Waste-to-Energy: An Opportunity to Increase Renewable Energy Share and Reduce Ecological Footprint in Small Island Developing States (SIDS)
Bibliographic record
Abstract
Small Island Developing States (SIDSs) are faced with challenges such as reducing the share of fossil energy and waste landfilling. This work summarizes the main aspects of 53 SIDSs that constrain economic development, energy sources, and waste management strategies. An integrative bibliographical review is conducted to synthesize the state-of-the-art of waste-to-energy (WtE) strategies and compare the technologies in light of their suitability to SIDS. The findings show that considering the large amount of waste produced annually, WtE technologies are of the utmost importance to reduce ecological footprints (EFs) and greenhouse gas (GHG) emissions, and to increase the share of renewable energy with the installation of incineration plants with energy recovery to replace fossil fuel power plants. Although WtE is recommended for all SIDSs, the Atlantic, Indian Ocean, Mediterranean, and South China Sea (AIMS) countries exhibit higher population density (1509 inhab/km2) and a high share of fossil fuel in their electricity mix, so that there is greater urgency to replace landfilling practices with WtE. The estimation of potential power generation capacity (MWh) from annual municipal solid waste (MSW) in each SIDS as well as the reduced land area required demonstrate the feasibility of WtE technologies. Only 3% of the landfill area is necessary for buildings and landscaping associated with a WtE plant able to treat 1 million tons of MSW, considering a 30 year lifespan. Furthermore, incineration with energy recovery benefits from high penetration worldwide and affordable cost among thermochemical processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".