Search

Composite Indicators (CIs, a.k.a. indices) are increasingly used as they can simplify interpretation of results by condensing the information of a plurality of underlying indicators in a single measure. This paper demonstrates that the strength of the correlations between the indicators is directly linked with their capacity to transfer information to the CI. A measure of information transfer from each indicator is proposed along with two weightoptimization methods, which allow the weights to be adjusted to achieve either a targeted or maximized information transfer. The tools presented in this paper are applied to a case study for resilience assessment of energy systems, demonstrating how they can support the tailored development of CIs. These findings enable analysts bridging the statistical properties of the index with the weighting preferences from the stakeholders. They can thus choose a weighting scheme and possibly modify the index while achieving a more consistent (by correlation) index., QC 20211026

We present the Multiple Criteria Decision Analysis Methods Selection Software (MCDA-MSS). This decision support system helps analysts answering a recurring question in decision science: Which is the most suitable Multiple Criteria Decision Analysis method (or a subset of MCDA methods) that should be used for a given Decision-Making Problem (DMP)?. The MCDA-MSS includes guidance to lead decision-making processes and choose among an extensive collection (over 200) of MCDA methods. These are assessed according to an original comprehensive set of problem characteristics. The accounted features concern problem formulation, preference elicitation and types of preference information, desired features of a preference model, and construction of the decision recommendation. The applicability of the MCDA-MSS has been tested on several case studies. The MCDA-MSS includes the capabilities of (i) covering from very simple to very complex DMPs, (ii) offering recommendations for DMPs that do not match any method from the collection, (iii) helping analysts prioritize efforts for reducing gaps in the description of the DMPs, and (iv) unveiling methodological mistakes that occur in the selection of the methods. A community-wide initiative involving experts in MCDA methodology, analysts using these methods, and decision-makers receiving decision recommendations will contribute to expansion of the MCDA-MSS.

Purpose: Weighting in life cycle assessment (LCA) incorporates stakeholder preferences in the decision-making process of comparative LCAs. Research efforts on this topic are concerned with deriving weights according to different principles, but few studies have evaluated the relationship between normalization and weights and their effect on single scores. We evaluate the sensitivity of aggregation methods to weights in different life cycle impact assessment (LCIA) methods to provide insight on the receptiveness of single score results to value systems. Methods: Sensitivity to weights in two LCIA methods is assessed by exploring weight spaces stochastically and evaluating the rank of alternatives via the Rank Acceptability Index (RAI). We assess two aggregation methods: a weighted sum based on externally normalized scores and a method of internal normalization based on outranking across CML-IA and ReCipE midpoint impact assessment. The RAI represents the likelihood that an alternative occupies a certain rank given all possible weight spaces, and it can be used to compare the sensitivity of final ranks to weight values in each aggregation method and LCIA. Evaluation is based on a case study of a comparative LCA of five PV technologies whose inventory is readily available in Ecoinvent. Results and discussion: Influence of weights in single scores depend on the scaling/normalization step more than the value of the weight itself. In each LCIA, aggregated results from a weighted sum with external normalization references show a higher weight insensitivity in RAI than outranking-based aggregation because in the former, results are driven by a few dominant impact categories due to the normalization procedure. Differences in sensitivity are caused by the notable variety (up two orders of magnitude) in the scales of normalized values for the weighted sum with external normalization and intrinsic properties of the methods including compensation and a lack of accounting for

After a short overview of the consistent development of renewable energies in the EU, this paper analyses the two main incentive schemes, FEED-IN and Tradable Green Certificates (TGC) mechanisms, adopted to stimulate the diffusion of "clean energy" in the European Union. In the first chapters, a brief description of these instruments is provided and considerations about positive and negative aspects with also suggestions to improve the systems are presented. The last part is dedicated to the assessment of these policies, by means of an analysis of their effectiveness, economic efficiency and expected revenues and profits for investors. In addition, the reasons why the FEED-IN system is more successful than the TGC one are illustrated, and general characteristics of a credible incentive system are presented.