Reduction in greenhouse gas and other emissions from ship engines: Current trends and future options
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it