MEASURING THE ENVIRONMENTAL PERFORMANCE OF COUNTRIES BY CODAS AND TOPSIS METHODS: G7 GROUP COUNTRIES EXAMPLE
Bibliographic record
Abstract
With the awareness of their environmental performance, countries can provide strategies and policies to improve their environmental performance. Thus, countries can contribute to their own economic development by increasing their environmental performance. Therefore, measuring the environmental performance of countries is of great importance. Environmental performance of countries can be measured by the Environmental Performance Index (EPI). EPI consists of two factors, environmental health and ecosystem vitality. Its factors are environmental protection components, and environmental protection components are environmental protection variables. In this context, the research has two purposes. The first of these,To measure the latest and up-to-date environmental performances of the G7 group countries for 2018, using CODAS and TOPSIS multi-criteria decision-making methods (MCDM) over the values of EPI components. The second is to determine which MCDM method can be used to explain the EPI values of countries the most. According to the findings, the ranking of countries' environmental performance with the CODAS method was determined as England, France, Japan, Germany, Canada, Italy and the USA. According to the TOPSIS method, this ranking was determined as England, France, Germany, Japan, Canada, Italy and the USA. According to another finding, it has been observed that there is a significant, positive and very high relationship between the EPI values of the countries and the values measured by the CODAS and TOPSIS methods. According to this result, it was evaluated that EPI can be explained by both methods. In addition, it has been concluded that the correlation value between TOPSIS values of EPI within the scope of the research is higher than the CODAS method, so it can be explained better with the TOPSIS method compared to the EPI CODAS method. In the literature, in order not to find a study measuring the environmental performance of countries with CODAS and TOPSIS methods, it was evaluated that the study in question contributed to the literature, since the findings obtained as a result of the research became a data set for future studies. Keywords: Environmental Performance, Environmental Performance Index, CODAS, TOPSIS
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".