The development of a framework to compare carbon capture and storage technologies as a means of decarbonizing cement production
Bibliographic record
Abstract
Cement production is hard to abate given that energy-efficiency measures and fuel switching have no impact on process emissions and a limited impact on total greenhouse gas emissions. Alternative cements and decarbonized raw materials can reduce process emissions; however, complete decarbonization requires carbon capture. Yet, most decarbonization roadmaps and studies generalize carbon capture without acknowledging differences between the technologies or regions in which they are implemented. To address this gap, we developed a bottom-up technology-explicit model of the cement sector to compare six technologies: chemical absorption, physical adsorption, membrane absorption, calcium looping, partial oxyfuel technology, and full oxyfuel technology. We explored energy and greenhouse gas impacts, capital costs, non-energy operating costs, energy costs, and carbon costs. A case study for Canada demonstrated that carbon capture technologies can be implemented at emissions abatement costs of −22 to 1 CAD/t CO 2 e, accounting for carbon price credits. Our findings show that energy can account for up to 81 % of the total costs, eroding the benefit of avoided carbon costs and increasing sensitivity to energy prices. However, carbon pricing still strongly influences the economics of carbon capture technologies and a minimum carbon price of 90 CAD/t CO 2 e by 2030 ensures carbon capture remains economical across Canada. The developed framework can used globally to help develop policy formulation and inform investment. • CCS increases sector energy demand 8-83% by 2050, depending on the CCS method. • Carbon pricing strongly influences emission abatement costs (EACs). • Energy costs account for as much as 81 % of total costs. • Under the CP170 baseline, Canada-wide EACs range from −22 to 1 CAD/t CO 2 e abated. • Regional breakeven carbon prices range from 50 to 60 to 210–220 CAD/t CO 2 e by 2030.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".