A significant challenge of an uncertainty assessment is the presentation of the results, since a quantitative uncertainty analysis dramatically increases the already considerable amount of data that needs to be communicated in an LCA study. This paper investigates three graphical options to interpret output samples from quantitative uncertainty analyses. The output samples are from case studies within the coalfired power generation sector, and include an assessment of empirical uncertainty from a stochastic uncertainty assessment and an assessment of uncertainty in decision variables from a parametric sensitivity analysis. Two commonly used representations of probabilistic samples are demonstrated, namely “box and whisker” plots and plots of the cumulative probability density function, as well as the multivariate geometric technique, principal component analysis (PCA). Cumulative probability plots are useful representations of uncertainty where a quantitative estimate of the relative uncertainty between options is required, but they become extremely tedious (many pair-wise combinations) and difficult to interpret when a large number of options are compared over many criteria. In such cases, PCA can be used to provide a valuable overview of the results, where it is able to clearly present any trade-offs that have to be made between selection criteria, and the “spread” of the options under consideration over the decision space. Box and whisker plots are good at representing the relative importance of empirical parameter uncertainty and the uncertainty arising from the choice of decision variables, and show the degree of shifting between the options as well as the full range over which the options potentially act. The three representations of uncertainty are found to complement each other, as each enhances different aspects of the results. The most appropriate graphical presentation method is found to depend on the particular decision context and the particular stage of the analysis.
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