Methodological Comparison of the Production Approach 2013 and 2019 for Quantifying the Carbon Stock in Harvested Wood Products in China
Bibliographic record
Abstract
Harvested wood products (HWP) play an important role in global climate change mitigation. The Production Approach, an internationally accepted HWP carbon accounting approach by the Intergovernmental Panel on Climate Change (IPCC), has been revised several times to enhance the accuracy of the accounting results. This study compared the methodological differences in the Production Approach elaborated in the 2013 Revised Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol (PA2013) and 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (PA2019). We used PA2019 to calculate the HWP carbon stock in China under different wood utilization scenarios. In terms of recycling and reusing of recovered paper, we found that PA2019 has a more accurate representation of the increasing proportion of paper and paperboard materials produced using recovered paper in the world. In 2019, the HWP carbon stock in China was 815.94 Mt (1 Mt = 106 t) using PA2019. In detail, the carbon stock of sawnwood, wood-based panels, and paper and paperboard were 208.52 Mt, 559.73 Mt, and 47.69 Mt, respectively. Scenario simulation results show that half-life was an important factor that affecting HWP carbon stock. China can improve HWP carbon stock by increasing the recovered paper utilization rate for paper products or increasing the utilization rate of HWP with long half-life in construction (structural material). The additional carbon stock obtained by changing the utilization structure of wood and improve the half-life of HWP will not be large until decades later but nonetheless important.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".