Behavioural frameworks to understand public perceptions of and risk response to carbon dioxide removal
Bibliographic record
Abstract
The adoption of carbon dioxide removal (CDR) technologies at a scale sufficient to draw down carbon emissions will require both individual and collective decisions that happen over time in different locations to enable a massive scale-up. Members of the public and other decision-makers have not yet formed strong attitudes, beliefs and preferences about most of the individual CDR technologies or taken positions on policy mechanisms and tax-payer support for CDR. Much of the current discourse among scientists, policy analysts and policy-makers about CDR implicitly assumes that decision-makers will exhibit unbiased, rational behaviour that weighs the costs and benefits of CDR. In this paper, we review behavioural decision theory and discuss how public reactions to CDR will be different from and more complex than that implied by rational choice theory. Given that people do not form attitudes and opinions in a vacuum, we outline how fundamental social normative principles shape important intergroup, intragroup and social network processes that influence support for or opposition to CDR technologies. We also point to key insights that may help stakeholders craft public outreach strategies that anticipate the nuances of how people evaluate the risks and benefits of CDR approaches. Finally, we outline critical research questions to understand the behavioural components of CDR to plan for an emerging public response.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".