Evaluating materiality disclosure in sustainability reports: A study of North American construction and engineering firms
Bibliographic record
Abstract
This research evaluates the sustainability reporting practices of 10 leading North American con-struction and engineering firms, focusing on adherence to the Sustainability Accounting Standards Board (SASB) Standards for the Engineering and Construction Services sector. The analysis covers five material topics: Ecological Impacts, Product Quality & Safety, Employee Health & Safety, Prod-uct Design & Lifecycle Management, and Business Ethics. Results reveal significant gaps in report-ing, with most firms failing to meet full disclosure for SASB metrics. Ecological Impacts and Business Ethics are the weakest areas, with limited disclosures on environmental risks and anti-competitive practices. Employee Health & Safety shows moderate compliance, with few firms reporting key metrics like Total Recordable Incident Rates (TRIR). The study highlights the urgent need for en-hanced transparency, standardized reporting, and robust governance frameworks. Improving alignment with SASB standards will foster accountability, strengthen stakeholder trust, and ad-vance sustainability within the sector.
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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 | 0.029 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".