Green Banking Practices in Bangladesh: A Critical Investigation
Bibliographic record
Abstract
Green banking or sustainable banking is one of the issues of the concern of all stakeholders of the world. Following this concern, this study has investigated the status of green banking practices of the non-bank financial institutions (NBFIs) and commercial banks of Bangladesh. Analyzing the contents of annual reports as well as websites of banks and NBFIs, the study finds that 44 out of 57 banks and 13 out of 33 NBFIs, to a varying degree, have exposures in direct or indirect green financing. But only 45 banks and 25 NBFIs conducted environmental risk rating. Most of the banks and NBFIs practice green banking only in a limited scale and volume and disclose green banking information in a semi structured manner in both the annual reports and corporate websites. However, except one, all the 56 scheduled banks and all the 33 non-bank financial institutions (NBFIs) have their own green banking policy guidelines. They also have green office guide for conducting in-house green activities. The study finds that green banking disclosures in their annual reports exceed that in their websites. It is also found that both private commercial banks (PCBs), and foreign commercial banks (FCBs) have surpassed state-owned commercial banks (SCBs) and state-owned specialized development banks (SDBs) in terms of the green financing.
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.
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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".