Distance‐based weighting methods for composite indicators, with applications related to energy sustainability

Abstract

In this paper, we propose a novel use of four distance‐based methods for constructing composite indicators: namely, the maximizing deviations method, the weighted least‐square (WLD) deviation from the mean method, the WLD deviation from the ideal method, and the WLD dissimilarity method. The main advantages of these methods are that they have analytical solutions. Thus, there is no need to solve the underlying programming problem for each application. They also result in common (but not necessarily equal) weights that allow complete comparison and ranking. We explore the potential of the suggested weighting method by using it to re‐estimate the Sustainable Energy Index and the Sustainable Development Goal 7 composite indicator.

The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators

“…Although current advances do not completely solve the lack of a weighting scheme and a perfect aggregation approach [26], these advances address important issues. They offer more realistic representations of multidimensional phenomena by reducing information loss during the aggregation of sub-indicators [27,28], considering spatial autocorrelation [29]; concerning discrepant or out-of-scale data [30], avoiding compensation between poor and above-average performance sub-indicators [31], and increasing the composite indicator discriminating power [32], among others [33]. This research focuses on improving methods that construct composite indicators based on Shannon's [34] information theory.…”