Sustainable visions: unsupervised machine learning insights on global development goals.
The 2030 Agenda for Sustainable Development of the United Nations outlines 17 goals for countries of the world to address global challenges in their development. However, the progress of countries towards these goal has been slower than expected and, consequently, there is a need to investigate the...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317412 |
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| author | Alberto García-Rodríguez Matias Núñez Miguel Robles Pérez Tzipe Govezensky Rafael A Barrio Carlos Gershenson Kimmo K Kaski Julia Tagüeña |
| author_facet | Alberto García-Rodríguez Matias Núñez Miguel Robles Pérez Tzipe Govezensky Rafael A Barrio Carlos Gershenson Kimmo K Kaski Julia Tagüeña |
| author_sort | Alberto García-Rodríguez |
| collection | DOAJ |
| description | The 2030 Agenda for Sustainable Development of the United Nations outlines 17 goals for countries of the world to address global challenges in their development. However, the progress of countries towards these goal has been slower than expected and, consequently, there is a need to investigate the reasons behind this fact. In this study, we have used a novel data-driven methodology to analyze time-series data for over 20 years (2000-2022) from 107 countries using unsupervised machine learning (ML) techniques. Our analysis reveals strong positive and negative correlations between certain SDGs (Sustainable Development Goals). Our findings show that progress toward the SDGs is heavily influenced by geographical, cultural and socioeconomic factors, with no country on track to achieve all the goals by 2030. This highlights the need for a region-specific, systemic approach to sustainable development that acknowledges the complex interdependencies between the goals and the variable capacities of countries to reach them. For this our machine learning based approach provides a robust framework for developing efficient and data-informed strategies to promote cooperative and targeted initiatives for sustainable progress. |
| format | Article |
| id | doaj-art-04b8808b93f74241a2dfcf25d41ea687 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-04b8808b93f74241a2dfcf25d41ea6872025-08-20T03:47:45ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01203e031741210.1371/journal.pone.0317412Sustainable visions: unsupervised machine learning insights on global development goals.Alberto García-RodríguezMatias NúñezMiguel Robles PérezTzipe GovezenskyRafael A BarrioCarlos GershensonKimmo K KaskiJulia TagüeñaThe 2030 Agenda for Sustainable Development of the United Nations outlines 17 goals for countries of the world to address global challenges in their development. However, the progress of countries towards these goal has been slower than expected and, consequently, there is a need to investigate the reasons behind this fact. In this study, we have used a novel data-driven methodology to analyze time-series data for over 20 years (2000-2022) from 107 countries using unsupervised machine learning (ML) techniques. Our analysis reveals strong positive and negative correlations between certain SDGs (Sustainable Development Goals). Our findings show that progress toward the SDGs is heavily influenced by geographical, cultural and socioeconomic factors, with no country on track to achieve all the goals by 2030. This highlights the need for a region-specific, systemic approach to sustainable development that acknowledges the complex interdependencies between the goals and the variable capacities of countries to reach them. For this our machine learning based approach provides a robust framework for developing efficient and data-informed strategies to promote cooperative and targeted initiatives for sustainable progress.https://doi.org/10.1371/journal.pone.0317412 |
| spellingShingle | Alberto García-Rodríguez Matias Núñez Miguel Robles Pérez Tzipe Govezensky Rafael A Barrio Carlos Gershenson Kimmo K Kaski Julia Tagüeña Sustainable visions: unsupervised machine learning insights on global development goals. PLoS ONE |
| title | Sustainable visions: unsupervised machine learning insights on global development goals. |
| title_full | Sustainable visions: unsupervised machine learning insights on global development goals. |
| title_fullStr | Sustainable visions: unsupervised machine learning insights on global development goals. |
| title_full_unstemmed | Sustainable visions: unsupervised machine learning insights on global development goals. |
| title_short | Sustainable visions: unsupervised machine learning insights on global development goals. |
| title_sort | sustainable visions unsupervised machine learning insights on global development goals |
| url | https://doi.org/10.1371/journal.pone.0317412 |
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