Latest revision as of 10:54, 18 March 2026

Abstract

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

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