Sustainable utilization of energy from waste: A review of potentials and challenges of Waste-to-energy in South Africa
Bibliographic record
Abstract
In South Africa, the burden placed on energy due to the rise in its demand coupled with the huge amount of untreated waste that ends up in landfills has called for the quest for sustainable energy utilization from waste resources. The literature has revealed a huge amount of theoretical energy potential recoverable from waste generated in Africa. However, many African countries are not yet exploiting the full potential energy inherent in waste to solve their energy and waste management crisis. To achieve success in WTE Industry, it is important to assess the peculiar local factors which impede its growth. The study evaluated the energy potential from waste in Africa with emphasis on South Africa’s case and the existing and proposed waste-to-energy (WTE) projects in South Africa. It was revealed that South Africa has the highest theoretical potential of energy from waste in Africa based on the quantity of generated and collected waste. About 104463 TJ/year and 22710 TJ/year can be recovered from incineration and landfill gas, respectively, in South Africa. Some of the barriers to full-operative WTE processing in South Africa were identified and discussed. Major resource-related barriers which are peculiar to South Africa’s waste management and energy system are the cheap and affordable coal resources and the low landfill tax. Recommendations for the future directions of WTE and sustainable energy recovery from waste in South Africa were made
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".