Overall economic assessments (OEAs) can provide a sound basis for decision-making in the areas of investments in new technologies and the application of existent technologies or operating practices. However, due to their long time horizons and complex nature, OEAs often contain m
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Overall economic assessments (OEAs) can provide a sound basis for decision-making in the areas of investments in new technologies and the application of existent technologies or operating practices. However, due to their long time horizons and complex nature, OEAs often contain many uncertain inputs, making a deterministic simulation insufficient to reflect the true value of the output. In order to incorporate these uncertainties, a systematic and efficient approach for uncertainty analysis is required. This paper sets out such a process, which consists of an iterative Uncertainty Quantification (UQ) based on importance measures for each uncertainty obtained from a Global Sensitivity Analysis (GSA). Methods for UQ and GSA are generally actively researched and well established in theory, but are infrequently applied on actual problems due to the computational and organisational complexity associated with integrative uncertainty assessments. To address this issue, the process is demonstrated on an interdisciplinary problem, namely the economic valuation of Engine Wash (EW) procedures using the cost-benefit tool LYFE. It is concluded that with this iterative uncertainty quantification procedure, the total uncertainty in the output distribution, measured using the 2.5th and 97.5th percentiles and expressed in terms of the Delta Net Present Value, is reduced from $45K - $983K to $78K - $584K. To achieve this reduction, additional modelling was carried out for only the two most important of the six uncertainties, determined using the GSA results, which illustrates the efficient allocation of modelling resources.
