Comparison of 16 national methods in the life cycle assessment of carbon storage in wood products in a reference building
Bibliographic record
Abstract
Abstract Wood and bio-based construction products are perceived as a way to use renewable resources, to save energy and to mitigate greenhouse gas (GHG)-emissions during production and to store carbon during the entire service life of the building. This article compares the carbon footprint per kilogram of wood products (softwood beams, plywood, oriented strand board panel, and fibre board) from the perspective of the life cycle assessment methodology for greenhouse gas (GHG) emissions of practitioners from 16 countries participating in the IEA Annex 72. These materials are used in PAL6 softwood structure multi-residential building. This article aims at comparing the carbon footprint accounting methods from 16 countries for PAL6 multi-residential building. Each national team applied the reference study period (RSP), life cycle modules covered, modelling rules, the geographical scope of inventory data as well as the LCA database according to its specific national method. The results show that there are three types of methodology to assess a building with biogenic content (0/0, -1/+1, -1/+1*). The results were more variable plywood, oriented strand board, and fibreboard than the softwood beams due to the variability in the wood transformation processes among the countries. A net negative carbon balance was obtained for the softwood beam for the countries using -1/+1* with a clear assumption of the fraction of the carbon permanently stored at the end-of-life (EoL). The carbon storage is only possible if it is secured at the EoL. Participating countries apply different definitions of permanence and EoL scenarios. Guideline on assessing, monitoring, and legally reporting carbon storage at the EoL are needed, based on concertation between standard, life cycle assessment, wood industry, and climate experts.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".