Development and demonstration of an uncertainty management methodology for life cycle assessment in a tiered-hybrid case study of an Irish apartment development

Purpose: It has been recognised by life cycle assessment (LCA) practitioners that uncertainty analysis needs to be incorporated into LCA studies to improve the reliability of the results; however, case studies still report results without uncertainty. Reasons for ignoring uncertainty include resource constraints or a lack of knowledge or expertise. This paper presents a structured uncertainty management method that aims to improve uncertainty reporting in LCA. Methods: The most common uncertainty classification for LCA is parameter, model and scenario; however, multiple classifications exist in literature. The latest classification published by Igos et al. (2019) divides uncertainty into three dimensions: location, level and nature, based on previous research (Walker et al. 2003; Warmink et al. 2010). In this paper, the three-dimensional uncertainty classification is further developed for practical implementation in LCA. The classification is incorporated into an uncertainty management methodology that is divided into five steps: identification, classification, quantification or qualification, reduction and reporting, and is integrated into the iterative steps of an LCA in accordance with ISO 14044 (2006). The method is demonstrated in a tiered-hybrid case study of an Irish apartment development from cradle-to-gate that focuses on climate change. The data sources include the bill of quantities, Ecoinvent datasets, Irish input-output tables and Irish environmental accounts data. Results and discussion: The initial uncertainty assessment of the case study found that the deterministic value likely underestimates the total tonnes of carbon dioxide equivalents (t CO₂-eq.) for the apartment development. The probability that the impact is greater than the deterministic value is approximately 93%, prior to uncertainty reduction. The main contributors to the total uncertainty were identified as the choice of Ecoinvent dataset, the sectoral emission intensities and the Intergovernmental Panel for Climate Change Global Warming Potentials. Therefore, work to reduce the total uncertainty should focus on identifying the most suitable dataset for the building material to reduce the input distribution for that material and on acquiring more product-specific data. Conclusions and recommendations: The developed uncertainty management method improves the way uncertainty is managed in practice in LCA case studies by providing a detailed and structured way for uncertainty to be identified, classified, measured and reported. It further identifies where resources can be focused to iteratively reduce the overall uncertainty of the results and thus improve their reliability. It is recommended that the developed method is tested across other case studies, life cycle stages and impact categories in further work.