Analysisof climate change performance of G7 countries based on AHP-CODAS methods
Bibliographic record
Abstract
Recently, due to the importance of climate change issues, it has been studied from different angles, and as a result, numerous articles have appeared in the literature. Nevertheless, there are few works on the analysis of climate change problems based on multi-criteria decision-making methods. The application of multi-criteria decision-making methods in this issue ensures as accurate results as possible because the weighting coefficients of the criteria are determined mathematically, and not based on subjective assessment. With this in mind, this study analyses the climate change performance of the G7 countries based on the AHP (Analytic Hierarchy Process) and CODAS (COmbinative Distance-based ASsessment) methods. The method was implemented based on available data of CCPI (Climate Change Performance Index) criteria for 2024. According to the AHP method, the most important criterion is greenhouse gas emissions. By reducing greenhouse gas emissions, the negative effects of climate change on the G7 countries can be mitigated. In terms of climate change performance, Germany ranks first. Ranking after Germany: European Union, United Kingdom, Italy, France, Japan, United States and Canada. Climate change is greater in the European Union than in the United Kingdom, Japan, the United States, and Canada. Climate changes in Germany are greater than in Italy and France. Climate change in Italy is greater than in France. However, regardless of the differences in climate change among the G7 countries, in order to mitigate the negative effect of climate change, it is necessary to reduce greenhouse gas emissions, increase the use of renewable energy in total consumption, and define an adequate climate policy strategy.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".