The Potential and Limits of Environmental Disclosure Regulation: A Global Value Chain Perspective Applied to Tanker Shipping
Bibliographic record
Abstract
Abstract Exploring how transnational environmental governance and the operation of global value chains (GVCs) intersect is key in explaining the circumstances under which mandatory disclosure can improve the environmental footprint of business operations. We investigate how the governance dynamics of the tanker shipping value chain (a major emitter of greenhouse gases) limits the effectiveness of the European Union (EU) monitoring, reporting, and verification (MRV) regulation, which mandates the disclosure of greenhouse gas emissions for ships calling at EU ports. Although MRV seeks to help shipowners and ship managers save fuel and reduce emissions, it does not address the complexity of power relations along the tanker shipping value chain and currently cannot disentangle how different actors influence the design, operational, commercial, and ocean/weather factors that together determine fuel consumption. In particular, the EU MRV neglects to reflect on how oil majors exert their power and impose their commercial priorities on other actors, and thus co-determine fuel use levels. We conclude that, in its current form, the EU MRV is unlikely to lead to significant environmental upgrading in tanker shipping. More generally, we argue that regulators seeking to facilitate environmental upgrading need to expand their focus beyond the unwanted behaviors of producers of goods and providers of services to also address the incentive structures and demands placed on them by global buyers.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".