Over the last decades, the contemporary way of living, as well as the technology development, has increased the household electricity consumption. The excessive use of electricity has a negative impact on the environment, increasing the carbon footprint and contributing to the climate change. Governments are more and more concerned about the way our societies consume energy and are committed to reduce the greenhouse emissions. As the residential sector contributes significantly to energy use, it is crucial to investigate the socio-economic parameters, dwellings’ characteristics, and climate conditions that determine the electricity consumption in households. The data of this study are collected from 1801 dwellings from all regions of Greece and two statistical models are built. Both of them conclude that the most significant determinants influencing the electricity consumption are the number of occupants, the size of the dwelling, the heating type, the heating and cooling hours, and the weather conditions.

Companies voluntary embody social and environmental actions via Corporate Social Responsibility (CSR). In this paper we indicate CSR polic ies in banking sector in Greece and we study the banks customers’ perceptions regarding their banks’ CSR polic ies. We present the ten biggest banks in Greece and we indicate the charitable programs and sponsoring that they offer in general during the last years in Greece. The present study empirically investigates the customers’ perception regarding CSR policies of banks; we study if they have really understood the meaning of CSR. Specifically, it is estimated whether customers believe that banks really wish to help society and the environment or they just wish to improve their sales and increase their profits. Under this framework, the results of empirical research are presented and the relevant findings are discussed

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.