The Impacts of Agricultural Energy Use and Cultivable Area on the Cropland Footprint in Light of the EKC Model: Evidence from the Top Ten Most Successful Countries in Agriculture
Bibliographic record
Abstract
This study examines the impact of agricultural production on environmental degradation. Specifically, it tests the agricultural energy use and arable land variables, along with the agricultural Environmental Kuznets Curve (EKC) hypothesis, in the ten most agriculturally successful countries: Australia, Canada, Mexico, Türkiye, United States, Argentina, Brazil, China, India, and Russia. Using annual data from 1992 to 2020, econometric analyses were conducted through Bootstrap ARDL (B-ARDL) and Bootstrap Toda-Yamamoto causality methods. The results only validate the agricultural EKC hypothesis in Türkiye among the countries in the sample. This finding highlights Türkiye's commitment to agricultural structural transformations. Recent developments and support in Türkiye’s agricultural sector align with the study's findings. Policy recommendations focused on how these developments could threaten food security.
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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 | 0.002 | 0.004 |
| 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 | 0.000 | 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".