Background: Digital transformation is increasingly present in modern logistics, especially in the context of sustainability and circularity pressures. The integration of technologies such as Internet of Things (IoT), Radio Frequency Identification (RFID), and automated platforms involves not only infrastructure but also a strategic vision, a flexible organizational culture, and the ability to support decisions through artificial intelligence (AI)-based systems. Methods: This study proposes an extended conceptual model using structural equation modelling (SEM) to explore the relationships between five constructs: technological change, strategic and organizational readiness, transformation environment, AI-enabled decision configuration, and operational redesign. The model was validated based on a sample of 217 active logistics specialists, coming from sectors such as road transport, retail, 3PL logistics services, and manufacturing. The participants are involved in the digitization of processes, especially in activities related to operational decisions and sustainability. Results: The findings reveal that the analysis confirms statistically significant relationships between organizational readiness, transformation environment, AI-based decision processes, and operational redesign. Conclusions: The study highlights the importance of an integrated approach in which technology, organizational culture, and advanced decision support collectively contribute to the transition to digital and circular logistics chains.

The European energy transition, marked by the increasing share of renewable sources in the production mix, brings to the fore the issue of maintaining power quality under conditions of high variability. This study proposes an adaptive monitoring model based on a zonal classification of electrical networks according to the volatility of net renewable production (wind and photovoltaic). The approach relies on a proprietary Renewable Variability Index (RVI), developed using publicly available European datasets, to assess the mismatch between electricity consumption and renewable generation in six representative countries: Germany, Denmark, Spain, Poland, Romania, and Sweden. Based on this index, the model defines three zonal risk levels and recommends differentiated power quality monitoring strategies: continuous high-resolution observation in critical areas, adaptive monitoring in medium-risk zones, and conditional event-based activation in stable regions. The results demonstrate a significant reduction in data acquisition requirements, without compromising the capacity to detect disruptive events. By incorporating adaptability, risk sensitivity, and selective allocation of monitoring resources, the proposed framework enhances operational efficiency in smart grid environments. It aligns with current trends in smart grid digitalization, enabling scalable, context-aware control and protection mechanisms that support Europe’s sustainability and energy security objectives while contributing to the broader goals of sustainable energy transition and long-term grid resilience.

Growing concerns about sustainability highlight the need to understand not only rational but also neurocognitive mechanisms that shape consumer decisions. This study examines how discrete emotions—such as empathy, moral satisfaction, and responsibility—interact with reflective cognitive control to influence green purchase intention, with neural loyalty functioning as a mediating mechanism. Grounded in dual-process theory, the proposed model is empirically tested through PLS-SEM using data from 276 consumers in Romania, Poland, and the Czech Republic, actively engaged with ecological products. The results demonstrate that both emotional and cognitive dimensions significantly predict purchase intention, while neural loyalty partially mediates these relationships, transforming temporary reactions into stable behavioral patterns. These findings suggest that bridging the intention–behaviour gap in sustainable consumption requires the integration of affective engagement, rational evaluation, and loyalty consolidation. The study contributes to sustainable marketing literature by positioning neurocognitive drivers as key antecedents of pro-environmental behaviour and by offering practical insights for designing interventions that effectively convert green intentions into consistent actions. All structural relationships were statistically significant (p < 0.05), confirming the robustness of the proposed model.

The transition to a low-carbon economy is profoundly reshaping European labour markets, creating both opportunities for sustainable employment and challenges for regions reliant on carbon-intensive sectors. Assessing how prepared EU Member States are for this shift remains difficult due to the lack of unified evaluation tools. This study introduces the Green Labour Market Readiness Index (GLMRI)—a composite measure assessing the adaptability of national labour markets to the energy-driven green transformation in nine EU countries: Germany, France, Sweden, Spain, Italy, Greece, Poland, Romania, and the Czech Republic. The index integrates five dimensions—education and skills, investment and infrastructure, policy and institutional quality, labour market structure, and innovation—based on harmonized data from 2010 to 2024. Panel econometric models (Fixed and Random Effects), combined with Hausman tests, are used to examine how structurally independent external energy-system characteristics, institutional capacity, and macro-structural labour-market conditions are associated with observed variation in labour-market readiness, as captured by the GLMRI composite outcome. Machine learning algorithms (Random Forest, XGBoost, LSTM) are employed to forecast readiness trajectories until 2040 under alternative policy scenarios. Results reveal persistent asymmetries between Northwestern and Southeastern Europe, showing that successful energy transition is closely associated not only with investment and innovation but also with human capital and governance quality. These associations are interpreted as diagnostic rather than causal, highlighting how external structural conditions shape the translation of energy-transition pressures into differentiated labour-market outcomes. The GLMRI provides a methodological and policy-relevant framework, helping decision-makers prioritize resources and design measures that make Europe’s energy transition sustainable, inclusive, and equitable.