TO ZERO AND BEYOND: Zero Energy Residential Buildings Study. 2016 Inventory of residential projects on the path to zero in the U.S. and Canada
Bibliographic record
Abstract
Buildings and their energy use account for 41% of carbon emissions in the United States (DOE 2011). The goals set out at the last United Nations Climate Change Conference (COP22) call for keeping the global temperature rise well below 2 degrees Celsius above pre-industrial levels and pursuing efforts to limit it to 1.5 degrees Celsius. In order to meet this imperative, the World Green Building Council (WGBC), in its recently published report, “From Thousands to Billions,” states that all new buildings must operate at net zero carbon by 2030 and 100% of ALL buildings must operate at net zero carbon by 2050. The next few years will be pivotal for the growing zero energy movement, with new cities and states adopting zero energy and zero carbon policies, solar costs plummeting to new record lows, and growing demand globally for zero energy buildings. Lux Research projects zero energy buildings and nearly-zero energy buildings will grow to a $1.3 trillion market by 2025. This illuminates a major market opportunity for the entire construction industry that will be driven and accelerated by NZEC’s collective action efforts.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".