Environmental hazards: The microgeography of land‐use negative externalities
Bibliographic record
Abstract
Abstract The decisions on the siting of hazardous facilities and compensation for nearby landowners depend on an accurate estimation of the negative externalities these facilities place on proximate land uses, primarily residential properties. In this paper, we highlight the sensitivity of these estimates to the treatment of distance from the hazard and to the presence of other nearby externality generating land uses identified at a highly granular geographic level. We find that estimated spillovers are quite sensitive to highly localized treatment of other land uses and that naive parametric specifications yield misleading results. Unlike previous work, we find proximity to a major oil pipeline results in lower house prices: properties adjacent to a property with a pipeline easement transact for 2.2% ($C 15.8k) less and those one property further away 1.6% ($C 11k) less than more distant residential properties. These effects vary by the type of land use on which the pipeline easement lies. Difference‐in‐differences tests indicate that the price effects of proximity respond to information shocks that remind potential buyers of pipeline risks but not those shocks that merely remind them of the presence of the pipeline. However, the effects of an information shock, in this case a nearby spill on the pipeline, dissipate within 18 months.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".