A Review of Municipal Waste Management with Zero Waste Concept: Strategies, Potential and Challenge in Indonesia
Bibliographic record
Abstract
Municipal waste management is still a significant problem for solid waste issues in Indonesia. Only 60 to 70% of the waste generated is disposed of in landfills, the rest is dispersed in different areas. The potential for leachate pollution, greenhouse gases, and a waste of non-renewable natural resources can occur due to municipal waste management problems not being optimal. Municipal waste management needs a holistic concept that would include upstream to downstream stages. This paper comprehensively reviews municipal waste management with a zero waste concept based on management, development, measuring, implementations, strategies, potentials, and challenges in Indonesia. The zero waste concept offers waste management, starting with waste elimination, recycling, reduction, and recovery of used goods. Several municipalities around the globe, such as Canberra, Adelaide (Australia), Stockholm (Sweden), Nova-Scotia (Canada), and San Francisco (United States), have decided on targets for zero waste cities. Indonesia is still implementing waste management that accentuates disposal in landfills, so there needs to be a literature study related to the management, development, measuring, implementations, strategies, potentials, and challenges of Indonesia’s zero waste concept.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".