Barriers or boosters? the role of governance pathways in deploying offshore carbon capture and storage: comparative implications from the EU and China
Bibliographic record
Abstract
Offshore carbon capture and storage (CCS) deployment has been hailed as a game changer in the ever-changing climate game in the era of Paris Agreement. In the European Union (EU), rigorous regulation within a legal framework governs cross-border offshore CCS projects, while China adopts a flexible policy-oriented approach. This article employs a multi-method research approach, combining legal doctrinal analysis, comparative studies, and discourse analysis, to examine the role of governance tools in offshore CCS deployment in the EU and China, highlighting their differing models and the implications for effective governance. The discrepancies in governance models for offshore CCS deployment between the EU and China arise from variations in legal traditions, disparities in the legal status of marine areas hosting offshore CCS projects, and differences in involved industries. The paradox between normative governance and offshore CCS deployment finds resonance and explanation in the “Collingridge Dilemma”. Experiences from both the EU and China underscore the significance of a tailored-made and well-balanced governance portfolio of legal and policy tools in regulating and facilitating offshore CCS deployment. Policy and law should act hands in hands as twin engines in a sound governance framework propelling the momentum of offshore CCS deployment forward.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".