炭素フットプリント予測のための説明可能なグラフ学習フレームワーク：SHAPベースの政策洞察と国類似性ネットワークの統合

An explainable graph learning framework for carbon footprint prediction: Integrating SHAP-based policy insights and country similarity networks (原題)

Sibel Barın Özkan

Energy Reports📚 査読済 / ジャーナル2026-10-31#気候リスク

DOI: 10.1016/j.egyr.2026.109840

原典: https://doi.org/10.1016/j.egyr.2026.109840

🤖 gxceed AI 要約

日本語

欧州27カ国の2015〜2023年データを用い、機械学習・説明可能AI・グラフニューラルネットワークを統合して炭素フットプリントを予測する枠組みを提案。LightGBMが線形手法を大きく上回り、SHAP分析で化石燃料依存や廃棄物、環境政策が主要決定要因と判明。国類似性ネットワークを組み込んだGraph Attention Networkが最高精度を示し、排出形成が国内指標と国際的構造関係の双方に駆動されることを実証した。

English

This study integrates machine learning, explainable AI (SHAP), and graph neural networks to predict carbon footprints across 27 European countries (2015-2023). Tree-based models, especially LightGBM, outperformed linear approaches, while SHAP identified fossil fuel dependency, waste generation, and climate initiatives as key drivers. Adding a country similarity network via a Graph Attention Network improved accuracy, showing emissions are shaped by both national indicators and cross-country structural relationships.

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

日本企業・政策にとっては、国別炭素予測に説明可能AIとグラフ構造を組み込む手法が、SSBJやTCFDシナリオ分析の定量高度化に示唆を与える。ただし欧州データ中心のため国内実装には追加検証が必要。

In the global GX context

For global disclosure scholarship, this offers a template for combining explainable ML with network structure in climate-risk modeling, relevant to ISSB/TCFD scenario analysis where predictive rigor and interpretability are increasingly demanded.

👥 読者別の含意

🔬研究者:説明可能AIとGNNを気候予測に統合する方法論的枠組みとして、炭素予測研究の新たなベンチマークになる。

🏢実務担当者:国別・地域別の炭素動向予測と要因分解を、シナリオ分析やリスク評価の定量根拠として活用できる。

🏛政策担当者:化石燃料依存や廃棄物などSHAPが示す主要因は、的を絞った気候政策設計の優先順位付けに有用。

📄 Abstract（原文）

Accurate prediction of carbon footprint dynamics is essential for climate policy design and sustainable development planning, yet complex nonlinear interactions among energy systems, environmental factors, and socioeconomic activities make estimation challenging. To address this gap, this study proposes an integrated framework combining machine learning, explainable artificial intelligence, and graph neural networks to predict carbon footprints across 27 European countries from 2015 to 2023. Evaluating several algorithms revealed that tree-based models substantially outperformed linear approaches, with LightGBM achieving the highest machine learning performance. SHAP analyses identified fossil fuel dependency, municipal waste generation, environmental impacts, protection capacity, and climate initiatives as the primary predictive determinants. Furthermore, incorporating a country similarity network into Graph Neural Network architectures improved predictive accuracy, with the Graph Attention Network (GAT) delivering the overall best predictive performance. Robustness checks confirmed model stability across various configurations, demonstrating that carbon footprint formation is driven by both national indicators and structural cross-country relationships. Ultimately, this framework bridges a methodological gap in environmental forecasting, offering robust predictive tools for policymakers and actionable, feature-level insights for targeted climate mitigation strategies.

🔗 Provenance — このレコードを発見したソース

openalex https://doi.org/10.1016/j.egyr.2026.109840first seen 2026-11-02 04:53:43 · last seen 2026-11-06 05:00:03

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