The effects of strict agricultural zoning on farmland values: The case of Ontario's Greenbelt
Bibliographic record
Abstract
This thesis is an investigation of the effects of the Greenbelt legislation on farmland values in Ontario. In 2004, this legislation imposed a moratorium on urban development of agricultural land within the Greenbelt, which encompasses approximately 1.8 million acres of land near the Greater Toronto Area (GTA). This legislation has generated considerable controversy, as landowners within the Greenbelt have claimed that the development restrictions imposed on their land would negatively impact its value, while the government has refuted these claims. In an attempt to resolve this controversy, this study conducts an empirical examination of the effects of the legislation on farmland values within the Greenbelt boundary. In addition, this study examines the effects on farmland values just outside the boundary, where the potential for leapfrog development exists. The theory on land values and growth controls indicates that imposing strict controls, which prohibit future development of agricultural land, would negatively impact the value of the affected land. The theory also indicates that when growth controls are imposed in one area, the value of land in surrounding areas increases. This study involves the use of an extremely detailed data set, comprised of thousands of farmland sales across southern Ontario. The level of detail of this data set, combined with the use of geographic information systems (GIS), allows for an in-depth examination of farmland values in Ontario and the effects of the Greenbelt on these values. A spatial autoregressive model is used for the empirical analysis to account for spatial correlation in the farmland price data. The results indicate that the Greenbelt legislation has influenced farmland values, both inside the Greenbelt and just beyond the outer boundary. However, these effects vary depending on the location of the farmland. The value of farmland located in close proximity to the GTA is found to be negatively affected by the Greenbelt legislation, but this negative effect diminishes as distance to the GTA increases. The value of farmland just outside the Greenbelt is found to be positively affected, providing evidence to support the existence of the leapfrog effect.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".