Public evaluations of four approaches to ocean-based carbon dioxide removal
Bibliographic record
Abstract
In the face of mounting global climatic pressures, negative emission technologies (NETs) for carbon dioxide removal (CDR) are increasingly proposed as necessary for meeting climate targets. While initial work has identified the potential of terrestrial NETs, a diverse set of marine/ocean-based NETs are gaining new and particular attention. Emerging studies on the feasibility of marine NETs are urgently needed, especially to explore the logics that public groups use to judge different approaches, and to ensure that design and governance of these technologies align with public values and priorities. This study explores factors of interest in understanding public views on four marine NETs, both perceptions of climate severity and urgency, and beliefs about marine environments. It uses a quantitative survey to explore how a representative sample of people in British Columbia, Canada and Washington state, United States evaluate four marine NETs: coastal restoration; ocean alkalinity enhancement; ocean fertilization; and offshore direct air carbon capture and storage. We find that perceived severity and urgency of climate change predicts greater comfort with all NETs studied, and views of marine environments as adaptable, fragile and manageable vary in predicting both greater and lesser comfort. Drawing upon these insights, the paper offers reflections on the conditional thinking linked with emerging views of marine NETs, concluding with methodological suggestions for future research on public perceptions as concerns the deployment of ocean-based CDR near and long term. Incorporating these insights into policy for ocean-based CDR will be important to ensuring responsible governance of these technologies.Key policy insights Incorporating research on public perceptions will be important to the design of marine NETs and accompanying policies.Public groups in both British Columbia and Washington expressed high levels of comfort with coastal restoration, some comfort with offshore direct air carbon capture and storage, and some discomfort with ocean alkalinity enhancement and ocean fertilization.Perceived severity and urgency of climate change predicted greater comfort with all approaches; this evidence aligns with a small but growing body of scholarship indicating openness to environmental intervention amongst public groups concerned with climate impacts.Beliefs about marine environments, namely whether they are ‘adaptable’, ‘manageable’ or ‘fragile’, also predicted comfort, suggesting that CDR in ocean contexts requires further examination regarding public perceptions.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".