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.

中文翻译：

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在爱尔兰公寓开发的分层混合案例研究中开发和论证用于生命周期评估的不确定性管理方法

目的

生命周期评估（LCA）的从业者已经认识到，不确定性分析需要纳入LCA研究中，以提高结果的可靠性。但是，案例研究仍然可以毫无不确定地报告结果。忽略不确定性的原因包括资源限制或缺乏知识或专业知识。本文提出了一种结构化的不确定性管理方法，旨在改善LCA中的不确定性报告。

方法

LCA最常见的不确定性分类是参数，模型和场景。但是，文献中存在多种分类。Igos等人发布的最新分类。（2019）根据先前的研究将不确定性分为三个维度：位置，水平和自然（Walker等，2003； Warmink等，2010）。在本文中，为在LCA中实际实施进一步开发了三维不确定性分类。该分类被纳入不确定性管理方法中，该方法分为五个步骤：识别，分类，量化或鉴定，减少和报告，并根据ISO 14044（2006）集成到LCA的迭代步骤中。该方法在爱尔兰公寓从摇篮到大门的开发（重点关注气候变化）的分层混合案例研究中得到了证明。数据源包括工程量清单，Ecoinvent数据集，爱尔兰投入产出表和爱尔兰环境账户数据。

结果与讨论

案例研究的初始不确定性评估发现，确定性值可能低估了公寓开发的总二氧化碳当量（t CO ₂当量）。在降低不确定性之前，影响大于确定性值的可能性约为93％。确定总不确定性的主要因素是选择Ecoinvent数据集，部门排放强度和政府间气候变化专门委员会全球变暖潜能值。因此，减少总不确定性的工作应集中在为建筑材料确定最合适的数据集以减少该材料的输入分布以及获取更多特定于产品的数据上。

结论与建议

所开发的不确定性管理方法通过提供详细，结构化的方式来识别，分类，测量和报告不确定性，从而改善了LCA案例研究中实际管理不确定性的方式。它进一步确定了可以集中资源的位置，以迭代方式减少结果的总体不确定性，从而提高结果的可靠性。建议在进一步的工作中对其他案例研究，生命周期阶段和影响类别进行测试。