Cement and Concrete Industries Contribution to Climate Change Mitigation
Bibliographic record
Abstract
The Cement Industry is continuously trying to find ways to reduce its environmental foot print through development of a comprehensive strategy for reducing emissions and energy use. Another way the Cement Industry can assist with climate change mitigation is to encourage the use of concrete based products in Canada's Infrastructure. This paper gives a brief overview on the Canadian Cement and Concrete Industry and how cement is made. In addition, a brief introduction is provided on Canada's Cement Sustainability Initiative (CSI) which identifies an action plan addressing performance related to six key issues. The main focus of the paper is to identifying the Cement Industry's climate change mitigation initiatives and several concrete based applications that help mitigate climate change. Details are provided on research showing the advantages of concrete such as the updated Athena study looking at pavement structures' energy use and global warming potential, NRC's truck fuel usage studies and research on concrete as a CO2 sink. Other topics such as optimized concrete mixes and ultra high strength concrete are also discussed. An example of the potential fuel savings and associated CO2, NOx and SO2 reductions when operating on concrete pavement is also provided.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".