Balancing Bio‐Energy Cropping Benefits and Water Quality Impacts: A Dynamic Optimization Approach
Bibliographic record
Abstract
The relationship between bio‐energy feedstock production and water quality has received little attention from economists. Here, an optimal control model is used to determine the optimal amount of land to convert to the production of energy feedstocks, specifically ethanol corn, taking into account potential impacts on water quality. Based on comparative static analyses of an optimal control model, and a numerical application, we find that the optimal proportion of land to shift into bio‐energy production from a baseline use, such as the Conservation Reserve Program, depends on key model parameters, specifically the rate of degradation of the pollutant and the link between the intensity of bio‐energy feedstock production and the rate of change in the pollutant stock. Yet, there is a limit to how much land should optimally be converted as society must trade‐off its desire to mitigate climate change against its willingness to accept a decline in water quality. Le lien entre la production de matières premières bioénergétiques et la qualité de l’eau a attiré peu d’attention de la part des économistes. Nous avons utilisé un modèle de contrôle optimal pour déterminer les superficies optimales à convertir à la production de matières premières énergétiques, en particulier le maïs destinéà la production d’éthanol, en tenant compte des répercussions potentielles sur la qualité de l’eau. D’après une simulation numérique et des analyses de statique comparative obtenues à l’aide d’un modèle de contrôle optimal, nous en sommes venus à la conclusion que les superficies optimales à convertir à la production de matières premières bioénergétiques à partir d’un instrument de référence, tel que le Conservation Reserve Program (Programme de réserve des terres sous conservation), dépend des paramètres clés du modèle, particulièrement du taux de biodégradation des polluants et du lien entre l’intensité de la production de matières premières bioénergétiques et le taux de variation du stock de polluants. Il existe tout de même une limite quant aux superficies à convertir étant donné que la société doit faire un choix entre son désir d’atténuer le changement climatique et son acceptation d’une diminution de la qualité de l’eau.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".