Building urban resilience with nature-based solutions: How can urban planning contribute?
Bibliographic record
Abstract
Cities face increasing environmental, social and economic challenges that together threaten the resilience of urban areas and the residents who live and work there. These challenges include chronic stresses and acute shocks, amplified by climate change impacts. Nature-based solutions have emerged as a concept for integrating ecosystem-based approaches to address a range of societal challenges. Nature-based solutions directly address and contribute to increased urban resilience. However, implementing nature-based solutions is inherently complex, given the range of ecosystem services, their multi-functionality and the trade-offs between functions, and across temporal and spatial scales. Urban planning can play a substantial role to support the implementation of nature-based solutions and to manage trade-offs and conflicts, as well as how social equity dimensions are considered. This paper presents a framework that guides the application of urban planning to nature-based solutions’ implementation, by addressing key trade-offs across temporal, spatial, functional and social equity aspects. The framework highlights the key questions, and the supporting information required to address these questions, to underpin the inclusion of nature-based solutions for urban resilience. We find that while urban planning can contribute substantially, there are continuing gaps in how the inherently anthropocentric urban planning processes can give voice to non-human nature.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.004 |
| 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".