An optimal sustainable planning strategy for national carbon capture deployment: A review on the state of<scp>CO<sub>2</sub></scp>capture in Canada
Bibliographic record
Abstract
Abstract This study reviews the steps Canada is taking to address sustainable decarbonization in the context of carbon capture. This work also presents a new optimal framework for national optimal deployment in need of strategic carbon capture implementation. This framework considers external environmental and social considerations often missing from implementation frameworks, which will aid policy makers in more well‐rounded deployment decisions. Thus far, Canada's carbon projects have captured a total of 36.3 Mt of CO2which has cost over $2.7 billion to implement. The Canadian case study utilizing the proposed optimal planning strategy shows that implementation of 58 post‐combustion carbon capture (PCC) plants located in seven provinces (Alberta, British Columbia, New Brunswick, Nova Scotia, Ontario, Quebec, and Saskatchewan) would result in Canada meeting the national targets. This implementation includes 16 plants removing emissions from the Electricity sector, 16 from the Heavy Industry sector, and 26 from the Oil and gas sector resulting in new emissions levels of 11.82 MtCO2, 27.63 MtCO2, and 107.01 MtCO2in each sector, respectively. Additional case studies examined the impact of Alberta's emissions and varying the national targets resulting in different optimal implementations plans. Through a sensitivity analysis on these targets, it was determined that plant distribution is heavily dependent on provincial energy and CO2transport prices. Additionally, if Alberta were to reduce their GHG emissions by 50% through alternative sustainable methods, only 35 PCC plants would be required to meet national targets. This framework provides a sustainable tool for decision‐makers to accelerate decarbonization.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".