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Record W4224212928 · doi:10.3390/w14091328

Development of a Water-Pricing Model for Domestic Water Uses in Dhaka City Using an IWRM Framework

2022· article· en· W4224212928 on OpenAlexfundno aff
Sumaia Kashem, M. Shahjahan Mondal

Bibliographic record

VenueWater · 2022
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsSewerageWater pricingExternalityGroundwaterWater resource managementRevenueWater resourcesTariffIntegrated water resources managementResource (disambiguation)Natural resource economicsEnvironmental scienceSocial equalityEquity (law)BusinessEnvironmental economicsEnvironmental engineeringEconomicsWater conservationEngineeringFinanceComputer science

Abstract

fetched live from OpenAlex

Dhaka city is experiencing high water use and rapid declination of groundwater. The current water price in the city is low and based on a uniform rate. To arrest the resource degradation along with pursuing cost recovery and promoting social equity, this paper develops a new pricing model for domestic water uses using the integrated water resources management principles. The development is accomplished through estimation of domestic water usage, evaluation of current water prices, and assessment of groundwater degradation externalities in the Tejgaon area of the city using both primary and secondary data. Two economic and two environmental externalities are incorporated. The model is based on an increasing block tariff strategy, and the estimated unit prices for the first and second blocks are respectively 5% and 75% higher than the existing price. The model has the potential to reduce the domestic water use in the city by up to 27%, increase the revenue for the Dhaka Water Supply and Sewerage Authority by up to 75%, and reduce the water bill for poor households by up to 67%. The model has a great potential for practical deployment and the concept can also be applied to other cities and water uses.

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 score0.169
Threshold uncertainty score0.372

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 head0.041
GPT teacher head0.245
Teacher spread0.204 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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