MétaCan
Menu
Back to cohort
Record W2944628801 · doi:10.1016/j.heliyon.2019.e01668

Economics of climate adaptive water management practices in Nepal

2019· article· en· W2944628801 on OpenAlexfundno aff
Rajesh Kumar, Kaustuv Raj Neupane, Roshan Man Bajracharya, Ngamindra Dahal, Suchita Shrestha, Kamal Devkota

Bibliographic record

VenueHeliyon · 2019
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsRainwater harvestingGroundwater rechargeWater scarcityDry seasonPurchasingBusinessAdaptive strategiesMetropolitan areaSocioeconomicsEnvironmental scienceGeographyEconomicsEngineeringAgricultureMarketingEcologyForestry

Abstract

fetched live from OpenAlex

This study analyses costs and benefits of the selected climate adaptive and equitable water management practices and strategies (CAEWMPS) in Dhulikhel Municipality and Dharan Sub-metropolitan city of Nepal. The CAEWMPS adopted the construction of water recharge pit at household level in Dharan and recharge ponds at community level in Dhulikhel. The results of household survey reveal that households have employed different coping strategies including minimizing consumption, purchasing from market, harvesting rain water and installing equipment for storing and pumping in both cities. In Dhulikhel, a significant number of households (18.56%) minimize consumption during the dry season but this is not the case in Dharan. Rather, around one-fifth (19.27%) of the households harvest rainwater in Dharan. In addition, households are forced to give-up their regular activities in order to implement coping strategies such as household chores, leisure time, meeting and gardening. The average estimated annual coping cost in Dharan (USD 87.5) is eight times higher than in Dhulikhel (USD 11.05); however, per unit coping cost is nearly equal in both the cities. In terms of benefit-cost ration, the community level recharge ponds in Dhulikhel (5.15) were found to be cost effective compared to the household level recharge pits of Dharan (1.72). These results provide policy makers with a comparative basis for adopting appropriate strategies to tackle problems related to water shortage under city-specific contexts.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.011
GPT teacher head0.197
Teacher spread0.187 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations22
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueHeliyonSame topicWater resources management and optimizationFrench-language works237,207