Newly established forests dominated global carbon sequestration change induced by land cover conversions
Bibliographic record
Abstract
Land cover conversions (LCC) have substantially reshaped terrestrial carbon dynamics, yet their net impact on carbon sequestration remains uncertain. Here, we use the remote sensing-driven BEPS model and high-resolution HILDA+ data to quantify LCC-induced changes in net ecosystem productivity (NEP) from 1981 to 2019. Despite global forest loss and cropland/urban expansion, LCC led to a net carbon gain of 229 Tg C. Afforestation and reforestation increased NEP by 1559 Tg C, largely offsetting deforestation-driven losses (-1544 Tg C), with newly established forests in the Northern Hemisphere driving gains that counterbalanced emissions from tropical deforestation. Regional carbon gains were concentrated in East Asia, North America, and Europe, while losses occurred mainly in the Amazon and Southeast Asia. Although smaller in area, newly established forests exhibited higher sequestration efficiency than degraded older forests, emphasizing the role of forest age in shaping global carbon sink dynamics. These findings highlight the critical importance of afforestation, forest management, and spatially informed land-use strategies in strengthening carbon sinks and supporting global carbon neutrality goals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".