Carbon footprint assessment of a wood multi-residential building considering biogenic carbon
Bibliographic record
Abstract
<title>Abstract</title> Wood and other bio-based building materials are often perceived as a good choice from a climate mitigation perspective. This article compares the life cycle assessment of the same multi-residential building from the perspective of 16 countries participating in the international project Annex 72 of the International Energy Agency to determine the effects of different datasets and methods of accounting for biogenic carbon in wood construction. Three assessment methods are herein considered: two recognized in the standards (the so-called 0/0 method and –1/+1 method) and a variation of the latter (–1/+1* method) used in Australia, Canada, France, and New Zealand. The 0/0 method considers neither fixation in the production stage nor releases of biogenic carbon at the end of a wood product’s life. In contrast, the –1/+1 method accounts for the fixation of biogenic carbon in the production stage and its release in the end-of-life stage, irrespective of the disposal scenario (recycling, incineration orlandfill). The -1/+1 method assumes that landfills offer only a temporary sequestration of carbon. In the –1/+1* variation, landfills and recycling are considered a partly permanent sequestration of biogenic carbon and thus fewer emissions are accounted for in the end-of-life stage. We examine the variability of the calculated life cycle-based greenhouse gas emissions calculated for a case study building by each participating country, within the same assessment method and across the methods. The results vary substantially. The main reasons for deviations are whether or not landfills and recycling are considered a partly permanent sequestration of biogenic carbon and a mismatch in the biogenic carbon balance. Our findings support the need for further research and to develop practical guidelines to harmonize life cycle assessment methods of buildings with bio-based materials.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".