Urban scaling of water and electricity demand across the United States
Bibliographic record
Abstract
Abstract As urban populations continue to grow, ensuring an adequate supply of water and electricity will be imperative. However, these resources are generally extracted in rural areas, creating tension during periods of limited availability. It has been argued, however, that urban areas are more efficient users of various resources in spite of their large populations. Here, we test this argument for water and electricity consumption across 46 US cities. Leveraging urban scaling theory, we show that water and electricity consumption scale sublinearly with population. This suggests that cities are using water and electricity more efficiently as their population increases. Further, the results show that this sublinear scaling exists regardless of season or year. Nonetheless, there were cities that deviated from the expected consumption value that would have been predicted by this model. We explored the role that precipitation and temperature might have on these deviates and found that temperature, in particular, can help explain why certain cities consume more electricity than expected based on their population. Understanding the relationship between consumption patterns and population is critical for planning investments for future infrastructure systems that will need to service higher populations with more limited resources.
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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 | 0.003 | 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 | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".