Advances Toward a Net-Zero Global Building Sector
Bibliographic record
Abstract
The building sector is responsible for 39% of process-related greenhouse gas emissions globally, making net- or nearly-zero energy buildings pivotal for reaching climate neutrality. This article reviews recent advances in key options and strategies for converting the building sector to be climate neutral. The evidence from the literature shows it is possible to achieve net- or nearly-zero energy building outcomes across the world in most building types and climates with systems, technologies, and skills that already exist, and at costs that are in the range of conventional buildings. Maximizing energy efficiency for all building energy uses is found as central to net-zero targets. Jurisdictions all over the world, including Brussels, New York, Vancouver, and Tyrol, have innovated visionary policies to catalyze themarket success of such buildings, with more than 7 million square meters of nearly-zero energy buildings erected in China alone in the past few years. Since embodied carbon in building materials can consume up to a half of the remaining 1.5°C carbon budget, this article reviews recent advances to minimize embodied energy and store carbon in building materials.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".