Stormwater Utilities: A Sustainable Answer to Many Questions
Bibliographic record
Abstract
One of the most complex and difficult questions to answer concerns how to organize and economically support public services of all kinds. In terms of services that involve a multiplicity of actors and objectives, as is the case with urban stormwater management, the difficulty is magnified and resources never seem to be sufficient. This paper reviews the successful approaches to stormwater management in a number of countries and concludes that it is both feasible and possible to successfully structure stormwater management in cities using a variety of models and incentives. With examples from cases practiced in the USA and Canada, based theoretically on the user-pays principle and on the fair distribution of impacts, the text innovates showing not only a technically and legally viable option, but an opportunity for users to become aware of the importance of reducing environmental impacts. By raising the possibility of delivering services out of the general public budget, reducing the taxation of all in exchange for charging only users and improving the performance, the discussion is directed, in an innovative way, to a very rarely questioned aspect and links the change in mentality from and economic way of thinking towards the new stormwater paradigm shift and SDGs.
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 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".