Pre-Feasibility Study of the Potential Market for Natural Gas as a Fuel for Power Generation in the Caribbean
Bibliographic record
Abstract
This study analyses the feasibility of introducing natural gas in 14 countries in the Caribbean. The current dependence on fuel oil in the countries in the Caribbean has led to high generation costs and electricity prices. Introducing natural gas would decrease both the cost and price of electricity - mainly due to the lower price of natural gas. Additionally, natural gas plants emit less carbon dioxide (CO2) per ton than fuel oil plants. Therefore, the net benefits of natural gas would be seen in lower financial and economic (environmental) costs. It is important to note that upon introducing natural gas, not all renewable energy (RE) and energy efficiency (EE) technologies that are viable in the current scenario - a scenario in which most electricity is generated with fuel oil - will still be viable. Furthermore, though natural gas proves viable under the current situation - where the price of natural gas is lower than that of fuel oil- there is no guarantee that this will always be the case. Lastly, there are some factors that need to be considered closely to fully assess if they will affect the viability of introducing natural gas in the Caribbean. For example, the introduction of natural gas may be hard to organize due to market structure disparities for each country. Additionally, it may not be feasible to completely phase out fuel oil. This report explains the above mentioned topics in further detail. Section A of this report assesses the potential of natural gas as a generation source, and presents the costs of supplying natural gas to the Caribbean. Section B analyses the implications of introducing natural gas on generation costs, electricity prices, and the viability of RE and EE technologies. Section B also includes a cost-benefit analysis that compares the savings in net benefits of three alternatives scenarios to the costs of the current scenario.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".