Financing methods for solid waste management: A review of typology, classifications, and circular economy implications
Bibliographic record
Abstract
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.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".