NRC-CAC Low Carbon Concrete Research and Development Workshops
Bibliographic record
Abstract
The production of cement and concrete for use in construction is a major source of greenhouse gases (GHGs), with cement production alone being responsible for 1.5% of Canadian GHGs and as much as 8% of the world emissions. Reducing cement and concrete GHG emissions is therefore a key component to meeting Canada’s 2030 and 2050 GHG reduction targets. As the world’s cement industry has also prioritized reaching net-zero by 2050, developing new technologies to support that goal also offers a significant export opportunity for Canadian industries. Reaching net-zero emissions from the concrete used in construction will require changes to industry practice as well as new materials and changes to cement production methods. Research, development and knowledge mobilization will be needed to support these changes. The National Research Council Canada (NRC) and the Cement Association of Canada (CAC) held a series of broadly based workshops at the end of 2021 to identify the key research needs that should be addressed by the Canadian construction industry. This document summarizes the results of those workshops. The seven workshops covered a total of 20 different topics, many in more than one session. The workshop sessions covered technologies related to combustion at the kiln, clinker production, cement, concrete and carbon sequestration in concrete. Geological sequestration of CO2 emissions from cement production was not addressed. A total of 75 different knowledge gaps were identified during the sessions, with 60 being related to cement and concrete and 15 to alternative fuels and cement production. Analysis showed that 54 of the knowledge gaps could be grouped into the categories of: • Development or improvement of test methods; • Concrete performance and durability; • Assessing GHG emissions reductions benefits; • Improvements to understanding of fundamentals • Supply chain issues; and • Changes to standards. This grouping applied to both the cement and concrete and the alternative fuels sessions. The remaining 21 knowledge gaps were related to individual technologies. The workshop sessions recommended research activities to address many of the knowledge gaps. Where possible, these recommendations were used as the basis to outline research pathways that would lead to the filling of the identified gaps. In some cases, no recommendations were given and the research pathways were developed from the authors’ knowledge of the topic. The overall results from each session were assessed based on the likely impact on GHG emissions from the cement and concrete industry and the estimated ease of adoption of the technology. Eighteen recommendations for research, development and knowledge mobilization activities were given, with eleven to support meeting Canada’s 2030 GHG emissions reductions targets and seven additional ones for further activities to support meeting the 2050 net-zero targets. Carbon capture, utilization and storage (CCUS) are likely to play a major role in the 2050 timeframe in particular, but the approaches described in this report will be critical for success in meeting the 2030 targets and important for minimizing the amount of carbon dioxide that needs to be captured once CCUS systems are fully in place. It should be noted that recommendations are based on the workshops and identified industry needs. The report does not indicate who should undertake which activities, nor is it intended as a description of future NRC activities.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.013 |
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".