The Inclusion of Natural Elements in Building Design: the Role of Green Rating Systems
Bibliographic record
Abstract
The awareness of the heavy impact that the building sector exerts on the natural environment is now widely shared, leading to a wide spread of tools (rules, regulations, voluntary rating) to control and guide towards building environmental sustainability.In this shared vision, the natural environment is perceived essentially as an asset to protect.But nature is not just something to be protected, it is also a key factor to improve the quality of our built environment and our well-being.Numerous studies analyze the positive impact of the introduction of natural elements in building design (i.e.green walls, indoor green, aquatic elements, etc.), including: reduced energy consumption, improved IAQ, benefits on users' attention capacity in office settings, stress-reducing effects in healthcare environments.However, despite this evidence, the use of natural elements in common building practice is still not quite widespread.There is therefore a need to promote awareness and use of the potential of the natural elements in design.This paper aims to assess and promote the enhancement of the natural elements in the voluntary green rating systems, as active tools in promoting environmental sustainability to all the actors of the building process.To this end, the study was developed through the following steps: 1. in the literature, identification of the elements of nature-based design with more evidence on environmental performance; 2. in green rating systems, identification of the weight given to the natural elements, evaluation of their current level of enhancement within the systems and identification of possible areas of development.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".