ANALYZING CLIMATE CHANGE PERFORMANCE OVER THE LAST FIVE YEARS OF G20 COUNTRIES USING A MULTI-CRITERIA DECISION-MAKING FRAMEWORK
Bibliographic record
Abstract
Today, limited resources are decreasing/depleting with the increase in the human population living on Earth. The increased human population brings with it various problems. Different events cause important climate events at the global level, such as the decrease or depletion of water resources with the increase in demand, damage to the ecosystem, health risks, and deterioration of biological diversity. Due to the use of fossil fuels, the formation of GHG (greenhouse gas) emissions and global warming cause significant climate changes. Climate change causes the restriction of environmental and vital activities, the increase of natural disasters, and the extinction of species. This study aimed to evaluate the climate change performance of G20 countries which emit more than 75% of the world’s GHG emissions from 2019 to 2023, using MCDM methods. An objective method, LOPCOW, was used to assign weights while SPOTIS, WISP, and RMSVC methods were used to determine the climate change performances of G20 countries. The findings showed that among G20 countries, the highest performance was found in the United Kingdom and India, while the United States, Canada and Saudi Arabia were found in the last ranks.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.001 | 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 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".