Global drivers of timber carbon stock from income-based perspective
Bibliographic record
Abstract
Introduction: Timber and its products are key carriers of carbon stocks and can cause a hysteresis effect of carbon release in the carbon cycle of forest ecosystems. The literature regarding the cross-regional flow of timber carbon stock mainly pays attention to production- and consumption-based perspectives, which cannot reflect how the primary inputs drive timber carbon stock flow. The income-based perspective accounting can identify the influence of primary input suppliers and supplement research on timber carbon stock embodied in trade. The goal of this paper is to explore the cross-regional flow of global timber carbon stock and identify the critical countries from an income-based perspective. Methods: We used the Ghosh-multi-regional input-output (MRIO) model to calculate the income-based timber carbon stock flow among 190 countries. Furthermore, combined with the Leontief-MRIO model, a comparative analysis is carried out to analyze the different results of the income-based, production-based, and consumption-based methods. Results: The results showed that the income-based timber carbon stock of the United States and China were among the top countries in imports and exports simultaneously. However, their export volumes were significantly larger, meaning that these countries have invested more primary resources in timber products. The timber carbon stock of the United States mainly flows into Canada and Brazil. In China, the largest flow went to Canada. Furthermore, the flow to the United States increased significantly. Moreover, comparing the three perspectives shows that the United States’ primary inputs have a greater impact on the global timber production chain than their production- and consumption-based roles. Brazil and Russia, as main primary resource suppliers of timber carbon stock, are more important than as final consumers. Discussion: The research can contribute to clarify the flow of forest resources embodied in global trade activities. Furthermore, it also provides a scientific basis to fairly account for carbon offset shares to achieve better the goal of forest resource protection agreed upon in COP26.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".