Energy Transition in Malta: Understanding the Implications on the Environment and Public Perception
Bibliographic record
Abstract
In Malta, different sources of fuel have been used to generate electricity, including the traditional coal fired systems and the recent natural gas power station. This study will investigate the possible implications on the air and soil quality, as well as the social perception of the energy transition from heavy fuel oil (HFO) to liquified natural gas (LNG) in Malta. The implications of air pollutants emitted from power stations are well documented and whilst they may affect the local air quality due to the mobile nature of such substances, implications can be considered to be both regional and subsequently global. 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 the willingness of the public to change the status quo on specific environmental issues. The parameters analysed for air quality were nitrogen oxides (NOx), carbon monoxide (CO), total suspended particulates (TSP) and sulfur dioxide (SO2). The results show that the oldest power station emitted high quantities of emissions when compared to the other power stations. Soil samples, from the surface and from the bottom, were collected in close proximity (within a radius of 1km to 5km) to the power stations and two control sites were sampled over a period of one-year. Two sites that were near the power stations demonstrated a high concentration of sulfates in the soil. However, a control site that was far away from the power stations depicted a higher level of sulfates. This could imply other sources of sulfates in soil other than from electricity generation. When it comes to public perception and disposition, survey 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".