Bankers Perspectives on Green Banking Practices in Commercial Banks: An Empirical Evidence from Nepal
Bibliographic record
Abstract
Going green, in recent days, has been a buzzword for both global banking and financial sectors as well as for the general public. Green banking as a part of going is a new way of performing the banking businesses considering the clean environmental issues and corporate social responsibility of banks. This paper endeavors to explore banker’s general understanding and factors affecting their perspective on green banking practices. The data was collected between June–October 2019 from banks in Kathmandu valley, Nepal. The sample of 326 banking employees has been collected by using a purposive sampling technique. This research employs an explanatory research design which estimates the causal relationship among dependent and independent variables. The paper uses descriptive and inferential methods of estimation. For understanding the bankers’ awareness level towards green banking awareness, an index has been calculated. Furthermore, primary and secondary data were collected to explore how bankers perceive green banking. The results show that many of the bankers are less aware of green banking practices in their banks, while only 5% of respondents are aware of green banking practices. The Probit regression results reveal that education, training for green banking, stationary cost, customer attraction, related parties’ instructions, and protection of the environment have significant and positive effects on green banking practices in banks. In conclusion, for adoption the green banking practices, first and foremost, banks should provide training to their employees and provide effective online services to their customers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".