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Record W4360861693 · doi:10.1111/deci.12595

The waste management supply chain: A decision framework

2023· article· en· W4360861693 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDecision Sciences · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsWestern University
Fundersnot available
KeywordsBusinessWaste managementCompostSupply chainEnvironmental scienceEnvironmental economicsEngineeringEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.009

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.

Opus teacher head0.028
GPT teacher head0.310
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it