The development of a framework for the selection of optimal sites for the location of municipal solid waste to value‐added facilities through the integration of a geographical information system and a fuzzy analytic hierarchy process
Bibliographic record
Abstract
Abstract This study develops a framework for quantification of the available municipal solid waste (MSW), defines the geographical point source locations for the MSW feedstock, and determines optimal locations for potential waste‐to‐value‐added (W2VA) facility sites. The framework developed here is applied to Canada. To determine optimal sites for W2VA facilities, a three‐stage decision‐making model comprising exclusion analysis, preference analysis, and network analysis was developed. The fuzzy analytic hierarchy process (FAHP) and geographic information systems (GIS) were used in an integrated decision‐making network to prioritize the preference factors and facility locations; with these, a land suitability map (LSM) was developed and suitable candidate sites for W2VA facilities were obtained for Canada. The identified candidate sites were then used in a network analysis to select optimal sites with minimal travel distances. This study was used to determine 10 and 15 optimal sites for W2VA facilities in Western Canada and Eastern Canada, respectively. This study prioritized optimal sites based on the minimization of transportation distances. The highest priority site in Western Canada is Site 1, located in Lethbridge County, Alberta. It is connected with 74 TSs and can receive 2.42 million tonnes of MSW. The highest priority site in Eastern Canada is Site 11, which is located in West Nipissing, Ontario, and is connected with 42 transport stations (TSs) and can receive 1 million tonnes of MSW. The adaptability of the applied decision‐making model, competency of the developed LSM, and flexibility of the network analysis provide a competent supporting tool for authorities to identify optimal locations for W2VA facilities.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".