Uncertainties in whole-building life cycle assessment: A systematic review
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
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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