A Process Oriented MCDM Approach to Construct a Circular Economy Composite Index

Abstract

The purpose of this contribution is to develop a Circular Economy Composite indicator to benchmark EU countries performance. Europe is at the forefront of the global transition towards a sustainable and circular economy. To this end, the European Commission has launched in 2015 a Circular Economy Action Plan including a monitoring framework to measure progress and to assess the effectiveness of initiatives towards the circular economy in the European Union (EU) and Member States. Still, this monitoring framework lacks a composite indicator at the national level to aggregate the circular economy dimensions into a single summary indicator. Although there is a wide range of sustainability composite indicators, no aggregate circular economy index exits to this date. We use a multi-criteria approach to construct a circular economy composite index based on TOPSIS (Technique for Order Preferences by Similarity to Ideal Solutions) methodology. In addition, we introduce a novel aggregation methodology for building a composite indicator where different levels of compensability for the distances to the ideal and anti-ideal (or negative-ideal) values of each indicator are considered. In order to illustrate the advantages of this proposal, we have applied it to evaluate the Circular Economy performance of EU Member States for the year 2016. This proposal can be a valuable tool for identifying areas in which the countries need to concentrate their efforts to boost their circular economy performance.

Circular Economy Concept in the Context of Economic Development in EU Countries

“…Silvestri et al built two composite indicatorsthe Circular Economy Static Index (CESI) and the Circular Economy Dynamic Index (CEDI)-that permitted both a static and a dynamic evaluation of the circular economy (CE) performance of European regions [53]. Authors Garcia-Bernabeu et al used a multi-criteria approach to construct a circular economy composite index based on Technique for Order Preferences by Similarity to Ideal Solutions methodology [54]. Robaina et al used cluster analysis with three econometric estimation methods: panel unit root test, panel cointegration tests, and vector autoregression model with the similar data like in this article [55].…”

A Multicriteria Extension of the Efficient Market Hypothesis

“…Given an m × 1 vector d j with elements sorted in increasing order, a sufficient condition for the percentile normalization described in Equations (17) and (18) to be equivalent to the linear max-min normalization in Equation (7) is that λ < 1/m.…”

“…since the m-th element of our sorted vector is its maximum. As a result, the pair of Equations (17) and (18) are equivalent to Equation (7).…”

“…Some other systems in which weights are directly obtained from data are Principal Component Analysis, Data Envelopment Analysis [17,25]. One of the advantages of obtaining weights from data is the possibility of avoiding redundancies by studying the correlation among criteria.…”

“…Here, compensability means the degree to which the achievement of any criterion is offset or balanced by the achievement of another one. This concept is a critical issue when categorizing aggregation methods such as the one described in this paper [15][16][17]; •…”