Abstract
Life Cycle Assessment (LCA) models for quantifying emissions and resources used as part of the life cycle inventory (LCI) step and for characterizing related impacts on human health, ecosystem quality, and natural resources as part of the life cycle impact assessment (LCIA) step together contribute considerable uncertainty and variability at different assessment phases. These contributions have led to questions about the ability of LCA results to be used in decision-making. Mainly, variability is related to spatiotemporal, technological, and interspecies and inter-individual differences, while uncertainty is further related to input data, model selection and choices, amongst other aspects. Currently, methods exist to assess and assign uncertainty and variability on LCI data as well as LCIA characterization results. However, often uncertainty is only assessed and reported qualitatively, is not comparable across impact categories and not consistently assessed and reported across levels of detail. Furthermore, many existing methods and models do not report uncertainty at all or limit their uncertainty assessment to a sensitivity analysis of selected input parameters, while ignoring variability, model uncertainty, and uncertainty related to choices and human errors. As part of the LCA Capability Roadmap, a committee of nearly 40 contributors under the auspices of the SETAC North America LCA Interest Group is currently working to identify research needs in the area of ill-characterized uncertainty. The group has investigated current best LCA practices, such as refinements to the pedigree matrix used to assess LCI data quality. In parallel, in the frame of UNEP-SETAC Life Cycle Initiative flagship project on providing Harmonization and Global Guidance for Environmental Life Cycle Impact Assessment Indicators, a task force focusing on uncertainty aspects has been established. This task force currently investigates best practices in existing LCIA methods and works on a minimum set of criteria for consistently reporting uncertainty in LCIA. These best practices and state of the art will be presented along with proposed milestones toward providing guidance of how to address and report uncertainty in LCA to improve current practice. Feedback is encouraged.
Info
Conference Abstract, 2017
UN SDG Classification
DK Main Research Area
Science/Technology
