Streamlined Life Cycle Assessment of an Innovative Bio-Based Material in Construction: A Case Study of a Phase Change Material Panel
Bibliographic record
Abstract
Research Highlights: This is the first study that analyzes the environmental performance of wood-based phase change material (PCM) panels. Background and Objectives: Life cycle assessment (LCA) is a powerful environmental management tool. However, a full LCA, especially during the early design phase of a product, is far too time and data intensive for industrial companies to conduct during their production and consumption processes. Therefore, there is an increasing demand for simpler methods to demonstrate a company’s resource efficiency potential without being data or time intensive. The goal of this study is to investigate the suitability of streamlined LCA (SLCA) tools and methods used in the building material industry, and to assess their robustness in the case study of a wood-based PCM panel. Materials and Methods: The Bilan Produit tool was selected as the SLCA tool and a matrix LCA was selected as the most commonly used SLCA method. A specific case study of a wood-based PCM panel was selected with a focus on its application in building construction in the province of Québec. Results: As a semi-quantitative LCA method, the matrix LCA provided a quick screening of the product life cycle and its hotspot stages, i.e., life cycle stages with high impact. However, the results of the full LCA and SLCA tools were quantitative and based on scientific databases. The use of the PCM panel and heating energy had the highest environmental impacts as compared to other inputs. The results of the full LCA and SLCA also identified energy consumption as a hotspot. Insufficient material or processes in the SLCA databases was one of the reasons for the difference between the results of the SLCA and full LCA. Conclusions: The examined SLCA methods provided proper explanations for the bio-based material in construction, but several limitations still exist, and the methods should be improved to make them more robust when implemented in such a specific sector.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".