Land use cover changes abated terrestrial ecosystem carbon sink in China during the past four decades
Bibliographic record
Abstract
Changes in land use and cover can strongly affect terrestrial carbon balance, which in turn can affect the calculation of carbon sinks that will keep future temperature within desired limits. Understanding how changes in land use and cover influence carbon sinks is challenging. Here, we simulated net carbon balance across China with full consideration of land use and land cover between 1981 and 2020 using the dynamic global vegetation model. The results indicated that carbon sink of terrestrial ecosystem in China have grown steadily particularly since 2001, the average values of the net primary productivity, net ecosystem productivity and net biome productivity were 3317 TgC • yr−1, 325 TgC • yr−1 and 70 TgC • yr−1. However, during the period, changes in land use and cover cumulatively reduced net primary productivity by 1,353.00 TgC, net ecosystem productivity by 1,290.71 TgC and net biome productivity by 226.93 TgC. Land use and cover changes have created a carbon source effect which abated terrestrial ecosystem carbon sink in China since 1981. Our findings may help guide policies to regulate land use in order to help China achieve carbon neutrality in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".