Examining Awareness and Preferences for Green Finance Among Commercial Banks in Somalia
Bibliographic record
Abstract
Climate change has received substantial attention from policymakers and academicians; hence, demonstrating the importance of environmental conservation.Fossil fuel energy use for industrialization and urbanization curtails environmental quality.This highlights the urgent need for green financing in green technologies and clean energy to preserve environmental quality.Contrary to the previous attempts that focus on the importance of green finance for mitigating climate change without examining how well-versed people are in the idea of green finance; however, this study aims to examine the awareness and preference for green finance among staff of commercial banks in Mogadishu, Somalia using a descriptive statistics research design.The results indicate more than 75% of the respondents are very familiar or familiar with green finance.However, less than 25% are unfamiliar with green finance.the result also indicates that the majority of the respondents prefer green finance more than 65% and just over 14% said they do not prefer green finance.Therefore, policymakers should implement policies aimed at reducing uncertainty and providing the banks with a conducive environment in green financing.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".