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Record W4404539861 · doi:10.1002/sd.3256

Financing methods for solid waste management: A review of typology, classifications, and circular economy implications

2024· review· en· W4404539861 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

VenueSustainable Development · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTypologyBusinessContext (archaeology)SustainabilityFinanceFlexibility (engineering)Circular economyMunicipal solid wasteWork (physics)Multiple-criteria decision analysisEnvironmental economicsEconomicsWaste managementEngineering

Abstract

fetched live from OpenAlex

Abstract The operations that underpin efficient municipal solid waste management delivery require economic funds. These funds are needed for both capital and recurrent expenditures. Municipalities (local governments) often being the main entities responsible for waste management in cities across the globe, have implemented several funding methods. Yet not all attempts at raising funds for waste management operations have been successful due to the existence of barriers preventing their sustainability in the long term. As such, municipal authorities and decision‐makers are frequently confronted with the dilemma of understanding different methods of financing waste management operations and making the appropriate choices among the available options for maximum operational flexibility. Based on locations, convenience, requirements, technical possibilities, institutional arrangement, and regulatory framework, several waste management financing methods have been adopted and applied with varying degrees of outcomes. However, this information is fragmented and scattered both in the academic and grey literature. In this work, we first collate and categorize the operating procedures of various municipal solid waste management finance strategies in a typology. We base our actions on several policy frameworks and areas of focus for waste management finance that have been implemented in many nations. Second, we provide a classification system by combining several strands of information on reported combinations of waste finance strategies, possibilities, and restrictions in the context of urban waste management. Finally, we discuss other essential elements related to financial sustainability and the implications on the circular economy. Therefore, this article presents a comprehensive review of the pros and cons of various municipal solid waste management financing methods.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.385
Teacher spread0.341 · 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