On the overall the 16 final scenario rankings are very similar in the sense that scenario E and C are always in a top cluster, scenario B is in a middle position and scenario A and D are always in a bottom cluster. A mean scenario ranking has been carried out to give an overall picture (see Figure 32 and Table A.8. in the Appendix). The individual scenario rankings according to the stakeholder weight sets are then presented in detail.
Figure 32.The mean ranking derived from all 16 scenario rankings, whereas the net flows of the complete ranking are the basis for the mean.
Table 14 gives an overview of all individual stakeholder scenario rankings. In essence, the overall rankings result in three clusters of scenarios, which can be identified in all the final scenario rankings. This structure is very robust in the sensitivity analyses: Scenario E “Large impact in small-scale use” and scenario C “Investment into the Future” rank high, while scenario B “Extension of competitive advantage” takes a middle position, and scenarios A “Fast and Known” and D “Extensive Use of Biomass” rank distinctively lowest
0,16 0,14
0,01
-0,14
-0,16 -0,20
-0,15 -0,10 -0,05 0,00 0,05 0,10 0,15 0,20
Φ scenario E Φ scenario C Φ scenario B Φ scenario A Φ scenario D
mean net flow Φ
None of the rankings of the scenarios are shifted in a profound way by the specific stakeholder weights set on the sustainability criteria. Only the ranks within the top and bottom cluster of scenarios vary due to the individual stakeholder weights sets.
Participant Complete ranking Partial ranking Institutional background of participant P1 C, E, B, D, A C / E, B, D, A Research Centre for Social Innovation P2 E, C, B, A, D E, C / B, A, D Representative of State Owned Forest
P3 E, C, B, A, D E, C, B, A / D Federal Ministry for Traffic, Innovation and Technology o
P4 E, C, B, A, D E, C, B, A / D Federal Ministry of Agriculture, Forestry, Environment, and, Water Management / Environment Section P5 C, E, B, A, D C / E / B, A / D Chamber for Agriculture
P6 E, C, B, A, D E / C, B, A / D Chamber for Labour
P7 E, C, B, A, D E / C, B, A / D AEE Renewable Energy Association, NGO P8 E, C, B, D, A E / C / B, D / A Electricity Utility, Wien Energie
P9 C, E, B, A, D C / E, B, A / D Euro Solar, NGO
P10 E, C, B, A, D E / C, B, A, D Global 2000, Environmental NGO
P11 E, C, B, A, D E, C, B, A / D Renewable Energy Representative, Small Hydro Power P12 E, C, B, D, A E / C, B, D / A Austrian Business Council for Sustainable Development
(ABCSD)
P13 E, C, B, A, D E / C, B, A / D Consumer Interest Council, VKI
P14 E, C, B, D, A E / C, B, D / A Renewable Energy Representative, Wind Power P15 C, E, B, A, D C / E, B, A / D WWF, Environmental NGO
P16 E, C, B, A, D E / C / B, A / D Industrialists’ Association
Table 14. Complete and partial scenario ranking results according to the different weight sets of the stakeholders. The “/” indicates a parallel position of scenarios in the partial rankings.
When summarizing the scenario ranking results according to the different weighting sets of the stakeholders (see Table 14), scenario E “Large impact in small-scale use” ranks first in 12 out of 16 scenario rankings (=75%). Yet despite the fact that it is only visible in the partial ranking, it stands alone at the top rank 4 times and in a parallel ranking with another scenario 8 times.
Whenever scenario E does not rank first, it ranks at least second.
Scenario C “Investment into the future” is in four cases rank 1 and 12 cases out of the 16 rank 2 (=75%). In the partial ranking, scenario C is always in the highest parallel ranked scenario cluster except in three cases.
Scenario B “Extension of competitive advantage” receives rank 3 of all the final stakeholder rankings. In the partial ranking it is in the highest parallel ranked scenario cluster 4 times. But
more often, 12 times out of 16, it is in a middle position between scenario clusters C, E and A, D.
Scenario A, “Fast and Known”, is in most cases rank 4 (12 of 16 rankings) and ranked last four times (=25%). The partial ranking reveals that scenario A is most often in the lowest ranked parallel scenario cluster (with D), ranked fourth on its own two times and ranked last on its own only once.
Scenario D “Biomass on a large scale” is ranked last in most stakeholder rankings (12 out of 16 times) and therefore ranked last most often (=75%) compared to all the other scenarios. In four rankings it is positioned on the fourth rank. The partial ranking shows that scenario D is two times ranked last on its own two times and also ranked fourth by itself on one occasion, but in most cases ranked in the lowest scenario cluster with A.
Complete ranking
The advantage of the complete ranking is to arrive to an unambiguous ranking and complete information according to the ranking distance between the scenarios. The overall net flow range is valuable information according the overall distance of the scenarios from each other. The general weakness is that this purported preciseness can not always be supported by the underlying data and assumptions. (see previous discussion in the Methods chapter).
Nevertheless, the complete ranking brings additional information and informs the results.
In this specific case the complete ranking has been used to identify the different rankings among the 16 final scenario rankings. Four different scenario rankings appear. The most common ranking shows that scenario E “Large impact in small scale use” is ranked first, followed by C
“Investment into the Future” and B “Extension of competitive advantage”. Scenario A “Fast and Known” and scenario D “Extensive Use of Biomass” clearly rank lower (see Table 15 and Figure A.4. in the appendix).
9 time E –C –B –A –D participant: 2, 3, 4, 6, 7, 10, 11, 13, 16 3 time E –C –B –D –A participant 8, 12, 14
3 time C –E –B –A –D participant: 5, 9, 15 1 time C –E –B –D –A participant: 1
Table 15. Summary of the final scenario rankings and the respective stakeholder groups
Two more types of scenario rankings occur among the respective three stakeholders: on the one hand, E, C, B, D, A, receive a ranking which is similar to the most common scenario ranking,
whereas D and A have switched places. And three participants have produced, on the other hand, the ranking C, E, B, A, D. In this case, the first rank is taken by scenario C “Investment into the Future”, which is in contrast to the most common ranking,. The least common ranking, emerging only once, is the same as the later sequence and the last rank is scenario A “Fast and Known”.
According to the complete ranking, the distance between the first two and the last two positioned scenarios is very small in all four groups (see Appendix for Figure A.4.). The total range of the net flows varies from ±0.13 (participant 2 in Figure A.4. in Appendix) to ±0.25 (participant 3 and 13 in Figure A.4. in Appendix), which is a moderate difference. In the rankings with an especially low net flow range, the scenarios are much closer together than it looks in the comparison. A small range of net flow suggests that the scenario impacts are due to the weight set in these rankings not as distinctly different inform the scenario rankings with a higher range of net flow. In other words, comparatively low net flows can be explained by the way that high weights were put on criteria which are characterised by low differences between the scenarios in the impact matrix.
Summarising the information of the complete ranking, the differences between the four scenario ranking groups, represented by the four different sets of rankings, is not considerably distinct.
The main reason for that is the fact that the overall robustness of the characterising scenario rankings is not high. The partial ranking promises to reveal more detailed information on the overall ranking pattern of the scenarios.
Partial ranking
The partial ranking provides additional information in the sense that the positive and negative performances from the comparison of the pairs of scenarios are accounted for in separate positive and negative outranking flows. Given the assumption that bad performances can not be compensated by good performances across criteria it provides important information. In the partial ranking, a scenario is only ranked higher than another one if it is better in the positive (higher Φ+) and negative performances (lower Φ-).The disadvantage of this kind of ranking is that the result is more complicated and presents scenarios in parallel position, if the performances in Φ- and in Φ+ are not better than the subsequent scenarios.
To investigate the groups of scenario rankings apparent from the complete ranking further Figure A.5. (see Appendix) is comparing the equivalent partial rankings. A comparison of the ranking patterns within group 1 shows that the majority of rankings include just one parallel cluster, comprised of either scenarios A and D or of E and C. A parallel position between C and B occurs only once in this group. But there are also “x-shaped” rankings with B in the middle
position and two parallel clusters. This shows that the first and/or the last rank are ambiguous in most cases. In the second group the partial rankings are all “x-shaped” rankings with B in the middle but the position of scenarios A and D points the other way. (see Figure A.5. in Appendix) This indicates that if the scenario A is in last position in this configuration, then only one can be in parallel ranking with scenario D. Group 3 presents rankings with scenario C on the first rank but always in parallel ranking with scenario E. The last cluster of scenarios is also exclusively in parallel position. The last group, consisting of just one ranking, presents a ranking with only one parallel cluster between scenarios E and C and is the only ranking with scenario A in last place by itself.
Comparing the patterns of the four groups of rankings revealed by the partial ranking, further illustrates the insufficient discriminatory power of the group differences which were found by the complete rankings. Nevertheless, there is interesting additional information from the partial ranking: the most common ranking, E –C –B –A –D is also the ranking with the least parallel scenario positions, so it is the least ambiguous ranking.