This study investigates managers’ perceptions of digitalization and artificial intelligence (AI) adoption within the framework of Industry 4.0, emphasizing the relationship between technological modernization, organizational culture, and sustainability. Drawing on empirical data collected in 2025 from 150 Romanian companies ’managers by applying a structured questionnaire, followed by a multivariate analytical approach supported by the Benjamini–Hochberg correction, the research identifies critical managerial perceptions that influence the success of digital transformation. The findings show that managers recognize digitalization as a strategic opportunity for process optimization and competitiveness. At the same time, they perceive it as a structural challenge driven by legacy systems, financial constraints, and limited digital competencies. Similarly, managers view AI as a valuable tool for data analysis and market forecasting, while also expressing concerns related to ethical, technical, and cybersecurity risks. The study further reveals managerial ambivalence toward Industry 4.0. Although automation and IoT are considered inevitable for maintaining competitiveness, their implementation remains constrained by logistical and cultural barriers. By integrating technological, organizational, and human dimensions, this research contributes to the literature on sustainable digital transformation. It provides an in-depth understanding of how managerial perceptions mediate the balance between innovation, responsibility, and long-term resilience. Finally, the results offer actionable insights for policymakers and business leaders seeking to align digitalization and AI initiatives with sustainable development objectives.

Ensuring a balance between economic growth and environmental sustainability is crucial to reduce the impact of CO₂ emissions and ensure sustainable economic development for future generations. The goal of this study is to investigate the influences of the adjusted gross dispensable income (GDI) per capita, the gross domestic product (GDP) per capita, energy consumption per capita, economic inequality in the population (GINI), and the median age on the carbon emissions in 27 European countries. In the dynamic panel analysis, CO₂ emissions represent the dependent variable, while adjusted GDI/capita, GDP/capita, energy use/capita, median age, and GINI are the independent variables. The valid results of the model show us that only the past values of CO₂ emissions, GDP/capita, and median age significantly influence the carbon emissions in the European countries analyzed. The median age and the GDP/capita have inversely proportional impacts on the carbon emissions in Europe. A 1% increase in GDP/capita produced a 0.006% decrease in carbon emissions in Europe. The younger the population is, the higher the carbon emissions. In contrast, the adjusted gross disposable income/capita, the energy consumption/capita, and economic inequality do not significantly influence CO₂ emissions/capita in the European countries and period analyzed.

To effectively reduce fuel consumption and emissions in personal transport, it is essential to understand how energy transfer inefficiencies arise under real-world driving conditions. This study investigates the behavioral and spatial determinants of energy transfer inefficiency in personal diesel vehicles using high-resolution vehicle telematics data. The research proposes a composite Energy Inefficiency Index (EII) derived from real-world indicators of driving behavior, including acceleration, braking, idling, speed variability, and trip structure. These indicators are normalized and weighted using principal component analysis to quantify inefficiency at trip and spatial levels. Geospatial analysis, including Global Moran’s I and heatmap visualization, is employed to identify spatial clustering of energy inefficiency across urban and extra-urban environments. The results reveal a moderate average level of energy inefficiency across the analyzed vehicle fleet, with braking frequency, acceleration frequency, trip duration, and idling time emerging as the primary behavioral drivers of inefficient energy transfer. A statistically significant positive spatial autocorrelation indicates pronounced clustering of inefficiency in dense urban areas characterized by congestion and stop–start traffic dynamics. Furthermore, this study evaluates potential fuel, cost, and CO₂ emission reductions achievable through improved driving behavior and compares these gains with those associated with vehicle electrification. The findings demonstrate that targeted behavioral interventions—such as eco-driving and idling reduction—can yield substantial efficiency improvements and emission reductions, complementing the benefits of electrification. Overall, this research provides a data-driven framework for managing energy transfer inefficiency in personal diesel vehicles by integrating behavioral analysis, spatial assessment, and telematics-based monitoring, offering practical insights for policymakers, transport planners, and vehicle technology developers.