Fuel consumption of global fishing fleets: current understanding and knowledge gaps
Bibliographic record
Abstract
Abstract Compared to a century ago, the world's fishing fleets are larger and more powerful, are travelling further and are producing higher quality products. These developments come largely at a cost of high‐fossil fuel energy inputs. Rising energy prices, climate change and consumer demand for ‘green’ products have placed energy use and emissions among the sustainability criteria of food production systems. We have compiled all available published and unpublished fuel use data for fisheries targeting all species, employing all gears and fishing in all regions of the world into a Fisheries and Energy Use Database ( FEUD ). Here, we present results of our analysis of the relative energy performance of fisheries since 1990 and provide an overview of the current state of knowledge on fuel inputs to diverse fishing fleets. The median fuel use intensity of global fishery records since 1990 is 639 litres per tonne. Fuel inputs to fisheries vary by several orders of magnitude, with small pelagic fisheries ranking among the world's most efficient forms of animal protein production and crustaceans ranking among the least efficient. Trends in Europe and Australia since the beginning of the 21st century suggest fuel use efficiency is improving, although this has been countered by a more rapid increase in oil prices. Management decisions, technological improvements and behavioural changes can further reduce fuel consumption in the short term, although the most effective improvement to fisheries energy performance will come as a result of rebuilding stocks where they are depressed and reducing over‐capacity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".