This study provides a comprehensive life-cycle assessment (LCA) of renewable energy sources, focusing on the ${\mathrm{CO}}_2$ emissions and ecological impacts associated with photovoltaic (PV) systems and wind energy technologies. The research evaluates emissions from raw material extraction, production, operation, and disposal, as well as the role of energy-storage systems. Photovoltaic systems exhibit life-cycle ${\mathrm{CO}}_2$ emissions ranging between 28–100 [g ${\mathrm{CO}}_2$eq/kWh], influenced by factors like production energy mix and panel efficiency. Wind turbines demonstrate lower emissions, approximately 7–38 [g ${\mathrm{CO}}_2$eq/kWh], with variations based on turbine type and operational conditions. Despite low operational emissions, the full environmental impact of renewables includes biodiversity disruptions, land use changes, and material recycling challenges. The findings highlight that while renewable technologies significantly reduce ${\mathrm{CO}}_2$ emissions compared to fossil fuels, their ecological footprint necessitates integrated sustainability strategies. The analysis supports policymakers and stakeholders in making informed decisions for a balanced energy transition, emphasizing the need for continued innovation in renewable technology life-cycle management.

Distributed operational data rarely translates directly into business decisions. Meanwhile, in almost all industries, including the automotive industry, especially in the premium segment, it is crucial to identify the factors conducive to closing the transaction at an early stage. The aim of this study is to develop classification models that make it possible to predict the probability of success of a particular Mercedes-Benz offer with regard to vehicle configuration. Such a tool enables optimal allocation of resources (salespeople’s time, media budgets, production capacity), which is confirmed by the literature on customer relationship management. This study evaluates the usefulness of four machine learning algorithms—Random Forest (RF), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine with an RBF kernel (SVM-RBF)—in forecasting sales, which was encoded as the binary variable Success. Among the tested models, Random Forest achieved the best results with an accuracy of 84.3%, F1-score of 0.73, and AUC of 0.90, indicating a very good ability to distinguish between successful and unsuccessful transactions. The results can be used for lead prioritization, dynamic discounting, optimization of marketing campaigns, and distribution/production planning.

The application of artificial intelligence (AI) in traffic management is expanding rapidly, offering innovative ways to optimize urban transportation systems. This paper provides a concise overview of key AI-driven traffic control strategies – including congestion forecasting, dynamic rerouting, and adaptive traffic signal control – that aim to reduce congestion and improve road safety. We combine insights from global research with case studies from Polish cities to highlight the practical benefits and challenges of implementing these AI solutions in real traffic systems. Our analysis confirms that early AI deployments are already yielding measurable improvements in traffic flow efficiency and safety. Looking ahead, integrating AI with connected and autonomous vehicles and with multimodal transport networks promises to further enhance the efficiency, safety, and sustainability of urban mobility.