Assessing Assisted Natural Regeneration as a Cost‐Efficient Mitigation for Climate Change and Biodiversity Loss in China
Bibliographic record
Abstract
Abstract Global deforestation results in climate change and biodiversity loss. Assisted natural regeneration (ANR) emerges as a promising approach to achieving global forest restoration targets, yet its potential and benefits for climate and biodiversity in China remain underexplored. Here, we assessed ANR potential across China and modeled spatial prioritization strategies targeting climate mitigation, biodiversity conservation, and cost savings, individually and in combination, as well as strategies considering spatial constraints from current forest restoration projects and fragmentation mitigation. From 1995 to 2015, 3.40 million hectares of land naturally regenerated into forests, with an additional 5.11 million hectares identified as potential regeneration areas, which could contribute to 12.41% of China's restoration goal in 2035. Spatial prioritization revealed limited overlap among the three single‐objective ANR strategies, while a multi‐objective optimization strategy emerged as the most effective solution to achieve synergies among goals. The top 30% of prioritized areas under the multi‐objective strategy could sequester 46.54 gigatons of CO 2 , reduce extinction risks of forest‐dependent species by 40.64%, and lower implementation costs by 31.55% compared to monoculture tree plantations. Our findings highlight that strategic spatial prioritization of ANR could mitigate climate change and biodiversity loss in a cost‐efficient manner and have the potential to reinforce current forest restoration projects.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".