Impacts of climate change on global agri-food trade
Bibliographic record
Abstract
Climate change and trade are closely related. Climate may alter the comparative advantages across countries, which may in turn trigger changes in trade patterns. Trade itself may constitute an adaptation strategy, moving excesses of agri-food supply to regions with shortages, and this in turn may explain changes in land-use. We investigate these linkages, showing that the changes in climate affect counties’ trade value and contribute to reshaping trade patterns. First, we quantify the long-term impacts of climate on the value of agri-food exports, implicitly considering the ability of countries to adapt, and show that higher marginal temperatures and rainfall levels tend to be beneficial for countries’ exports. Following a gravity model approach, we then link the evolving trade patterns to climate change adaptation strategies. We find that the larger the difference in temperatures and rainfall levels between trading partners, the higher the value of bilateral exports. Furthermore, while developed and developing exporters are both sensitive to climate change and to cross-countries heterogeneity in climate, we found their responses to changes in climate to be quite diverse.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".