Harvesting benefits: Exploring the effects of second‐best policies on enhancing soil organic carbon stocks in agriculture
Bibliographic record
Abstract
Abstract Agricultural subsidies can be an effective policy tool to enhance soil organic carbon sequestration. This paper assesses the effectiveness of a second‐best hypothetical policy which subsidizes additional canola hectares optimally for each soil zone in Saskatchewan in an effort to increase soil organic carbon. I develop a simulation model that includes on‐farm acreage responses and employs a novel field‐level dataset from the Saskatchewan Crop Insurance Corporation to measure changes in soil organic carbon stocks attributable to changes in cropping choices. I find that a policy offering optimal subsidies specific to each soil zone for additional hectares of canola, implemented in 2019 and continuing indefinitely for all insured fields in Saskatchewan, generates an external social benefit worth 14.7 billion Canadian dollars when the subsidy is set to maximize the net external social benefit, and 29.4 billion Canadian dollars when it is set to maximize the change in total welfare. This paper highlights the potential environmental and social benefits of second‐best policies as a cost‐effective alternative to traditional first‐best policies. It also shows how economic and biophysical models can be combined to estimate soil characteristics, thereby avoiding the high cost of direct measurement.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".