Deep Learning Approaches for Carbon Pricing Mechanism Design in Green Transportation Supply Chains
グリーン交通サプライチェーンにおける炭素価格設定メカニズム設計のための深層学習アプローチ (AI 翻訳)
Z. Gao
🤖 gxceed AI 要約
日本語
本研究はLSTMとGCNにマルチヘッドアテンションを統合したハイブリッド深層学習モデルを提案し、グリーン交通サプライチェーンにおける動的炭素価格最適化を実現。従来の静的モデルと比較して予測精度23.7%向上、コスト最適化18.5%改善。実ケースでは平均21.6%の排出削減と15.5%のコスト削減を達成。多目的最適化フレームワークが経済性と環境便益のバランスを実証した。
English
This study proposes a hybrid deep learning model integrating LSTM, GCN, and multi-head attention for dynamic carbon pricing optimization in green transportation supply chains. It improves prediction accuracy by 23.7% and cost optimization by 18.5% over static models. Real case studies show 21.6% emission reduction and 15.5% cost reduction. The multi-objective framework balances economic and environmental benefits.
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GXリーグや排出量取引制度の拡大が進む中、動的炭素価格設定の技術的基盤は実務上重要。本手法はサプライチェーン全体の最適化に寄与し、企業の脱炭素戦略やSBT達成を支援する可能性がある。
In the global GX context
Globally, carbon pricing mechanisms increasingly require dynamic, data-driven approaches. This paper addresses the limitations of static models by leveraging deep learning for real-time optimization, relevant to jurisdictions like the EU ETS, California cap-and-trade, and China's national ETS.
👥 読者別の含意
🔬研究者:Novel integration of LSTM, GCN, and attention for dynamic carbon pricing in supply chains; ablation and sensitivity analyses provide robust validation.
🏢実務担当者:Method can be adapted for real-time carbon pricing and cost optimization in green logistics, supporting Scope 3 emission reductions.
🏛政策担当者:Demonstrates technical feasibility of dynamic carbon pricing mechanisms, offering inputs for next-generation policy design.
📄 Abstract（原文）
This study uniquely integrates Long Short-Term Memory networks (LSTM) and Graph Convolutional Networks (GCN) with a multi-head attention mechanism to address dynamic carbon pricing optimization in green transportation supply chains, overcoming the limitations of traditional static models. As global climate change issues become increasingly severe, the design of carbon pricing mechanisms for green transportation supply chains has become a key factor in promoting sustainable development. We construct a hybrid deep learning model that simultaneously captures temporal dependencies in carbon emission data and spatial relationships in supply chain network structures. Traditional carbon pricing methods often rely on static models and simplified assumptions, making it difficult to adapt to complex and dynamic supply chain environments. Experimental results show that the proposed deep learning method improves carbon price prediction accuracy by 23.7% compared to traditional methods and achieves 18.5% improvement in supply chain cost optimization. Furthermore, the method achieved an average 21.6% carbon emission reduction and 15.5% cost reduction in three real green transportation supply chain cases, demonstrating its effectiveness in practical applications. The multi-objective optimization framework successfully balances the trade-off between economic and environmental benefits through organic integration of genetic algorithms and deep learning models. Ablation experiments validated the importance of each model component, and sensitivity analysis confirmed the rationality of parameter settings. This method provides strong technical support for formulating more precise and dynamic carbon pricing policies, offering significant theoretical value and practical significance for promoting sustainable development of green transportation supply chains.OPEN ACCESS Received: 17/09/2025 Accepted: 24/11/2025 Published: 20/03/2026
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.23967/j.rimni.2025.10.73399first seen 2026-05-05 22:53:58
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。