LCA impact assessment categories

Abstract

A technical framework is presented to evaluate the strengths and the limitations of LCA impact assessment categories to yield accurate, useful results. The framework integrates the inherent characteristics of life-cycle inventory (LCI) data sets, characteristics of individual impact categories, how impact categories are defined, and the models used to characterize different categories. The sources for uncertainty in impact assessment are derived from the basic LCI procedures and the complexity of environmental processes and mechanisms. The noteworthy LCI procedures are: (1) the collection and aggregation of data across a comprehensive product system, (2) co-product and recycling allocation for releases and resources, and (3) the conversion of these data by functional unit calculations. These operations largely remove spatial and temporal considerations, resulting in analytical and interpretive limitations that vary in magnitude for different impact assessment categories. The framework shows two groups of categories where LCA results may be insufficient for making comparisons: (1) categories that involve local and/or transient processes and (2) categories that involve non-mass loading, biological parameters, such as biodiversity, habitat alteration, and toxicity. The framework also shows that how impact categories are defined complicates their use. Some categories are based on objective stressor-effect networks using known environmental mechanisms. In contrast, other categories are defined using various levels of subjective judgment to address either highly complex or unknown mechanisms. Finally, the framework shows that differences in the quality and detail of information provided by various models used during characterization also influence the accuracy and usefulness of the results. In summary, the framework indicates that (1) the various uncertainties in each individual category have a a number of different technical origins and that (2) the degree of uncertainty varies significantly between categories. As a result, interpretation and valuation cannot presume an equivalency of processes or merit behind numerical values for different categories. The framework can be used to initially identify and track these uncertainties to improve LCA impact assessment interpretation and application.