Rethinking Growth in the Gulf: The Role of Renewable Energy, Electricity Use, and Economic Openness in Oil-Rich Economies
Bibliographic record
Abstract
This paper investigates how renewable electricity production, energy consumption, and economic openness influence economic growth in the Gulf Cooperation Council (GCC) countries from 2008 to 2023. Using annual panel data for six countries—Saudi Arabia, UAE, Qatar, Bahrain, Kuwait, and Oman—we apply both the Pooled Mean Group (PMG) and Dynamic Fixed Effects (DFEs) estimators to explore short-run dynamics and long-run equilibrium relationships. These methods are preferred because they balance flexibility with efficiency where PMG allows country differences in short-run dynamics, while DFE provides robustness under small-sample conditions, making them more suitable than the Mean Group (MG) estimator or standard Fixed Effects (FE) models for our short panel of six countries. The results show that traditional electricity consumption significantly supports economic growth in the long run, while renewable energy, despite its potential, has yet to show a statistically significant growth-enhancing effect, likely due to its currently small share in the energy mix. Foreign direct investment and trade openness display mixed impacts, with their significance varying across models. Short-run dynamics highlight the importance of energy efficiency and infrastructure readiness in shaping how energy translates into growth. Overall, the findings suggest that while energy remains central to GCC economies, the transition to renewables must be better aligned with broader development and investment strategies. These insights are highly relevant for policymakers navigating the twin goals of energy diversification and sustainable economic growth under Vision 2030 agendas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".