Agricultural Greenhouse Gas Emissions in Türkiye: A Comparison with EU-27 Countries and Machine Learning–Based Forecasting
トルコの農業温室効果ガス排出：EU-27カ国との比較と機械学習に基づく予測 (AI 翻訳)
Çağdaş Civelek
🤖 gxceed AI 要約
日本語
トルコの農業GHG排出量をEU-27カ国と比較し、2025-2030年の排出量を機械学習で予測。農業排出は全体の約12%を占め、排出強度は低いが総量は2番目に多い。Random Forestが最良の予測性能（R²=0.503）を示した。
English
This study compares Türkiye's agricultural GHG emissions with EU-27 countries and forecasts 2025-2030 emissions using machine learning. Agricultural emissions account for ~12% of total, ranking second in total but ninth lowest in intensity. Random Forest achieved the best prediction performance (R²=0.503).
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では農業分野のGHG排出削減が課題であり、SSBJ開示や農林水産省の政策に資する。他国との比較や予測手法は日本の農業排出インベントリ改善に参考となる。
In the global GX context
This paper provides a comparative framework for agricultural GHG emissions and demonstrates ML forecasting, relevant for global disclosure and policy alignment with EU regulations. It offers insights for countries like Japan seeking to benchmark agricultural emissions.
👥 読者別の含意
🔬研究者:農業排出予測へのML適用と国際比較の方法論を参考にできる。
🏢実務担当者:農業セクターの排出削減計画や報告に役立つベンチマークを提供。
🏛政策担当者:EU規制との整合や排出削減目標設定に示唆を与える。
📄 Abstract（原文）
Greenhouse gas (GHG) emissions in Türkiye have shown a continuous increase over the past 35 years, with a total rise of 155.3%. In comparison, agricultural greenhouse gas emissions have increased by 41.8% and currently account for approximately 12% of total GHG emissions, highlighting the sector's significant contribution. Accurately determining and planning future emission levels is crucial for implementing effective mitigation strategies and aligning with European Union regulations. This study presents a comparative analysis of Türkiye's agricultural greenhouse gas emissions with those of the 27 European Union countries for the period 2015–2023 and provides forecasts for the years 2025–2030. The findings indicate that, although Türkiye ranks as the second-highest country in terms of total agricultural greenhouse gas emissions, it ranks ninth lowest in terms of emission intensity. Among the three prediction models evaluated, the Random Forest Regression model demonstrated the best performance, achieving a coefficient of determination (R²) of 0.503, compared to 0.482 for Ridge Regression and −2.048 for Linear Regression.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.54740/ros.2026.043first seen 2026-08-01 07:30:16 · last seen 2026-08-02 06:58:34
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