Improving Carbon Stock Estimates for In-Use Harvested Wood Products by Linking Production and Consumption—A Global Case Study
Bibliographic record
Abstract
We developed a method to better estimate the carbon stocks of in-use harvested wood products (HWP) by using the Eora multiregional input-output tables to link global HWP production and end uses, compared to existing global-scale studies that focused on semifinished HWP. Using the new method, we allocated global HWP to country-specific end uses, including solid HWP used in (1) construction, (2) furniture production, and (3) other end uses, and as (4) household and sanitary paper and (5) other paper and paper products, while the HWP carbon stocks in these end uses were estimated using the Stock Change Approach. We reported that HWP produced globally contained an annual average of 277.7 teragram carbon in 1992-2015, of which 63.0, 12.6, 76.7, 9.1, and 116.3 teragram carbon were consumed by the above five end uses, respectively. By 2015, the carbon stocks of global in-use HWP produced since 1992 accumulated to 2938 teragrams of carbon, of which the above five HWP end uses accounted for 1489, 268, 890, 0, and 291 teragrams of carbon, respectively. Country-specific HWP production and consumption varied significantly, with the eight leading consuming countries (United States, China, Japan, Canada, Germany, Russia, United Kingdom, and France) accounting for 69% of the global in-use HWP carbon stocks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".