Assessing Carbon Sequestration Potential in State-Owned Plantation Forests in China and Exploring Feasibility for Carbon Offset Projects
Bibliographic record
Abstract
In the pursuit of carbon neutrality, state-owned forests are prime candidates for carbon offset projects due to their unique tenure and management characteristics. Employing methodologies endorsed by the International Panel on Climate Change and logistic growth curves, this study assesses the carbon stocks and sequestration potential of established state-owned plantation forests across 31 Chinese provinces from 2023 to 2060, encompassing seven forestry industry groups. This study projects that by 2060, these forests will amass a carbon stock of 558.25 MtC, with the highest stock in Northeast China (122.09 MtC) and the lowest in Northwest China (32.27 MtC), notably showing the highest growth rate at 91.15%. Over the forecast period, they are expected to accumulate a carbon sink of 637.07 MtCO2e, translating to an average annual carbon sink of 17.22 MtCO2e and an average annual carbon sink per unit of 1.41 tons of CO2 per hectare per year. Additionally, state-owned forests have the potential to offset approximately 0.15%–0.17% of annual carbon emissions, aligning with international climate goals. However, it is essential to note that the conversion of these carbon sinks into tradable carbon credits is subject to specific methodology requirements. Therefore, the future development of carbon offset projects in China’s state-owned forests should consider the advancement of carbon market mechanisms, including the Chinese Certified Emission Reduction and the introduction of a carbon inclusion mechanism and natural forest methodology, to fully realize their potential contributions to carbon neutrality. In summary, these findings offer valuable insights for shaping the future of carbon offset initiatives within China’s state-owned forests.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".