Environmental scan and issue awareness: risk management challenges for CCS
Bibliographic record
Abstract
Long lists of issues relevant to carbon capture and storage projects have been provided in a number of sources, encompassing the broad categories of technological risks, health and environmental risks and societal risks. From these long lists a selection of ten major issues, broken down into three broad categories, has been made. The selected issues are: 1) government and industry factors (competent regulatory oversight; adequate risk assessment and risk management frameworks; and supportive public policy architecture); 2) environmental risk factors (adequate site-specific characterisations of geological formations for CCS storage sites worldwide; credible monitoring of storage site performance; and the possibility of leaking from storage); 3) socio-economic factors (tolerable economic costs; public perceptions of risks and benefits; information provision, effective communication and stakeholder engagement; and social and public acceptability, including the use of decision support mechanisms). The paper emphasises that what is unique about carbon capture and storage, considered as a major set of risk issues of global proportions, is how proactively these relevant major risks and risk factors have been identified and characterised by major institutional actors, especially industry and governments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".