The Impacts of Climate Change on Agricultural Trade in the MENA Region
Bibliographic record
Abstract
Human-induced climate change has been one of the most widely discussed issues of scientific and political spheres in the recent decades, and it has been overwhelmingly agreed that climate change poses a very serious threat for the environment and the economy. It has been observed that increasing temperatures and extremities in weather patterns create a serious challenge for agriculture and food security especially in various disadvantaged regions. Even in the most optimistic scenarios, where global mean temperatures rise by around 2°C by 2100, serious negative effects are expected on agricultural production and crop yields over the next century.The Middle East and North Africa (MENA) is one of the most vulnerable regions as one of the most food-import dependent region in the world. Water resources are scarce and irrigation is not sufficiently developed in the region, and climate change hurts the already vulnerable agricultural supply, where on the other hand increasing population continuously fosters the demand for agricultural products.The aim of this paper is to examine the impacts of climate change on agricultural trade in the MENA region. The indicators for climate change includes variables such as precipitation patterns and temperatures, and the effect of the change in the climate change indicators on agricultural exports and imports will be analyzed through a panel data analysis, where the impacts of GDP, per-capita oil use and trade integration will also be added as variables.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".