Reference Service Life Dataset for non-structural building envelope materials: current state, knowledge gaps and inconsistencies
Bibliographic record
Abstract
The construction sector plays a central role in Canada’s Emissions Reduction Plan, which aims to cut greenhouse gas emissions by 40% from 2005 to 2030. Life Cycle Assessment (LCA) offers a comprehensive method for evaluating the environmental impacts of buildings, including material extraction, construction, operation, and demolition. A key source of uncertainty in LCAs is the Reference Service Life (RSL) of building materials, particularly non-structural components, whose durability is often less well-documented than that of structural ones. This study is intended to emphasize the need for a reliable RSL dataset of non-structural building envelope materials. The approach involved includes: (1) compiling survey-based and experimentally derived service life data as obtained from the literature; (2) extracting all available RSL values from Environmental Product Declarations (EPDs) for non-structural building envelope materials available in North America, and; (3) organizing the findings into a detailed dataset and analyzing the data to identify existing inconsistencies and knowledge gaps. The findings from this study indicate that nearly half of the EPDs reviewed entirely ignore mentioning the RSL values for non-structural building envelope products, thereby providing no basis for informed life cycle analysis. Among those that do include an RSL, many rely on the default to a 75-year building lifespan, often following standard assumptions, without any evidence or justification. The remaining information present widely differing values for identical products. Such variability leads to fragmented and inconsistent information, which reduces the reliability of LCAs. The results highlight the urgent need for more standardized and transparent RSL reporting in EPDs, particularly for non-structural components. Improved datasets will enable more accurate environmental impact assessments, better inform material selection, and ultimately support Canada’s broader climate goals.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".