Potential Contribution of Waste-to-Energy to Power Consumption in the Gaza Strip
Bibliographic record
Abstract
Sustainable energy supply is one of the main challenges that people will face over the coming decades. Biomass can make a substantial contribution to supplying future energy demand in a sustainable way. Currently it is the largest global contributor of renewable energy, and has significant potential to expand the production of heat, electricity and fuels for transport. Municipal solid waste is an enormous renewable resource that has high energy capacity because it contains a high proportion of biomass materials. This kind of sustainable waste management typically called waste-to-energy is critical for reducing the reliance on fossil fuels and non-renewable materials. Waste-to-energy is a reliable and alternative form of energy that has become the basis for many of the most successful solid waste management systems in many countries. Energy recovery from waste is the conversion of waste materials into useable heat, electricity, or fuel through a variety of processes. This study assesses the potential contribution of waste-to-energy facilities to total Gaza peak power demand up to the year 2040 based on three scenarios: incineration, anaerobic digestion and landfill gas recovery. Three dumping sites are distributed along the Gaza Strip, Johr El-deek, Deir El-balah and Rafah. The analysis shows a potential to produce about 1100 MWh per day based on the anaerobic digestion scenario, about 580 MWh per day based on incineration of municipal solid waste scenario, and about 130 MWh per day based on landfill gas recovery scenario. These values accounts to 275%, 145% and 33% of the year 2014 peak electricity demand of 400 megawatt from the three scenarios, respectively. The forecasted results of the three scenarios can be used to design future waste-to- energy facilities in the main cities of the Gaza Strip. The production cost of energy was 7¢/kWh, 5¢/kWh and 17¢/kWhfor incineration, anaerobic digestion and landfill gas recovery scenarios, respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".