Uncertainties in whole-building life cycle assessment: A systematic review
Bibliographic record
Abstract
Environmental impacts (EIs) of building stocks have been receiving significant attention in recent decades as they consume more than 40% of the world's energy, release one third of total greenhouse gas emissions, and account for 30% of global landfill waste. Prior efforts have focused on mitigating EIs during the operation stage of buildings, while the environmental performance of other stages is relatively overlooked. Addressing this, whole-building life cycle assessment (WBLCA) has gained prominence from a life-cycle perspective to ensure the best environmental performance. However, there is an array of factors that can affect WBLCA results, and such uncertainties render decisions made for sustainable development untenable. Aiming to understand the comprehensive uncertain sources of WBLCA (what) and their corresponding solutions (how), this paper systematically reviews existing publications on WBLCA, presents its status and challenges, and analyses the taxonomy of uncertainties and eight uncertainty methods and variants thereof. Accordingly, a framework is developed that enables LCA practitioners to readily understand the correlation between WBLCA uncertainties and solutions, and conveniently locate and appraise them throughout the WBLCA process. Upon answering the known-what and known-how questions, this study contributes to the body of knowledge of LCA by providing a comprehensive and systematic methodology to evaluate the EIs of buildings.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".