Strategies for low carbon concrete: primer for federal government procurement: low carbon assets through life-cycle assessment (LCA)² initiative
Bibliographic record
Abstract
A large amount of carbon emissions is generated by the harvesting, transportation, manufacture and end of life disposal/recycling of construction materials. These emissions are referred to as “embodied carbon” and are measured using a technique called life cycle assessment (LCA). As buildings become more efficient in their annual operating energy and thus lower their operational carbon, the construction industry and policy makers around the world have become increasingly concerned with the growing relative impacts of embodied carbon. An LCA conducted on a LEED-certified Ontario government building that was built in 2013 shows that over a 60-year time frame, embodied carbon accounts for over 40% of the total whole life carbon footprint of a building. A 2018 Intergovernmental Panel on Climate Change report indicated that approximately a 12-year window remained for significant emission reduction before catastrophic effects of climate change become unavoidable. If the 12-year timeframe is considered instead of the standard LEED 60-year timeframe, embodied carbon becomes the dominant source of emissions, accounting for closer to 75% of emissions (Figure 1). That same LCA study found that the concrete in the building was responsible for approximately 50% of the embodied carbon. Therefore, in the first 12-years of a building’s life cycle, concrete alone is responsible for nearly 40% of the building’s total carbon impact.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".