New framework for quantifying WTP to consider equity in cost allocation of NPS pollution abatement in TMDL framework
Bibliographic record
Abstract
Non-Point Source pollution abatement at regional watershed level depends largely on the participation of all polluters in applying mitigation actions and for extended periods of time. Typically, Regulators have limited capacity to monitor polluter’s compliance with mitigation policies that impose additional costs to polluters. The willingness of polluters to implement mitigation actions is key factor to achieve successful NPS pollution abatement. Social acceptability of mitigation policies is a significant indicator of polluter’s willingness to apply mitigation actions. Social acceptability is valuable measure for decision making because it allows regulators to evaluate mitigation policies based on their likelihood to succeed. Today, the lack of practical approach to evaluate social acceptability is limiting its use in NPS pollution management. Social acceptability depends on economic and social factors. Equity in distributing mitigation costs among polluters emerges as a practical indicator of social acceptability. In this work, a framework is developed to quantify polluter’s Willingness to Pay to implement equity in mitigation cost allocation at the polluter level (i.e. farmer). The suggested framework represents new application to integrate equity in decision making in NPS management. A practical application of the new framework is provided using phosphorus loading reduction in the Fishtrap Creek Watershed in the Nooksack River Basin in northwestern Washington State.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".