Uncertainty in Quantification of Material Use and Embodied Greenhouse Gas Emissions of Single-Family Dwellings
Bibliographic record
Abstract
Reducing embodied greenhouse gas (GHG) emissions in the construction of buildings is globally recognized as important for meeting climate targets. However, quantitative understanding of building material use is limited and employing material efficiency strategies to reduce building embodied and global GHG emissions is underutilized. This dissertation advances the understanding of building material intensity (MI) and embodied GHG intensity with specific focus on single-family dwellings (SFD). The thesis examines the uncertainty and variability associated with current and practical approaches to built environment industrial ecology and building regulations, the variation in MI and embodied GHG emissions within and between cities, and the potential for material efficiency strategies to reduce embodied GHG emissions in SFDs. The three central chapters build an evidence-based approach to advance understanding of MI in SFDs and then combine MI with material GHG intensity to investigate embodied GHG emissions. Chapter 3 investigates variations in MI and implications for uncertainty in MI research focusing on buildings within one city (Toronto, Canada). Chapter 4 expands on this and examines differences in MI both within and between places with investigation of buildings in Toronto, Canada, Perth, Australia, and Luzon, Philippines. In Chapter 5, embodied GHG intensity is estimated and examined to determine whether the observed ranges of MI or material GHG intensity drive overall embodied GHG emissions of buildings. Three design and material strategies (light-weight design of structures, more intensive building use, very low GHG material substitution) are evaluated for their potential to reduce embodied GHG emissions of housing construction. Through the study of 80 buildings across three locations, this dissertation identifies important implications of uncertainty and variability within and between locations, determines implications of functional unit selection on interpreting MI, and identifies large contributors and drivers of building material use and embodied GHG emissions. The findings support uncertainty examination in bottom-up MFAs and inform building regulations on the use of functional units when developing benchmarks for building material and embodied GHG estimates. Material intensity and embodied GHG interventions for housing are location and context specific, but regardless of location, constructing smaller houses would reduce overall MI and embodied GHG emissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".