NetZeroML: Hybrid PCA-XGBoost Framework for Global Decarbonization and Renewable Energy Forecasting

Abstract

The accelerating urgency of climate change mitigation has intensified global efforts toward renewable energy adoption; however, a comprehensive, data-driven understanding of its multidimensional impacts remains limited. This study presents a machine learning-based analytical framework to examine the relationships among renewable energy penetration, climate indicators, and socioeconomic factors across over 150 countries from 1990 to 2026. The proposed methodology integrates exploratory data analysis, Principal Component Analysis (PCA), K-means clustering, gradient boosting regression, and ARIMA-based time-series forecasting to identify latent sustainability patterns. Empirical findings indicate that countries with renewable energy shares exceeding 40% achieve an average reduction of 2.3 tons in CO₂ emissions per capita while maintaining stable economic growth. Clustering analysis reveals five distinct sustainability archetypes: Pioneer Economies, Fossil-Dependent Industrials, Emerging Green Transitions, Renewable-Rich Developing Nations, and Stagnant Emitters. The XGBoost model demonstrates strong predictive capability (R² = 0.94, RMSE = 0.31), outperforming baseline linear regression models. Forecasting results suggest global renewable energy adoption may reach 48.7% by 2035 under current trajectories, with potential expansion to 61.2% under accelerated scenarios. These findings provide valuable insights into evidence-based policy formulation and strategic energy planning.