This article embarks on a crucial exploration of the implementation and integration of environmental, social, and governance (ESG) criteria within the energy sector of European countries, with the ultimate objective of refining the energy legislation in Ukraine. The research delves into the evolving role of ESG standards as a cornerstone in corporate strategy and investment, transitioned from its roots in socially responsible investing to a comprehensive framework that intertwines ethical, environmental, and governance considerations with financial analysis. The main thrust of the study is the following: firstly, it provides a panoramic overview of ESG applications within European energy projects, underscoring pivotal policies, strategies, and instances of successful ESG standard incorporation. Secondly, it benchmarks and compares ESG practices across Europe’s energy sector to distill best practices and their consequential trends and impacts. Lastly, this study converges these European insights to forge pragmatic recommendations for Ukraine’s energy legislation, aiming for an elevated integration of ESG standards that are in step with European benchmarks. The anticipated outcomes offer recommendations for enhancing Ukraine’s energy sector with robust ESG standards, proposing steps that align with the country’s newly adopted Energy Strategy 2050 and international sustainability goals.

Purpose: This article presents a novel approach that leverages advanced data analytics and machine learning techniques to enhance marketing strategies. By precisely targeting and segmenting audience groups based on their descriptive profiles, the study aims to significantly improve the efficacy of marketing campaigns. Methods: The study employs several clustering and community detection algorithms, including Louvain Community, Greedy Modularity, and Label Propagation. These methods are applied to diverse datasets to identify distinct groups within the audience that exhibit specific behavioral and preference patterns. The approach emphasizes data-driven decision-making, which involves making decisions based on the analysis of data rather than intuition or observation, to optimize marketing outcomes. Results demonstrate that employing advanced clustering techniques can significantly refine the segmentation process, leading to more targeted marketing efforts. These methods successfully identified nuanced sub-groups within the datasets, which corresponded closely with customer behaviors and preferences variations, thereby allowing for more tailored marketing strategies. Discussion: The study's findings underscore the imperative for marketers to embrace sophisticated analytical techniques. Machine learning has the potential to transform marketing strategies by providing deeper insights into customer segmentation. This research highlights the importance of staying ahead of the curve in the face of the complexities of consumer markets and evolving business environments.

Modern aviation is one of the main consumers of petroleum-based fuels, consuming nearly 100 million gallons of fuel per year, and this consumption continues to grow. On the other hand, airlines have committed to achieving net-zero carbon dioxide (CO₂) emissions in the industry by 2050. Fulfilling this commitment necessitates the investigation of new and the optimization of existing processes for the production of alternative, renewable, and environmentally safe feedstocks. This article was prepared as part of the research project “Development of Technological Solutions for Obtaining Composite Motor Fuels from Secondary Raw Materials to Enhance Energy Security”.

The study employed a simulation based on a detailed dataset containing over 307,046 unique order identifiers and 1,050 unique product identifiers. This dataset included information such as order placement dates, product codes, quantities, and precise locations within the warehouse, including coordinates. The simulation modeled the order-picking route using the Single-Picker Routing Problem (SPRP) algorithms to minimize distance and travel time. The methods compared various wave-picking strategies and grouping methods (single-line and multi-line) for their effectiveness.The applied method significantly reduced the travel distance required by the order picker in the warehouse. The key to this optimization was consolidating orders into waves of specific sizes, achieving a fourfold distance reduction for the studied dataset. Additionally, the solution proposed grouping products by location within the warehouse, either in a single aisle or across multiple aisles based on proximity. Although this method often enhances efficiency, it did not in this particular case. However, it was included as it may yield better results with different datasets and further reduce travel distances in the warehouse. The research underscored the critical role of efficient routing and grouping strategies in warehouse operations. Although wave picking significantly reduced travel distances, the effectiveness of clustering strategies depended on the characteristics of the specific dataset, suggesting the need for tailored solutions based on the warehouse layout and the features of the orders. Future research could extend to integrating product volume and weight variations, which may further optimize order-picking strategies.

The quality of transport services is not only a measure of operational efficiency but also an important factor of strategic logistics management in the pursuit of sustainable development. This study identifies five key transport service quality indicators (timeliness, routing, economy, safety, efficiency) and uses data from a logistics monitoring system to assess them with Harrington’s desirability function. Each indicator’s performance is converted into a partial desirability score and these scores are combined into a single overall desirability score (D), with weights determined from the data. Notably, a threshold around D = 0.63 emerged as the benchmark for acceptable service quality. This numeric threshold provides managers with a clear KPI target—if the service quality index falls below 0.63, it signals the need for corrective action, whereas consistently achieving values near 0.8 reflects very good performance aligned with strategic sustainability goals. Based on the proposed approach, an algorithm and software tool were developed to automate the assessment process. The obtained results show how improvements in service reliability, safety and efficiency can be aligned with broader sustainability goals in automotive transportation. The proposed approach offers managerial decision makers a robust tool to guide policy and investment, ensuring that enhancements in transport service performance also advance environmental and social sustainability. In doing so, the framework advances SDG 9 by turning logistics telemetry into an actionable management index that strengthens resilient transport infrastructure and fosters practical innovation at the enterprise level.

Objectives: Entrepreneurship is defined as a process in which not only business is a participant, but also local governments and social organizations (more broadly, the public and social sectors). In the management process, entrepreneurship is manifested, for example, by choosing the right...