The full climate impacts of nature-based climate solutions must be considered to achieve climate goals
Bibliographic record
Abstract
Nature-based climate solutions (NBCSs) refer to actions that seek to protect, restore and better manage natural landscapes to reduce greenhouse gas (GHG) emission or remove CO2 from the atmosphere. NBCSs are integral part of many countries’ roadmaps to reach net zero GHG emissions by mid century in compliance with the Paris Agreement climate goals. Implementation of NBCSs not only affects cycling of CO2 and other GHGs in the Earth system, but impacts the energy balance at the Earth’s surface through biophysical effects including changes in reflectivity (albedo), surface roughness and the water cycle, with effects on surface temperature. Furthermore, storage of the sequestered CO2 in above-ground biomass is often vulnerable to natural and anthropogenic disturbances, with the risk of re-release after a few decades. Yet, frameworks that seek to balance residual GHG emissions with nature-based CO2 removal often only consider the CO2 sequestration potential, and do not take the full climate impacts of NBCSs and the vulnerability of carbon stocks into account. By implementing large-scale reforestation as an example of a NBCS in an Earth system model we show that offsetting fossil-fuel CO2 emissions with nature-based CO2 removals to achieve net zero CO2 emissions could result in additional warming compared to the case where the CO2 emissions are avoided, if biophysical effects are not considered and nature-based CO2 storage is temporary. We provide recommendations for taking into account the full climate impacts of NBCSs in net zero accounting frameworks and lay out directions for future research.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".