Analysis of decision-making around sulfur regulation in marine shipping using the graph model for conflict resolution
Bibliographic record
Abstract
Reducing the environmental and health impacts arising from marine transportation is a central mandate for the International Maritime Organization (IMO), but policy implementation is challenged by overlapping jurisdictions and compartmentalized policy design. Here, we investigate decision-making strategies after the IMO 2020 policy, aimed at reducing sulfur emissions by more stringently regulating fuel sulfur content, came into force. Our analysis highlights the consequences of an exception in IMO 2020 allowing vessels to use exhaust gas cleaning systems as an alternative to fuel-based compliance. Focusing on an example in the Port of Vancouver, Canada, we present a formal model capturing the key decision-makers as they respond to protect their own interests upon the implementation of IMO 2020. We trace how responses to this policy have evolved since 2020, leading to a critical decision point from which a set of alternative technology pathways for marine shipping futures arise. These pathways diverge meaningfully in terms of how they can contribute to policy goals beyond sulfur emissions (e.g., water quality and decarbonization). We then provide recommendations for supporting industry decision-making around those alternatives. While we focus on how this policy manifested in Vancouver, Canada, IMO 2020 has a global impact, and our model provides a framework for systematically investigating its consequences in other jurisdictions. We also provide transferable insights to other multi-scale governance contexts, contributing to policy design that includes co-benefits and trade-offs relevant to interrelated goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".