Slow-Steaming Climate Strategies For Abatement Efforts In Maritime Shipping
Bibliographic record
Abstract
Maritime shipping is a major contributor to climate change – accounting for 2.89\% of Global CO2 emissions in 2018. Given the “light hand” of regulatory institutions governing commercial activities on the high seas, attempts to reduce the emissions of ocean-going ships have not been successful. In this thesis, the impacts of emission policies and incentives for cooperation in the international maritime shipping industry are examined. The International Maritime Organization (IMO) – the regulator – GHG Strategy puts forth both “speed optimisation” and “speed reduction” as candidate measures for short-term emission abatement. These terms are poorly defined, however, leading to different interpretations. Slow steaming, or deliberately reducing ships’ speed, allows firms to decrease fuel consumption and therefore, emissions. Grounded in this rationale, a flexible numerical simulation model is developed for a market comprised of heterogeneous shipping companies to investigate maritime shipping abatement dynamics under various slow steaming policies. First, we project firms' business-as-usual (BAU) operations and then analyse both policies: Speed reduction – relative to BAU levels and Speed optimisation – as firms' climate strategy response to meet various emission caps. The simulation results suggest that firms already slow-steam when economically optimal (i.e. by evaluating the trade-off between fuel savings and time-dependent operating costs). Even more so, they show that speed optimization -as an abatement strategy- provides firms with the flexibility to derive their optimal Slow-Steaming rates to sustain a regulator's environmental policy. In contrast, we find that Slow-Steaming - as a command and control policy- shifts regulatory focus and is difficult to enforce in international waters. The simulation model was also used to analyze a two-stage, cooperative game of coalition formation with heterogeneous firms and individual abatement strategies. In the first stage, firms decide whether to join a coalition or not (membership decision). Coalition signatories adopt the operational slow-steaming climate strategy over the planning horizon and choose the abatement levels that maximise the sum of their payoffs under a joint emission budget constraint. On the other hand, non-signatories to the coalition (singletons) optimise their own abatement level by maximizing individual payoffs, subject to their own individual caps. Our results indicate that cooperation allows firms with heterogeneous abatement cost curves to pool resources and properly allocate speed reduction endeavours to sustain an emission target. Thus, industry-level climate strategies withhold the potential to improve environmental sustainability through cooperation for ocean shipping.
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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 | 0.000 |
| 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) | 0.006 | 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".