Uncollected Urban Plastic Waste in Bandung: A Geo-Referenced Material Flow Analysis Revealing Spatial Inequalities and Management Challenges
Bibliographic record
Abstract
With an ever-increasing population and growing consumption, plastic waste management has become one of the most challenging global problems. Both mismanagement and illegal dumping pose significant environmental and public health risks, leading to severe issues such as the release of harmful chemicals and heavy metals into the air through burning, and significant ocean pollution from riverine plastic discharge. Indonesia is estimated to be one of the top emitters of riverine plastics and a significant portion of the country’s municipal solid waste is either burned or uncollected. Despite the recognized importance of tackling mismanaged plastic waste, comprehensive data on plastic waste flow remain largely unavailable. This study presents a plastic Material Flow Analysis (MFA) in Bandung, Indonesia, using a bottom-up, geo-referenced approach to tackle the absence of data.Our methodology involves quantifying the volume of uncollected waste and identifying its specific locations through georeferenced mapping and spatial analysis. The findings reveal that household plastic waste consumption ranges from 14 to 20 kg per capita per year. On average, over 50% of plastic waste is sent to landfills, 20-25% is source-separated and recycled, 12% remains uncollected, and 1-2% is burned. Limited infrastructure and collection capacity result in higher rates of uncollected waste and burning. These mismanaged waste hotspots are often located near riverbanks or open spaces adjacent to households.Accessibility analysis indicates that areas with higher uncollected waste are farther from waste collection points and lack adequate infrastructure, including roads and transport systems, increasing reliance on informal disposal methods such as burning and dumping. This suggests that mismanaged waste is not only an environmental issue but also a predictor of social inequalities within cities, as affected communities often face poor living conditions and inadequate access to basic services such as clean water. By providing data-driven insights and actionable recommendations, this research contributes to the development of sustainable and equitable waste management strategies in Indonesia. Furthermore, this study tests the utility of applying a bottom-up georeferenced Material Flow Analysis to measure plastic waste flows, contributing to the growing body of research in this field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".