The waste management supply chain: A decision framework
Bibliographic record
Abstract
ABSTRACT The alarmingly increasing trends in worldwide waste generation call for a holistic analysis of waste management supply chains. Using a comprehensive end‐to‐end (i.e., waste generation to waste disposal) decision framework, this article analyzes key decisions of a waste management firm (WMF) focused on the proportions of dry (and wet) waste to recycle (and compost). This framework is applied to assess the impact of: (i) the preprocessing of generated waste at source (i.e., the “upstream factors”) and (ii) the market prices of recycled dry and composted wet wastes (i.e., the “downstream factors”) on WMF's decisions. One key insight is that the WMF will choose to process more waste when the market prices for processed wastes are high, and/or when more waste is preprocessed at source. Improvements in presorting can be more economical and offer a long‐term sustainable solution to efficient waste management. From a policy perspective, we observe that taxing a WMF for waste disposal could dissuade the WMF from participating in waste processing especially when its marginal processing costs are high. The decision framework and the corresponding model are calibrated to different world regions using secondary data on these regions, classified by their income levels. It is observed from our data analysis that uniform “one‐size‐fits‐all” policies are dominated by region‐specific tailored policies for efficient waste management. Hence, prescriptions should be carefully formulated based on the type of waste generated and the processing/disposal options available in a region. For example, composting more wet waste at source is a better choice in low‐, low‐middle‐, and middle‐high‐income regions, whereas this is not the case in high‐income regions. The proposed decision framework also provides an explanation of the negative impact on recycling initiatives at a local level stemming from decreasing recycled material prices. Given that this is the first study to characterize and analyze the waste management supply chain, the article also highlights some areas for future research.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".