Beyond density: Examining overlooked drivers of housing and neighborhood greenhouse gas emissions
Bibliographic record
Abstract
• Life cycle assessment was used to calculate housing and neighborhood GHG emissions. • Embodied and operational GHG impacts of the built environment are estimated. • Detached houses are more GHG intensive per capita than denser housing built forms. • GHG emissions are associated with built environment and socioeconomic characteristics. The built form of housing impacts virtually all infrastructure provision and lifestyle choices, particularly daily mobility patterns. The environmental impacts of these built environment interactions have been captured in previous housing life cycle assessments (LCA), but they have focused on ubiquitous built forms including detached homes and high-rise multi-unit residential buildings (MURB) and rarely examined the impacts of different neighborhood types. This study builds on previous LCA research that considered the impacts of buildings, transportation, and road infrastructure. Here, we critically examine the greenhouse gas (GHG) emissions of newly built housing forms including attached homes and low-rise MURBs, in addition to detached homes and high-rise MURBs. The GHG estimates are then extended to neighborhood levels and statistically analyzed using built environment and socioeconomic characteristics. Using this approach, we can better understand relationships between GHG emissions, household, and community parameters. Four housing built forms and 529 neighborhood case studies from Toronto, Canada were analyzed. The results show that attached homes and MURBs are 45–62 % less GHG intensive per capita than detached homes. Considering results at the neighborhood level, GHG emissions per capita decrease with higher density. We also find negative associations between GHG emissions per capita and household characteristics including household size, tenancy, and household income. The findings of this study highlight the need to integrate built forms, mobility, and transport infrastructure when assessing the potential greenhouse gas emissions of housing and neighborhoods. Besides aiming for density, the provision of walkable neighborhoods, low-emission modes of transport, and high-density housing forms (e.g., multi-unit residential buildings) can lead to low-GHG neighborhoods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".