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N. Pankow et al.
It has also proved useful to review the causal relationship. In the next stages of
the indicator development, this step should be included to ensure the relevance in
the specific department.
The trial based on case examples has shown a solid data availability in the
sewage treatment sector. The variants evaluated with the utility analysis and the
dashboard of sustainability were practical in their application and displayed easy to
interpret graphics. Due to the direct ranking method the value benefit analysis has
a subjective value attitude. A stronger focus on the weighting method as suggested
by Sartorius et al. (2017) with the involvement of experts and decision-makers is
recommended. The Dashboard of Sustainability simplified the results with its colour
representation even more than the value benefit analysis. The colour assignment
made the identification of “good” and “bad” rated variants easy. The high degree of
simplification is accompanied by a loss of transparency. The direct comparison of
variants could lead to wrong conclusion especially for non-expert users.
The partial order and the program PyHasse could not be used for the variant
evaluation. The small number of variants and the high number of indicators
lead to many incompatibilities. Examples with more than 3 variants could show
comparability and hence better interpretable results.
The program PyHasse, however, showed usability for the assessment of the MIS.
The study of separability points to differing indicators. The dominance analysis has
shown that no sustainability dimension dominates. This suggests a diverse set of
indicators that covers different aspects. However, the indicator rating used with
PyHasse could be further developed. Since the number of variants was too small
to statistically secure the results.
As clarified at the beginning of this work, the MIS can be developed in an
iterative process. As a result, the changes of social, legal, technical or scientific
nature occurring over time can be included. Furthermore, this procedure offers the
possibility to test the set of indicators with current obtained data. Once a stable
number of indicators has been reached, the analyses already performed by PyHasse
can be reapplied and expanded.
During the work, further questions related to the indicators and the evaluation
methods have emerged: The evaluation of qualitative indicators has so far been
subjective. For further application it is appropriate to develop a rating scheme to
facilitate the evaluation of these indicators.
The causal relationship and the quality of the indicators were only briefly
addressed in this paper. Further work requires a closer examination of the indicators
in terms of their causal relationship to a specific field in civic engineering and their
assessability. As a result, the evaluation of sustainability by indicators and thus the
decision could be hedged better.
In this work three multicriteria evaluation methods were considered. In the next
phases, further multi-criteria evaluation methods could be tested by case studies
within the implementation phases. The testing of new methods could also lead to
the creation of a proprietary algorithm that can be used for planning and purchasing.
N. Pankow et al.
It has also proved useful to review the causal relationship. In the next stages of
the indicator development, this step should be included to ensure the relevance in
the specific department.
The trial based on case examples has shown a solid data availability in the
sewage treatment sector. The variants evaluated with the utility analysis and the
dashboard of sustainability were practical in their application and displayed easy to
interpret graphics. Due to the direct ranking method the value benefit analysis has
a subjective value attitude. A stronger focus on the weighting method as suggested
by Sartorius et al. (2017) with the involvement of experts and decision-makers is
recommended. The Dashboard of Sustainability simplified the results with its colour
representation even more than the value benefit analysis. The colour assignment
made the identification of “good” and “bad” rated variants easy. The high degree of
simplification is accompanied by a loss of transparency. The direct comparison of
variants could lead to wrong conclusion especially for non-expert users.
The partial order and the program PyHasse could not be used for the variant
evaluation. The small number of variants and the high number of indicators
lead to many incompatibilities. Examples with more than 3 variants could show
comparability and hence better interpretable results.
The program PyHasse, however, showed usability for the assessment of the MIS.
The study of separability points to differing indicators. The dominance analysis has
shown that no sustainability dimension dominates. This suggests a diverse set of
indicators that covers different aspects. However, the indicator rating used with
PyHasse could be further developed. Since the number of variants was too small
to statistically secure the results.
As clarified at the beginning of this work, the MIS can be developed in an
iterative process. As a result, the changes of social, legal, technical or scientific
nature occurring over time can be included. Furthermore, this procedure offers the
possibility to test the set of indicators with current obtained data. Once a stable
number of indicators has been reached, the analyses already performed by PyHasse
can be reapplied and expanded.
During the work, further questions related to the indicators and the evaluation
methods have emerged: The evaluation of qualitative indicators has so far been
subjective. For further application it is appropriate to develop a rating scheme to
facilitate the evaluation of these indicators.
The causal relationship and the quality of the indicators were only briefly
addressed in this paper. Further work requires a closer examination of the indicators
in terms of their causal relationship to a specific field in civic engineering and their
assessability. As a result, the evaluation of sustainability by indicators and thus the
decision could be hedged better.
In this work three multicriteria evaluation methods were considered. In the next
phases, further multi-criteria evaluation methods could be tested by case studies
within the implementation phases. The testing of new methods could also lead to
the creation of a proprietary algorithm that can be used for planning and purchasing.