Clustering and Predictive Analysis of Renewable Energy’s Influence on National Economies

Global movement towards renewable energy systems is reconfiguring national economic paradigms. This study assesses the impact of renewable energy usage on national economies through the application of advanced regression modeling, machine learning classification, and clustering to ascertain cause-and-effect relationships between renewable energy adoption and Gross Domestic Product (GDP) growth. The research uses World Bank data of renewable energy consumption and GDP through linear and multiple regression models to quantify the impact of renewable energy on economic growth. Random Forest, Decision Trees, and Support Vector Machines (SVM) are used in predictive analysis, whereas K-Means clustering categorizes countries based on trends of renewable energy consumption and economic performance. The study finds statistically significant positive correlation between the adoption of renewable energy and economic growth, with the strength of association varying geographically. Policy lessons drawn from this study will assist policymakers in optimizing energy transition policies to achieve sustainable economic growth, offering a framework for evaluating the long-term effects of renewable energy adoption on national economies.