The Siting of Livestock Facilities and Environmental Regulations
Bibliographic record
Abstract
The significant variation in the stringency of environmental regulations across Ontario municipalities in the late 1990s created the possibility of pollution havens where expanding livestock operations would locate in those municipalities with the lowest regulatory compliance costs. Using actual building permit data rather than aggregate regional production numbers, the study examined the factors affecting spatial changes. Instead of locating to reduce environmental compliance costs as suggested by the pollution haven hypothesis, barns are being built largely where the livestock sector is concentrated suggesting the existence of agglomeration economies. The result suggests that the normalization of standards across the province through the new Nutrient Management Act will not significantly influence the location decisions of expanding or new livestock facilities which are largely determined by economic factors associated with livestock infrastructure support. L'importante variation de sévérité des règlements environnementaux dans les municipalités de l'Ontario vers la fin des années 1990 a créé la possibilité de refuges pour pollueurs: les fermes d'élevage en expansion allaient s'installer dans les municipalités où les coûts de conformité aux règlements étaient les plus faibles. À l'aide de données sur les permis de construire plutôt que de données agrégées sur la production régionale, la présente étude a examiné les facteurs qui influaient sur les changements spatiaux. Au lieu de s'installer là où les coûts de conformité aux règlements sont faibles comme le laisse supposer l'hypothèse des refuges pour pollueurs, les entreprises choisissent de s'installer là où le secteur de l'élevage est concentré, ce qui laisse supposer l'existence d'économies d'agglomération. Les résultats semblent indiquer que l'uniformisation des normes à l'échelle provinciale en vertu de la Loi sur la gestion des éléments nutritifs n'aura pas d'influence significative sur le choix des endroits où seront construits les bâtiments des exploitations en expansion ou des nouvelles exploitations puisque ce choix est en grande partie motivé par des facteurs économiques liés au soutien à l'infrastructure de l'élevage.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".