THE EFFECT OF TRADABLE DISCHARGE PERMIT (TDP) \nPROGRAMS ON THE RELIABILITY OF WATER QUALITY IN RIVERS
Bibliographic record
Abstract
Tradable Discharge Permit (TDP) programs have shown, both in practice and in theory, to have \ntremendous potential as cost-effective methods of pollution control. Nevertheless, there are still many \nuncertainties regarding TDP programs that if not adequately addressed, might impair their success. \nConcerns range from issues of market failure that prevents optimal trading, to political agendas that differ \nfrom a typical TDP program in their priorities, to modeling difficulties that might cause erroneous \npredictions of cost savings and environmental performance. The hopelessness of trying to overcome \nthese concerns all at once is recognized. And therefore, apart from a brief discussion where the more \ncommon of these uncertainties are identified and discussed, attention is focused only on the uncertainty \nassociated with environmental modeling, specifically that associated with the stochastic aquatic \nenvironment. \nNumerous studies have been carried out to predict the potential impacts of TDP programs, whether \npositive or negative, on the environment they are intended to protect. These studies have been \ninvaluable in laying essential groundwork for the further understanding and actual implementation of such \nprograms. However, many of these studies assumed deterministic environmental models when in reality \nnothing is ever constant. The environment is an open system vulnerable to, amongst many other agents, \nweather variations and changes in microbial behavior. It is therefore, this study's goal to attempt to \nadvance a step forward by re-assessing those same questions asked many times before, but this time \nwithout disregarding the stochastic nature of the environment. \nThe Willamette and Athabasca Rivers in Oregon, USA and Alberta, Canada, respectively are used as \nexample case studies. These systems are simulated to predict how they might respond if discharge \npermit trading were implemented. The Mean-Value First-Order Second-Moment (MFOSM) method is \nused to evaluate the reliability of each system's dissolved oxygen (DO) concentration meeting set \nstandards, as a function of its BOD wasteload distribution and environmental randomness. The results \nshow that trading does indeed influence environment quality. For the Willamette River, trading improves \nthe water quality reliability. For the Athabasca River, trading makes the reliability worse. However, these \neffects are quite minimal in that, for any target reliability to be achieved that is reasonable, trading is \nfound not to change the reliability significantly in comparison to that attained under a policy of no trading.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".