Managing water-related energy in future cities – a research and policy roadmap
Bibliographic record
Abstract
Water-related energy accounts for around one-quarter of California's energy use. Most of the influence is within cities. This project aimed to identify research and policy needs associated with managing energy related to urban water. A workshop was convened with diverse representation from water and energy sectors in federal (US), state (California) and municipal governments, research and regulatory agencies, universities, utilities, not-for profit and private sectors. The workshop established a vision of future cities, including elements of success, research needs and barriers. A subsequent on-line survey was used to estimate the potential, effort and ‘potential-to-effort’ ratio of each suggested element. First suggested steps in the roadmap include: development of educational programmes, combined standards, guidelines, funding and planning for water and energy efficiency, improved understanding and management of factors motivating consumers, and improved methods to quantify and track targets of ‘water-related energy and related greenhouse gas emissions’. The ‘roadmap’ could help streamline future effort and sequencing action. The authors note and reflect on the importance of representation at such a workshop, and an effort is made to understand sources of variability in viewpoints. The semi-quantitative method used could have relevance to wider resource management issues and complex problem resolution.
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 | 0.001 | 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 | 0.001 |
| 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".