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Clustering of EU Countries by the Level of Circular Economy: An Object-Oriented Approach

Author

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  • Konstantin Gomonov

    (Department of Economic and Mathematical Modelling, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia)

  • Svetlana Ratner

    (Department of Economic and Mathematical Modelling, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia
    Economic Dynamics and Innovation Management Laboratory, V.A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, 65 Profsoyuznaya Street, 117997 Moscow, Russia)

  • Inna Lazanyuk

    (Department of Economic and Mathematical Modelling, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia)

  • Svetlana Revinova

    (Department of Economic and Mathematical Modelling, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia)

Abstract

In order to effectively regulate the circular economy (CE) at the national and international levels, it is essential to have a unified and informative system of indicators for monitoring the progress in the CE. The lack of standard indicators for measuring the progress of cyclicality leads to contradictions and misunderstandings, which is a problem for the implementation of CE strategies. This paper aims to adapt dynamic clustering approaches to solving strategic management problems of circular production and consumption processes. To achieve this goal, the authors performed the following tasks: (1) tested clustering algorithms by ranking EU countries by the level of development of the circular economy; (2) identified the approach that allows the best classification of EU countries, considering changes in the indicators of the level of CE development in 2000–2019 (dynamic classification); (3) developed a software module using python libraries to classify and visualize the results. The results illustrate that the k-means algorithm has a good discriminatory ability in division of all countries of the training sample (EU countries) into several clusters with different dynamics in the development of the CE. The best quality of classification is obtained by the indicator “Generation of municipal waste per capita”; satisfactory quality of the classification is obtained by the indicator “Generation of waste excluding major mineral wastes per GDP unit”. The study results demonstrate the fundamental applicability of the object-oriented and classical statistical approach to solving strategic management problems of the CE and their potential effectiveness in terms of the clarity and information content of reflecting cyclical processes.

Suggested Citation

  • Konstantin Gomonov & Svetlana Ratner & Inna Lazanyuk & Svetlana Revinova, 2021. "Clustering of EU Countries by the Level of Circular Economy: An Object-Oriented Approach," Sustainability, MDPI, vol. 13(13), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7158-:d:582336
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    References listed on IDEAS

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    1. Zvonimira Sverko Grdic & Marinela Krstinic Nizic & Elena Rudan, 2020. "Circular Economy Concept in the Context of Economic Development in EU Countries," Sustainability, MDPI, vol. 12(7), pages 1-13, April.
    2. Roman Lacko & Zuzana Hajduová & Marcin Zawada, 2021. "The Efficiency of Circular Economies: A Comparison of Visegrád Group Countries," Energies, MDPI, vol. 14(6), pages 1-13, March.
    3. Nikolay Sterev & Vania Ivanova, 2021. "Circular Economy: New Opportunities for Growth," Eurasian Studies in Business and Economics, in: Mehmet Huseyin Bilgin & Hakan Danis & Ender Demir & Sofia Vale (ed.), Eurasian Economic Perspectives, pages 339-357, Springer.
    4. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    5. Willi Haas & Fridolin Krausmann & Dominik Wiedenhofer & Markus Heinz, 2015. "How Circular is the Global Economy?: An Assessment of Material Flows, Waste Production, and Recycling in the European Union and the World in 2005," Journal of Industrial Ecology, Yale University, vol. 19(5), pages 765-777, October.
    6. Marcus Linder & Steven Sarasini & Patricia Loon, 2017. "A Metric for Quantifying Product-Level Circularity," Journal of Industrial Ecology, Yale University, vol. 21(3), pages 545-558, June.
    7. Jaime Sánchez-Ortiz & Vanesa Rodríguez-Cornejo & Rosario Del Río-Sánchez & Teresa García-Valderrama, 2020. "Indicators to Measure Efficiency in Circular Economies," Sustainability, MDPI, vol. 12(11), pages 1-15, June.
    8. Barbara Fura & Małgorzata Stec & Teresa Miś, 2020. "Statistical Evaluation of the Level of Development of Circular Economy in European Union Member Countries," Energies, MDPI, vol. 13(23), pages 1-23, December.
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    1. Joanicjusz Nazarko & Ewa Chodakowska & Łukasz Nazarko, 2022. "Evaluating the Transition of the European Union Member States towards a Circular Economy," Energies, MDPI, vol. 15(11), pages 1-24, May.
    2. Claudia Diana Sabău-Popa & Alexandra Maria Bele & Anca Otilia Dodescu & Marcel Ioan Boloș, 2022. "How Does the Circular Economy Applied in the European Union Support Sustainable Economic Development?," Sustainability, MDPI, vol. 14(16), pages 1-16, August.

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