A Novel WENSLO and ALWAS Multicriteria Methodology and Its Application to Green Growth Performance Evaluation
Bibliographic record
Abstract
Green growth has managed to gain the interest of scholars and politicians recently since it is focused on the fact that the economic development of countries can take place by respecting and protecting the environment. To sustain green growth, it is critical to determine the current situation of countries in this regard and to identify deficiencies as a result. As such, this study proposes a novel multicriteria decision support tool called Weights by ENvelope and SLOpe (WENSLO) and Aczel-Alsina Weighted ASsessment (ALWAS) to identify the green growth performance of countries. The WENSLO method is introduced to objectively decide the criteria' weight values, whereas the ALWAS method is developed to rank the existing alternatives in a decision-making problem. We display the model introduced via green growth application at the country scale in G7. Concerning the findings, environmental factors are more vital than economic and social dimensions in the green growth of countries, and carbon dioxide emissions, water, and marine protected areas are the foremost factors. We highlighted that in terms of green growth level, Canada comes first, then the U.K., and finally Germany. The results of this research provide specific recommendations to guide authorities of G7 countries on green growth planning. The findings can also shed light on what developing countries need to achieve regarding green growth.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".