Exploring the current challenges and emerging approaches in whole building life cycle assessment
Bibliographic record
Abstract
The environmental impacts of building stock have received significant attention as buildings release one-third of the total greenhouse gas emissions. Whole-building life cycle assessment (WBLCA) has become a trend to address this limitation by ensuring the best environmental performance of a building. However, the current WBLCA development faces many challenges, which makes it difficult to create reliable and comparable results. This study aims to conduct a critical literature review to summarize the current challenges in WBLCA applications and the emerging approaches that might address these challenges. Three main challenges are listed: variances in goal and scope definition, building structure complexity, and varieties in the LCA database and methods. Emerging approaches are also presented to address these challenges, including the integration of building information modeling into WBLCA and environmental product declaration applications in impact assessments. The findings of this study could support researchers and decision-makers with the most popular approaches to conduct WBLCA and achieve reliable outputs.
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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 | 0.027 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".