Can reforestation help stabilize the climate in net-zero pathways? 
Bibliographic record
Abstract
Reforestation is a nature-based climate solution (NbCS) that can serve to sequester and store large quantities of atmospheric carbon dioxide. It requires no new technological advancements for deployment, is relatively cost-effective, and it would lead to important co-benefits for ecosystems and ecosystem services. For these reasons reforestation is a key measure in deep-mitigation and net-zero pathways. However, reforestation at scale alters land-surface biophysical properties (albedo, evapotranspiration and latent heat release, and sensible heat flux) that can induce either a warming or cooling effect on surface temperature. The magnitude and sign of this temperature response depend on the background climate state and latitude over which reforestation is implemented. Therefore, depending on the scale and region of reforestation, these biophysical effects could lead to additional warming in emission pathways that use reforestation to compensate for residual CO2 emissions. Our research investigates the effectiveness of reforestation at stabilizing global mean temperature when used to compensate for residual CO2 emissions. Using a climate model of intermediate complexity (the UVic-ESCM v2.10) we conduct a set of idealized simulations where fossil fuel emissions decline towards zero by 2050 but remain at 1 and 5, Gt CO2/yr between 2050 to 2100 to represent emissions that are difficult to eliminate. Meanwhile reforestation is implemented globally and in different latitudinal zones (tropics, mid-latitudes, and high-latitudes) at an areal coverage appropriate to sequester the ongoing emissions so that cumulative CO2 emissions between 2050 and 2100 are net-zero. From these simulations we quantify the effectiveness of reforestation at stabilizing global mean temperature under consideration of biogeochemical and biophysical effects and feedbacks. While we expect our results to show that the carbon sequestration from reforestation could be effective at stabilizing global mean temperature, the biophysical effects could also induce important variations in global mean temperature. As such, our research is intended to provide an Earth system analysis of reforestation that can inform forestation carbon markets and net-zero policy frameworks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.134 | 0.054 |
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".