Updated and expanded analysis of fuel tax concessions for the commercial fishing fleet of the United Kingdom
Bibliographic record
Abstract
Fuel represents one of the most substantial cost items for commercial fishing enterprises. Despite growing government commitments to reducing carbon emissions and improving energy efficiency, fuel subsidies for fisheries persist globally, including in the United Kingdom (UK), where Fuel Tax Concessions (FTCs) provide tax relief to the fishing industry thus reducing their costs. This study updates and expands previous work examining the economic implications of FTCs for the UK commercial fishing fleet, incorporating the latest financial data up to 2022 as well as new insights into the large-scale pelagic fleet segment. Results show that over the past decade, FTCs have totalled approximately £1480 million in the UK, and in 2022 alone amounted to about £ 121 million in foregone government revenue. The UK fleet generated approximately £1020 million in income in 2022, therefore without FTCs, the fleets’ collective profits would have been around £ 1 million, effectively breaking even, all else remaining equal. While FTCs are currently important for maintaining overall fleet profitability, ensuring international competitiveness, and therefore supporting the livelihoods and food provision that these fleet segments provide, there are concerns about their alignment with sustainability and net-zero targets. This study highlights the need for greater transparency and informed debate between industry, government and civil society on the future of FTCs to balance economic support with social and environmental objectives, and the need to further invest in the acceleration of fuel efficiencies and cost-effective alternative fuel sources.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".