Forest carbon storage and sink estimates under different management scenarios in China from 2020 to 2100
Bibliographic record
Abstract
Forests play a crucial role in mitigating climate change through carbon storage and sequestration, though environmental change drivers and management scenarios are likely to influence these contributions across multiple spatial and temporal scales. In this study, we employed three tree growth models-the Richard, Hossfeld, and Korf models-that account for the biological characteristics of trees, alongside national forest inventory (NFI) datasets from 1994 to 2018, to evaluate the carbon sink potential of existing forests and afforested regions in China from 2020 to 2100, assuming multiple afforestation and forest management scenarios. Our results indicate that the Richard, Hossfeld, and Korf models provided a good fit for 26 types of vegetation biomass in both natural and planted Chinese forests. These models estimate that in 2020, carbon stocks in existing Chinese forests are 7.62 ± 0.05 Pg C, equivalent to an average of 44.32 ± 0.32 Mg C/ ha. Our predictions then indicate this total forest carbon stock is expected to increase to 15.51 ± 0.99 Pg C (or 72.26 ± 4.6 Mg C/ha) in 2060, and further to 19.59 ± 1.36 Pg C (or 91.31 ± 6.33 Mg C/ha) in 2100. We also show that plantation management measures, namely tree species replacement, would increase carbon sinks to 0.09 Pg C/ year (contributing 38.9 %) in 2030 and 0.06 Pg C/ year (contributing 32.4 %) in 2060. Afforestation using tree species with strong carbon sink capacity in existing plantations would further significantly increase carbon sinks from 0.02 Pg C/year (contributing 10.3 %) in 2030 to 0.06 Pg C/year (contributing 28.2 %) in 2060. Our results quantify the role plantation management plays in providing a strong increase in forest carbon sequestration at national scales, pointing to afforestation with native tree species with high carbon sequestration as key in achieving China's 2060 carbon neutrality target.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".