Are water-related nature-based solutions (NbS) assessed for their full multi-benefit potential? A review from an urban perspective
Bibliographic record
Abstract
Abstract Nature-based solutions (NbS) leverage natural processes to address societal and environmental challenges. In recent years, they have gained significant global attention as integrated strategies that enhance resilience and sustainability amid global change. NbS are particularly relevant in complex and rapidly evolving urban environments, where water management is critical for mitigating hazards and preserving resources. However, assessing NbS performance remains challenging due to their multidisciplinary nature and local socio-geographical dependencies. This study presents a systematic literature review to evaluate whether current water-related NbS performance assessments adequately capture their full range of benefits within urban environments. Based on an analysis of 111 peer-reviewed scientific studies, this review examines: (1) the backgrounds of experts reporting on NbS and the types of NbS assessed, (2) existing evaluation methods, (3) the extent to which global interconnected challenges, such as climate change and water resiliency, are addressed, and (4) the involvement of stakeholders and citizens in NbS methodologies. Key findings indicate that assessment parameters collectively address multiple benefits but remain fragmented and narrowly focused, highlighting the lack of genuinely integrated multi-benefit assessments. This was related to the fact that most researchers interested in NbS performance assessment were concentrated in a few research areas and relied on a limited number of parameters, while stakeholder involvement remained very limited. Frameworks addressing global interconnected challenges (i.e., the SDGs and Resource Nexus) also proved difficult to apply as evaluation tools. It was identified that incorporating citizen science can fill empirical data gaps and strengthen post-implementation assessments while enhanced transdisciplinary collaboration across scientific, policy, and community domains is crucial for developing comprehensive assessment frameworks. As the first comprehensive synthesis dedicated to performance assessment methods for water-related NbS in urban settings, this review establishes a benchmark for the field. Researchers, policymakers, and practitioners are encouraged to collaborate in advancing NbS and translating these insights into action.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".