Mind the Gap: An Evaluation of Indicator Discrepancies between Sustainability Standards and Certifications in the Built Asset Industry
Bibliographic record
Abstract
The built asset industry impacts our global environment significantly, contributing notably to environmental degradation. Various sustainability standards and certifications, such as LEED, DGNB, BREEAM, ISO 14001, and GRI 200 series, have been established to guide the industry toward sustainable practices. Despite their intended purpose, the diversity of these systems has led to a complex and inconsistent landscape. This paper undertakes a review of 25 certifications and 26 standards in the built asset industry, identifying and analyzing gaps and discrepancies in their measuring indicators. Using a rigorous process, we consolidated the diverse measuring indicators from each scheme into a list of 189 specific indicators, for comparative analysis. This analysis revealed notable gaps and inconsistencies within these schemes, illuminating differences in their emphasis and coverage of sustainability indicators. These findings highlight the need for increased standardization and inclusiveness in sustainability assessments within the industry. This study contributes to the discourse on industry standardization, policy decisions, sector transparency, and further research, marking a crucial step towards a more integrated approach to sustainability in the built asset industry. Keywords: Sustainability, built asset industry, Sustainability standards, Green Building Certification
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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 | 0.125 | 0.255 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".