Nature-Based Solutions for Urban Climate Change Adaptation: Linking Science, Policy, and Practice Communities for Evidence-Based Decision-Making
Bibliographic record
Abstract
Nature-based solutions offer an exciting prospect for resilience building and advancing urban planning to address complex urban challenges simultaneously. In this article, we formulated through a coproduction process in workshops held during the first IPCC Cities and Climate Science Conference in Edmonton, Canada, in March 2018, a series of synthesis statements on the role, potential, and research gaps of nature-based solutions for climate adaptation and mitigation. We address interlocking questions about the evidence and knowledge needed for integrating nature-based solutions into urban agendas. We elaborate on the ways to advance the planning and knowledge agenda for nature-based solutions by focusing on knowledge coproduction, indicators and big data, and novel financing models. With this article, we intend to open a wider discussion on how cities can effectively mainstream nature-based solutions to mitigate and adapt to the negative effects of climate change and the future role of urban science in coproducing nature-based solutions.
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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 | 0.276 | 0.240 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.032 | 0.038 |
| Open science | 0.007 | 0.043 |
| Research integrity | 0.015 | 0.024 |
| 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".