This study examines the causality relationships between foreign direct investments (FDI), exports, unemployment and economic growth in the fifteen old EU members using panel data covering the period 1970-2015. The Hausman test is applied for choosing between Fixed Effect and Random Effect approach in order to estimate the panel VAR equations for Granger causality tests. The results revealed three bidirectional causalities between economic growth and exports, exports and FDI, and exports and unemployment and three unidirectional causalities running from FDI to economic growth, FDI to unemployment and from economic growth to unemployment. Policy implications are then explored in the conclusions.

The energy sector plays a pivotal role in economic development, societal progress, and environmental sustainability, yet heavy reliance on fossil fuels remains a major challenge for achieving climate neutrality. Within this context, the European Union (EU-27) has committed to ambitious climate goals, including achieving carbon neutrality by 2050, making it a critical region for studying energy transition. This study analyzes the determinants of fossil fuels’ share (SFF) in final energy consumption at the aggregate EU-27 level over a 19-year period (2004–2022) and forecasts trends in the region’s energy transition through 2030. Using a random forest (RF) regressor, complex nonlinear relationships between SFF and six key predictors—GDP, population, industrial production, CO₂ emissions, renewable energy share (SRE), and energy intensity—were modeled. Model interpretability was enhanced through Shapley additive explanations (SHAP) and partial dependence plots (PDPs), revealing CO₂ emissions and SRE as the dominant predictors with opposing effects on SFF. Interaction effects highlighted the synergistic role of emission reduction and renewable energy adoption in minimizing fossil fuel reliance. GDP, while less influential overall, exhibited a significant negative relationship with SFF during early growth stages. Forecasts indicate a steady decline in fossil fuel reliance, from 1.8% in 2022 to 1.33% by 2030, supporting the EU’s climate objectives by emphasizing the importance of renewable energy adoption and emission control. This study demonstrates the transformative potential of machine learning and explainable AI (XAI) techniques in providing actionable insights to advance the EU-27’s sustainability journey.

As digital transformation accelerates across Europe, cloud computing plays an increasingly central role in modernizing public services and private enterprises. Yet adoption rates vary markedly among EU member states, reflecting deeper structural differences in digital capacity. This study employs explainable machine learning to uncover the drivers of national cloud adoption across 27 EU countries using harmonized panel datasets spanning 2014–2021 and 2014–2024. A methodological pipeline combining Random Forests (RF), XGBoost, Support Vector Machines (SVM), and Elastic Net regression is implemented, with model tuning conducted via nested cross-validation. Among individual models, Elastic Net and SVM delivered superior predictive performance, while a stacked ensemble achieved the best overall accuracy (MAE = 0.214, R² = 0.948). The most interpretable model, a standardized RF with country fixed effects, attained MAE = 0.321, and R² = 0.864, making it well-suited for policy analysis. Variable importance analysis reveals that the density of ICT specialists is the strongest predictor of adoption, followed by broadband access and higher education. Fixed-effect modeling confirms significant national heterogeneity, with countries like Finland and Luxembourg consistently leading adoption, while Bulgaria and Romania exhibit structural barriers. Partial dependence and SHAP analyses reveal nonlinear complementarities between digital skills and infrastructure. A hierarchical clustering of countries reveals three distinct digital maturity profiles, offering tailored policy pathways. These results directly support the EU Digital Decade’s strategic targets and provide actionable insights for advancing inclusive and resilient digital transformation across the Union.