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Record W1898380934 · doi:10.1142/s2382624x15500071

More Water Please, It's Getting Hot! The Effect of Climate on Residential Water Demand

2015· article· en· W1898380934 on OpenAlexaff
Oscar Zapata

Bibliographic record

VenueWater Economics and Policy · 2015
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEndogeneityClimate changeConsumption (sociology)Environmental sciencePrecipitationNatural resource economicsWater useWater conservationWater supplyEconomicsWater resourcesAgricultural economicsWater resource managementGeographyEnvironmental engineeringEconometricsEcologyMeteorology

Abstract

fetched live from OpenAlex

Climate change is expected to alter the supply and demand for water in the residential sector. Existing studies exploit the differences in climate across seasons mostly in North America and Europe, and identify changes in consumption levels attributed only to households' short-term responses. The results from models that simulate household consumption of water are sensitive to the parameters that govern the behavior of climate variables and household responses in the upcoming decades, and fail to consider short-term determinants of water consumption. The findings in the literature suggest an inexistent or small effect of climate on residential water demand. This paper studies the relationship between climate conditions and residential water consumption that corresponds to households' long-term adaptation to climate, while controlling for the effect of short-term determinants of water demand. I take advantage of the geographic variation in climate conditions across municipalities of Ecuador to identify the effect of temperature, precipitation and humidity on water demand. I adopt average prices and an IV technique to address the endogeneity problem between water prices and quantities that arise from the use of increasing-block water tariffs. I find a large and significant effect of temperature on residential water demand, whereas precipitation and humidity have a small effect. Temperature also has a stronger effect on water demand among low-income households.

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 score0.755
Threshold uncertainty score0.306

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 head0.206
Teacher spread0.199 · 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
Published2015
Admission routes1
Has abstractyes

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