Municipal Solid Waste Management and Potential Revenue from Recycling in Malaysia
Bibliographic record
Abstract
Municipal Solid Wastes (MSW) issues have become talk of the day worldwide because of the current and the future threats it has to both life and the environment. Malaysia, like other developing nations, has been facing serious problems in recent years in terms of MSW and its management due to the nation’s rapid economic growth. The objective of this paper is to review and present the current state of MSW and its management in Malaysia and to estimate the economic potentials of some recyclables as well. MSW generation in Malaysia has increased significantly in recent years, ranging between 0.5 - 2.5kg per capita per day (or a total of 25000 - 30000 tons per day). Generally, the waste contains high amount of organics, moisture content and bulk density. More than 70% of the generated wastes are collected using both curbside and communal centers with a collection frequency varying from daily to every two days. In addition, both compactor trucks and open lorry trucks are used. Landfilling is the main disposal method practiced; about 90 - 95% of the collected wastes is still disposed in landfills, with a recycling rate of 5 -10% despite the fact that 70 - 80% of the waste is recyclable. Estimation of the amount of recyclables and their revenue generation potential shows an impressive result. Recycling and composting of the municipal solid waste is therefore recommended.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".