Price Elasticity of Water Demand in a Small College Town: An Inclusion of System Dynamics Approach for Water Demand Forecast
Bibliographic record
Abstract
The relationship between water demand and pricing using the price elasticity of water demand in the City of Pullman, Washington, between 2000 and 2006 shows that the current amount of water depletion is not sustainable. Three different economic scenarios were developed by altering variables in regression equations to investigate the influence of individual variables on estimating the final price elasticity of water demand. Single-family households, total residential households, and total population water use of the City of Pullman, Washington were the three different economic scenarios developed for calculating the price elasticity of water demand. The regression results show that the price elasticity of marginal price is inelastic. The exponents for median household income, fixed price, and precipitation had the expected signs in all applied scenarios. An economic model based on the regression equation of price elasticity was developed using a systems dynamic approach. The economic model projected a decline in water demand when the independent variables were assumed to grow linearly over the coming 25 years. When the household size with higher elasticity values was excluded from the regression equation, the developed economic model was able to forecast reasonable water demand. The time series data with exact service connections are recommended to reduce the uncertainty in the computation of the price elasticity of water demand. Further sensitivity analysis is recommended to understand interrelationship of water demand and pricing from the developed economic model using system dynamics approach.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".