations to extract lower concentration or lower value resources. This results in additional efforts for the extraction of the same amount of resource which can be translated into higher energy or costs. The endpoint of the impact pathway for resource use is often assessed as the future consequences of resource extraction. Schneider et al. (2014) went further in the pathway with the development of a new model for the assessment of resource provision including economic aspects that influence the security of supply and affect the availability of resources for human use


Resource use Decreased availability Regeneration (natural growth) Damage to availability of resources for human wealth Future availability & effort needed Damage to ecosystems (less water, less prev) Damage to human health (less food, less shelter,...) Recovery (urban & mining) Future provision of needs stock size regeneration rates, recovery fractions geological distribution, technology development societal demand, substitution Fig. 10.28 Impact pathway for resource depletion [adapted from EC-JRC (2010b)] R.K. Rosenbaum et al.


Several classification schemes exist for resources (Lindeijer et al. 2002), classifying them according to their origin into Abiotic resources (inorganic materials— e.g. water and metals, or organic materials that are non-living at the moment of their extraction—fossil resources) and Biotic resources (living at least until the time of their extraction or harvest from the environment, and hence originating in the biomass). A further classification may be done according to the ability of the resource to be regenerated and the rate by which it may occur. Here resources are classified into: • Stock resources exist as a finite and fixed amount (reserve) in the ecosphere and are not regenerated (metals in ores) or regenerated so slowly that for practical purposes the regeneration can be ignored (fossil resources) • Fund resources regenerate but can still be depleted (like the stock resources) if the rate of extraction exceeds the rate of regeneration


Depletion can be temporary if the resource is allowed to recover but it can also be permanent for biotic fund resources where the species underlying the resource becomes extinct. Biotic resources are fund resources but there are also examples of abiotic resources like sand and gravel where the regeneration rate is so high that it is meaningful to classify them as fund resources • Flow resources are provided as a flow (e.g. solar radiation, wind and to some extent freshwater) and can be harvested as they flow by. Flow resources cannot be globally depleted but there may be local or temporal low availability (notably for freshwater—see Sect. 10.15) Stock resources are also referred to as non-renewable resources while fund and flow resources jointly are referred to as renewable resources. Resources may also be classified as exhaustible, i.e. they can be completely used up, and inexhaustible, which are unlimited


Resources may also be classified as exhaustible, i.e. they can be completely used up, and inexhaustible, which are unlimited. 10.16.3 Existing Characterisation Models Impacts resulting from resource use are often divided into three categories following the impact pathway (see Fig. 10.28): 1. Methods aggregating natural resource consumption based on an inherent property 2. Methods relating natural resource consumption to resource stocks or availability 3. Methods relating current natural resource consumption to consequences of future extraction of natural resources (e.g. potential increased energy use or costs). Category 1 methods focus for example on exergy [expressing the maximum amount of useful work the resource can provide in its current form, (Dewulf et al. 2007)], energy (Frischknecht et al. 2015) and solar energy (Rugani et al. 2011). Life Cycle Impact Assessment


While being very reproducible and also easy to determine, the relevance of exergy loss to the scarcity and future availability of the resource is not obvious and therefore these methods are not recommended by the European Commission (EC-JRC 2011). However, the cumulative energy demand (CED) method (Frischknecht et al. 2015) is still used frequently as a resource accounting method in LCA studies and is also part of various comprehensive LCIA methods like CML-IA for fossil fuels (Guinée et al. 2002), ReCiPe (Goedkoop et al. 2012) and the Ecological Scarcity method (Frischknecht and Büsser Knöpfel 2013). Viewing resource use from a sustainability perspective, the characterisation at midpoint level in the environmental mechanism (Fig. 10.28) should address its impact on the future availability of the resource for human activities


10.28) should address its impact on the future availability of the resource for human activities. Several category 2 methods do this through incorporating a measure of the scarcity of the resource, expressed by the relationship between what is there and what is extracted, i.e. between the size of the stock or fund and the size of the extraction. However, there are different measures to determine the size of the stock or fund yet to be extracted. Figure 10.29 shows a terminology for classifying a stock resource into classes according to their economic extractability and whether they are known or unknown. Here we will describe those most used in LCIA. The reserves are the part of the resource which are economically feasible to exploit with current technology


Here we will describe those most used in LCIA. The reserves are the part of the resource which are economically feasible to exploit with current technology. The reserve base is the part of the demonstrated resource that has a reasonable potential to become economically and technically available if the price of the resource increases or if more efficient extraction technology becomes available. Ultimate reserves are the resources that are ultimately available in the earth’s crust, which include nonconventional and low-grade materials and common rocks. This reserve Fig. 10.29 Resource/reserve classification for minerals [taken from U.S. Geological Survey (2015)] R.K. Rosenbaum et al.


estimate refers to the quantity of resources that is ultimately available, estimated by multiplying the average natural concentration of the resources in the earth’s crust by the mass of the crust. Lately, the extractable geologic resource, also called ultimate recoverable resource and ultimately extractable reserves, has also been adopted by a few LCIA methods. This reserve type is the amount of a given metal in ore in the upper earth’s crust that is judged to be extractable over the long term, e.g. 0.01% (UNEP International Panel on Sustainable Resource Management 2011). Each reserve estimate has pros and cons. Reserves are known and economically viable to extract, but this amount can fluctuate considerably with changes in prices and discoveries of new deposits. Reserve base has not been reported by the US Geological Survey since 2009 because its size also increases and decreases based on technological advances, economic fluctuations and new discoveries, etc


Consequently, basing the characterisation factoron reserves or reserve base has the problem that it changes with time. Ultimate reserves are calculated on basis of the average concentration of metals in the earth’s crust so they are more stable but this is not a good indicator of the quantity of the resource that can realistically be exploited. Finally, the extractable geologic resource seems to be a quite certain reserve estimate but authors are still debating how to quantify it (Schneider et al. 2015). From the category 2 methods, CML-IA and EDIP are the most widely used. The CML-IA method for characterisation of abiotic stock resources defines an Abiotic Depletion Potential, ADP with a characterisation factor based on the annual extraction rate and the reserve estimates. In Guinée et al. (2002) only the ultimate reserves are included, but Oers et al. (2002) defined additional characterisation factors on the basis of reserves and reserve base estimates


In Guinée et al. (2002) only the ultimate reserves are included, but Oers et al. (2002) defined additional characterisation factors on the basis of reserves and reserve base estimates. CML-IA using reserve base estimates is the method recommended in the ILCD Handbook for LCIA in the European context (EC-JRC 2011). An alternative approach inspired by the EDIP method (Hauschild and Wenzel 1998) bases the assessment for the abiotic stock resources on the reserve base and defines the characterisation as the inverse person reserve, i.e. the amount of reserve base per person in the world. For renewable resources, the EDIP inspired characterisation is based on the difference between the extraction rate and the regeneration rate. If the regeneration rate exceeds the extraction rate, it is considered that there is no resource availability issue, and the characterisation factor is given the value 0


If the regeneration rate exceeds the extraction rate, it is considered that there is no resource availability issue, and the characterisation factor is given the value 0. Further, down the impact pathway, category 3 methods have been developed expressing the future consequences of current resource consumption. Some methods quantify these consequences as additional energy requirements: Eco-Indicator 99, IMPACT 2002+; some methods quantify this effort as additional costs: ReCiPe and Surplus Cost Potential on basis of relationships between extraction and cost increase (Ponsioen et al. 2014; Vieira et al. 2016b), EPS 2000 and the Stepwise method based on willingness to pay; and some methods quantify this effort as additional ore material that has to be dealt with: Ore Requirement Indicator ORI (Swart and Dewulf 2013) and Surplus Ore Potential SOP (Vieira et al. 2016a) used in the LC-IMPACT LCIA method


2016a) used in the LC-IMPACT LCIA method. These methods suffer from a strong dependency on rather uncertain assumptions about the future efficiencies and energy needs of Life Cycle Impact Assessment


mining and extraction technologies, but they seem to better capture the issue of concern which is assuring a supply of resources to future generations. Schneider et al. (2014) defined a semi-quantitative method expressed as the economic resource scarcity potential (ESP) for evaluating resource use based on life cycle assessment. This method includes elements typically used in the discipline of raw materials criticality, like governance and socio-economic stability, trade barriers, etc., for which each element are scaled to the range 0–1. For metal resources, characterisation factors are mostly applied to the metal content in the ore, not the mineral that is extracted. The relevant inventory information is thus the amount of metal used as input, not the amount of mineral. This is also how life cycle inventory (LCI) databases model elementary flows of mineral and metal resources. Schneider et al


This is also how life cycle inventory (LCI) databases model elementary flows of mineral and metal resources. Schneider et al. (2015) considers not only the geological stock not yet extracted, but also the anthropogenic stock in circulation in products and goods. The geographic scale at which it is relevant to judge the availability and depletion of a resource depends on the relationship between the price and the density/transportability of the resource. The scale is global for the valuable and dense stock and fund resources that are easy to transport and hence traded on a world market (metals, oil, coal, tropical hardwood), while it is regional for the less valuable and/or less dense stock and fund resources that are used and extracted regionally (natural gas, sand and gravel, limestone) or even locally. For further details see Chap. 40 and Hauschild and Huijbregts (2015)


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WHO: Indoor air quality: organic pollutants. Report on a WHO Meeting, Berlin, 23–27 August 1987. EURO Reports and Studies 111. Geneva, Switzerland (1989) WHO: Health risks of particulate matter from long-range transboundary air pollution, Bonn, Germany (2006) WMO: Scientific Assessment of Ozone Depletion: 2014, World Meteorological Organization, Global Ozone Research and Monitoring Project Report No. 55. Geneva, Switzerland (2014) Author Biographies Ralph K. Rosenbaum LCA expert and environmental modeller focusing on LCIA development since early 2000s. Contributed to several UNEP/SETAC working groups towards global harmonisation of LCA methodology. Interested in LCIA modelling of emissions and water/soil resource use, operationalisation of uncertainty management and spatial differentiation. Michael Hauschild Involved in development of LCIA methodology since the early 1990s


Michael Hauschild Involved in development of LCIA methodology since the early 1990s. Has led several SETAC and UNEP/SETAC working groups and participated in the development of the ISO standards and the ILCD methodological guidelines. Main LCA interests are chemical impacts, spatial differentiation and science-based boundaries in LCIA. Anne-Marie Boulay LCA and water footprint expert focusing on water use impacts assessment in LCA since 2008. Chaired UNEP/SETAC and FAO working groups towards harmonisation of water use impact assessment methodologies, and has been involved in ISO standards development on water footprint, providing training on the topic for UNEP and ISO worldwide. Peter Fantke Develops methods for LCIA, health impact assessment and chemical alternatives assessment since 2006. Has contributed to UNEP/SETAC LCIA working groups and is USEtox Manager


Peter Fantke Develops methods for LCIA, health impact assessment and chemical alternatives assessment since 2006. Has contributed to UNEP/SETAC LCIA working groups and is USEtox Manager. Interested in quantifying and characterising chemical emissions, uncertainty analysis, consumer exposure, chemical substitution and model parameterisation. Alexis Laurent Working with LCA since 2010 with a strong LCIA focus, particularly on normalisation aspects. Main LCA interests include development of LCIA methods, LCA applications and footprinting of large-scale systems for policy-making (e.g. nations, sectors), and LCA applied to various technology domains, including energy systems. Montserrat Núñez Environmental scientist with interest in LCA, agriculture, and modelling of environmental impacts from resource use in agricultural activities. Involved in development of LCIA methods to assess impacts of land use and water use since 2007


Involved in development of LCIA methods to assess impacts of land use and water use since 2007. Marisa Vieira Both as researcher and consultant developing and applying LCA, LCM and footprints since 2007. Main interests are the application of life cycle thinking in companies, branch associations and policy and capability development through training on LCA, SimaPro and Environmental Footprint. R.K. Rosenbaum et al.


Chapter 11 Uncertainty Management and Sensitivity Analysis Ralph K. Rosenbaum, Stylianos Georgiadis and Peter Fantke Abstract Uncertainty is always there and LCA is no exception to that. The presence of uncertainties of different types and from numerous sources in LCA results is a fact, but managing them allows to quantify and improve the precision of a study and the robustness of its conclusions. LCA practice sometimes suffers from an imbalanced perception of uncertainties, justifying modelling choices and omissions. Identifying prevalent misconceptions around uncertainties in LCA is a central goal of this chapter, aiming to establish a positive approach focusing on the advantages of uncertainty management. The main objectives of this chapter are to learn how to deal with uncertainty in the context of LCA, how to quantify it, interpret and use it, and how to communicate it


The main objectives of this chapter are to learn how to deal with uncertainty in the context of LCA, how to quantify it, interpret and use it, and how to communicate it. The subject is approached more holistically than just focusing on relevant statistical methods or purely mathematical aspects. This chapter is neither a precise statistical method description, nor a philosophical essay about the concepts of uncertainty, knowledge and truth, although you will find a little bit of both. This chapter contains (1) an introduction of the essential terminology and concepts of relevance for LCA; (2) a discussion of main sources of uncertainty and how to quantify them; (3) a presentation of approaches to calculate uncertainty for the final results (propagation); (4) a discussion of how to use uncertainty information and how to take it into account in the R.K


Rosenbaum (&) IRSTEA, UMR ITAP, ELSA Research Group and ELSA-PACT—Industrial Chair for Environmental and Social Sustainability, 361 rue Jean-François Breton, BP 5095, 34196 Montpellier, France e-mail: ralph.rosenbaum@irstea.fr S. Georgiadis Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark S. Georgiadis Global Decision Support Initiative, Technical University of Denmark, Kongens Lyngby, Denmark P. Fantke Division for Quantitative Sustainability Assessment, Department of Management Engineering, Technical University of Denmark, Kongens Lyngby, Denmark © Springer International Publishing AG 2018 M.Z. Hauschild et al. (eds.), Life Cycle Assessment, DOI 10.1007/978-3-319-56475-3_11


interpretation of the results; and finally (5) a discussion of how to manage, communicate and present uncertainty information together with the LCA results


Learning Objectives After studying this chapter, the reader should be able to: • Explain the importance and usefulness of addressing uncertainty in LCA • Distinguish types and sources of uncertainty and variability and explain important misconceptions of uncertainty in the context of LCA • List the dominating sources of uncertainty in a typical LCA • Explain the relevant concepts and vocabulary of uncertainty • Analyse sensitivity, uncertainty and variability and use these insights to reduce overall uncertainty when performing an LCA • Express and communicate uncertainty in an appropriate way, catering to the purpose of the analysis • Apply uncertainty information in results interpretation and decision support 11.1 The British mathematician, science historian, author and inventor Jacob Bronowski wrote that “Knowledge is an unending adventure at the edge of uncertainty”. This is a perfect motto and inspiration for this chapter


This is a perfect motto and inspiration for this chapter. Before learning how to deal with uncertainty in the context of LCA, how to quantify it, interpret and use it, or communicate it, which are the main objectives of this chapter, it is useful to truly understand the concept of uncertainty in a broader sense. It is for that reason that we have chosen to approach the subject much more holistically than just focusing on relevant statistical methods, mathematical aspects and the like. This chapter is neither a precise statistical method description, nor a philosophical essay about the concepts of uncertainty, knowledge and truth, although you will find a little bit of both. First of all, uncertainty is always there, it is the elephant in the room no matter what we are doing or talking about


First of all, uncertainty is always there, it is the elephant in the room no matter what we are doing or talking about. From individuals to the entire humanity, from a child to a stock market broker to the most accomplished Nobel laureate, many of our daily efforts are related to knowing more, doing better, being more precise and more accurate. Acquiring knowledge and information and reducing the uncertainty around them is a driving force behind all human advancement, mobilising incredible amounts of resources worldwide. It is in fact one (if not the) driving force behind most things we do. Uncertainty is also often the elephant in the room when people talk about or apply LCA. It is always there but some may fear it and ignore it deliberately, some may use it to criticise or even discredit LCA. An oversimplified understanding of R.K. Rosenbaum et al.


uncertainty is a good part of the problem’s root in both cases. Uncertainty is indeed frequently perceived as potentially discrediting LCA and its results as being too uncertain, unreliable, and insufficiently capable of distinguishing the compared options. The often considerable resources required for quantifying and managing uncertainty in an LCA study is an important barrier for their adequate consideration. Nevertheless, the presence of uncertainties of different types and from numerous sources in LCA results is a fact and ignoring them may be more detrimental than managing them in an integrated manner which allows their meaningful use to quantify and improve the precision of a study and the robustness of its conclusions. LCA practice sometimes suffers from an imbalanced perception of uncertainties and their use in justifying modelling choices and omissions (e.g. excluding impact categories due to their perceived uncertainty)


excluding impact categories due to their perceived uncertainty). Identifying prevalent misconceptions, in some cases “myths”, around uncertainties is another central goal of this chapter. The ambition is to help balancing the discussions around uncertainty in LCA and establish a positive discourse that focuses on the advantages of uncertainty management. Proper uncertainty management allows for more robust results and conclusions in support of science-based decision-making, grounded on the (accurate) recognition and discussion of inevitable and ubiquitous uncertainties. Consider the following conceptual and simplified example to illustrate how fundamentally useful uncertainty assessment and management are in LCA. Figure 11.1 shows the results of an LCA study, performing a comparison of two alternative options A and B, for a given impact category like water use for example. The point estimate (i.e


Figure 11.1 shows the results of an LCA study, performing a comparison of two alternative options A and B, for a given impact category like water use for example. The point estimate (i.e. reproducible, single value output from the LCA model without considering variations in inputs) impact score is 4 for option A and 6 for option B, which may suggest that option A is preferable, i.e. less environmentally impacting, over option B by a factor of 1.5. However, considering the uncertainties (including correlations between both options), the impact scores can be shown as superposed distributions as demonstrated in Fig. 11.1 (even though this may not be the best way to compare scenarios as discussed later in this chapter). Where the distributions are overlapping, option B has certain chances to be preferable over option A, the opposite of the conclusion drawn above from only looking at the point estimates


Where the distributions are overlapping, option B has certain chances to be preferable over option A, the opposite of the conclusion drawn above from only looking at the point estimates. The more the distributions overlap, the higher the chances that option A may not be preferable to option B. In the left plot, there is a relatively small overlap of both distributions, and hence a relatively low chance to take the wrong decision when preferring option A over option B. In the centre plot, it is essentially impossible to discern the impact scores of both options and the chances to make the wrong conclusion would be high, no matter which option is chosen. In the right plot, the dispersions of both options are different (which will usually be the case in practice) and need to be evaluated in order to derive more reliable results. How to deal with such cases is discussed further-on in the chapter. That means that if the uncertainty cannot be further reduced (e.g


How to deal with such cases is discussed further-on in the chapter. That means that if the uncertainty cannot be further reduced (e.g. by using more certain data or models), both options are basically equal in terms of their potential environmental impact on water use. The consideration and communication of uncertainties related to results obtained via modelling and/or measurements is vital for their correct interpretation. This is Uncertainty Management and Sensitivity Analysis


often hampered by the difficulty to assign and propagate uncertainty information of the usually numerous parameters of a model as required by uncertainty assessment methods. This problem becomes even more apparent when modelling large systems as usually done in LCA, not to mention that there is more to overall uncertainty of a model result than just what parameters contribute. In current daily LCA practice, this often leads to complete omission of this important and integral aspect of any model result, while it may potentially influence or even change the conclusions of a study. Uncertainty thus refers to everything we do not know and we cannot be certain about, regardless whether we are aware of it or not. In order to create a common basis of understanding when using technical terms and vocabulary around uncertainty, a thorough definition of important terms and concepts will be provided as starting point in the following section


11.2 Essential Concepts and Definitions In order to provide an accessible and operational angle on the subject, we have deliberately chosen to use simplified terminology and explanations that do not always capture everything there is to say. The focus of this chapter is on what is relevant for LCA students and practitioners, not on covering all aspects around statistical concepts, terms and definitions. For many concepts there may be multiple terms that are used synonymously in literature and in some cases there may not even be consensus on specific terms and their definition, such as what a sensitivity analysis exactly is for example. The implicit imprecision may be shocking to experts in statistics, but avoiding to capture the full complexity substantially helps getting a first grasp and understanding of the main concepts and how they are used in LCA practice, which is the main purpose of this chapter. Fig


Fig. 11.1 Illustrative comparison of impact scores from two options A and B with the point estimates of 4 and 6, respectively, in all graphs, but with different uncertainties, low in the left, high in the centre and mixed in the right graph R.K. Rosenbaum et al.


11.2.1 Defining Uncertainty, Variability and Sensitivity The term uncertainty is used with a fairly large variation in its definition, including or excluding (somewhat) adjacent concepts like variability and sensitivity. It is therefore difficult if not impossible to give a universally valid and accepted definition of uncertainty. For the sake of defining a common understanding within the scope of this book, we use the definition of uncertainty as comprising everything we do not know, expressed as the probability or confidence for a certain event to occur. More precisely, the “unknown” includes both random and systematic errors (of estimating, measuring or collecting data), mistakes, and epistemological (or epistemic) uncertainty (i.e. lack of scientific knowledge and consequent misinterpretations). To put it a bit bluntly, uncertainty in principle describes the degree to which we may be off from the truth


lack of scientific knowledge and consequent misinterpretations). To put it a bit bluntly, uncertainty in principle describes the degree to which we may be off from the truth. In reality it is of course impossible for us to know that, otherwise we would not have to face uncertainty since we would know the truth (and we will avoid attempting to define what “truth” itself means). Therefore, in practice we define reference points that we assume to represent truth or at least to be close to it. A typical example for such a reference point would be a measurement. If we trust the measuring method and protocol we trust that a measurement represents a sort of truth at a specific point in space and time and the difference between a modelled estimate and a corresponding measured value is then used as an indicator for uncertainty. Ciroth et al. (2004) discuss and nicely illustrate this discrepancy between measured and true value and what uncertainty represents in that respect


Ciroth et al. (2004) discuss and nicely illustrate this discrepancy between measured and true value and what uncertainty represents in that respect. It is then important to keep in mind that the measured value inevitably comes with its own uncertainty due to possible measurement errors (and mistakes) and due to the uncertainty of how suitable the measurement method and how representative the sampling was regarding the actual “truth”. Uncertainty can thus be quantified and reduced by knowing more, which usually requires us to invest more resources in order to gain more knowledge (e.g. by performing additional measurements or collecting more data and refining the model). However, no matter how many resources we have available, we can never be certain that we have eliminated (or at least minimised) uncertainty. In order to define variability, let’s take the example of body weight distributions in a human population


In order to define variability, let’s take the example of body weight distributions in a human population. Many observations we can make will always have more than one value, as soon as we measure more than one sample (i.e. a sub-set of data points from a population of measured data), human body weight being an intuitive example. We are thus faced with a natural variability that simply represents the variety or spread in the data that we will always observe. With enough resources at hand that allow us to take every possible sample, we can perfectly well measure and quantify this variability, but we can never reduce it. In the context of LCA, we are typically faced with three different types of variability: (1) temporal variability (e.g. seasonal changes in temperature), (2) spatial or geographical variability (e.g. population density in different regions), and (3) inter-individual variability of humans, animals, other species (e.g. differences in diets) or technologies


population density in different regions), and (3) inter-individual variability of humans, animals, other species (e.g. differences in diets) or technologies. Uncertainty Management and Sensitivity Analysis


In LCA practice, the terms variability and uncertainty are often not distinguished or overarching one another (i.e. variability is often included as one aspect of uncertainty). However, for their important differences described before, it is recommendable and good practice to quantify and maintain both well separated as this will allow us to put this information to good use when interpreting and improving LCA results. We will come back to that later. The sensitivity of a model describes the extent to which the variation of an input parameter or a choice (e.g. time horizon in the functional unit) leads to variation of the model result


The sensitivity of a model describes the extent to which the variation of an input parameter or a choice (e.g. time horizon in the functional unit) leads to variation of the model result. A model is sensitive toward a parameter if a small change in this parameter will result in a large change in the model result, whereas a model is insensitive toward a parameter if any change in this parameter will have no (or negligible) effect on the model result (which in certain cases might indicate that this parameter may not be needed in the model, or at least that it is not an important input parameter for this particular value of the model result). Sensitivity may be analysed for both continuous and discrete input parameters, and it can also be analysed for choices leading to discrete sets of input values. For example, the choice of LCIA method is always a discrete choice between a certain number of fixed options (i.e. available methods)


For example, the choice of LCIA method is always a discrete choice between a certain number of fixed options (i.e. available methods). It is worth noting that the term sensitivity is used in various and inconsistent ways throughout literature and no agreement on its exact definition exists. Two main uses could be distinguished: (1) For some authors sensitivity includes the effect of uncertainty and thus considers the range of variation of input parameters as a function of their uncertainty (which hence needs to be known), varying them all at the same time. This is also called global sensitivity analysis and is essentially what this chapter refers to as uncertainty analysis. (2) Others define sensitivity solely as the effect of a certain change in input on the output applying a predefined variation without considering the uncertainty. This is analysed by varying one parameter at a time and also called local sensitivity analysis


This is analysed by varying one parameter at a time and also called local sensitivity analysis. In the context of this book and many publications in the LCA community, sensitivity only describes the variation of a result due to variation of an input or choice, without considering its uncertainty, i.e. local sensitivity. 11.2.2 Defining Accuracy and Precision in the LCA Context When talking about uncertainty, a number of terms are often used in conjunction or interchangeably which seem to be synonyms but in fact are not. Two such terms are accuracy and precision. The definition of these terms in general English dictionaries varies to some extent, the Oxford English Dictionary for example defines accuracy as technical noun being “The degree to which the result of a measurement, calculation, or specification conforms to the correct value or a standard” and precision as technical noun being “Refinement in a measurement, calculation, or specification...”


Therefore, both terms are independent and while accuracy refers to the correctness of a value, precision relates to the relationship among multiple measurements or calculation results. It is therefore useful to have a closer look at the R.K. Rosenbaum et al.


actual meaning of these terms in a technical or scientific context and what they imply for LCA. Accuracy describes the closeness of a measured or modelled value to its “true” value. Precision represents the quality of being reproducible in amount or performance (i.e. any repetition of a calculation, experiment, model run, etc. gives a similar result when precise or a wide spread when imprecise), but a reproducible result does not necessarily have to be very accurate or even “true”. In consequence, the accuracy of a model result may be high while its precision can be low as illustrated in Fig. 11.2. This means that the average of such model results will still represent meaningful information even though the results’ spread (i.e. the standard deviation) may be large. In contrast, a very precise measurement or model Fig. 11.2 Illustration of precision and accuracy Uncertainty Management and Sensitivity Analysis


result (i.e. with a small standard deviation) is not necessarily meaningful if it comes with low accuracy regarding the information one is actually looking for. In the LCA context, this can be illustrated using the different time horizons of the global warming potential (GWP). When intending to capture potential impacts from global warming of greenhouse gas (GHG) emissions, the GWP is integrated over 20, 100 and until the 4th IPCC Assessment Report (IPCC 2007) even over 500 years. It is intuitive that precision decreases with an increasing time horizon due to the assumptions necessary to model and predict far into the future, but does accuracy also automatically decrease with longer time horizons? In order to answer that question, we need to consider that most GHGs stay much longer in the atmosphere than 20 years. GWP20 is a very precise and probably accurate indicator for the cumulative radiative forcing (i.e