Sustainability in Maritime: Human-Centered Decarbonization Through Behavioral Change
Bibliographic record
Abstract
Abstract This paper explores a transformative approach to maritime decarbonization by emphasizing human behavior—termed "Carbon Culture"—as a central lever for reducing emissions. The maritime sector is responsible for approximately 2.89% of global CO2 emissions, translating to more than 1 billion metric tons annually. While most decarbonization strategies center around technical retrofits, alternative fuels, and regulatory compliance with frameworks like EEXI, CII, and national / local regulations, this study proposes a complementary, human-centric model. It focuses on real-time reporting, crew awareness, continuous training, and performance-based incentives to drive behavioral change towards optimizing fuel efficiency. The core methodology integrates behavioral science with digital operations, embedding sustainability directly into day-to-day workflows. Crews and shore teams are encouraged to make deliberate, fuel-saving choices through data-informed voyage planning, maintenance , engine load optimization, trim management, and energy-efficient auxiliary usage. These behavioral shifts are enabled and reinforced through user-friendly software platform, which provides mobile dashboards, gamified leaderboards, and microlearning modules to engage seafarers and reinforce sustainability principles. Global industry benchmarks suggest that operational inefficiencies account for 10–15% of excess fuel usage. According to Clarkson Research, a typical medium-sized tanker consumes 25–35 metric tons of fuel oil per day. Behavioral interventions can reduce this consumption by 2.5–3.5 tons per day. Fleet-wide data analyzed in this study indicates a 5–10% reduction in daily fuel consumption, resulting in direct cost savings of $1,000–$2,000 per vessel per day, depending on fuel price and voyage profile. These findings affirm that human behavior, when properly supported by digital tools and incentives, can deliver tangible environmental and financial benefits. This approach offers an accessible, scalable, and cost-effective pathway to help maritime sector fleets meet evolving IMO, EU ETS, and national decarbonization mandates. It also serves as a strategic bridge during the industry's transition to future fuel systems, helping close short-term compliance gaps while positioning ships crews for long-term sustainability roles. In conclusion, the integration of human factors with digital maritime operations presents a timely and replicable solution to support the global maritime sector's decarbonization ambitions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".