The Importance of Sustainable Finance Management Systems in the Digital Era
Bibliographic record
Abstract
Sustainable finance has gained significant attention in recent years, fueled by the rise of the circular economy. It emphasizes evaluating investments with a focus on environmental, social, and governance (ESG) factors, aligning financial activities with sustainable development goals for long-term resilience in economic systems. This study aims to analyze co-authorship and co-citation trends in research on “Sustainable Finance Management Systems (SFMS) and the Circular Economy,” using data from 37,132 articles collected from Dimensions AI, and analyzed through VOSviewer software. The bibliometric analysis highlights growth patterns and trends in sustainable finance and circular economy research, illustrating valuable connections between authors, citations, and countries. It also identifies emerging research areas and sector-specific demands, providing insights for global collaboration and interdisciplinary partnerships in sustainable finance and the circular economy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".