Nature-based solutions as enablers of circularity in water systems: A review on assessment methodologies, tools and indicators
Bibliographic record
Abstract
Water has been pushed into a linear model, which is increasingly acknowledged of causing cumulative emissions of pollutants, waste stocks, and impacting on the irreversible deterioration of water and other resources. Moving towards a circular model in the water sector, the configuration of future water infrastructure changes through the integration of grey and green infrastructure, forming Nature-based Solutions (NBS) as an integral component that connects human-managed to nature-managed water systems. In this study, a thorough appraisal of the latest literature is conducted, providing an overview of the existing tools, methodologies and indicators that have been used to assess NBS for water management, as well as complete water systems considering the need of assessing both anthropogenic and natural elements. Furthermore, facilitators and barriers with respect to existing policies and regulations on NBS and circularity have been identified. The study concludes that the co-benefits of NBS for water management are not adequately assessed. A holistic methodology assessing complete water systems from a circularity perspective is still needed integrating existing tools (i.e. hydro-biogeochemical models), methods (i.e. MFA-based and LCA) and incorporating existing and/or newly-developed indicators.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".