Are water-related Nature-based Solutions (NbS) assessed to their full multi-benefit potential? A systematic literature review.
Bibliographic record
Abstract
Nature-based Solutions (NbS) leverage and mimic natural processes to address societal and environmental challenges. In recent years, they have attracted global interest for their significant contributions to the Sustainable Development Goals, offering integrated approaches to address multiple dimensions of resilience and sustainability in the context of global change. This potential is particularly promising in complex and rapidly evolving urban environments, where water resources represent both managing hazards and protecting resources. However, assessing and quantifying the full potential and impacts of NbS remains challenging, as their impacts span multiple disciplines and depend on local socio-geographical contexts and initial implementation goals. Holistic assessment frameworks are urgently required[ES1] to demonstrate performance, capture the diverse effects of NbS along the process-impact chain, and enable stakeholders to monitor progress over time. This study presents a systematic literature review to map the current state of the art in NbS performance evaluation. 111 articles were reviewed to assess whether NbS evaluation methods associated with urban water resources provide holistic and transferable approaches while addressing the complexity of human-natural systems. Preliminary results indicate that most studies focused on existing sites where NbS were considered for implementation, often using modeling approaches. Performance evaluations spanned 16 parameter categories, with the majority addressing quantitative and qualitative hydrological aspects, consistent with the authors’ disciplinary backgrounds. Although many methods demonstrated reusability and supported decision-making processes, most studies assessed limited parameters, partly due to modeling assumptions. Notably, social aspects were frequently acknowledged, particularly regarding the involvement of local governments during the implementation phase. The results of this literature review can support scientists in developing robust assessment frameworks and provide stakeholders with a comprehensive overview of the current state of the art in NbS multi-benefit characterization. This, in turn, will provide stakeholders with greater confidence to invest in NbS, upscale their use, and influence NbS policies.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".