A New Ranking of Environmental Performance Index Using Weighted Correlation Coefficient in Intuitionistic Fuzzy Sets: A Case of ASEAN Countries
Bibliographic record
Abstract
Growth in number of population and development nowadays indicate a good sign for nation’s development. However, the development sometimes might neglects the preservation and conservation of nature and can reflects in environment performance. Concerning on this matters, Environmental Performance Index (EPI) has been introduced since 2006 to depict the environment performance for most of the countries in the world. The index considers ten policy categories associated with environmental public health and ecosystem sustainability. The main mathematics operation in establishing EPI is arithmetic mean of all ten policy categories. One of the weaknesses in the arithmetic mean is the operation might neglects some extreme values in data. Recently, Wan Ismail and Abdullah introduced the EPI using analytic hierarchy process (AHP) but the weight of policy category was not considered. This paper proposes a new ranking of EPI using a decision making tool of weighted correlation coefficient based on intuitionistic fuzzy sets (IFS). An original data of policy categories were converted into IFSs which benefiting in considering two-sided of membership and non membership. Criteria weights for alternatives in fuzzy correlation coefficient were utilized to set new EPI for nine ASEAN countries. A new ranking EPI among ASEAN countries show that Thailand is the highest EPI followed by Malaysia. The new ranking may offer an alternative measure in evaluating environmental performance particularly for ASEAN countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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".