The economic and energy efficiencies of GCC states: A DEA approach
Bibliographic record
Abstract
The six GCC states share similar economic, geographic and socio-cultural characteristics and also face with similar challenges in terms of energy perspective. This study plans to focus on the economic and energy efficiency of the six GCC states. In the process, the study ranks the GCC states in terms of their efficiency scores. These efficiencies are computed through Data Envelopment Analysis. The economic efficiency is calculated for all six GCC states. Capital and labor are the inputs and GDP is the output. In this survey, Saudi Arabia maintains the highest efficiency score of 0.94, closely followed by Qatar (0.92), Kuwait (0.89), Bahrain (0.83), Oman (0.81) and UAE (0.67). There is a huge gap between the economic efficiency scores of Saudi Arabia and UAE. The environmental efficiency scores are calculated using CO2 emissions as output and electric power consumption and energy as input. Again, the highest efficiency score is for Saudi Arabia (0.91) followed by Oman (0.87), Kuwait and Bahrain have a tie for the 3rd position with a score of 0.74. Finally, the laggards are UAE (0.65) and Qatar (0.62). Again, there is a huge gap between the best and the worst performers. The case of two countries is worth mentioning. Qatar is ranked second in terms of economic efficiency while it was ranked sixth in terms of economic efficiency. Oman was ranked fifth in terms of economic efficiency while it was ranked second in terms of environmental efficiency. Finally, an average of economic and environmental efficiency are taken to compute the composite index. Saudi Arabia has the first place followed by Oman, Kuwait, Bahrain, Qatar and UAE.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".