A composite indicator for the waste management in the EU via Hierarchical Disjoint Non-Negative Factor Analysis

Abstract

In the last years, the quantity of information and statistics about waste management are more and more consistent but so far, few studies are available in this field. The goal of this paper is of producing a modelbased Composite Indicator of ''good'' Waste Management, in order to provide a useful tool of support for EU countries' policy-makers and institutions.Composite Indicators (CIs), usually, are multidimensional concepts with a hierarchical structure characterized by the presence of a set of specific dimensions, each one corresponding to a subsets of manifest variables. Thus, we propose a CI for Waste Management in Europe by using a hierarchical model-based approach with positive loadings. This approach guarantees to comply with all the good properties on which a composite indicator should be based and to detect the main dimensions (i.e., aspects) of the Waste Management phenomenon.In other terms, this paper provides a hierarchically aggregated index that best describes the Waste Management in EU with its main features by identifying the most important high order (i.e., hierarchical) relationships among subsets of manifest variables. All the parameters are estimated according to the maximum likelihood estimation method (MLE) in order to make inference on the parameters and on the validity of the model.

Development of the Waste Management Composite Index Using DEA Method as Circular Economy Indicator: The Case of European Union Countries

“…The seven first-ranked countries in both illustrations primarily are Germany, the Netherlands and Austria, followed by Belgium, Denmark, Sweden and Luxembourg, Making these countries a suitable example of best practice within the Waste management area. Also, the results obtained by the model proposed in this paper are similar to those obtained by Cavicchia, Sarnacchiaro and Vichy [56] who developed the CI composite index for Waste Management in Europe by using Hierarchical Disjoint Non-Negative Factorial Analysis (HDNFA). As conclusion, Scenario 3 is accaptable for WMCI evaluation and monitoring and further below, we will discuss the achievement of the top four countries according to its results.…”

A multi-group higher-order factor analysis for studying the gender-effect in Teacher Job Satisfaction

“…In order to minimize the discrepancy function, a descent coordinates algorithm has been developed requesting that all the items on the diagonal of B and the elements of c must be nonnegative. 2O-DFA was used by Cavicchia, Sarnacchiaro and Vichi [4] in order to build a consistent Composite Indicator for Waste Management in EU.…”

Solid Municipal Waste Management for Sustainable Development

“…Educational work with the population, which is actively carried out by the media, house masters, has become widely known. The observance of cleanliness is strictly monitored by the "garbage police", who punish those who throw out garbage in unspecified places with impressive fines [25,26]. European countries resort to different ways of motivating the population to carefully sort garbage.…”

“…European countries resort to different ways of motivating the population to carefully sort garbage. For example, Berlin teenagers who collect garbage and hand it over for recycling receive a financial reward for this [25,26]. The Dutch authorities issue special environmental loyalty coupons to citizens of the country who actively participate in the separate garbage collection program.…”

“…The Dutch authorities issue special environmental loyalty coupons to citizens of the country who actively participate in the separate garbage collection program. In Barcelona, various garbage collection events occur quite often, in the form of significant discounts on utility bills [26]. There is a powerful incineration industry in Switzerland: heating of many settlements is carried out at the expense of garbage burned at special plants.…”