Bayesian-Based Green Financial Risk Modeling With Fuzzy Logic and Attention-Driven Feature Selection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate risk assessment is essential for advancing sustainable finance and supporting low-carbon growth. Existing methods struggle to capture complex temporal dynamics, manage uncertainty, and select key features from heterogeneous data. To address these challenges, this paper proposes GreenRiskNet, a Bayesian framework integrating three components: TimesNet for multi-scale temporal feature extraction, a fuzzy Bayesian network to represent uncertainty in green finance data, and TabNet's attention-based feature selection to enhance interpretability and focus on predictive factors. Experiments on the Climate Bonds Initiative and Yahoo Finance datasets show GreenRiskNet outperforms baselines, achieving up to 9.5% accuracy and 8.3% F1-score improvements. These results demonstrate the model's effectiveness in robustly predicting green financial risks. This work provides a practical, interpretable tool aiding institutions and policymakers in sustainable financial decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it