Weighing risks against <scp>GHG</scp> reduction benefits in emerging green technologies
Bibliographic record
Abstract
Abstract Green technologies represent promising strategies for mitigating carbon emissions and combating global warming. However, their implementation in civil and industrial applications is not without risks, as intrinsic uncertainties and latent safety concerns can threaten their effective deployment. To quantitatively evaluate the trade‐off between environmental benefits and safety issues, this work develops an innovative methodological approach. This approach aims to determine the risk–benefit profile of emerging green solutions through the definition and calculation of a new key performance indicator, the risk of CO 2 avoided index (RCAI). By doing so, it provides a comprehensive understanding of the effectiveness of the green technology implementation and serves as a powerful tool for supporting stakeholder decision‐making. In order to demonstrate the systematic nature and versatility of the methodological approach, it has been applied to a case study involving a carbon capture and storage (CCS) system retrofitted onto a power plant. The results underscore its flexibility and effectiveness, highlighting the importance of sustainable and safe technological advancements in the fight against global warming.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| 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".