Exploring public acceptability of direct air capture and storage: Climate urgency, moral hazards and perceptions of the whole versus the parts of a carbon dioxide removal system
Bibliographic record
Abstract
Abstract Negative emission technologies (NETs) or the drawdown of atmospheric carbon is increasingly essential to meeting climate targets. Many options (e.g., afforestation) may not however meet the scale of removal and permanence of storage needed. Scientists and engineers are thus turning to new alternatives involving technology bundles using direct air capture of C02 and storage. However, the social acceptability of these is presumed unlikely given the sheer complexity of their components, governance arrangements, perceived advantages and disadvantages, and the different moral and value positions at play. This paper explores public perceptions of a proposed system including above sea and potentially more positively viewed components (wind energy to power direct air capture of carbon) alongside deep-ocean and potentially more negatively perceived components (injection and storage as carbonate rock). Using a representative survey of n = 2120 US and Canadian residents nearest a proposed system pilot, analysis reveals two very different profiles of perceivers, pro and con. Rejection of the system as a whole is driven by concern for storage or below sea components, physical risks (e.g., leakage), and belief that such a system constitutes a moral hazard, enabling continued fossil fuel dependence. Conversely, those who support such a system perceive it as economically, climatically, and ethically beneficial now and for future generations, express a strong sense of climate severity and urgency, and see themselves as responsible for natural systems. We close with cautions as to the social licence for negative emission technologies, and the fragility of hope as these possibilities unfold.
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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 | 0.006 | 0.013 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".