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

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides empirical evidence on renewable energy determinants using hybrid ML-econometric approach, useful for further research.

🏢実務担当者:Highlights R&D and trade openness as key drivers, informing corporate renewable energy investment strategies.

🏛政策担当者:Suggests policies promoting R&D and trade integration to accelerate renewable energy adoption.

The Gulf Cooperation Council (GCC) countries face the challenge of balancing their re-liance on hydrocarbon resources with ambitious renewable energy transition goals, in-cluding 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 that drive renewable energy consumption. This study investigates the macro-economic determinants of renewable energy consumption in the six GCC countries over the period 2000-2024 using a hybrid methodology of stepwise regression analysis and Random Forest 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 determinants 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 dropped significantly from the final model because of multicollinearity and the struc-tural 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), demonstrating satisfactory predictive performance. Sub-period analysis further shows that the influence of R&D expenditure and trade openness become more pronounced after 2015, alongside the implementation of national energy transition strategies across the GCC. These findings underline the importance of technological innovation and economic openness in sup-porting the region’s energy transition that greater investment in R&D and stronger in-ternational trade integration can help renewable energy adoption.

• Research Square https://doi.org/10.20944/preprints202608.0150.v1first seen 2026-08-08 04:33:14