EVALUATION OF SUSTAINABLE FINANCIAL PERFORMANCE WITH ENTROPY-BASED ARAS METHOD: A STUDY ON G-20 COUNTRIES
Bibliographic record
Abstract
Nowadays, it is not possible to consider business processes successful where only economic factors are taken into consideration and environmental, social and management elements are ignored. At this point, sustainable finance can be defined as creating economic, environmental, social and management value in individual and corporate investments in order to contribute to the sustainable development of financial models, financial products and services, and financial markets, and directing investment processes by taking these elements into consideration. This study aims to provide a solution to the difficulty in finding data on countries' sustainable finance levels and to calculate the sustainable financial performance of G-20 countries through multi-criteria decision-making methods using economic, environmental, social and governance-related indicators between 2010-2022. For this purpose, 14 indicators related to economic, environmental, social and governance were determined and the sustainable financial success levels of G-20 countries were examined with the Entropy-based ARAS method. According to the results obtained, when the average of the values between 2010 and 2022 was taken, the most successful country was Australia with 72%. Australia is followed by Germany with 68%; by Canada with 67%; by America and England with 65%; by Japan followed with 60%. According to average values, the three least successful countries in terms of sustainable finance, are India, which ranks 19th with 30%; Argentina ranked 18th and Turkey ranked 17th, with performance values of 34% each.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".