Abstract: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.
| Subjects: | Machine Learning (cs.LG); Computers and Society (cs.CY); Systems and Control (eess.SY) |
| Cite as: | arXiv:2605.03304 [cs.LG] |
| (or arXiv:2605.03304v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.03304 arXiv-issued DOI via DataCite (pending registration) |
From: Jiachen Shen [view email]
[v1]
Tue, 5 May 2026 02:48:15 UTC (6,794 KB)
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