Prediction of average SDG scores by using support vector machine: a machine learning approach

The sustainable development goals (SDGs) are a global initiative established by the United Nations to address pressing social, economic, and environmental challenges. The SDGs consist of 17 interconnected goals designed to create a sustainable future for all. Understanding progress toward these goals is essential for effective planning and decision-making. This study focuses on predicting the average SDG score, a composite measure that reflects the overall performance of countries in achieving the 17 SDGs. SDG average score data from 166 countries that covers the period from 2000 to 2022 are analyzed. The dataset was divided into 80% for training and 20% for testing to ensure accurate model evaluation. A time lag of three years was used as input to the model to capture the temporal relationships within the data. Support vector machine (SVM) model, known for its robustness and efficiency, was employed to predict the average SDG scores for the years 2025 to 2030. In addition, the study selects countries with the highest and lowest average SDG scores from both the Global North and South and forecasts all 17 SDGs for 2030 that provides a detailed analysis of the interplay among goals to show how progress in one area can influence outcomes in others.

Text 122891.pdf - Published Version Restricted to Repository staff only Download (1MB) Official URL or Download Paper: https://link.springer.com/article/10.1007/s10098-0...

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