This study examines the integration and price behavior of European Union electricity markets using a Vector Error Correction Model (VECM). Employing daily wholesale day-ahead electricity prices from 24 EU countries spanning October 2017 to September 2024, the research identifies seven regional clusters of markets based on similarities in price trends. The analysis reveals strong long-term equilibrium relationships and dynamic short-term adjustments, highlighting the interconnectedness of these markets. Central players, such as Germany in Block 1 and France in Block 2, emerge as pivotal in driving regional stability, while markets like Romania and Bulgaria (Block 3) demonstrate significant interconnections. Scandinavian and Baltic regions (Blocks 4 and 5) showcase unique balancing mechanisms influenced by shared infrastructure. Aggregated inter-block dynamics underscore the critical role of central hubs like Blocks 1 and 3 in bridging market disparities. Despite progress, regional heterogeneity persists, with slower adjustments observed in certain clusters. The findings emphasize the need for targeted policies to enhance cross-border electricity trading and infrastructure investments, ensuring equitable integration across all regions. By addressing these disparities, the EU can bolster market efficiency and resilience, contributing to its overarching energy strategy and transition to sustainable energy systems.

The integration of Artificial Intelligence (AI) into modern economies holds transformative potential, but its environmental impact, particularly its carbon footprint, is a growing concern. This paper explores the hidden costs associated with AI, focusing on its substantial greenhouse gas (GHG) emissions. Training large-scale AI models, particularly those based on deep learning, is highly energy demanding, resulting in substantial GHG emissions. Operationally, the continued use of AI systems further exacerbates the environmental toll, especially in data centres powered by non-renewable energy sources. This paper highlights mitigation strategies, including the transition to renewable energy sources for powering AI infrastructures and the development of more energyefficient algorithms. Techniques such as model pruning, quantisation, and knowledge distillation are identified as crucial in reducing energy consumption during the training and operational phases of AI models. Additionally, the role of AI in sustainability efforts is examined, suggesting that AI could facilitate resource efficiency in industries such as agriculture, commerce, and manufacturing, thereby contributing to the global transition towards a low-carbon economy. While AI promises significant advancements across multiple sectors, it is essential to address its environmental costs through sustainable practices. Failure to do so may result in AI accelerating climate change, overshadowing its potential benefits.