Nature-based solutions as enablers of circularity in water systems: A review on assessment methodologies, tools and indicators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it