Modeling Adaptive Technologies and Clean Fuels Towards Climate-Neutral Shipping
Bibliographic record
Abstract
The Greek maritime sector faces multiple techno-economic, environmental and development challenges, requiring careful investment decisions. In this paper we present the application of a free, open-source Investment Decision Support Tool, called MaritimeGCH: a least-cost linear optimization model that reflects operational and investment variables and constraints within the shipping industry. The model aims to optimize fleet composition under techno-economic, environmental, operational factors and European environmental regulations such as the FuelEU Maritime Regulation, requiring a transition to cleaner fuels. Through the tool, we test the effect of different technologies that increase fuel efficiency such as new propulsion systems, engine optimization, and hull maintenance. We can estimate each technology’s respective cost and carbon abatement potential within the Greek shipping fleet. The study also tests a set of scenarios from slow to fast transition to cleaner fuels within the Greek shipping sector and explores their effect on fleet optimization decisions. This set of scenarios reflects the potential evolution of some fuels phasing out (e.g. Oil and RefPO), being replaced by the transition fuels (LNG and LPG), while others will ultimately become more prevalent in the future (MeOH, NH3 and H2). Results indicate a growing fleet rise increases emissions and costs, spurring the adoption of efficiency technologies while cleaner fuels gain prominence in later years. The tool’s flexibility highlights a key insight: decarbonization is not a binary choice between profitability and sustainability, but a nuanced optimization problem that requires sophisticated analytical capabilities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".