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Energy companies' carbon reduction, low-carbon transformation, and green innovation for deep learning algorithms under the carbon neutrality goal

カーボンニュートラル目標下における深層学習アルゴリズムを用いたエネルギー企業の炭素削減・低炭素転換・グリーンイノベーション (AI 翻訳)

Xiaohui Xie

Journal of Renewable and Sustainable Energy📚 査読済 / ジャーナル2026-05-01#炭素会計Origin: CN
DOI: 10.1063/5.0320236
原典: https://doi.org/10.1063/5.0320236

🤖 gxceed AI 要約

日本語

本論文は、グラフニューラルネットワークを用いた炭素排出モデリングフレームワークを提案。生産設備・エネルギー消費ユニット・排出係数をノードとするヘテロジニアスグラフを構築し、マルチスケールグラフ畳み込みと動的アテンション機構により重要ノードを特定。ゲート付き回帰ユニットで時間発展をモデル化し、炭素排出量と生産コストの二目的最適化を実現。実験では設備警告のF1値0.89、30%削減制約下で限界費用209.41元/トンと従来モデルより低コストを達成。

English

This paper proposes a carbon emission modeling framework based on graph neural networks. It constructs a heterogeneous graph with production equipment, energy consumption units, and emission factors as nodes, using multi-scale graph convolution and dynamic attention for key node identification. A gated recurrent unit models temporal evolution, and a dual-objective optimization balances total carbon emissions and production costs. Experiments show an F1 score of 0.89 for equipment warning and a marginal cost of 209.41 yuan/ton under a 30% reduction constraint, outperforming traditional models.

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

中国の電力企業を対象とした研究だが、グラフニューラルネットワークによる炭素排出モデリング手法は日本のエネルギー企業にも応用可能。特に、設備レベルの詳細な排出原単位可視化やコスト最適化は、日本のGX実践における排出削減投資判断に示唆を与える。

In the global GX context

This Chinese study presents a graph neural network-based approach for carbon emission modeling that can be applied globally to energy companies. Its focus on cost-effective emission reduction through AI-driven optimization is relevant for corporate decarbonization strategies under frameworks like TCFD and ISSB, though direct applicability requires adaptation to local data and regulations.

👥 読者別の含意

🔬研究者:Provides a novel hybrid deep learning approach (GNN+GRU) for carbon emission modeling and dual-objective optimization, advancing AI applications in carbon accounting.

🏢実務担当者:Offers a technical blueprint for energy companies to model emissions at equipment level and optimize production costs under carbon constraints, useful for sustainability teams implementing digital tools.

🏛政策担当者:Demonstrates cost-effective emission reduction potentials through AI, informing policy design for industrial decarbonization subsidies or carbon pricing mechanisms.

📄 Abstract（原文）

To address the problems faced by energy companies, such as complex carbon emission data modeling and insufficient decision support, as well as the difficulty of traditional methods in meeting both real-time and accuracy requirements, this paper proposes a carbon emission modeling framework based on a graph neural network. This paper first constructs a heterogeneous graph with production equipment, energy consumption units, and emission factors as nodes, and defines edge weights through material/energy flows; then, this paper uses multi-scale graph convolution to aggregate equipment layer and plant layer features, introduces a dynamic attention mechanism, calculates node weights, and combines emission thresholds to enhance key node identification, embeds a gated recurrent unit to model temporal evolution, and fuses graph features and historical states through reset gates and update gates; finally, this paper establishes a dual-objective optimization function for total carbon emissions and production costs, balancing the two with weight coefficients. Experiments show that the F1 value of the equipment warning performance of the proposed model reaches 0.89; under the 30% emission reduction constraint, the marginal cost is 209.41 yuan/ton, which is 115.38 yuan/ton lower than that of the graph convolutional networks model. This cost advantage lowers the threshold for green innovation for enterprises; by coordinating plant-level energy dependencies, this method provides a technical path for coordinated emission reduction in upstream and downstream industrial chains, and provides accurate modeling and optimized decision-making support for the low-carbon transformation of energy enterprises.

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