Environmental and technical efficiency of French suckler sheep farms under pollution‐generating technologies: A multi‐equation stochastic frontier approach using info‐metrics
Bibliographic record
Abstract
Abstract Reducing the negative environmental impact of production activities without (substantial) loss of production is a crucial challenge for the agricultural sector. Investigating farms' environmental and technical efficiency (TE) levels and drivers can contribute to addressing this issue. In this regard, based on recent theoretical developments on the appropriate handling of undesirable outputs in the modeling of production technologies, this paper introduces a multi‐equation stochastic frontier framework for technical and environmental efficiency (EE) analysis. This framework is applied to a sample of French suckler sheep farms. The results indicate that, on average, farms in the sample can increase their desirable output by 20% without using more inputs while reducing their greenhouse gas emissions by 24%. Findings also show that relatively high (low) levels of TE are associated with relatively low (high) levels of EE and that the likelihood for a farm to be both technically and environmentally efficient is relatively low. Only 32% of the farms in the sample have a high level of TE and EE. Drivers such as decoupled direct payments are positively associated with EE and negatively associated with TE, while no significant effect is found for green direct payments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".