Farmland Protection and Agricultural Land Values at the Urban‐Rural Fringe: British Columbia's Agricultural Land Reserve
Bibliographic record
Abstract
Abstract Farmland conservation policies typically use zoning and differentiated taxes to prevent urban development of farmland, but little is known about the effectiveness of these policies. This study adds to current knowledge by examining the impact of British Columbia's Agricultural Land Reserve (ALR), established in 1973, which severely restricts subdivision and nonagricultural uses for more than 4.7 million hectares of farmland. To determine the extent to which the ALR preserves farmland by reducing or removing the development option, a multilevel hedonic pricing model is used to estimate the impact of land use, geographic, and zoning characteristics on farmland value near the capital city of Victoria on Vancouver Island. Using sales data from 1974 through 2008, the model demonstrates a changing ALR impact over time that varies considerably by improved and unimproved land types. In 2008, landowners paid 19% less for the typical improved farmland parcel within the ALR versus that outside it. This suggests that would‐be developers expect permanency in the zoning law, and prefer non‐ALR zoned land. However, ALR land that is unimproved has a premium of 55%, suggesting that this land is more valuable for agriculture than for development. Farmland located closer to the city or the commuting highway commands a premium if it has a residence on it, with a residence also explaining why smaller agricultural properties sell at higher prices. However, it appears that zoning by itself is insufficient to protect farmland; other policies likely need to be implemented in conjunction with zoning to protect agricultural land.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".