Multi-criteria decision-making in the evaluation of environmental quality of OECD countries
Bibliographic record
Abstract
Purpose The purpose of this paper is to use multi-criteria decision-making methods to assess environmental quality of the Organization for Economic Co-operation and Development (OECD) countries. Design/methodology/approach Weights of criteria are determined by means of entropy weight method. VIKOR method is used to rank different OECD countries based on their environmental quality. Findings The results show the best and the worst environmental quality of different OECD countries. The top five countries of environmental quality are Spain, Israel, Belgium, Japan and the USA. These countries have the best quality of environment. By contrast, the worst five countries of environmental quality are Iceland, Australia, New Zealand, Canada and Chile. Originality/value The findings have implications regarding environmental quality. The results suggest that governments should engage in policy-making that improves their environmental quality. Specifically, those having poor quality of environment should protect the environment and reduce the negative impact on environment. For example, reduce emission of CO 2 to lessen the impact of climate change, improve the quality of air and water, reduce waste generation, increase biodiversity and enhance forest resources. Improvement of environmental quality will improve our social and economic life as well as health conditions.
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How this classification was reachedexpand
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 | 0.066 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".