Green Finance Practices by Nepalese Commercial Banks: Fostering Sustainable Development in Nepal
Bibliographic record
Abstract
Intending to integrate environmental, social, and governance (ESG) issues into financial choices, the global financial landscape has changed its growing pressure for sustainable development.Recently, green finance practices are gaining popularity as a key strategy in many countries in the world.However, Nepal, which is renowned for its natural beauty, suffers from several environmental issues.For instance, international initiatives, of the sustainable development goals (SDGs) of the United Nations (UN), emphasize the alignment of financial flows with sustainable development.The study has adopted a qualitative research method to investigate the practices and barriers preventing Nepalese commercial banks from implementing green finance practices.This study reveals the complex issues specific to Nepal through in-depth interviews with senior executives, risk managers, and sustainability officers.The findings of the study demonstrate several obstacles such as the implementation of regulatory framework, few green investment opportunities, perceived financial risks, a lack of knowledge and experience among banking professionals, and the requirement for strong institutional support and leadership commitment.To get rid of these barriers, it is recommended that Nepal's commercial banks embrace green finance practices widely, fit into the nation's sustainable development objectives and support global environmental efforts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".