Analysis of Climate Change Performances of G7 Group Countries: An Application Using the MEREC-based RAFSI Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The activities of major economies regarding climate change can influence the global climate, the global economy, and the climate change strategies of other countries. In this context, analyzing the climate change performance of G7 countries is considered important. In this research, the climate change performances of G7 countries for the year 2023 were measured using the MEREC-based RAFSI method, based on the Climate Change Performance Index (CCPI) criteria. According to the findings, the most significant climate change criteria for G7 countries within the scope of the MEREC method were identified as Greenhouse Gases Emissions and Climate Policy. According to the MEREC-based RAFSI method, the climate change performance values of the countries were ranked as follows: Germany, the UK, France, Italy, the USA, Japan, and Canada. Furthermore, it was observed that the countries with performance values above the average climate change performance value were Germany, the UK, France, and Italy. Consequently, for the improvement of global climate change and contributions to the global economy, it is assessed that G7 countries need to show development particularly in Greenhouse Gas Emissions and Climate Policy criteria, and that the USA, Japan, and Canada need to undertake activities to enhance their climate change performance. From a methodological perspective, it was concluded that the MEREC-based RAFSI method is sensitive in measuring the climate change performances of countries according to sensitivity analysis, credible and reliable according to comparative analysis, and robust and stable according to simulation analysis. Therefore, based on the results of sensitivity, comparative, and simulation analyses, it was determined that the climate change performances of countries can be measured with MEREC based RAFSI in the scope of the CCPI.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it