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.

Greece has suffered the worst from the late 2000s global financial crisis. Despite the completion of the economic adjustment program the rate of recovery in Greece is still weak. The liquidity constraints in the Greek economy imply that the investment shock, which will accelerate its recovery rate, will be an outcome of investment inflows by privatizations and/or other opportunities arising to foreign investors (inward FDI) due to the devaluation of the assets in Greece caused by the crisis. However, the level of attracted by the Greek economy has always been well below the European Union average. This paper investigates the factors favoring inward FDI in Greece after the crisis. A survey is made by the use of a questionnaire for the collection of primary data on the activity of multinationals in Greece and non-parametric methods are used for investigating the attractiveness of the Greek market and the decision to invest.

Greece's slow recovery from the severe economic depression caused by the great global financial and economic crisis that erupted in late 2000s was interrupted by the effects of COVID-19 pandemic on economic activity. This chapter looks into the factors shaping Greece's competitive position and discusses the potential to achieve a V-shaped recovery at the end of the pandemic.

Purpose: This study investigates the impact of institutional factors on firms investing in R&D.Design/methodology/approach: The study uses data from the World Bank's Enterprise Surveys for the Republic of Cyprus, Greece, Italy and Portugal. A model with institutional factors as explanatory variables and firm performance factors as control variables is estimated with weighted least squares heteroscedasticity corrected regression. The reverse causality problem is addressed by using a two-stage least squares regression approach.Findings: The findings indicate that institutional quality has a significant influence on firms' R&D expenditure. The results have several implications in relation to findings of previous research.Research limitations/implications: The inclusion of more countries that were affected by the European economic crisis will probably give more insights about the effect of institutional factors on R&D.Practical implications: Policy makers have to address short-comings in institutional quality, particularly in terms of the labor regulation burden. Policy makers should prioritize anti-corruption measures to foster an environment that would attract more R&D in the Republic of Cyprus and Greece.Originality/value: This study contributes to the growing body of literature investigating the impact of institutional factors on R&D. It focuses on four developed European countries that bore the brunt of the European economic crisis and have to implement their recovery and resilience plans successfully, in order to recover from the effects of the COVID-19 pandemic on their economic activity.