Carbon Capture, Utilization, and Storage: Public Confidence in Risk Decision-Making
Bibliographic record
Abstract
Abstract Canada has developed extensive expertise and experience in carbon capture, utilization, and storage (CCUS). Although CCUS has repeatedly been identified as an important option for carbon dioxide emissions reductions in the last fifteen years, it has not reached its potential largely because the technology faces challenges across a range of socioeconomic and political risks. This chapter identifies the key risks influencing public confidence in CCUS and government decision-making processes and develops recommendations for decision-makers to support public confidence in risk decision-making for CCUS. It includes in-depth interviews with decision-makers from a variety of sectors related to CCUS policy and implementation along with a comprehensive review of academic, industry, and government publications. Using the REACT framework of risk management tools (regulatory, economic, advisory, community-based, and technology), the chapter recommends a variety of risk management options that can help to strengthen public confidence in CCUS and regulatory frameworks underpinning its development. The analysis suggests that a wide variety of actions is needed in order for CCUS to make the contribution to climate mitigation that continues to be envisioned for large industrial sites.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".