Using demand elasticity as an alternative approach to modelling future community water demand under a conservation-oriented pricing system: An exploratory investigation
Bibliographic record
Abstract
Water managers lack practical and readily available tools to inform them about what impact price changes (or changes in other drivers of water use) will have on demand – and therefore revenue – over both the short and long term. This paper examines how the concept of demand elasticity can be used to model changes in annual aggregate water use in response to future changes in major demand drivers including water and electricity prices, average income, population, level of business activity and climate. It does so by describing a pilot investigation completed in York Region in Southern Ontario, where a range of assumptions about price elasticities were used to calculate the rate of growth for water demand over a 40-year period. This investigation was deliberately exploratory and the findings can only be considered indicative and preliminary. However, with further development, the modelling approach described could provide an additional tool to help water managers understand changes in demand, and communities make the transition to a conservation-oriented water pricing system.Les gestionnaires de l’eau n’ont pas suffisamment d’outils pratiques et facilement accessibles à leur disposition pour pouvoir déterminer quelle sera l’incidence des fluctuations de prix (ou fluctuations liées à d’autres facteurs entourant l’utilisation de l’eau) sur la demande et, par conséquent, sur les revenus, aussi bien à court terme qu’à long terme. La présente communication examine la façon dont le concept de l’élasticité de la demande peut servir à modéliser les changements touchant l’utilisation globale annuelle de l’eau en réponse aux changements futurs liés aux principaux facteurs entourant la demande, notamment les prix de l’eau et de l’électricité, le revenu moyen, la population, le niveau d’activité commerciale et le climat. Dans cette optique, la présente communication décrit une étude pilote menée dans la région de York dans le sud de l’Ontario, où une gamme d’hypothèses à propos de l’élasticité-prix a servi à calculer le taux de croissance de la demande en eau sur une période de 40 ans. Cette étude était délibérément de nature exploratoire et les résultats peuvent uniquement être considérés comme étant indicatifs et préliminaires. Cependant, moyennant un développement plus poussé, l’approche de modélisation décrite pourrait fournir un outil supplémentaire qui aiderait les gestionnaires de l’eau à comprendre les fluctuations de la demande et les collectivités à entreprendre la transition vers un système de tarification de l’eau axé sur la conservation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".