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Record W4403469960 · doi:10.1002/wwp2.12229

Water tariffs and social equity: Towards water service connections and pricing instruments for the poor

2024· article· en· W4403469960 on OpenAlexaff
Philamer C. Torio, Leila M. Harris

Bibliographic record

VenueWorld Water Policy · 2024
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEquity (law)BusinessSocial equalityWater pricingWater industryEnvironmental economicsEconomicsEnvironmental scienceWater resourcesWater supplyWater conservationMarket economyEcologyPolitical scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract Aside from operational sustainability and efficiency goals, water tariffs also need to address the requirements of affordability and equity, in most instances, by providing subsidies to poor households. Yet, poor households are unable to enjoy the benefits of such cross‐subsidization and linked conveniences as they are unable to get water service connections. Even with a well‐designed water tariff, the inability of poor households to get piped connections results in the payment of premiums for water that is difficult to access and, at times, of questionable quality. In this paper, we assess the pro‐poor projects of three Philippine municipal water systems and suggest that providing water service connections to poor households allows these households to avoid paying the water poverty premium, as well as experience the related public health and quality of life benefits accruing from such connections. We argue that designing the best tariff for existing socio‐economic, political, and environmental realities must go hand in hand with programs that provide water service connections to poor households as this will ensure not only operational viability but also equitable water provision.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.410

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 head0.020
GPT teacher head0.253
Teacher spread0.233 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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