Public perceptions of Carbon Capture and Storage technology in Alberta: Applying an integrative framework
Bibliographic record
Abstract
Carbon capture and storage (CCS) has emerged as a technological option for meeting greenhouse gas emissions reduction targets in the Canadian province of Alberta.Public support is likely to affect the feasibility of widespread implementation of CCS projects.This study explores citizens' perceptions of CCS, including knowledge, and stated support, and develops a method to characterize their attitudes towards CCS using a framework that includes psychological perceptions, values, environmental concerns, and socio-demographic variables.A web-based survey was conducted with a representative sample of Alberta citizens (n=1076) in 2013.The data suggest that respondents' knowledge of CCS has increased over the last decade, though climate change knowledge remains limited.The majority (53%) of respondents support the use of CCS, and 85% consider CCS at least "somewhat important" for inclusion in the province's emissions-reduction strategy.A minority of respondents (18%) are opposed to CCS.Regression analysis reveals that respondent support for CCS is associated with perceptions of outcome efficacy (belief that CCS is a useful climate change mitigation strategy), trust in the regulator and industry, and distributive fairness.Respondent support is also associated with beliefs of several benefits of CCS implementation, including the ability to balance economic development with emissions reductions, the continued ability to use fossil fuels, and the potential to export CCS technology to other countries in the future.On the other hand, respondent opposition to CCS is associated with perceptions of risk, including concern about potential groundwater contamination and that CCS would potentially displace investments in renewable energy.These empirical insights suggest that CCS outreach and engagement efforts could be enhanced by carefully considering citizen 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".