An evaluation of existing environmental buildings’ rating systems and suggested sustainable material selection assessment criteria
Bibliographic record
Abstract
Environmental ranking systems for buildings' sustainability and green design are widely used all around the world.Ranking systems are used as planning guidelines to implement sustainability into buildings in the design stage.Although sustainability ranking systems may have their pitfalls, these systems have shed the light on sustainability and has led to businesses investing in green design.Many rating systems are available to assess buildings in terms of sustainability, where a set of criteria are assessed using a quantitative approach and a score is provided, the question that arises is are these systems reliable?Different rating systems are available, among these systems are Leadership in Energy and Environmental Design (LEED) in the USA, Building Research Establishment's Environmental Assessment Method (BREEAM) in the United Kingdom, Comprehensive Assessment System for Building Environmental Efficiency (CASBEE) that has been developed for the Japanese market, Green Globes that has been implemented in Canada, Sustainable Building Tool (SBTool) as an international toolkit and framework for rating the sustainable performance of buildings and projects.All rating systems are designed towards assessment of the building impact on the environment.All assessment methods have limitations which questions the usefulness of these methods and advocates the need for a better sustainability assessment system.This paper is an attempt to highlight the pitfalls of current environmental ranking systems and to raise awareness towards the need for a system which addresses sustainable material and system selection in project planning stage individually rather than assessment of the building in terms of sustainability as a whole.Suggested criteria for an effective sustainable system www.witpress.
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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".