DGNB vs. LEED: A comparative analysis
Bibliographic record
Abstract
Nowadays, with an intensifying climate change, resources running short, economic complexity and debts going sky high, the idea of "sustainability" becomes more and more important. However, the views on this topic are inconsistent and partially affecting themselves. In the building sector with its high demand for energy as well as resources and corresponding costs the issue is also on the agenda. There are plenty of approaches and methods how to measure, evaluate and control the impacts of buildings on the environment. Most promising are rating methods for a standardized and scientific based assessment of buildings. In Germany for example the so called "energy certificate" intends to create transparency regarding the energy efficiency of buildings. To promote sustainable buildings, several organizations were founded in the past years, developing implementation strategies in the form of certification systems, based on ecological, economical and social aspects. In 1999 the World Green Building Council (WGBC) based in Canada was founded. Essential objectives are the promotion of sustainable buildings, transfer of information and innovation between the countries as well as the support of effective certification systems. The members support measurable and assessable buildings by the development and enhancement of certification systems or alternatively the adaptation of existing systems to the requirements of their countries. In Germany the German Sustainable Building Council (DGNB) was founded three years ago. It is a WGBC member and meanwhile offers the assessment method "German Sustainable Building Certification". The system aims to close the gap left by well established methods like the Building Research Establishment Environmental Assessment Method (BREEAM) or the Leadership in Energy and Environmental Design (LEED): the assessment of a building's whole life-cycle. This comparative overview shows the standards and guidelines the systems are based on, their main targets, the weightings of the indicators and also which aspects are neclected.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 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".