Geography of nature-based solutions in the global public water sector
Bibliographic record
Abstract
Water is a basic human right essential for sanitation, health, food security, cultural practices, and livelihoods. Yet as of 2022, a quarter of the world’s population still lacked access to clean drinking water. While proposing solutions to water insecurity is as complex as water systems themselves, nature-based solutions (NbS) are one potential means of increasing water access in equitable ways. NbS are defined as systems that are designed to conserve or rehabilitate ecosystems in order to improve natural processes responsible for ecosystem services, such as the filtration and distribution of water. An important hub for improving the health of water resources and ensuring equitable water access is the public water sector. When implemented in public water municipalities, NbS can reduce water insecurity by improving the quality and quantity of water resources and managing water-related risks. To date, there are not yet studies that systematically explore how the global municipal water sector is incorporating NbS into new designs. Using semi-structured interviews and online surveys to collect data, this study sought to fill this research gap by investigating the usage of nature-based water management techniques in water municipalities spanning four continents. These municipalities were identified using Artificial Intelligence (AI) platforms. Synthesizing the data allowed for conclusions to be drawn related to how and why nature-based solutions were implemented, how effective they were at meeting their intended aims, and what improvements could be made for future applications of NbS. More specifically, results indicated that nature-based solutions are implemented in context-specific ways, largely due to geographic and economic considerations. Additionally, various water issues led to the application of NbS, such as climatic changes, drought, and water pollution. Overall, NbS were employed to build resilience and protect water resources in response to pressing water issues. This research found that NbS can be improved by gaining more public support, along with expanding and optimizing systems to accommodate for pressing water-related challenges.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".