Integrating Machine Learning and Econometric Models to Uncover the Macroeconomic Determinants of Renewable Energy Consumption in the GCC Countries

The Gulf Cooperation Council (GCC) countries face the challenge of balancing their reliance on hydrocarbon resources with ambitious renewable energy transition goals, including initiatives such as Saudi Vision 2030. Despite these commitments, renewable energy deployment in the region remains relatively limited, highlighting the need to better understand the factors associated with renewable energy consumption. This study investigates the macroeconomic factors associated with renewable energy consumption in the six GCC countries over the period 2000–2024 using a hybrid methodology combining panel econometric methods and machine learning. Panel data were compiled from the World Bank and the International Energy Agency. The econometric results identify research and development (R&D) expenditure and trade openness as the two most important predictors of renewable energy consumption, jointly explaining approximately 63% of the variation (adjusted R2 = 0.629). The random forest model supports these findings by ranking R&D expenditure as the most influential predictor, followed by trade openness. GDP, foreign direct investment, and inflation were not found to be significantly associated with renewable energy consumption in the final models and the structural characteristics of GCC economies. The random forest model also achieved an out-of-sample R-squared of 0.425 with a low RMSE (0.032), suggesting satisfactory predictive performance for an initial model. Sub-period analysis further suggests that the associations of R&D expenditure and trade openness strengthened after 2015, coinciding with the implementation of national energy transition strategies across the GCC. These findings suggest that technological innovation and economic openness may support the region’s energy transition, with greater investment in R&D and stronger international trade integration potentially facilitating renewable energy adoption.