Sustainability in the Construction Industry: A Critical Analysis Between Sustainable Development Indicators and Assessment Tools
Bibliographic record
Abstract
Nowadays, different organizations and institutions have advanced methodologies and strategies that make it possible to assess, through parameters and indicators, the sustainability of construction projects that materialize the concept of “sustainable building”. The main aim of this research is to carry out a critical analysis between the Sustainable Development Indicators (SDI), released by the IBGE/Brazil, and the most used assessment tools in Brazil, namely: LEED and AQUA. Thus, with regard to the “green buildings” certified by these tools in the Brazilian territory, data collection was carried out in those organizations considering the parameters: the level of certification and the region of Brazil. The lack of synchronization between the data from the SDI and the assessment tools was found. Finally, recommendations are presented that aim to reduce the inconsistencies found in the assessment tools.
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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 | 0.079 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".