Reduction in greenhouse gas and other emissions from ship engines: Current trends and future options
Bibliographic record
Abstract
The impact of ship emission reductions can be maximised by considering climate, health and environmental effects simultaneously and using solutions fitting into existing marine engines and infrastructure. Several options available enable selecting optimum solutions for different ships, routes and regions. Carbon-neutral fuels, including low-carbon and carbon-negative fuels, from biogenic or non-biogenic origin (biomass, waste, renewable hydrogen) could resemble current marine fuels (diesel-type, methane and methanol). The carbon-neutrality of fuels depends on their Well-to-Wake (WtW) emissions of greenhouse gases (GHG) including carbon dioxide (CO2), methane (CH4), and nitrous oxide emissions (N2O). Additionally, non-gaseous black carbon (BC) emissions have high global warming potential (GWP). Exhaust emissions which are harmful to health or the environment need to be equally removed using emission control achieved by fuel, engine or exhaust aftertreatment technologies. Harmful emission species include nitrogen oxides (NOx), sulphur oxides (SOx), ammonia (NH3), formaldehyde, particle mass (PM) and number emissions (PN). Particles may carry polyaromatic hydrocarbons (PAHs) and heavy metals, which cause serious adverse health issues. Carbon-neutral fuels are typically sulphur-free enabling negligible SOx emissions and efficient exhaust aftertreatment technologies, such as particle filtration. The combinations of carbon-neutral drop-in fuels and efficient emission control technologies would enable (near-)zero-emission shipping and these could be adaptable in the short- to mid-term. Substantial savings in external costs on society caused by ship emissions give arguments for regulations, policies and investments needed to support this development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".