Carbon Balance of China-Made Wood Products Assessed Using a Trade-Linked Approach
Bibliographic record
Abstract
Harvested wood products (HWPs) contribute to forestry carbon removal and, therefore, to climate change mitigation. However, the complex international trade of raw wood materials and HWPs and the associated biogenic carbon conversion along the global supply chain are not properly accounted for in the existing HWP carbon accounting schemes, precluding effective climate policy design. To address the gap, we developed a generalizable Trade-Linked Approach to account for trade and the associated carbon stocks and emissions. In the case study for China-made HWPs in 1990–2020, trade partners supplied 22% (1425 MtCO 2 e, or 389 MtC) of raw materials and consumed 13% (691 MtCO 2 e, or 189 MtC) of the HWPs, with the latter double that of the FAOSTAT statistics that were used in the existing studies. In the same period, the trade partners contributed 13% (570 MtCO 2 e) of the total HWP carbon stocks. The HWPs consumed overseas provided a 43 MtCO 2 e yr –1 carbon sink (15% of the total) from 2016 to 2020, which is close to the “forest and land use carbon credits” transacted in global voluntary carbon markets in 2020. In addition to adequately allocating carbon removals to each country, we appeal for a global HWP-associated carbon payment scheme to incentivize HWP-based mitigation activities along the supply chain.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| 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 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".