Greening of Industries in Bangladesh: Pollution Prevention Practices
Bibliographic record
Abstract
Industrial pollution is largely responsible for the environmental degradation in Bangladesh. The environment-polluting industries have contributed to serious and widespread deterioration in the quality of water, land and air. The objectives of the study are: to document pollution prevention options and their current use in Bangladesh; to compare practices across five different highly polluting industries; and to contribute to the pollution prevention literature from a developing country’s perspective. The study is an exploratory one, using both primary and secondary data. Five industries were selected from the top-ten environment polluting industries in Bangladesh; these are the tannery, pulp & paper, fertilizer, textile and cement industries. From each industry group, two sample plants were selected with five executives participating from each plant. This study highlights the reality of Bangladeshi industrial plants in applying pollution prevention initiatives. It reveals that compared to leading firms in developed countries, pollution prevention initiatives in Bangladesh are underutilized. This study finds that the tannery, pulp and paper, fertilizer, textile and cement industries are still generating pollutants through their various manufacturing processes, likely causing adverse impacts on human health, the natural environment and socio-economic aspects, resulting in a social cost for the country.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".