Table 2 The MCDM methods used in this study. Table 2
Related Figures (9)
Fig. 1. Energy sustainability assessment within a system of systems framework. 4 Assumed to be the same as biomass woodchip. b Assumed to be the same as geothermal (Jacobson, 2009). Table 1 Overall ranking of the energy sources based on different MCDM methods. Fig. 2. Relative aggregate footprint (RAF) of different energy sources (0-100). Fig. 3. Comparison of energy sources’ RAF sensitivity to the considered efficiency criteria (C, carbon footprint; W, water footprint; L, land footprint; and $, cost). The dashe horizontal line shows the RAF = 50 level to facilitate comparison. The CWL$ line shows the RAF scores when all four criteria are considered (as shown previously in Fig. 2). Table 4 provides the mean and standard deviation of the energy RAFs, calculated based on 100,000 rounds of Monte-Carlo sampling. Mean RAFs are within 5% of the RAFs calculated in the previous step by applying Eq. (1) at the end of the Monte-Carlo selection process using the overall ranks, as opposed to applying Eq. (1) at the end of each round of Monte-Carlo selection using the ranks specific to that round of selection only. This shows the reasonable reliability of the calculated RAF values in the previous step, which required significantly less computational effort. The Monte-Carlo MCDM method used here orders energy alternatives under each MCDM method based on their winning probabilities at each rank. Once the option with the highest win- ning probability is determined as the best alternative (rank 1) based on a given method, this option is removed from the alternatives Mean and standard deviation of relative aggregate footprint scores. Table 4 Fig. 4. Individual energy RAF resource sensitivity charts, reflecting RAF sensitivity to the considered efficiency criteria (C, carbon footprint; W, water footprint; L, lan footprint; and §, cost). The RAF value is 0 at the center of the radar chart and 100 at the edge of the chart. In addition to the degree of robustness information, deci- sion makers can benefit from learning about the sensitivity of RAF scores to exclusion/inclusion of different criteria from the analysis. Sensitivity analysis also helps with understanding the major drawbacks of different energy sources that are in need of being addressed in order to improve their efficiency scores significantly. For example, learning the fact that the high water footprint of biofuels is one of the main reasons for their inefficiency would encourage investment and research in the technological improvements that can help with minimizing the biofuels’ water footprint. Moreover, given that the importance of different sus- tainability criteria (carbon footprint, water footprint, etc.) varies regionally depending on the availability of local resources, the sensitivity analysis information can help selecting the best energy
