Exploring the current challenges and emerging approaches in whole building life cycle assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it