Toward actionable insights for carbon neutrality: A sustainable spatio-temporal forecasting framework fusing socio-economic dependencies
カーボンニュートラルに向けた実践的洞察：社会経済的依存関係を融合した持続可能な時空間予測フレームワーク (AI 翻訳)
Aiting Xu, Senhao Fang, Jiapeng Chen, Zheyu Chen
🤖 gxceed AI 要約
日本語
本研究は、社会経済理論（STIRPAT）と適応的ニューラルアーキテクチャ（GSGDN）を統合した炭素排出予測フレームワークを提案。地理的加重回帰で地域依存関係を捉え、スパースゲーティング機構で計算資源を動的配分し、運用時の炭素フットプリントを削減。森林コピュラで不確実性を定量化し、MAPE 6.619%と低炭素運用を達成。高排出・サンプル外シナリオでも優れた性能を示し、炭素ガバナンスの意思決定支援に貢献。
English
This study proposes a carbon emission forecasting framework integrating STIRPAT economic theory with an adaptive neural architecture (GSGDN). It captures regional dependencies via geographically weighted regression and reduces operational carbon footprint through a sparse gating mechanism. A forest copula quantifies uncertainty, achieving a MAPE of 6.619% with low carbon emissions. The framework outperforms benchmarks in point and interval forecasting, especially in high-emission and out-of-sample scenarios, offering a sustainable decision-support tool for carbon governance.
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GX文脈では、SSBJ開示やカーボンニュートラル政策が進む中、地域別の排出予測と不確実性評価は自治体や企業の排出削減計画に有用。AIによる予測精度向上と計算コスト削減は、日本のデータ駆動型カーボン管理に貢献する。
In the global GX context
Globally, this framework aligns with TCFD/ISSB disclosure needs by providing robust carbon forecasting and uncertainty quantification. It supports proactive climate policy and early-warning systems, relevant for jurisdictions implementing carbon pricing and transition planning. The integration of socio-economic factors enhances the credibility of emission projections for investors and regulators.
👥 読者別の含意
🔬研究者:Provides a novel AI-based forecasting method that balances accuracy and computational sustainability, useful for advancing carbon emission modeling.
🏢実務担当者:Offers a decision-support tool for carbon governance, enabling differentiated regulation and proactive policy-making for corporate sustainability teams.
🏛政策担当者:Supports evidence-based carbon neutrality strategies with high-accuracy forecasts and uncertainty bounds, aiding policy design and early-warning systems.
📄 Abstract（原文）
Accurate carbon emission forecasting (CEF) is critical for achieving carbon neutrality. However, existing models often fail to capture complex socio-economic interdependencies and entail high computational carbon costs. To address these limitations, this study hypothesizes that integrating socio-economic theory with an adaptive and efficient neural architecture can simultaneously enhance prediction accuracy and computational sustainability. This study proposes a novel CEF framework that combines STIRPAT economic theory, a Graph Sparse Gating Decision Network (GSGDN), and a forest copula function. The framework first constructs multi-view socio-economic graphs via geographically weighted regression to represent regional dependencies. It then employs a GSGDN that fuses socio-economic and spatio-temporal information through dedicated modules while dynamically allocating computational resources via a sparse gating mechanism to reduce carbon footprint. Finally, a forest copula models cross-regional prediction-error dependencies to provide robust uncertainty quantification. Comprehensive evaluations show that the framework achieves a mean absolute percentage error (MAPE) of 6.619% with an operational carbon footprint of only 58.561 g/h, outperforming benchmark models in point and interval forecasting, as well as in high-emission and out-of-sample scenarios. These results demonstrate that the proposed framework offers a sustainable and precise decision-support tool for carbon governance, enabling differentiated regulation, proactive policy-making, and early-warning systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1080/15435075.2026.2710114first seen 2026-07-31 05:45:02
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