Assessing the Climate Change Impacts of Biogenic Carbon in Buildings: A Critical Review of Two Main Dynamic Approaches
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
Wood is increasingly perceived as a renewable, sustainable building material. The carbon it contains, biogenic carbon, comes from biological processes; it is characterized by a rapid turnover in the global carbon cycle. Increasing the use of harvested wood products (HWP) from sustainable forest management could provide highly needed mitigation efforts and carbon removals. However, the combined climate change benefits of sequestering biogenic carbon, storing it in harvested wood products and substituting more emission-intensive materials are hard to quantify. Although different methodological choices and assumptions can lead to opposite conclusions, there is no consensus on the assessment of biogenic carbon in life cycle assessment (LCA). Since LCA is increasingly relied upon for decision and policy making, incorrect biogenic carbon assessment could lead to inefficient or counterproductive strategies, as well as missed opportunities. This article presents a critical review of biogenic carbon impact assessment methods, it compares two main approaches to include time considerations in LCA, and suggests one that seems better suited to assess the impacts of biogenic carbon in buildings.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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