Application of linear ordering methods to assess the level of inequality in the context of sustainable development goal 10 in the European Union

• Autorzy: Mikuła Aneta , Raczkowska Małgorzata

Identyfikatory

DOI

10.29119/1641-3466.2024.200.20

• Języki publikacji: EN

Abstrakty

EN

Purpose: The purpose of this article is to compare the results of linear ordering conducted using various methods to assess the achievement level of the tenth sustainable development goal, i.e., reducing inequalities in European Union countries in 2022. A specific aim of the study is also to identify the technique that shows the greatest agreement with other forms of ordering. Design/methodology/approach: In this study, three methods of linear ordering were used to assess the diversity of inequality levels in the European Union. The reference methods include Hellwig's development measure and the classical TOPSIS method. A non-reference method was also used based on the averaged values of normalized features. The synthetic variables were computed using indicators of the tenth sustainable development goal, which were applied to assess progress in goal achievement in EU countries. To assess the consistency of the obtained rankings, Kendall's rank correlation coefficients were calculated, and similarity measure vectors were computed. Findings: The study addresses the issue of choosing a linear ordering method to determine the level of achievement of the 10th Sustainable Development Goal, which aims to reduce inequalities in the European Union countries in 2022. Based on the analysis, it can be concluded that the choice of synthetic variable construction procedure affects the ranking of the examined objects. Based on the similarity measure vectors of the rankings, it was found that the ranking constructed using the TOPSIS method with zero unitarization is the closest to all other rankings. Research limitations/implications: The authors acknowledge that the choice of variables for the study does not fully capture the level of socio-economic inequalities in EU countries. The study used only the indicators of the 10th Sustainable Development Goal, monitored and published by Eurostat, to assess the level of inequality. Additionally, the final ordering of objects within linear ordering methods depends on the variable selection and normalization method adopted by the researchers. Practical implications: The study's results may be useful for policymakers in the European Union countries. Using an aggregate synthetic measure to assess a multidimensional phenomenon can facilitate evaluating progress in achieving the goal. Additionally, the application of an appropriate method to identify the countries best achieving the goal provides an opportunity to identify processes contributing to success and apply them in countries with lower levels of goal achievement. Originality/value: The study proposes the use of a multi-criteria analysis approach, which is currently considered an effective method for measuring and describing multifactor phenomena. Several methods of variable aggregation often yield different results. Therefore, it seems appropriate to compare several methods of evaluating a given phenomenon and choose the one with the greatest agreement with others. This procedure is not commonly used as a standard quantitative tool in assessing the diversity of inequality levels in EU countries.

Słowa kluczowe

EN

linear ordering; non-reference methods; reference methods; country ranking; inequalities; sustainable development

PL

porządkowanie liniowe; metody referencyjne; metody niereferencyjne; ranking krajów; nierówności; rozwój zrównoważony

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2024
• Tom: z. 200
• Strony: 269--279
• Opis fizyczny: Bibliogr. 35 poz.

Twórcy

autor

Mikuła Aneta

• Warsaw University of Life Sciences

• https://orcid.org/0000-0001-7129-6898

autor

Raczkowska Małgorzata

• Warsaw University of Life Sciences

• https://orcid.org/0000-0002-8540-8958

Bibliografia

• 1. Ardielli, E. (2019). Use of TOPSIS method for assessing of good governance in European Union countries. Review of Economic Perspectives, Vol. 19, No. 3, pp. 211-231, doi: 10.2478/revecp-2019-0012.
• 2. Bąk, A. (2018a). Analiza porównawcza wybranych metod porządkowania liniowego. Research Papers of Wrocław University of Economics, Vol. 508, pp. 19-28, doi: 10.15611/pn.2018.508.02.
• 3. Bąk, A. (2018b). Zastosowanie metod wielowymiarowej analizy porównawczej do oceny stanu środowiska w województwie dolnośląskim. The Polish Statistician, Vol. 63(1), pp. 7-20, doi: 10.5604/01.3001.0014.0521.
• 4. Balcerzak, A.P. (2020). Quality of institutions in the European Union countries. Application of TOPSIS based on entropy measure for objective weighting. Acta Polytechnica Hungarica, Vol. 17, No. 1, pp. 101-122, doi: 10.12700/APH.17.1.2020.1.6.
• 5. Eurostat. Indicators of sustainable development. Retrieved from: http://www.ec.europa.eu/eurostat/web/main/data/database, 12.01.2024.
• 6. Gavurova, B., Megyesiova, S. (2022). Sustainable health and wellbeing in the European Union. Frontiers in Public Health, Vol. 10, doi: 10.3389/fpubh.2022.851061.
• 7. Guijarro, F., Poyatos, J.A. (2018). Designing a sustainable development goal index through a goal programming model: The Case of EU-28 Countries. Sustainability, Vol. 10, No. 9, 3167, doi: 10.3390/su10093167.
• 8. Hellwig, Z. (1968). Zastosowanie metody taksonomicznej do typologicznego podziału krajów ze względu na poziom ich rozwoju oraz zasoby i strukturę wykwalifikowanych kadr. Statistical Review, Vol. 4(1968), pp. 307-326.
• 9. Hwang, C.L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. New York: Springer-Verlag.
• 10. Kendall, M.G. (1938). A new measure of rank correlation. Biometrika, Vol. 30, No. 1/2, pp. 81-93.
• 11. Kisielińska, J., Borkowski, B., Czech, K.A., Górska, A., Koszela, G., Krawiec, M., Zielińska-Sitkiewicz M. (2021a). Wielowymiarowa analiza danych w ekonomice rolnictwa, Warszawa: SGGW.
• 12. Kisielińska, J., Roman, M., Pietrzak, P., Roman, M., Łukasiewicz, K., Kacperska, E. (2021b). Utilization of Renewable Energy Sources in Road Transport in EU Countries- TOPSIS Results. Energies, Vol. 14, No. 22, 7457, doi: 10.3390/en14227457.
• 13. Kukuła, K. (1999). Metoda unitaryzacji zerowanej na tle wybranych metod normowania cech diagnostycznych. Acta Scientifica Academiae Ostroviensis, Vol. 4, pp. 5-31.
• 14. Kukuła, K. (2020). O pewnych dylematach związanych z budową rankingu obiektów ze względu na poziom zjawiska złożonego. Scientific Journal Warsaw University of Life Sciences Problems of World Agriculture, Vol. 20(2), pp. 12-21. doi:10.22630/PRS.2020.20.2.9.
• 15. Kukuła, K., Luty, L. (2015). Propozycja procedury wspomagającej wybór metody porządkowania liniowego. Statistical Review, Vol. 62(2), pp. 219-231.
• 16. Malina, A. (2004). Wielowymiarowa analiza przestrzennego zróżnicowania struktury gospodarki Polski według województw. Krakow Review of Economics and Management. Special Series, Monographs, Vol. 162.
• 17. Mikuła, A. (2016). Nierówności społeczne w Polsce. Social Inequalities and Economic Growth, Vol. 47, pp. 442-453, doi: 10.15584/nsawg.2016.3.32.
• 18. Navarro, V. (2000). Assessment of the World Health Report 2000. The Lancet, Vol. 356, No. 4, pp. 1598-1601, doi: 10.1016/S0140-6736(00)03139-1.
• 19. Reiff, M., Surmanová, K., Balcerzak, A.P., Pietrzak, M.B. (2016). Multiple criteria analysis of European Union agriculture. Journal of international studies, Vol. 9, No. 3, pp. 62-74, doi: 10.14254/2071-8330.2016/9-3/5.
• 20. Roszko-Wójtowicz, E., Grzelak, M.M. (2020). Macroeconomic stability and the level of competitiveness in EU member states: a comparative dynamic approach. Oeconomia Copernicana, Vol. 11, No. 4, pp. 657-688, doi: 10.24136/oc.2020.027.
• 21. Roszkowska, E., Filipowicz-Chomko, M. (2020). Measuring sustainable development in the education area using multi-criteria methods: a case study. Central European Journal of Operations Research, Vol. 28, No. 4, pp. 1219-1241, doi: 10.1007/s10100-019-00641-0.
• 22. Roszkowska, E., Filipowicz-Chomko, M. (2021), Measuring sustainable development using an extended Hellwig method: A case study of education. Social Indicators Research, Vol. 153, No. 1, pp. 299-322, doi: 10.1007/s11205-020-02491-9.
• 23. Sachs, J., Schmidt-Traub, G., Kroll, C., Lafortune, G., Fuller, G. (2018). SDG index and dashboards report 2018: global responsibilities: implementing the goals. New York: Bertelsmann Stiftung and Sustainable Development Solutions Network (SDSN).
• 24. Sompolska-Rzechuła, A. (2020). Zastosowanie liniowego porządkowania obiektów do oceny aktywności ekonomicznej ludności w ujęciu województw. The Polish Statistician, Vol. 65(03), pp. 46-61, doi: 10.5604/01.3001.0014.0456.
• 25. Szymańska, A. (2021), Reducing socioeconomic inequalities in the European Union in the context of the 2030 agenda for sustainable development. Sustainability, Vol. 13, No. 13, 7409, doi: 10.3390/su13137409.
• 26. Ture, H., Dogan, S., Kocak, D. (2019). Assessing Euro 2020 strategy using multi-criteria decision making methods: VIKOR and TOPSIS. Social Indicators Research, Vol. 142, pp. 645-665, doi: 10.1007/s11205-018-1938-8.
• 27. Tutak, M., Brodny, J. (2022). Evaluating differences in the Level of Working Conditions between the European Union Member States using TOPSIS method. Decision Making: Applications in Management and Engineering, Vol. 5, No. 2, pp. 1-29, doi: https://doi.org/10.31181/dmame0305102022t.
• 28. Ulbrych, M.A., Lesiak, J. (2022). Determinanty konkurencyjności produkcji przemysłowej krajów Grupy Wyszehradzkiej. Optimum. Economic Studies, Vol. 4, No. 110, 133-150, doi: 10.15290/oes.2022.04.110.09.
• 29. UN, (2015). Przekształcamy nasz świat: Agenda na rzecz zrównoważonego rozwoju 2030, A/RES/70/1. Retrieved from: https://www.unic.un.org.pl/files/164/Agenda 2030_pl_ 2016_ostateczna.pdf, 12.01.2024.
• 30. Utzig, M., Raczkowska, M., Mikuła, A. (2023). Wybrane aspekty zrównoważonego rozwoju w obszarze społecznym w powiatach województwa mazowieckiego. Journal of Tourism and Regional Development, Vol. 19, pp. 117-126, doi: 10.22630/TIRR.2023.19.11.
• 31. Vasilić, N., Semenčenko, D., Popović-Pantić, S. (2020). Evaluating ICT usage in enterprises in Europe: Topsis approach. Economic Themes, Vol. 58, No. 4, pp. 529-544, doi:10.2478/ethemes-2020-0030, DOI:10.2478/ethemes-2020-0030.
• 32. Vavrek, R., Chovancová, J. (2019). Assessment of economic and environmental energy performance of EU countries using CV-TOPSIS technique. Ecological Indicators, Vol. 106, 105519, doi:10.1016/j.ecolind.2019.105519.
• 33. Wawrzyniak, D. (2016). Standard of living in the European Union, Comparative Economic Research. Central and Eastern Europe, Vol. 19, No. 1, pp. 141-155, doi: 10.1515/cer-2016- 0008.
• 34. Wypych, M. (1982). Syntetyczna miara rozwoju w badaniach ekonomicznoprzestrzennych. Statistical Review, Vol. 3-4.
• 35. Zhou, K., Wang, Y., Hussain, J. (2022). Energy poverty assessment in the Belt and Road Initiative countries: Based on entropy weight-TOPSIS approach. Energy Efficiency, Vol. 15, No. 7, 46, doi: 10.1007/s12053-022-10055-8.