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.

翻译：联合国《2030年可持续发展议程》为世界各国应对发展中的全球性挑战制定了17项目标。然而，各国在这些目标上的进展均慢于预期，因此有必要探究其背后的原因。本研究采用一种新颖的数据驱动方法，通过无监督机器学习技术分析了107个国家超过二十年（2000-2022年）的时间序列数据。我们的分析揭示了某些可持续发展目标之间存在显著的正向与负向关联。研究结果表明，可持续发展目标的进展深受地理、文化和社会经济因素的影响，且没有任何国家有望在2030年前实现全部目标。这凸显了需要采取因地制宜的系统性可持续发展路径，该路径应承认各目标间复杂的相互依存关系以及各国实现目标的能力差异。为此，我们基于机器学习的方法为制定高效、数据驱动的战略提供了稳健框架，以推动促进可持续进展的合作性、针对性倡议。