Embracing clean waste-to-energy solutions in Sub-Saharan Africa: A countrified residential perspective
Bibliographic record
Abstract
This study explores the adoption of Waste to Energy (WTE) as a panacea to waste management challenges by assessing whether rural households will embrace WTE solutions while ascertaining the determinants of residents’ subscriptions to WTE. Simple random sampling was employed in selecting respondents, while stratified sampling was employed in reaching respondents in the old town and new t site. The study found that 66% of the respondents were willing to subscribe to WTE technologies, while 59 (34%) were reluctant to subscribe to the technology even if it was readily available. Respondents willing to subscribe were motivated by the thought that WTE technology would help reduce waste-related diseases and improve waste management. The three paramount reasons why some respondents were unwilling to subscribe to WTE technologies are that the technology might come with charges, the technology has no personal benefit, and the respondents were not convinced about WTE technology. From the logistic regression, the determinants of residents’ willingness to subscribe to WTE technologies were established as age, education, income, waste sorting practice, and the perception of WTE as a panacea. The findings of this study have important implications for community engagement in waste and energy projects. Thus, the study recommends a pre-requisite for close community engagement for every community-level project, including WTE projects. • Environmental awareness is a requirement for modeling waste-to-energy solutions in rural areas. • Rural residents are willing to subscribe to waste management services. • Majority, 66% of the rural households perceive WTE as a panacea for waste management, and the same quota is willing to subscribe to WTE technology. • Socio-economic factors such as age, education, and income significantly influence rural folks’ willingness to subscribe to waste management services.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".