Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.03304 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917460944355328 |
|---|---|
| author | Shen, Jiachen Shi, Jian Wang, Dan Zhu, Han |
| author_facet | Shen, Jiachen Shi, Jian Wang, Dan Zhu, Han |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03304 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Will the Carbon Border Adjustment Mechanism Impact European Electricity Prices? A GNN-Based Network Analysis Shen, Jiachen Shi, Jian Wang, Dan Zhu, Han Machine Learning Computers and Society Systems and Control 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. |
| title | Will the Carbon Border Adjustment Mechanism Impact European Electricity Prices? A GNN-Based Network Analysis |
| topic | Machine Learning Computers and Society Systems and Control |
| url | https://arxiv.org/abs/2605.03304 |