Capacity Enhancement among Banks’ Employees for ESRM Skill Enhancement: A Case of Nepalese Commercial Banks
Bibliographic record
Abstract
This study examines capacity enhancement initiatives for Environmental and Social Risk Management (ESRM) skill development across Nepal’s commercial banks, categorized by ownership. Through a quantitative evaluation of training programs and competency gaps, the research reveals a decisive, large-scale strategic commitment to ESRM. Financial investments are substantial and varied for the period of F/Y 2021-22 to F/Y 2024-25: Joint Venture banks lead with NPR 29.6 million allocated, followed by Nepalese Investors’ Owned (NPR 18.1 million) and Government-Owned banks (NPR 6.5 million). Training scale also differs; Nepalese Investors’ Owned banks conducted the most sessions (294) for the largest audience (7,104 attendees), whereas Joint Venture banks focused on 246 trainings for 5,269 employees, suggesting deeper engagement. Government banks conducted fewer, more concentrated sessions. This data signifies an industry-wide evolution from building specialist cadres towards embedding a resilient, bank-wide ESRM culture as a ‘first line of defense.’ The maturation is evident in the exponential growth in funding, training frequency, and employee reach. The study concludes that to sustain this progress, banks must advance towards structured competency-based frameworks, supported by regulatory reinforcement and industry collaboration. Strengthening ESRM proficiency is critical for improving risk management, ensuring compliance, and promoting sustainable financial practices in Nepal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".