Is the Greenest Building the One Already Standing?
Bibliographic record
Abstract
Buildings contribute 39% to global greenhouse gas emissions (GHG) and 17% to Canada’s GHG emissions. Addressing carbon emissions from the built environment is an urgent, critical need to progress towards our global climate targets. There are two primary sources of GHG emissions connected to buildings: embodied carbon, which is the carbon emitted in materials extraction, manufacturing, transport, construction, and decommissioning, and operational carbon, which is the carbon emitted to power and heats the building while in use. Taken together, these two types of emissions are called whole-life carbon. As power grids in Canada decarbonize and on-site energy generation becomes more common, embodied carbon will contribute a more significant percentage to a building’s carbon profile and be a more impactful avenue for intervention. However, delaying action on represented carbon risks “locking in” a higher carbon profile in the built environment for the next 50 to 60 years, the typical lifespan of Canadian buildings. \n \nThis synthesis map explores the full carbon profile of buildings to understand better the influences, challenges, and opportunities to reduce carbon emissions from the building sector. \n \nReading greenest building \nWe invite you to read this map starting from the introduction on the left side, then explore the five sections – which are titled, coded with stakeholder icons, and have brief descriptions – in the order that interests you. The legend on the bottom left explains the icons used to identify the critical stakeholders for each map section. \n \nThe design brief provides a more detailed exploration of the topic, including the background and context for each element of the synthesis map and a roadmap for reaching a net-zero building sector by 2050.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".