Getting the message right on nature‐based solutions to climate change
Bibliographic record
Abstract
Nature-based solutions (NbS)-solutions to societal challenges that involve working with nature-have recently gained popularity as an integrated approach that can address climate change and biodiversity loss, while supporting sustainable development. Although well-designed NbS can deliver multiple benefits for people and nature, much of the recent limelight has been on tree planting for carbon sequestration. There are serious concerns that this is distracting from the need to rapidly phase out use of fossil fuels and protect existing intact ecosystems. There are also concerns that the expansion of forestry framed as a climate change mitigation solution is coming at the cost of carbon rich and biodiverse native ecosystems and local resource rights. Here, we discuss the promise and pitfalls of the NbS framing and its current political traction, and we present recommendations on how to get the message right. We urge policymakers, practitioners and researchers to consider the synergies and trade-offs associated with NbS and to follow four guiding principles to enable NbS to provide sustainable benefits to society: (1) NbS are not a substitute for the rapid phase out of fossil fuels; (2) NbS involve a wide range of ecosystems on land and in the sea, not just forests; (3) NbS are implemented with the full engagement and consent of Indigenous Peoples and local communities in a way that respects their cultural and ecological rights; and (4) NbS should be explicitly designed to provide measurable benefits for biodiversity. Only by following these guidelines will we design robust and resilient NbS that address the urgent challenges of climate change and biodiversity loss, sustaining nature and people together, now and into the future.
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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 | 0.055 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.032 | 0.032 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".