Global potential for natural regeneration in deforested tropical regions
Bibliographic record
Abstract
Extensive forest restoration is a key strategy to meet nature-based sustainable development goals and provide multiple social and environmental benefits1. Yet achieving forest restoration at scale requires cost-effective methods2. Tree planting in degraded landscapes is a popular but costly forest restoration method that often results in less biodiverse forests when compared to natural regeneration techniques under similar conditions3. Here we assess the current spatial distribution of pantropical natural forest (from 2000 to 2016) and use this to present a model of the potential for natural regeneration across tropical forested countries and biomes at a spatial resolution of 30 m. We estimate that an area of 215 million hectares—an area greater than the entire country of Mexico—has potential for natural forest regeneration, representing an above-ground carbon sequestration potential of 23.4 Gt C (range, 21.1–25.7 Gt) over 30 years. Five countries (Brazil, Indonesia, China, Mexico and Colombia) account for 52% of this estimated potential, showcasing the need for targeting restoration initiatives that leverage natural regeneration potential. Our results facilitate broader equitable decision-making processes that capitalize on the widespread opportunity for natural regeneration to help achieve national and global environmental agendas. An estimated area of 215 million hectares has the potential for natural forest regeneration across tropical forested countries and biomes, representing an above-ground carbon sequestration potential of 23.4 Gt C.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".