Abstract
Type of the article: Research ArticleAbstractThis study explores potential factors associated with renewable energy consumption in Azerbaijan by examining the relationships among economic growth, human development, per capita CO₂ emissions, and urban population growth. The analysis applies an exploratory machine learning framework based on the XGBoost algorithm combined with SHapley Additive exPlanations (SHAP) to examine possible nonlinear associations and evaluate the relative contribution of selected socioeconomic and environmental variables. Correlation analysis indicates a negative relationship between renewable energy consumption and CO₂ emissions (−0.55), while human development and economic growth exhibit generally positive associations with renewable energy use, suggesting a possible role of socioeconomic development in the energy transition process. The exploratory model produced performance indicators of R² = 0.81, RMSE = 1.18, and MAE = 1.04, suggesting that the model captures variation within the available sample; however, given the limited dataset, these results should not be interpreted as evidence of strong or robust predictive performance. SHAP-based interpretation suggests that human development and environmental pressure may represent relatively important variables within the model framework, while the effects of economic growth and urbanization appear comparatively moderate and context-dependent. Overall, the study provides preliminary exploratory evidence regarding possible nonlinear patterns linking socioeconomic development, environmental conditions, and renewable energy consumption in Azerbaijan, offering directions for future research using larger datasets and complementary empirical approaches.