Comparative Assessment of Climate Change Performance: Türkiye vs. G7 Countries Using a Hybrid MPSI-MABAC Approach
Bibliographic record
Abstract
Climate change has become one of the most pressing global challenges, with its impacts intensifying in recent years. For Türkiye, addressing climate change is critical due to its growing economy, rising emissions, and vulnerability to environmental risks. This study underscores the importance of comparing Türkiye's climate change performance with G7 countries using Environmental Performance Index (EPI) data to identify gaps and opportunities for improvement. The aim of this study is to compare Türkiye's climate change performance with that of G7 countries by utilizing Environmental Performance Index (EPI) data and applying a hybrid MPSI-MABAC methodology. A hybrid Multi-Perspective Strategic Integration (MPSI) and Multi-Attributive Border Approximation Area Comparison (MABAC) methodology was applied to rank the countries based on climate-related criteria. Among these, "Projected cumulative emissions to 2025 relative to carbon budget" (C10) was identified as the most significant factor. The findings reveal that the United Kingdom, Germany, and France lead in performance, while Türkiye and Canada are the lowest-ranked. This analysis provides valuable insights to governments, businesses and researchers for shaping national policies and fostering international cooperation to combat climate change effectively.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".