Leveraging building permit data for large-scale embodied carbon assessment of residential building construction
Bibliographic record
Abstract
Abstract The construction sector must balance reducing embodied greenhouse gas (GHG) emissions with meeting the rising global demand for infrastructure driven by population growth. While existing research on additional housing provision often focuses on the environmental implications of new buildings, a push towards circular economy initiatives has shed light on the renovation of existing building as an alternative pathway for additional dwelling creation. This study quantifies embodied GHG emissions for over 65 000 residential construction projects from 2018 to 2023 by analyzing open-source building permit data from six North American municipalities. Through a hybrid approach that combines regional input–output models and reported construction costs, we estimate embodied emissions for newly built residential dwellings and dwellings added through renovations of existing buildings. Our results show that new single-dwelling buildings have a higher average GHG emission intensity than new multi-unit residential buildings and are also ∼10 times more GHG intensive than single-dwelling additions to existing buildings. In contrast, units in new multi-unit buildings with over 10 dwellings are on average 1.5–3 times more GHG-intensive than additions to existing buildings. We show that best-in-class dwelling additions have 30%–90% less embodied GHG compared to the median GHG intensity of new dwellings. However, dwellings added through the most GHG-intensive renovations exceeded the emissions of newly built units in up to 40% of cases in large multi-unit buildings. This study provides insight into the scale and intensity of renovation activities while demonstrating the utility of building permit data for embodied GHG and circularity assessments, providing valuable insights for sustainable housing and resource management policies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".