Cost Effective Targeting Of Land Retirement To Improve Water Quality: A Multi-Watershed Analysis
Bibliographic record
Abstract
An integrated watershed management framework that combines economic, hydrologic and GIS modeling is developed to study cost effective land retirement in multiple watersheds to achieve off-site sediment reduction goal. This integrated framework examines two alternative standards-a uniform standard under which each watershed is required to achieve the same sediment reduction goal and a non-uniform standard under which marginal cost of sediment abatement is equal across watersheds. Furthermore, for each standard, costs of abatement under two alternative rental instruments based on marginal cost of sediment abatement ($/ton) and uniform payments per acre ($/acre) are examined. Then the cost effectiveness of the four policy options (uniform standard with $/ton and $/acre instrument, non-uniform standard with $/ton and $/acre instrument) is discussed. The integrated framework is applied to 12 agricultural watersheds in Illinois Conservation Reserve Enhancement program (CREP) region. The watersheds varied in size between 29, 995 and 70, 849 acres. Cropland within 900 feet of streams-129, 955 acres (33.4% of all cropland in the 12 watersheds)-is considered eligible for enrollment into the CREP. Consistent with Illinois' program, a sediment reduction goal of 20% is selected for all of the simulations. Policy implications from the empirical results are quite interesting. With either a $/ton or a $/acre instrument, the non-uniform standard, which equalizes marginal cost of abatement across watersheds, outperforms the uniform standard policy. With either a uniform or non-uniform standard, a $/ton instrument outperforms a $/acre instrument. The least preferred policy option, the uniform standard with a $/acre instrument, is 2.5 times as costly as the most preferred policy option, the non-uniform standard with a $/ton instrument. These results suggest that program administrators may want to consider a program that includes a non-uniform standard and a rental payment instrument based on marginal cost of abatement in order to achieve their objectives at least cost.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".