Embracing clean waste-to-energy solutions in Sub-Saharan Africa: A countrified residential perspective
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it