Enhancing consistency in consequential life cycle inventory through material flow analysis
Bibliographic record
Abstract
Abstract Wood products are gaining interest in the building sector, due to their potential in sequestering greenhouse gas emissions. However, increasing wood materials use in the built market can have unforeseen changes in the material supply chains. Consequential Life Cycle Assessment (CLCA) allows the assessment of changes in material supply chain. To quantify and link those consequences, the affected physical flows need to be estimated. Material Flow Analysis (MFA) can bring to CLCA modelling more representative and quantitative information than the commonly used hypothesis in consequential modelling. Indeed, MFA considers physical constraints (technology performances and material availability), in addition to account for mass balance. The main objective of this presentation is to illustrate how such consistency is added to CLCA through an MFA of wood products in non-residential (NR) buildings in the province of Québec (Canada). Wood flows are tracked to identify their end-use markets and trends in consumption. To overcome the lack of data and bring insights on the sector’s dynamics, such as stock variations and potential discarded flows that supply recycling markets, residence time model and also extrapolation and correlation between physical and economic parameters are used. Results show how flows can increase in the market before reaching their physical constraints, such as the available wood stock in the forest. These insights will significantly enhance the data collection for CLCA. In conclusion, the MFA brings support to CLCA by proposing a framework to model changes in the construction market.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".