Assessing the Europe 2020 Strategy Implementation Using Interval Entropy and Cluster Analysis for Interrelation between Two Groups of Headline Indicators
The research analyzes the progress of Member States in the implementation of Europe 2020 strategy targets and goals in 2016–2018. Multiple criteria decision-making approaches applied for this task. The set of headline indicators was divided into two logically explained groups. Interval entropy is pr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/23/3/345 |
| _version_ | 1797541371363459072 |
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| author | Natalja Kosareva Aleksandras Krylovas |
| author_facet | Natalja Kosareva Aleksandras Krylovas |
| author_sort | Natalja Kosareva |
| collection | DOAJ |
| description | The research analyzes the progress of Member States in the implementation of Europe 2020 strategy targets and goals in 2016–2018. Multiple criteria decision-making approaches applied for this task. The set of headline indicators was divided into two logically explained groups. Interval entropy is proposed as an effective tool to make prioritization of headline indicators in separate groups. The sensitivity of the interval entropy is its advantage over classical entropy. Indicator weights were calculated by applying the WEBIRA (weight-balancing indicator ranks accordance) method. The WEBIRA method allows the best harmonization of ranking results according to different criteria groups—this is its advantage over other multiple-criteria methods. Final assessing and ranking of the 28 European Union countries (EU-28) was implemented through the α-cut approach. A k-means clustering procedure was applied to the EU-28 countries by summarizing the ranking results in 2016–2018. Investigation revealed the countries–leaders and countries–outsiders of the Europe 2020 strategy implementation process. It turned out that Sweden, Finland, Denmark, and Austria during the three-year period were the countries that exhibited the greatest progress according to two headline indicator groups’ interrelation. Cluster analysis results are mainly consistent with the EU-28 countries’ categorizations set by other authors. |
| first_indexed | 2024-03-10T13:14:04Z |
| format | Article |
| id | doaj.art-f94fb0fabdf549db9c34c9ddfff23ff7 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T13:14:04Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-f94fb0fabdf549db9c34c9ddfff23ff72023-11-21T10:32:35ZengMDPI AGEntropy1099-43002021-03-0123334510.3390/e23030345Assessing the Europe 2020 Strategy Implementation Using Interval Entropy and Cluster Analysis for Interrelation between Two Groups of Headline IndicatorsNatalja Kosareva0Aleksandras Krylovas1Department of Mathematical Modelling, Vilnius Gediminas Technical University, Saulėtekio al. 11, 10223 Vilnius, LithuaniaDepartment of Mathematical Modelling, Vilnius Gediminas Technical University, Saulėtekio al. 11, 10223 Vilnius, LithuaniaThe research analyzes the progress of Member States in the implementation of Europe 2020 strategy targets and goals in 2016–2018. Multiple criteria decision-making approaches applied for this task. The set of headline indicators was divided into two logically explained groups. Interval entropy is proposed as an effective tool to make prioritization of headline indicators in separate groups. The sensitivity of the interval entropy is its advantage over classical entropy. Indicator weights were calculated by applying the WEBIRA (weight-balancing indicator ranks accordance) method. The WEBIRA method allows the best harmonization of ranking results according to different criteria groups—this is its advantage over other multiple-criteria methods. Final assessing and ranking of the 28 European Union countries (EU-28) was implemented through the α-cut approach. A k-means clustering procedure was applied to the EU-28 countries by summarizing the ranking results in 2016–2018. Investigation revealed the countries–leaders and countries–outsiders of the Europe 2020 strategy implementation process. It turned out that Sweden, Finland, Denmark, and Austria during the three-year period were the countries that exhibited the greatest progress according to two headline indicator groups’ interrelation. Cluster analysis results are mainly consistent with the EU-28 countries’ categorizations set by other authors.https://www.mdpi.com/1099-4300/23/3/345Europe 2020 strategyEU-28 countriessmartsustainable and inclusive growthheadline indicatorsWEBIRA |
| spellingShingle | Natalja Kosareva Aleksandras Krylovas Assessing the Europe 2020 Strategy Implementation Using Interval Entropy and Cluster Analysis for Interrelation between Two Groups of Headline Indicators Entropy Europe 2020 strategy EU-28 countries smart sustainable and inclusive growth headline indicators WEBIRA |
| title | Assessing the Europe 2020 Strategy Implementation Using Interval Entropy and Cluster Analysis for Interrelation between Two Groups of Headline Indicators |
| title_full | Assessing the Europe 2020 Strategy Implementation Using Interval Entropy and Cluster Analysis for Interrelation between Two Groups of Headline Indicators |
| title_fullStr | Assessing the Europe 2020 Strategy Implementation Using Interval Entropy and Cluster Analysis for Interrelation between Two Groups of Headline Indicators |
| title_full_unstemmed | Assessing the Europe 2020 Strategy Implementation Using Interval Entropy and Cluster Analysis for Interrelation between Two Groups of Headline Indicators |
| title_short | Assessing the Europe 2020 Strategy Implementation Using Interval Entropy and Cluster Analysis for Interrelation between Two Groups of Headline Indicators |
| title_sort | assessing the europe 2020 strategy implementation using interval entropy and cluster analysis for interrelation between two groups of headline indicators |
| topic | Europe 2020 strategy EU-28 countries smart sustainable and inclusive growth headline indicators WEBIRA |
| url | https://www.mdpi.com/1099-4300/23/3/345 |
| work_keys_str_mv | AT nataljakosareva assessingtheeurope2020strategyimplementationusingintervalentropyandclusteranalysisforinterrelationbetweentwogroupsofheadlineindicators AT aleksandraskrylovas assessingtheeurope2020strategyimplementationusingintervalentropyandclusteranalysisforinterrelationbetweentwogroupsofheadlineindicators |