Reducing external costs of nitrogen pollution by relocation of pig production between regions in the European Union
Bibliographic record
Abstract
Abstract This paper tests the hypothesis that relocation of pig production within the EU27 can reduce the external costs of nitrogen (N) pollution. The external cost of pollution by ammonia and nitrate from agriculture in the European Union (EU27) in 2008 was estimated at 61–215 billion € (0.5 to 1.8% of the GDP). Per capita it ranged from more than 1000 € in north-west EU27 to 50 € in Romania. The average contribution of pig production was 15%. Using provincial data (224 NUTS2 regions in EU27), the potential reduction of external N cost by relocation of pig production was estimated at 14 billion € (10% of the total). Regions most eligible for decreasing the pig stock were in western Germany, Flemish region, Denmark, the Netherlands and Bretagne, while Romania is most eligible for increasing pig production. Relocating 20 million pigs (13% of the total EU stock) decreased average external costs per capita from 900 to 785 € in the 13 NUTS2 regions where pigs were removed and increased from 69 to 107 € in 11 regions receiving pigs. A second alternative configuration of pig production was targeted at reducing exceedance of critical N deposition and closing regional nutrient cycles. This configuration relocates pigs within Germany and France, for example from Bretagne to Northern France and from Weser-Ems to Oberbayern. However, total external cost increases due to an increase of health impacts, unless when combined with implementation of best N management practices. Relocation of the pig industry in the EU27 will meet many socio-economic barriers and realisation requires new policy incentives.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".