Export Potential of Recycled Plastic: A Study on Bangladesh
Bibliographic record
Abstract
In the age of sustainable business practice, the usage of plastic is a matter of great concern. Bangladesh, being a developing country, has a huge amount of plastic waste. However, there is a dearth of empirical research that investigates the potential of recycled plastic industry development in Bangladesh. Therefore, the purpose of this study is to examine the external and internal factors affecting the recycled plastic industry of Bangladesh and to provide recommendations to develop the recycled plastic industry as a potential source of export. To this end, an exploratory study was conducted, and ten officials from Bangladesh based small-and-medium enterprises were interviewed. The results reveal that Bangladesh has huge potential for the recycled plastic industry operation. However, the industry lacks government and institutional support. If public and private sectors can come forward to promote the recycling sector, then this industry has the potential to be one of the most profitable industries in Bangladesh. Based on the expectations of the interviewees, some policy recommendations are suggested to develop the recycled plastic industry. Recommendations have highlighted effective and efficient waste management systems, proper planning, efficient technology usage, infrastructural development, the developed value chain for the collection of plastic wastes, among others. Most importantly, coordinated efforts of government, consumers, recycling industries, and plastic product manufacturers can contribute to the establishment of the plastic recycling sector as a major productive industry in Bangladesh.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".