Taking stock of caps on water use: fostering sustainability or falling short?
Bibliographic record
Abstract
ABSTRACT Balancing water demands with available supplies on a scale that enables sustainable water use constitutes a complex governance challenge. Water managers face the daunting task of balancing water demands amidst variable supplies impacted by climate change and unsustainable extraction leading to drying lakes, depleted rivers, and shrinking aquifers. Traditionally, water managers have focused on bolstering water supplies to meet rising demands. However, this strategy is failing in many regions because of exorbitant costs, alongside environmental, legal, and political obstacles. Despite growing focus, no single solution ensures sustainable water use. Among governance options, capping water extractions holds theoretical promise, but evidence of their efficacy is limited and inconsistent due to shortcomings in their implementation, as detailed in this study. Our paper contributes to the literature on water governance by organizing empirical evidence of different types of caps. We developed a database to analyze 47 cases spanning 14 countries utilizing various types of caps (e.g., volumetric, water level limit, and moratorium) applied to different sources of water (e.g., aquifers, lakes, and rivers). We assess their efficacy in terms of enforceability, adaptability, and performance. We illustrate the strengths and weaknesses of capping as a water management strategy and offer recommendations to enhance its effectiveness.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".