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Record W3213452879 · doi:10.1002/wwp2.12063

Urban water security under a changing climate: Is Nepal's water policy on the right track?

2021· article· en· W3213452879 on OpenAlexfundno aff
Hemant Ojha, Kamal Devkota, Chandra Lal Pandey, Krishna K. Shrestha, Dil Khatri, Kaustuv Raj Neupane, Basundhara Bhattarai, Anthony B. Zwi

Bibliographic record

VenueWorld Water Policy · 2021
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
FundersVetenskapsrådetInternational Development Research Centre
KeywordsWater securityClimate changeEnvironmental planningIncentiveWater supplyAdaptation (eye)Water resourcesWater conservationBusinessEnvironmental resource managementNatural resource economicsPolitical scienceGeographyEconomicsEnvironmental scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.008
GPT teacher head0.207
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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