Beyond the Carbon Ceiling: Neural Econometric Decoupling of GCC Energy Portfolios — Bridging Growth Theory and Deep Learning
Year: 2026
Type: preprint
Abstract: The Gulf Cooperation Council (GCC) states have to find a way to balance their reliance on hydrocarbon resources and their ambitious aims on moving to renewable energy (for instance, the Saudi Vision 2030.) Despite the policies in place, the implementation of renewable energy technologies in the region is relatively low indicating that more needs to be understood about the reasons behind the use of renewable energy. The present research will analyze the macroeconomic determinants of renewable energy consumption in the 6 GCC states for the periods 2000-2025 using a combination of panel econometric techniques and machine learning. Panel data were constructed from data from the World Bank and the International Energy Agency. The econometric results show that R&D expenditure and trade openness are the two most important determinants of renewable energy consumption, which jointly explain about 63% of the variation (adjusted R² = 0.629). The random forest model is consistent with these findings, with R&D expenditure as the most important predictor followed by trade openness. No statistically meaningful relationship was found between GDP, foreign direct investment, inflation and renewable energy consumption in the final models due to the structural characteristics of GCC economies. The random forest model also had a low RMSE value of 0.032 and an out-of-sample R-squared of 0.425, indicating satisfactory predictive performance for an initial model. Sub-period analysis also indicates that the relationships between R&D expenditure and trade openness became stronger after 2015, which coincides with the introduction of national energy transition strategies in the GCC. The study implies that innovation in terms of technology as well as the open nature of economy could promote energy transition in the region whereby high amount of investments in research and development are required as well as deeper international trade linkages might be supporting the acceptance of green energy approach.
Source: Preprints.org
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7212425243