Global synthesis and regional insights for mainstreaming urban nature-based solutions
Bibliographic record
Abstract
Nature-based solutions (NbS) have emerged as a key strategy for sustainably addressing multiple urban challenges, with rapidly increasing knowledge production requiring synthesis to better understand whether and how NbS work in different social, ecological, economic, or governance contexts. Insights in this Perspective are drawn from a thematic review of 61 NbS review articles supported by an expert assessment of NbS knowledge in seven global regions to examine key challenges, fill gaps in Global South assessment, and provide insights for scaling up NbS for impact in cities. Eight NbS challenges emerged from our review of NbS reviews including conceptual, thematic, geographic, ecological, inclusivity, health, governance, and systems challenges. An additional expert assessment reviewing literature and cases in seven global regions further revealed the following: 1) Local context-based ecological knowledge is essential for NbS success; 2) Improved technical knowledge is required for planning and designing NbS; 3) NbS need to be included in all levels of planning and governance; 4) Putting justice and equity at the center of urban NbS approaches is critical, and 5) Inclusive and participatory governance processes will be key to long-term success of NbS. We synthesized findings from the NbS review results and regional expert assessments to offer four critical pathways for scaling up NbS: 1) foster new NbS research, technological innovation, and learning, 2) build a global NbS alliance for sharing knowledge, 3) ensure a systems approach to NbS planning and implementation, and 4) increase financing and political will for diverse NbS implementation.
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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 | 0.053 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".