Testing the pollution haven hypothesis for the Ontario livestock sector
Bibliographic record
Abstract
Fewer but larger farms coupled with pressures from a growing non-farm rural population has resulted in tighter public controls on farm production practices. Municipalities were the key institution authorizing the establishment of livestock production facilities in Ontario prior to the Nutrient Management Act. This decentralized regulatory approach resulted in a range of environmental policies and regulations involving the issuance of building permits. Differences in nutrient management by-laws could affect the location decision of new livestock facilities. This variation allows for the existence of potential "pollution havens" in which barns located in those regions with lax regulations and thereby lowest compliance costs. While there could be differences in the costs of meeting local bylaws in the establishment of a new or expanded livestock farm, these may be offset by other factors influencing location choice. The purpose of this study is to determine the factors influencing the location of livestock production facilities in southwestern Ontario from 1996 to 2001. Research into the determinants of livestock production facility location is timely in light of the administrative changes taking place in the province. First, a current picture of the degree of variation in environmental stringency across the municipalities is required but lacking. Secondly, little is known about the various location determinants driving the establishment of livestock production facilities in Ontario. This study will help determine whether new livestock operations have been built in townships with lax environmental by-laws or has the decision to build been based on other factors. Third, it will assess which of the environmental regulations have impacted firm location which will aid municipalities trying to design policies to attract (or deter) future livestock growth in their region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".