Urban water security under a changing climate: Is Nepal's water policy on the right track?
Bibliographic record
Abstract
Abstract Nepal's urban regions are facing increasing levels of water insecurity under a changing climate. The country has a long history of water policy development while climate‐related policies are also emerging at different levels of the new federal republic. It is unclear whether public policy is on the right track to ensure urban water security in the face of growing demand and increasingly variable supply. In this paper, we assess how Nepal's water policy is shaping urban adaptation. We focus on how policies have defined institutional arrangements for water supply and management, allocation of water rights, and the conservation of water source catchments. Policy attention to these issues is crucial for the adaptation of water systems to climate change. Drawing on the review of policy texts and the status of policy implementation in two towns (Dharan and Dhulikhel), we conclude that current policy frameworks and strategies neglect opportunities to facilitate urban water adaptation to climate change. The neglect is seen in escalating conflicts among institutions, an unclear framework for water rights, and inadequate incentives for catchment conservation. We highlight the need for a more coherent and risk sensitive approach to water policy, underpinned by deliberative, research‐informed, and learning‐based strategies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".