Implications of using systematic decomposition structures to organize building LCA information: A comparative analysis of national standards and guidelines- IEA EBC ANNEX 72
Bibliographic record
Abstract
Abstract Introduction:The application of the Life Cycle Assessment (LCA) technique to a building requires the collection and organization of a large amount of data over its life cycle. The systematic decomposition method can be used to classify building components, elements and materials, overcome specific difficulties that are encountered when attempting to complete the life cycle inventory and increase the reliability and transparency of results. In this paper, which was developed in the context of the research project IEA EBC Annex 72, we demonstrate the implications of taking such approach and describe the results of a comparison among different national standards/guidelines that are used to conduct LCA for building decomposition.Methods:We initially identified the main characteristics of the standards/guidelines used by Annex participant countries. The “be2226” reference office building was used as a reference to apply the different national standards/guidelines related to building decomposition. It served as a basis of comparison, allowing us to identify the implications of using different systems/standards in the LCA practice, in terms of how these differences affect the LCI structures, LCA databases and the methods used to communicate results. We also analyzed the implications of integrating these standards/guidelines into Building Information Modelling (BIM) to support LCA.Results:Twelve national classification systems/standards/guidelines for the building decomposition were compared. Differences were identified among the levels of decomposition and grouping principles, as well as the consequences of these differences that were related to the LCI organization. In addition, differences were observed among the LCA databases and the structures of the results.Conclusions:The findings of this study summarize and provide an overview of the most relevant aspects of using a standardized building decomposition structure to conduct LCA. Recommendations are formulated on the basis of these findings.
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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 | 0.190 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.027 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".