Impact of the European Green Deal (EDG) on the Agricultural Carbon (CO2) Emission in Turkey
Bibliographic record
Abstract
The European Union (EU) hopes to motivate a sustainable green transition in response to widespread concern that the earth is heading toward environmental calamities due to climatechange issues.The present study focused on analyzing the impact of the EGD on Agricultural carbon emissions in Turkey.European Union and Turkey have strong trade relations; Turkey exports a significant portion of its exports to the European Union.This fact made it compulsory for Turkey to follow the regulations implemented in the EU regarding trade.In reaction to the European Union's EGD, Turkey formulated EGD Action Plan, this plan laid down the roadmap for Turkey to follow the regulations under the EU's EGD.Agriculture carbon emissions in Turkey and EGD are taken as the variables for the study.In the present report, an attempt has been made to analyze impact of the EGD on agriculture carbon emissions.In study, we consider agriculture CO2 emissions as the dependent-variable and the EGD as the independentvariable.Secondary data from the various published sources have been collected and analyzed with statistical tools, and findings are drawn from them.Statistical tools like Unit Root Tests, the Ordinary Least Square Test, and Auto regressive distributed lag model, are used to interpret the impact.The result of our study shows that EGD significantly impacts agriculture carbon emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".