Natural forests exhibit higher carbon sequestration and lower water consumption than planted forests in China
Bibliographic record
Abstract
Abstract Large‐scale planted forests (PF) have been given a higher priority in China for improving the environment and mitigating climate change relative to natural forests (NF). However, the ecological consequences of these PF on water resource security have been less considered in the national scale. Moreover, a critically needed comparison on key ecological effects between PF and NF under climate change has rarely been conducted. Here, we compare carbon sequestration and water consumption in PF and NF across China using combination of remote sensing and field inventory. We found that, on average, NF consumed 6.8% (37.5 mm per growing season) less water but sequestered 1.1% (12.5 g C m −2 growing season −1 ) more carbon than PF in the period of 2000–2012. While there was no significant difference in water consumption ( p = 0.6) between PF and NF in energy‐limited areas (dryness index [DI] < 1), water consumption was significantly ( p < 0.001) higher in PF than that in NF in water‐limited regions (DI > 1). Moreover, a distinct and larger shift of water yield was identified in PF than in NF from the 1980s to the 2000s, indicating that PF were more sensitive to climate change, leading to a higher water consumption when compared with NF. Our results suggest NF should be properly valued in terms of maximizing the benefits of carbon sequestration and water yield. Future forest plantation projects should be planned with caution, particularly in water‐limited regions where they might have less positive effect on carbon sequestration but lead to significant water yield reduction.
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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".