Selection of Impact Categories, Category Indicators and Characterization Models in Goal and Scope Definition

Abstract

This chapter aims to provide practical guidance and a factual overview of implemented Life Cycle Impact Assessment (LCIA) methods available in Life Cycle Assessment (LCA) software. Currently available midpoint and endpoint characterization methods are presented and their specific properties are qualitatively compared in detailed tables.

Selecting impact categories, category indicators, and characterization models or LCIA methods is a task every LCA practitioner faces frequently. Although it is quite an essential decision that requires sufficient understanding of a number of concepts and a good overview of available LCIA methods, very little guidance is available in the literature. The ISO 14044 standard establishes both requirements and recommendations for the choice of impact categories, category indicators and characterization models to be used in LCIA as part of an LCA study. This selection process must be done at the outset of a study, during the goal and scope definition phase. However, with the increasing number of LCIA methods and indicators becoming available, the task of choosing which ones to use has become a significant effort requiring the practitioner to understand the main characteristics of these methods and keep up-to-date with the latest developments. Furthermore, in practice, the selection of impact categories and LCIA methods is also driven by criteria that go beyond those given in ISO 14044.

The primary objectives of this chapter are to: (1) provide a structured overview of selection criteria from different sources and angles, as well as (2) establish guidelines to make an informed and conscious choice by providing essential information needed to support such a choice.

Appendices

Annex

Preamble

The tables have not been published before. All information contained in the tables is publicly available information with the exception of the IMPACT World + and LC-Impact descriptions. A reference list to the annex is provided beneath the tables.

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 following tables provide a complete and updated qualitative comparison of widely used LCIA methods available in current LCA software. Only models integrated into LCIA methods (and thus readily available for practitioners in LCA software and databases) are represented here with the exception of the latest methods IMPACT World + and LC-Impact, which by the time of writing (early 2016) were not yet fully implemented into LCA software but readily available to be imported manually (see respective websites for further information). It is worth mentioning that the authors of the LC-IMPACT method intend to provide both midpoint and endpoint characterization factors (CFs). So far, endpoint CFs have been published, while midpoint CFs are not yet available but foreseen for later publication and thus not included in Table 2.1 The author is also aware of a potential major update of the ReCiPe 2008 method, but the currently available version (from 2013) in LCA software and the method’s website is as described in Tables 2.1 and 2.2, therefore the description of the (major 2015) update was not included here. The Japanese LCIA method LIME has been updated to version 3.0 some time ago, but to the author’s knowledge no documentation in another language than Japanese is available, which is why only version 2.0 is covered in here.

Further models (published but not yet integrated into LCIA methods) are discussed in the ILCD handbooks on LCIA (EC-JRC 2010, 2011) and of course in current scientific literature. Models not based on mechanistic cause-effect chain modeling, such as regulatory-based distance-to-target approaches like the Swiss Eco-scarcity method (Frischknecht et al. 2009) or the MEEuP approach based on emission limit values (Kemna et al. 2005) were also excluded from this overview. Such approaches require specific interpretation , different from cause-effect-based methods, due to their non-mechanistic and often policy-priority-based nature. The content of these tables is restricted to facts, while judgements on quality etc. were excluded as far as possible. For a further evaluation including expert judgements, e.g. on scientific validity, environmental relevance , or stakeholder acceptance, the reader is referred to the ILCD handbook on LCIA (EC-JRC 2010, 2011). Given the large amount of information contained in these tables, mistakes cannot be excluded, but as much information as possible has been verified in the original documentation of the methods (and if required corrected when taken from the ILCD handbook, which contains a number of small errors in the method descriptions). LCIA methods are under constant improvement and may be updated and corrected over time. Consequently, the information contained in Tables 2.1 and 2.2 is a snapshot of the situation and available information by the time of writing of this chapter (early 2016) and is likely to change over time.

Tables 2.1 and 2.2 contain a qualitative comparison of a number of specific properties of available LCIA methods. Each column represents an LCIA method, while the rows are structured by impact category . This allows easy identification of the differences (and similarities) in these properties per impact category among methods and choosing the most suitable one for a given goal and scope.

Table 2.1 lists the most important midpoint characterization methods, while Table 2.2 contains methods providing endpoint damage assessment characterization factors . A number of methods were published before 2000, but are not included in this overview as they are outdated and obsolete for today’s LCA practice. As a support to using and interpreting the tables, a brief description of each property and its meaning are given (note that not all properties may apply to each impact category):

• Aspects/diseases/ecosystems considered: lists which kinds of impacts are considered, e.g. which kinds of resources (for resource use), or which kinds of diseases (for human health), or which ecosystems out of freshwater, marine water, and terrestrial ecosystems are covered by a method.

• Characterization model : gives the name (if applicable) and points to the main reference(s) for the corresponding characterization model used to calculate the characterization factors for a given impact category and LCIA method.

• Human health effects: details about which kind of health effects were included.

• Ecosystem effects: details about which kind of effects on ecosystems were included.

• Biotic resources effects: consideration of potential impacts on biotic resources is still a rare property, but is included in some methods and may be an important point for some studies.

• Fate modeling: details about how the modeling of the distribution of an emission in the environment is considered (the concept of fate may also be applied for modeling a part of the cause-effect chain of a resource extraction instead of an emission).

• Exposure modeling: details about how the transfer of a substance from the environment into a given target (e.g. human population or an ecosystem) is considered (the concept of exposure may also be applied for modeling a part of the cause-effect chain of a resource extraction instead of an emission).

• Effect modeling: details about how the effect(s) of a substance transferred from the environment into a given target (e.g. human population or an ecosystem) is considered (the concept of exposure may also be applied for modeling a part of the cause-effect chain of a resource extraction instead of an emission).

• Marginal/average: these terms are used in different ways and meanings in the LCA context; here they describe two different impact modeling principles or choices: a marginal impact modeling approach represents the additional impact per additional unit emission/resource extraction within a product system on top of an existing background impact which is not coming from the modelled product system. This allows, e.g., considering non-linearities of impacts depending on local conditions like high or low background concentrations to which the product systems adds an additional emission/resource extraction. An average impact modeling approach is strictly linear and represents an average impact independent from existing background impacts, which is similar to dividing the overall effect by the overall emissions.

• Emission compartment(s): for which emission compartment(s) the method provides characterization factors .

• Time horizon: details on the time horizon(s) used to calculate potential impacts. A prominent example are the GWP-time horizons of 20, 100, and until IPCC (2007a) also 500 years. The essential difficulty with time horizons is that a short time horizon may exclude an important amount of future potential impacts from the assessment (risking violating the sustainability principle of inter-generational equality). Whereas, a long time horizon may ‘dilute’ large short-term impacts over a longer time (i.e. making them look smaller), which would give a small but permanently continuing impact a similar impact potential than that of a large impact occurring within a short time. In other words, it would give the same importance to a large impact within one generation as to a small impact affecting several generations of humans for example. An important and widely ignored issue in current LCA practice is the inconsistency among time horizons between different impact categories , with some representing 100 years and others several hundreds to even thousands of years. An inconsistency that, in principle, would disallow adding up endpoint scores into areas of protection or normalizing and weighting midpoint scores. Its importance, however, needs further study and most likely it is far from being a large source of uncertainty relative to other issues in LCA.

• Region modelled/valid: details on which region(s) has been modelled (i.e. which region is represented by the parameters used in the characterization model ). A model may either represent one or several specific region(s) (the larger the region, the more averaging is applied and the less specific the model is representing a region) or a global (or sometimes continental) average, also referred to as generic.

• Level of spatial differentiation: if the characterization model represents more than one region, it is spatially (or geographically) differentiated. The level of differentiation may range from coarse (e.g. continental, sub-continental, countries, etc.) to fine (e.g. small grid-cells of a few km or sub-watersheds). The finer the spatial differentiation, the better a model captures variability of local conditions which may influence potential impacts by up to several orders of magnitude for some impact categories , such as toxicity or water consumption.

• Number of substances/land use types/resources: the more substances or land-use types/resources are covered by a method, the more likely it will consider all important (= highly contributing to impact) emissions or resource extractions of a product system . A missing characterization factor for any given elementary flow automatically leads to its omission in the impact profile.

• Unit: the dimension of the indicator.

• “n/a” means that information was not available or that a property is not applicable.

Not all these properties may be of equal relevance for choosing an LCIA method for each practitioner or study, but are intended to represent the most relevant and fact-based properties.

Table 2.1 Detailed characteristics of available midpoint characterization methodologies [Extended and updated from ILCD handbook on LCIA(EC-JRC 2010, 2011)]

Table 2.2 Detailed characteristics of available endpoint characterization methodologies [(extended and updated from ILCD handbook on LCIA (EC-JRC 2010; EC-JRC 2011)]

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