International experiences in stormwater fee
Bibliographic record
Abstract
Stormwater management (SWM) includes a wide range of services aimed at environmental protection, enhancement of water resources and flood control. Local governments are responsible for managing all these aspects within their jurisdiction, but they often present limitations in generating revenues. Thus, many municipalities have been seeking a dedicated funding source for these programs and practices. This publication provides a brief overview of current legal issues associated with stormwater funding focusing on the most used method: fees. It is a successful mechanism to fund legal obligations of municipalities; however, it must have a significant value to motivate the reduction of runoff. Through literature, we found stormwater fees in Australia, Brazil, Canada, Ecuador, France, Germany, Poland, South Africa and the United States (USA). France had the highest average monthly fee, but this financing experience was suspended in 2014. Brazil has the lowest fee by m², comparable to the US fee. While in Brazil overall SWM represents low priority investments, the USA represents one of the most evolved countries in stormwater funding practices. It was noticed by reviewing the international experience that charging stormwater fees is a successful mechanism to fund the legal obligations and environmental protection.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".