An Analysis Of The Effectiveness And The Side-Effects Of The German Wastewater Fee Law
Bibliographic record
Abstract
Abstract It is virtually an article of faith among economists and many other policy analysts that taxes or fees on pollution emissions have the potential to bring about environmental benefits at lower cost than command-and-control approaches.3 However, the details are critical. The establishing law and its implementing regulations and practices must ensure that the fees cover both the concentration and the quantity of the pollutants of greatest concern. The fees must be set high enough to have the desired ‘steering’ effect. Put differently, it must serve to induce polluters to undertake investments to reduce pollution if economically warranted, but not be so high as to have serious consequences for important sectors of the economy. Potential adverse side-effects from the imposition of such a scheme must be considered. Less frequently mentioned are beneficial side-effects. In this chapter, we report on the actual effects, both adverse and beneficial, of one such statute that imposes fees on pollutants contained in wastewater discharges, the German Abwasserabgabengesetz.4 Germany is an important country in its own right, but in comparative environmental law, it is especially relevant to North American observers because it is the country that utilizes the civil law system that is otherwise most similar to that of the United States and Canada: a democracy, a federation,5 a major industrial
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".