Multi-Criteria Analysis of Waste-to-Energy Technologies in Developed and Developing Countries
Bibliographic record
Abstract
The main objective of this paper is to utilize a multi-criteria analysis (MCA) to evaluate Waste-to-Energy (WTE) technologies and identify constraints when examining the placement of a WTE facility. From this, the focus is best summarized by determining the optimal WTE technology in developed countries and how the process would change if implemented in developing nations. In this study, incineration, gasification, and pyrolysis technologies were reviewed and evaluated. The MCA evaluated the different WTE technologies based on a variety of criteria considering environmental, financial, social, technical, and waste quality and quantity. Different weighted factors were used for the two MCAs and different alternative weighted factor scenarios were produced to perform a sensitivity analysis on the results. Overall, pyrolysis was found to be the preferred option for the developed and the developing nation in all scenarios. For developed countries, the highest difference in the overall index score (7 %) was found in incineration between the baseline and scenario 4. In developing countries, the highest differences in the overall index scores were found in scenario 3 for incineration (9 %) and pyrolysis (10 %). Although pyrolysis had the highest overall capital cost due to it being the newest technology, the environmental, social, associated risk, and waste benefits were seen to be more significant on the findings.
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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) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".