Effective Risk Management Policy choices under Climate Change: An Application to Saskatchewan Crop Sector
Bibliographic record
Abstract
There is growing concern about the impact of climate change on agriculture and the potential need for better risk management instruments that respond to a more risky environment. This is based on the widespread assumption that climate change will increase weather and yield variability and will expose farmers to higher levels of risk. But it is not obvious what will be the net impact of those on the distribution of yields and its correlation with weather indexes. Five stylized scenarios for crop yields are built on the basis of the available empirical information: baseline, marginal climate change without adaptation, with adaptation and with misalignment of expectations, and an extreme events scenario. A micro simulation model is calibrated using micro farm level data from the Canadian province of Saskatchewan. Four alternative policy measures are analyzed: three types of subsidized insurance (individual yield, area-yield and weather index) and an ex post disaster payment. Results on insurance uptake, budgetary costs and impacts on diversification, farmers’ welfare and farm income variability, are presented for three different types of farms. The paper goes beyond indentifying the effectiveness of risk management instruments under stylized climate change scenario and analyze the policy decision criteria when policy makers face ambiguous climate change contingencies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".