Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1828
Title: An Exploratory DEA and Machine Learning Framework for the Evaluation and Analysis of Sustainability Composite Indicators in the EU
Authors: Tsaples, Georgios
Papathanasiou, Jason
Georgiou, Andreas C.
Type: Article
Subjects: FRASCATI::Social sciences::Economics and Business
FRASCATI::Social sciences::Economics and Business::Business and Management
Keywords: data envelopment analysis
two-stage DEA
exploratory modeling and analysis
sustainability
increased discriminatory power
machine learning
Issue Date: 29-Jun-2022
Publisher: MDPI
Source: Mathematics
Volume: 10
Issue: 13
First Page: 2277
Abstract: One method that has been proposed for the measurement of sustainability is Data Envelopment Analysis (DEA). Despite its advantages, the method has limitations: First, the efficiency of Decision-Making Units is calculated with weights that are favorable to themselves, which might be unrealistic, and second, it cannot account for different perceptions of sustainability; since there is not an established and unified definition, each analyst can use different data and variations that produce different results. The purpose of the current paper is twofold: (a) to propose an alternative, multi-dimensional DEA model that handles weight flexibility using a different metric (an alternative optimization criterion) and (b) the inclusion of a computational stage that attempts to incorporate different perceptions in the measurement of sustainability and integrates machine learning to explore country sustainability composite indices under different perceptions and assumptions. This approach offers insights in areas such as feature selection and increases the trust in the results by exploiting an inclusive approach to the calculations. The method is used to calculate the sustainability of the 28 EU countries.
URI: https://doi.org/10.3390/math10132277
https://ruomo.lib.uom.gr/handle/7000/1828
ISSN: 2227-7390
Other Identifiers: 10.3390/math10132277
Appears in Collections:Department of Business Administration

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