The Restructuring of South American Soy and Beef Production and Trade Under Changing Environmental Regulations
Bibliographic record
Abstract
In response to the extensive loss of forests caused by soy and cattle expansion in South America, several countries have increased their legal restrictions on deforestation , and stepped up their enforcement. In addition, in the Brazilian Amazon, new private agreements were initiated in 2006 and 2009 to limit the purchase of soy and cattle linked with deforestation. One concern is that such policies, because they are spatially heterogeneous or focus on a subset of relevant actors, might generate negative spillovers in the form of leakage of agricultural activities and deforestation to less-regulated areas, and/or a redistribution of non-compliant product sales to non-participants. In this study, we use panel data on soy and beef production and trade in agricultural frontiers of South America to examine how changes in deforestation regulations in South America have altered soy and cattle expansion and exports in this region, and to understand how these changes, if they have occurred, influence the overall effectiveness of deforestation regulations. We find no evidence of a change in soy or pasture area expansion patterns due to changes in regulations, except within the Amazon biome where pasture expansion slowed in response to more stringent regulations and coincided with pasture intensification. We do find, however, a decrease in beef imports from biomes with more stringent deforestation regulations. While this decrease may indicate the existence of leakage to countries outside the study area, it is likely offset by pasture intensification, continued opportunities for deforestation, and increasing domestic consumption from these biomes. These results point to the potential role of substitution effects between local and international consumer markets, and between different actors, in diminishing the overall effectiveness of deforestation regulations.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".