From Heavy Fuel Oil to Liquified Natural Gas: Electricity Generation Transition in Malta
Bibliographic record
Abstract
This study focuses on the shift in the source for electricity generation in Malta.This is done by comparing the emissions from two power stations (Marsa; MPS and Delimara; DPS) which ran on heavy fuel oil (HFO), with the emissions originating from the liquified natural gas (LNG) unit.This study also investigates the possible implications on the environment and the social perception of electricity generation in Malta over a period of 10 years.The pollutants emitted from power stations may affect the local air quality as well as regional and global environment through dispersal.These pollutants may act directly on different environmental matrices since they can eventually end up in soil and water through precipitation.Understanding the public perception is important since it will help in identifying particular knowledge gaps and misinformation, as well as their willingness to change the status quo on specific environmental issues.The air pollutants analysed were nitrogen oxides (NOx), carbon monoxide (CO), total suspended particulates (TSP) and sulfur dioxide (SO2).The air quality analysis showed that overall, the oldest power station, situated in Marsa, emitted high quantities of emissions when compared to the other power stations especially with regards to NOx, TSP, and SO2.When it comes to public perception and disposition, results showed that the public noted and acknowledged a change in air quality over time.According to the findings, the public is aware and has been well informed with regards to the advantages related to liquified natural gas as an energy source, yet, the public seems not to be aware of the advantages related to heavy fuel oil.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".