Global Energy Systems Modeling : Structure and Environmental Impacts

Abstract

The global energy landscape undergoes transformative changes characterized by unprecedented challenges in climate and geopolitical dynamics. This study analyzes energy systems across 66 countries using machine learning methods, based on comprehensive energy performance and environmental indicators. Using the K-means clustering method, three distinct country clusters were identified. The baseline cluster includes 62 countries with moderate energy parameters. China forms a unique cluster, characterized by exceptionally high total energy consumption and maximum carbon emissions, yet relatively low per capita consumption. The third cluster comprises the United States, the Russian Federation, and India, which are distinguished by diversified energy policies with high nuclear and natural gas consumption. Statistical analysis reveals robust correlations between primary energy consumption and environmental indicators, with correlation coefficients exceeding 0.9. The research demonstrates that primary energy consumption patterns and fuel balance structure, rather than emission volumes directly, are primary determinants of environmental impact. Coal-based energy generation emerges as the predominant source of anthropogenic environmental challenges globally. These findings underscore the urgent necessity for developing individualized national energy transition strategies that account for each country's unique energy profile and economic circumstances. The methodology established provides a framework for evidence-based policy formulation toward sustainable energy futures.