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Record W1999090689 · doi:10.1111/1467-8489.12030

Nondiscretionary residential water use: the impact of habits and water‐efficient technologies

2013· article· en· W1999090689 on OpenAlexaff
María Á. García-Valiñas, Wasantha Athukorala, Clevo Wilson, Benno Torgler, Robert Gifford

Bibliographic record

VenueAustralian Journal of Agricultural and Resource Economics · 2013
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsWater consumptionStock (firearms)Water useConsumer behaviourSubsistence agricultureConsumption (sociology)Price elasticity of demandEconomicsCapital (architecture)Environmental economicsWater resource managementEnvironmental scienceBusinessNatural resource economicsMicroeconomicsMarketingGeographyEcology

Abstract

fetched live from OpenAlex

Several studies published in the last few decades have demonstrated a low price‐elasticity for residential water use. In particular, it has been shown that there is a quantity of water demanded that remains constant regardless of prices and other economic factors. In this research, we characterise residential water demand based on a Stone‐Geary utility function. This specification is not only theory‐compatible but can also explicitly model a minimum level of consumption not dependent on prices or income. This is described as minimum threshold or nondiscretionary water use. Additionally, the Stone‐Geary framework is used to model the subsistence level of water consumption that is dependent on the temporal evolution of consumer habits and stock of physical capital. The main aim of this study is to analyse the impact of water‐saving habits and water‐efficient technologies on residential water demand, while additionally focusing attention on nondiscretionary uses. This is informed by an empirical application using data from a survey conducted among residents of Brisbane City Council, Australia. The results will be especially useful in the design of water tariffs and other water‐saving policies.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.963
Threshold uncertainty score0.184

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 head0.007
GPT teacher head0.173
Teacher spread0.166 · 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

Citations16
Published2013
Admission routes1
Has abstractyes

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