Low-carbon concrete materials: current and emerging technologies
Bibliographic record
Abstract
Concrete production is a major contributor to global greenhouse gas (GHG) emissions. This white paper highlights the urgent need for innovative approaches that can achieve substantial GHG reduction while preserving the performance and integrity of concrete structures. Categorized emerging low-carbon concrete materials and technologies are explored and information-gathering questions are suggested to evaluate their suitability for an application. These technologies are aligned with the roadmap toward net-zero carbon concrete by 2050, developed by Innovation, Science and Economic Development (ISED) Canada in partnership with the Cement Association of Canada (CAC). In section 2 of the paper categorizes low-carbon concrete innovations into the following key areas: Cement Replacements and Alternatives; Novel Concrete Additives and Fillers; Aggregate Substitutions; Carbon Capture, Utilization, and Storage (CCUS); Alternative Energy Sources and Production Methods; Innovative Construction Practices. Each material and technology category is analyzed in terms of benefits, challenges, and practical implementation considerations. The white paper also highlights the critical role of Life Cycle Assessment (LCA) and Environmental Product Declarations (EPDs) in quantifying the environmental impact of these solutions. Section 4 provides an overview of the fundamentals of EPDs, various types of EPDs, and Product Category Rules (PCRs). It includes a list of available industry-average EPDs for cement and concrete in Canada and discusses the importance of EPDs and PCRs in evaluating the environmental impact of concrete technologies. The section underscores the necessity for standardized tools and methodologies to ensure the transparency and comparability of environmental data, which are important for making informed decisions in construction projects. In conclusion, this white paper provides a comprehensive overview for engineers and project managers considering new and emerging low-carbon concrete materials and technologies by categorizing and detailing innovative approaches to reducing emissions. It compares the emerging and established technologies within the supply chain to achieve net-zero carbon concrete by 2050. With increased use and demonstration, these new materials and technologies will expand the repertoire of strategies available to the construction sector. Thus, providing more potential efficiencies in cost, time, and GHGs for the future. This is the second publication in a series on low-carbon concrete. Readers are encouraged to review the first publication “Low-Carbon Concrete: Sustainable Performance at an Affordable Price” (Greenis et al., 2023) as a foundation. https://doi.org/10.4224/40003155.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".