用图神经网络分析碳关税如何影响欧洲电价，发现低碳国家受益、高碳国家承压。
Will the Carbon Border Adjustment Mechanism Impact European Electricity Prices? A GNN-Based Network Analysis
- 构建时空图神经网络，模拟八国电力市场联动效应。
- 低碳国家电价或下降，高碳国家成本上升，出现结构性分化。
- 揭示市场优序机制变革是核心驱动，适合能源政策研究者参考。
欧盟碳边境调节机制（CBAM）给互联的欧洲电力市场带来复杂挑战。传统静态分析常忽略跨边界溢出效应，难以全面理解该政策影响。本文提出一种时空图神经网络（GNN）框架，同时量化CBAM对电力价格与碳强度（CI）的影响。研究构建了涵盖八个国家的子图模型。结果显示，CBAM并非单一税率，而是重塑市场的工具，导致结构分化。在模拟情景中，法国、瑞士等低碳国家可能获得竞争优势，国内电价下降；而波兰等高碳国家则面临成本双重压力。根本原因在于市场优序机制发生转变。
原文摘要 · Abstract (English)
The European Union's Carbon Border Adjustment Mechanism (CBAM) creates a complex challenge for the interconnected European electricity market. Traditional static analyses often miss the cross-border spillover effects that are vital for understanding this policy. This paper addresses this gap by developing a spatio-temporal Graph Neural Network (GNN) framework. It quantifies how CBAM affects electricity prices and carbon intensity (CI) at the same time. We modeled a subgraph of eight European countries. Our results suggest that CBAM is not just a uniform tax. Instead, it acts as a tool that transforms the market and creates structural differences. In our simulated scenarios, we observe that low-carbon countries like France and Switzerland can gain a competitive advantage. This suggests a potential decrease in their domestic electricity prices. Meanwhile, high-carbon countries like Poland face a double burden of rising costs. We identify the primary driver as a fundamental shift in the market's merit order.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。