THE EFFECT OF TRADABLE DISCHARGE PERMIT (TDP) \nPROGRAMS ON THE RELIABILITY OF WATER QUALITY IN RIVERS
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».