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Record W2737296756 · doi:10.1177/2399654417719558

A proposal for the analysis of price escalation within water tariffs: The impact of the Water Framework Directive in Spain

2017· article· en· W2737296756 on OpenAlexaff
Marta Suárez-Varela, Roberto Martı́nez-Espiñeira

Bibliographic record

VenueEnvironment and Planning C Politics and Space · 2017
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsWater Framework DirectiveUnit priceOrder (exchange)EconomicsSample (material)Water resourcesMeasure (data warehouse)BusinessUnit (ring theory)SustainabilityWater pricingEnvironmental economicsMicroeconomicsWater conservationWater qualityComputer scienceFinance

Abstract

fetched live from OpenAlex

During the last few decades, numerous international organizations have emphasized the role of pricing policy as a tool to achieve objectives of efficiency, environmental sustainability, and cost-recovery in the management of water resources. Incorporating a certain level of price escalation within water tariffs by adopting increasing block rates is commonly advocated as a key element for controlling water demand and fulfilling these objectives. However, despite its widespread use, there exists no established procedure to measure the levels of price escalation embodied in water tariffs. We propose a measure of price escalation within water tariffs at the level of the water supply management unit (the municipality, in our study). In order to illustrate the usefulness of our measure, we analyse the evolution of price escalation in residential water tariffs between 2000 and 2014 in a sample of 952 Spanish municipalities and examine the factors influencing this evolution.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.008
Science and technology studies0.0000.001
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations11
Published2017
Admission routes1
Has abstractyes

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