Embodied GHG of missing middle: Residential building form and strategies for more efficient housing
Bibliographic record
Abstract
Abstract This research addresses two critical problems facing communities today: the growing demand for housing and the need to reduce material consumption to mitigate the impacts of climate change. Material production and use accounts for more than 25% of annual global greenhouse gas (GHG) emissions and must be reduced to meet the Paris Climate Agreement's 2°C target. At the same time, increasing urban populations are accelerating the demand for housing and construction materials. Strategies for supplying more materially efficient housing are urgently needed. Here, we quantify the impact of residential form on embodied emissions. Specifically, we look at the reduction potential of missing middle (low‐rise multi‐unit) housing, compare missing middle to single‐family and mid/high‐rise buildings, and identify opportunities for optimizing efficiency within forms. Forty‐two new material quantifications are calculated using an ontology based on MasterFormat and UniFormat. Minimum, maximum, and most likely GHG emissions factors are used to convert material mass to CO 2 eq. We observe embodied GHG of missing middle buildings varying between 5540 and 39,600 kgCO 2 eq/bedroom. On average, multi‐unit missing middle buildings have significantly lower embodied GHG per bedroom than single‐family and mid/high‐rise buildings, but variability within forms is greater than between forms, indicating a large potential to reduce embodied GHG through building design. Best‐in‐class design strategies include reducing substructure size and indoor parking, limiting mid/high‐rise slab thickness, and choosing low‐GHG insulation products. Building missing middle homes in the 1st quartile of embodied GHG efficiency with minimum insulation emissions factors could reduce future embodied residential emissions in Ontario, Canada by 46.7%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".