9791221501063-17.pdf

Environmental sustainability is one of the main goals of all countries in the world. Sustainable Development Goals (SDGs) have been proposed by the United Nations in 2015. The purpose of this paper is to explore if and how European countries achievethe goals of environmental sustainability (tracked...

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Γλώσσα:English
Έκδοση: Firenze University Press, Genova University Press 2023
Διαθέσιμο Online:https://books.fupress.com/doi/capitoli/979-12-215-0106-3_17
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spelling oapen-20.500.12657-748892023-08-03T17:59:37Z Chapter Sustainable development goals: classifying European countries through self-organizing maps Davino, Cristina Nicola, D’Alesio Environmental Sustainability Artificial Neural Networks Self-Organizing Maps Sustainable Development Goals bic Book Industry Communication::J Society & social sciences Environmental sustainability is one of the main goals of all countries in the world. Sustainable Development Goals (SDGs) have been proposed by the United Nations in 2015. The purpose of this paper is to explore if and how European countries achievethe goals of environmental sustainability (tracked by the SDGs number 13, 14, and 15). In particular, SDG 13 refers to climate change and its impacts; SDG 14 refers to the conservation of water and marine resources while the last one; SDG 15 deals with the preservation of forests. The reference methodology of the paper are the Self-Organizing Maps proposed by Kohonen in 1982 as an unsupervised clustering method in the framework of artificial neural networks. The proposed analysis considers the 23 indicators related to the three SDGs of environmental sustainability and aims to explore and identify groups of countries with similar characteristics through a dimensionality reduction. Such clusters will be visually represented in a two-dimensional map. The proposed analysis considers the most recent data for all the above SDGs, which is 2018, with the aim of classifying the countries in terms of environmental sustainability and highlighting possible implications for policymakers. An analysis of the network accuracy is shown, using appropriate indicators. These results allow us to see which countries have achieved these goals and how they have deviated from them. 2023-08-03T15:05:45Z 2023-08-03T15:05:45Z 2023 chapter ONIX_20230803_9791221501063_85 2704-5846 9791221501063 https://library.oapen.org/handle/20.500.12657/74889 eng Proceedings e report application/pdf Attribution 4.0 International 9791221501063-17.pdf https://books.fupress.com/doi/capitoli/979-12-215-0106-3_17 Firenze University Press, Genova University Press ASA 2022 Data-Driven Decision Making 10.36253/979-12-215-0106-3.17 10.36253/979-12-215-0106-3.17 9223d3ac-6fd2-44c9-bb99-5b98ca9d2fad 863aa499-dbee-4191-9a14-3b5d5ef9e635 9791221501063 134 6 Florence open access
institution OAPEN
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language English
description Environmental sustainability is one of the main goals of all countries in the world. Sustainable Development Goals (SDGs) have been proposed by the United Nations in 2015. The purpose of this paper is to explore if and how European countries achievethe goals of environmental sustainability (tracked by the SDGs number 13, 14, and 15). In particular, SDG 13 refers to climate change and its impacts; SDG 14 refers to the conservation of water and marine resources while the last one; SDG 15 deals with the preservation of forests. The reference methodology of the paper are the Self-Organizing Maps proposed by Kohonen in 1982 as an unsupervised clustering method in the framework of artificial neural networks. The proposed analysis considers the 23 indicators related to the three SDGs of environmental sustainability and aims to explore and identify groups of countries with similar characteristics through a dimensionality reduction. Such clusters will be visually represented in a two-dimensional map. The proposed analysis considers the most recent data for all the above SDGs, which is 2018, with the aim of classifying the countries in terms of environmental sustainability and highlighting possible implications for policymakers. An analysis of the network accuracy is shown, using appropriate indicators. These results allow us to see which countries have achieved these goals and how they have deviated from them.
title 9791221501063-17.pdf
spellingShingle 9791221501063-17.pdf
title_short 9791221501063-17.pdf
title_full 9791221501063-17.pdf
title_fullStr 9791221501063-17.pdf
title_full_unstemmed 9791221501063-17.pdf
title_sort 9791221501063-17.pdf
publisher Firenze University Press, Genova University Press
publishDate 2023
url https://books.fupress.com/doi/capitoli/979-12-215-0106-3_17
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