Is the Greenest Building the One Already Standing: A Synthesis Map
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 make 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 processes related to materials extraction, manufacturing, transport, construction, and decommissioning; and operational carbon, which is the carbon emitted to power and heat 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 greater percentage to a building’s carbon profile and be a more impactful avenue for intervention. However, delaying action on embodied 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 whole carbon profile of buildings to better understand the influences, challenges, and opportunities to reduce carbon emissions from the building sector. We invite you to read this map starting from the introduction on the left side, then to 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 key stakeholders for each section of the map. \n \nThe associated 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 how to reach 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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".