This paper examines the impact of clean energy integration on CO2 intensity in European power systems over 2015–2024, based on a dataset of 38 European countries. The study applies three analyses: (i) descriptive statistics on cross-country patterns, (ii) fixed-effects regression on clean energy and CO2 intensity, and (iii) crisis-period modelling for 2021–2022. The descriptive results reveal significant heterogeneity. The average clean energy share is 30.8%, ranging from below 5% (Malta, Moldova) to above 45% (France) and approximately 38% (Sweden). Accordingly, CO2 intensity varies between 23.6 gCO2/kWh (Sweden) and 982 gCO2/kWh (Kosovo), indicating different decarbonization rates across Europe. Regression analysis provides statistically significant evidence that clean energy is associated with lower carbon intensity. A one-point increase in the share of wind and solar power corresponds to a reduction of 12.18 gCO2/kWh (p < 0.01), while a similar increase in other clean sources (hydroelectric, nuclear) corresponds to a decrease of 12.24 gCO2/kWh (p < 0.01). Electricity demand has no statistically significant relationship with CO2 intensity. Crisis-era modeling shows insignificant interaction terms for 2021-2022, suggesting stability of the relationships during the crisis. Overall, the findings support the importance of clean energy integration, highlight persistent inequalities across countries, and the value of policy resilience during major energy shocks.

With the widespread use of renewable energy sources (RES) in the smart grid, the next generation power system, short-term load forecasting (STLF) is of critical importance in grid stability and energy optimization. Traditional STLF models include issues such as high computational cost, dependency on cloud infrastructure, and latency issues, which are undesirable for real-time energy management. To solve these issues, the EdgeAI paradigm, which combines edge computing and artificial intelligence (AI), can be a promising solution. EdgeAI reduces the dependency on cloud-based systems by processing data close to the data source, offering advantages such as lowlatency and lowbandwidth. Thus, it increases the response speed by processing data in real time, making it suitable for STLF applications. In order to benefit from all these advantages, the EdgeAI-driven Hybrid Echo State Network and Gated Recurrent Unit (ESN-GRU) model is introduced for real-time and efficient STLF in this paper. ESN-GRU combines the fast training capabilities of ESN and sequential learning capabilities of GRU, and offers the advantages of high prediction performance and low latency inference in STLF. Experimental results show that the proposed model improves the R² score by 2.5% and significantly reduces the MAPE value from 129 to 0.101 compared to existing models. Benchmark results in edge environment prove that ESN-GRU provides up to 79% and 92% faster inference compared to state-of-the-art methods.