Techno-economic and environmental analysis of the application of onshore power supply onboard a bulk carrier : a case-study approach
Bibliographic record
Abstract
The shipping industry plays a significant role in global greenhouse gas emissions, and efforts to reduce its environmental impact are crucial. This thesis investigates the potential of onshore power supply (OPS) to mitigate emissions by analyzing its impact on the Carbon Intensity Indicator (CII) of a bulk carrier vessel trading between Canada and China. \nThe study addresses several research questions, including how OPS affects the CII, whether OPS adoption alone can meet international emission reduction targets, and the financial implications for shipowners and operators considering fuel savings. Operational data from the specific vessel, MV BulkCarrier-N1, was collected from voyage reports and performance monitoring databases for the period from January 2021 to December 2022. \nTwo scenarios, IMO-Low and IMO-High, based on International Maritime Organization (IMO) trajectories, were developed to project future emission reduction targets. A Python model was then developed to analyze the impact of OPS and speed optimization on CIIs and fuel savings. The model incorporated inputs such as drydocking schedules, OPS installation timing, capital and operating expenses for OPS, annual revenues, speed ranges, and fuel prices. \nThe findings indicate that speed optimization alone can meet the IMO-Low targets until 2028 and the IMO-High targets until 2027. However, sustaining the desired efficiency levels beyond these years would require additional measures. When OPS was combined with speed optimization, the impact on CIIs was found to be minimal, primarily due to the relatively small contribution of the auxiliary engine's fuel consumption to the overall fuel usage. \nAlthough OPS showed minor improvements in CIIs, it did contribute to fuel savings. The comparison between the IMO-Low and IMO-High scenarios revealed higher short-term financial gains under the more ambitious trajectory. However, achieving long-term sustainability and meeting ambitious emission reduction targets will necessitate the implementation of additional efficiency measures. \nThis study underscores the complex relationship between OPS adoption, speed optimization, and the achievement of international emission reduction goals. While OPS and speed optimization have potential benefits, they may not be sufficient on their own to achieve long-term sustainability in the shipping industry strategies and measures are needed such as the use of biofuels or alternative fuels (i.e. green methanol, ammonia etc.), to drive significant emissions reductions and ensure a more environmentally friendly shipping sector.
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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) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".