The EU energy sector became a very important one as a result of the war in Ukraine. On the other hand, the EU started in defining and implementing new strategies regarding green economy and sustainability. Even though these strategies cover short and medium periods, they have as a main goal the decrease in the EU’s dependence of energy imports. This research is focused on present challenges, risks, and uncertainties related to energy production and consumption in all EU member states. In order to achieve the research objectives, a huge statistical database, which covered 2012–2021, was used. The analysis is based on specific indicators regarding primary energy production; imports and exports; gross available energy; final energy consumption; non-energy consumption; energy dependency; and energy intensity. There are at least three research procedures used in this paper: a meta-analysis, a statistical analysis, and an econometric analysis, as well. Finally, the analysis points out the disparities between member states regarding energetic resources and energy dependency using a new model for quantifying risk factors in the European energy system. Moreover, new public policies are proposed by the authors under a cluster approach of the EU’s regions.

One of the important problems of sustainable logistics is routing vehicles in a sustainable manner, the green vehicle routing problem, or vehicle routing problems which aim to reduce CO₂ emissions. In the literature research, it was seen that these problems were solved with heuristic, metaheuristic, or hyper-heuristic methods and hybrid approaches since they are in the NP-hard class. This work presents a parallel multi-process genetic algorithm that incorporates problem-specific genetic operators to minimize CO₂ emissions in the capacity-constrained vehicle routing problem. Unlike previous research, the algorithm combines parallel computing with tailored genetic operators in order to enhance the diversity of solutions and speed up convergence. Genetic algorithm models were developed to minimize total distance, CO₂ emissions, and both objectives simultaneously. Two genetic algorithm models were developed to minimize total distance and CO₂ emissions. Experimental results using the reference CVRP examples such as A-n32-k5 and B-n44-k7 show that the proposed approach reduces CO₂ emissions by 1.2% more than hybrid artificial bee colony optimization, 1.3% more than ant colony optimization, and 4% more than the traditional genetic algorithm. Experimental results using benchmark CVRP instances demonstrate that the proposed approach outperforms hybrid artificial bee colony optimization, ant colony optimization, and traditional genetic algorithms for most of the test cases. This is done by exploiting multi-core processors, and the parallel architecture has improved computational efficiency; the modules compare and update solutions against the global optimum. Results obtained show that prioritizing CO₂ emissions as the only objective yields better results compared to multi-objective models. This study makes two significant contributions to the literature: (1) it introduces a novel parallel genetic algorithm framework optimized for CO₂ emission reduction, and (2) it provides empirical evidence underscoring the advantages of emission-focused optimization in CVRP.