The Role of Nature-Based Solutions in Supporting Social-Ecological Resilience for Climate Change Adaptation
Bibliographic record
Abstract
Social-ecological systems underpinning nature-based solutions (NbS) must be resilient to changing conditions if NbS are to contribute to long-term climate change adaptation. We develop a two-part conceptual framework linking social-ecological resilience to adaptation outcomes in NbS. Part one determines the potential of NbS to support resilience based on assessing whether NbS affect key mechanisms known to enable resilience. Examples include social-ecological diversity, connectivity, and inclusive decision-making. Part two includes adaptation outcomes that building social-ecological resilience can sustain, known as nature's contributions toadaptation (NCAs). We apply the framework to a global dataset of NbS in forests. We find evidence that NbS may be supporting resilience by influencing many enabling mechanisms. NbS also deliver many NCAs such as flood and drought mitigation. However, there is less evidence for some mechanisms and NCAs critical for resilience to long-term uncertainty. We present future research questions to better understand how NbS can continue to support social-ecological systems in a changing world.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".