Decarbonizing Conventional Building Materials for Net-Zero Emissions: A Feasibility Study in Canada
Bibliographic record
Abstract
Abstract Governments around the world are aiming for net-zero carbon emissions by 2050. To drive broader decarbonization efforts, the building industry is challenged by the mission to reduce embodied carbon emissions, which stem from building materials and systems. Most efforts in buildings are focused on operational energy, but up to 80% of a building’s emissions occur before use and occupancy, from extraction to construction phases. These emissions are irreversible and contribute around 11% of global carbon emissions. This study emphasizes the need to prioritize the decarbonization of conventional building materials during production using low carbon constituents to mitigate associated environmental impact. Quantitative data analysis and reviews have identified alternative low-carbon options for possible application in mix design. Examples include substituting general use (GU) Portland cement with Portland limestone cement, using supplementary cementitious materials (SCMs) to reduce cement content, and utilizing low-carbon concrete masonry units. Achieving net-zero embodied carbon requires promoting circular bio-based products and reducing conventional materials’ carbon throughout their life cycle. At material level analysis, this is possible not only by promoting the use of circular bio-based materials but also by purposefully reducing the embodied carbon of conventional building materials throughout their life cycle, as well as communicating the best practices as lessons learnt to promote broader adoption by the architecture, construction and engineering (ACE) industry. However, collaboration among designers, contractors, and manufacturers is essential. This study provides a preliminary pathway to overall decarbonization efforts, understanding that conventional, existing building materials will play a significant role in attaining the net-zero commitments of the future built environment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".