Assessing the sustainability of renewable energy technologies using multi-criteria analysis: Suitability of approach for national-scale assessments and associated uncertainties

Multi-criteria analyses (MCAs) are often applied to assess and compare the sustainability of different renewable energy technologies or energy plans with the aim to provide decision-support for choosing the most sustainable and suitable options either for a given location or more generically. MCAs are attractive given the multi-dimensional and complex nature of sustainability assessments, which typically involve a range of conflicting criteria featuring different forms of data and information. However, the input information on which the MCA is based is often associated with uncertainties. The aim of this study was to develop and apply a MCA for a national-scale sustainability assessment and ranking of eleven renewable energy technologies in Scotland and to critically investigate how the uncertainties in the applied input information influence the result. The developed MCA considers nine criteria comprising three technical, three environmental and three socio-economic criteria. Extensive literature reviews for each of the selected criteria were carried out and the information gathered was used with MCA to provide a ranking of the renewable energy alternatives. The reviewed criteria values were generally found to have wide ranges for each technology. To account for this uncertainty in the applied input information, each of the criteria values were defined by probability distributions and the MCA run using Monte Carlo simulation. Hereby a probabilistic ranking of the renewable energy technologies was provided. We show that the ranking provided by the MCA in our specific case is highly uncertain due to the uncertain input information. We conclude that it is important that future MCA studies address these uncertainties explicitly, when assessing the sustainability of different energy projects to obtain more robust results and ensure better informed decision-making.