Trade flows and carbon emissions: How far are we from the climate targets?
貿易フローと炭素排出：気候目標達成までの距離 (AI 翻訳)
Bardia Moghtader, Füsun Ülengin, Eda Helin Yıldız, Emel Aktaş, Y. Ilker Topcu
🤖 gxceed AI 要約
日本語
本研究は、PyCaretを使用して機械学習モデル（ARIMA、AdaBoost、Theta）を訓練し、2030年までの輸送部門のCO2排出量を予測。EUグリーンディールの目標達成可能性を評価した結果、ギリシャとイタリアのみが目標達成可能であることが判明。貿易フローデータを用いた貨物モーダルシフトの分析も行い、政策提言を提示している。
English
This study uses PyCaret to train ML models (ARIMA, AdaBoost, Theta) to forecast transport CO2 emissions up to 2030 and assess EU Green Deal targets. Only Greece and Italy are on track; other countries need policy reform to shift freight to lower-emission modes. Data-driven mitigation strategies are proposed.
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
日本の運輸部門の脱炭素目標評価にも応用可能な予測手法を提供。ただし、分析対象はEU諸国であり、日本の政策文脈には直接対応しない。
In the global GX context
This paper provides a replicable ML framework for assessing transport emission targets. It highlights the gap between current policies and climate goals, useful for global policymakers and researchers focusing on transport decarbonization.
👥 読者別の含意
🔬研究者:Researchers can adopt the ML methodology for forecasting emissions and clustering countries by emission profiles.
🏢実務担当者:Corporate sustainability teams in logistics can use the findings to advocate for modal shift strategies.
🏛政策担当者:Policymakers can leverage the data-driven mitigation strategies to design targeted transport decarbonization policies.
📄 Abstract（原文）
Policymakers are increasingly calling for tangible actions to meet the United Nations targets for reducing emissions to 1990s levels. This study assesses whether climate targets are attainable by 2030 for a selected set of countries, with a particular focus on the transportation sector. PyCaret’s Python library is used to train and compare a range of time series models and derive transport emission forecasts of countries up to 2030. We employ a range of machine learning models, including ARIMA, AdaBoost, and Theta forecaster, using historical transport emissions data to forecast CO2 emissions and assess the likelihood of countries achieving the EU Green Deal’s decarbonization objectives. To further support policy design, we analyze freight modal emissions using trade flow data and group countries using the K-Means clustering based on their emission profiles. Our findings show that only two countries—Greece and Italy—are currently on track to meet the 2030 targets. The remaining nations require urgent and targeted policy reform, particularly in shifting freight transport toward lower-emission modes. We conclude by offering realistic, data-driven mitigation strategies for those unlikely to meet their commitments.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.6084/m9.figshare.32091650first seen 2026-05-05 19:42:53
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。