Expert Assessments of Maritime Shipping Decarbonization Pathways by 2030 and 2050
Bibliographic record
Abstract
Abstract International shipping conveys over 80% of global trade by volume and emits an estimated 3% of the world's greenhouse gases (GHGs). There are many potential pathways and barriers to decarbonizing the diverse and fragmented international shipping sector, with numerous uncertainties. Here, we employ expert elicitation, gathering perspectives from 149 world‐leading experts in maritime shipping and decarbonization, to characterize uncertainties in shipping decarbonization pathways. These experts predict a 30%–40% (25th–75th percentile range) carbon intensity reduction by 2030 compared to 2008, meeting the International Maritime Organization's (IMO) target. By 2050, they anticipate an approximate 40%–75% cut in GHG emissions, falling short of the IMO's 2050 net‐zero GHG goal. Responding experts see decarbonization occurring through three types of measures: operational, technological, and alternative energy sources. In the short‐term, decarbonization is predicted to be dominated by operational measures, while in the long‐term, it will be dominated by alternative energy, although there is no consensus on which fuels will dominate. Technological upgrades are expected to play crucial supporting roles. The experts believe that differences in business models and governance may lead to different decarbonization pathways by ship segment. The experts' qualitative responses highlight: alternative energy systems, ship fleet turnover, spillover effects from other sectors, reducing industry pessimism, and the supply chain as critical leverage points that can propel shipping toward sustainable decarbonization pathways. Navigating this transition demands support from key levers identified in this study: politics and policy, maritime governance, and contractual architecture.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".