Environmental Externalities, Comparative Advantage, and the Location of Production: An Application to the Canadian Dairy Industry
Bibliographic record
Abstract
The traditional theory of comparative advantage has not been well integrated with the theories of externalities and location. In this paper we develop a conceptual framework which undertakes such an integration at the firm and regional levels. We call this the General Equilibrium with Individual Spatial Heterogeneity and Externalities (GEISHE) model. We use the model to study the effect of spatial heterogeneity in emission intensity on the spatial distribution of production under a uniform emission standard. This model suggests that the introduction of an emission restriction can have differential effects on the spatial patterns of production, depending on local production intensity. We also present empirical analysis of intraprovincial movement of production for the Canadian dairy industry using 1996 and 2006 Census data. Ceteris paribus, areas that had higher dairy production intensities in 1996 also tended to experience higher declines in their dairy cow populations between 1996 and 2006, which is consistent with the GEISHE model. These results suggest that environmental pressure may change the patterns of comparative advantage within a supply managed industry, even if relocation of production across provincial boundaries is not permitted. Expression of environmental comparative advantages seems to be taking place within provinces.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".