More Water Please, It's Getting Hot! The Effect of Climate on Residential Water Demand
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".