Comparative Analysis of OECD Countries Based on Energy Trilemma Index: A Clustering Approach
Bibliographic record
Abstract
This study analyzes OECD countries in the context of the energy trilemma index and clusters countries with similar characteristics. In the study, the k-means clustering technique is used. The optimum number of clusters was determined using the Elbow method in combination with the Silhouette Index. Moreover, all results are visualized to enhance comprehensibility. The results show that countries such as Austria, Canada, Finland, and Denmark are in the high energy trilemma group with index scores of 82.2, 82.3, 82.7, and 83.3, respectively. Countries in the high group have achieved a high level of balance between energy security, energy equity, and environmental sustainability. In addition, countries such as Belgium, Hungary, Australia, the Czech Republic, and Estonia are in the medium energy trilemma group with index scores of 76.4, 76.6, 77.1, 77.6, and 78.7, respectively. Countries in the medium group have made progress in balancing the dimensions of the energy trilemma but have not yet reached excellence. However, countries such as Mexico, Türkiye, Colombia, and Costa Rica are in the low energy trilemma group with index scores of 63.1, 64.1, 64.8, and 69.3, respectively. These low energy trilemma group countries face significant challenges in balancing energy security, energy equity, and environmental sustainability and need to make improvements in these areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".