Detailed analyses of the impact matrix that tested the overall robustness of the results revealed inherent structures and offered a better understanding of the distinct results on the final scenario ranking. An analysis was performed that aimed at examining which criteria are supportive of or in conflict to each other, information that allows detailed analysis of trade-offs and synergies within the sustainability dimensions.
The GAIA plane visualises the discrimination direction of the 17 sustainability appraisal criteria. The plates provide two types of information: the direction of each indicator, and the length of its vector. Whereas the direction offers relative information - similar directions indicate supportive structures (synergies), opposite directions indicate conflicting structures (trade-offs), and the length indicates the discriminative power of the indicator for the overall ranking. (Bana e Costa 1990, Geldermann and Zhang 2001) The most discriminative criteria in this case study are (a) Phosphor Water Emissions, (b) Biological and (c) Chemical Oxygen Demand, (d) Noise, and (e) Security of Supply. Also rather strongly discriminative are the criteria (f) Effect on Public Budget, (g) Quality of Landscape, and (h) Regional Self Determinacy. The criteria (a), (b), and, (c) represent quantitative criteria, whereas all the others are qualitatively appraised criteria. A detailed investigation showed that those criteria that are characterised by a large range of the individual scenario results are the most discriminative criteria. In other words, criteria that have a high relative standard deviation (standard deviation divided by the mean of the results) are more influential on the final scenario ranking than criteria with a low standard deviation. The investigations showed, however, that the length of the vector is very sensitive to minor changes of the preference functions resulting in weakness.
Therefore, the length of the GAIA vectors is not considered a reliable proxy in this case study
for the identification of highly influential criteria. However one methodological insight is gained: despite the Water Quality indictors, which already showed poor data reliability, all the other highly discriminative criteria are qualitative criteria. This fact raises the question of whether the quantitative and qualitative criteria are treated differently in the GAIA interpretation, due to the different characteristics of the data. A possible explanation might be that there is a tendency for qualitative criteria, the full appraisal range was used by the experts, in order to put more differential weight to the individual scenarios, whereas for the quantitative the range of results is the outcome of a quantitative modelling exercise and that can, at least in principle, generate very similar results for each scenario.
On the contrary, the relative information of the positioning of the appraisal criteria towards each other is experienced as a valuable asset to the GAIA visualisation. The overall picture reveals that the environmental, social, technological, and economic criteria cover quite a large range. In particular the environmental dimension covers a very large range and is least homogeneous. A few criteria seem to point in opposing directions, for example the indicator ‘Cumulated Material Effort’ which indicates a more economic-dominated direction and ‘Employment’ which leans in the direction of social sustainability criteria (see Figure 41).
The environmental dimension covers the widest range in the plane, spreading across three quarters of the full circle. Most indicators are in supportive or neutral positions towards each other, but there are conflicts within the dimension. The indicators ‘Cumulative Material Effort’
and ‘SO2 equivalents’ seem to be in conflicting positions. This shows that SO2 emissions are not linearly linked to the amount of material used, but that certain technologies use processes that are related to especially high sulphur emissions. The environmental dimension strongly overlaps with the social and technological criteria range.
Figure 41. Visualisation by GAIA of the overall impact matrix representing the sustainability criteria impacts across the five scenarios
The social dimension covers an area of 90° and is in this sense very compact. None of the social criteria are in a conflicting position to each other. Remarkably, there is much overlap of technological criteria, such as Security of Supply, Import Dependency, and Technological Leadership. Also striking is the economic criterion ‘Employment’ which is in a supporting position to many social criteria and even completely overlaps the social criterion ‘Social Cohesion’. In contrast, the other economic criteria are in conflicting positions with the social criteria. The economic dimension is represented by merely three criteria which cover a rather large range of 160°. This clearly indicates that they are in conflict with each other. The criteria
‘Costs and Effect on Public Spending’ stands in contrast to the criteria ‘Employment’, which indicates that technologies that are minimising micro- and macro economic costs are not necessarily creating additional jobs. Moreover, the economic criteria are in opposition to most of the social and technological criteria as well as to some of the environmental indicators as well. As mentioned, one exception is ‘Cumulative Material Effort’, which is closely related to the economic criteria ‘Costs’ and ‘Effect on Public Budget’. The technological criteria spread across an area of 90° , similar to the social criteria. The GAIA plane presents the technological criteria in a supporting or neutral position towards the social criteria and environmental criteria and is in direct conflict to Costs and Effect on Public Budget.