Rules for assessment and declaration of buildings with net-zero GHG-emissions: an international survey – A Contribution to IEA EBC Annex 72:Energy in Buildings and Communities Technology Collaboration Programme
Bibliographic record
Abstract
Around 40% of global CO2 emissions can be attributed to the construction, maintenance, and use of buildings. Reducing these greenhouse gas (GHG) emissions is an essential goal in the context of sustainable development (IEA 2019). This is expressed, among other things, in SDG 13: Climate change. Reducing these emissions requires considerable efforts from all those involved in the construction and building sector as actors, decision makers and service providers, including upstream and downstream industries. In order to be able to design and implement appropriate reduction measures, the calculation and assessment of GHG emissions in the life cycle of buildings with the help of indicators, calculation rules, assessment methods and benchmarks is a prerequisite. Particularly benchmarks provide the basis for requirements for carbon performance as part of the environmental performance of buildings. They can be used both in the context of sustainability assessment systems, funding programs, building standards, and policymakers’ actions as well as provide the basis for individual design targets. A new approach is the top-down derivation of benchmarks in an effort to respect planetary boundaries. This involves protecting the natural basis of life by ensuring that future new construction and refurbishment measures lead to buildings with (almost) no negative effects on the climate during their lifecycle which led to the “climate-neutral building” approach in line with numerous are global initiatives.
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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 | 0.031 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".