The object of the research is the alignment of innovative energy technologies with the Sustainable Development Goal (SDG) 12, which pertains to sustainable consumption and production. The problem to be solved – there is an ambiguity regarding how these technologies contribute to SDG 12. This study aims to conduct a strategic analysis of external factors influencing innovative energy technologies from the perspective of SDG 12. The results show that innovative energy technologies, like bioenergy, geothermal energy, solar energy, wind energy, hydropower, and ocean energy, enhance energy efficiency, cut emissions, and reduce waste and toxins. These technologies align with SDG 12 and are shaped by various legislations. They intersect with the shift from fossil fuels, requiring analysis in the context of other UN SDGs. Legal frameworks must adapt to innovative energy technologies. The findings highlight that innovative energy technologies support SDG 12 by ensuring energy efficiency and reducing emissions. These technologies address economic and environmental challenges, promote sustainable progress and economic growth, enhance business competitiveness, stabilize prices, diversify energy sources, ensure national energy security and reduce energy poverty. The scope and conditions for practical application of the results of this research provide valuable insights for the scientific community, energy companies, policymakers, and investors in the energy sector. They can be used to inform decisions and strategies aimed at aligning energy technologies with the objectives of SDG 12. However, the implementation and effectiveness of these technologies also depend on regulatory support, industry collaboration, and public acceptance of new energy solutions.

This study investigates the severity of injuries sustained by over seven million participants involved in road traffic incidents in Poland between 2015 and 2024, with a view to supporting sustainable mobility and the United Nations Sustainable Development Goals. Road safety is a crucial dimension of sustainable development, directly linked to public health, urban liveability, and the socio-economic costs of transportation systems. Using a harmonised participant-level dataset, this research identifies key demographic, behavioural, and environmental factors associated with injury outcomes. A novel five-level injury severity variable was developed by integrating inconsistent records on fatalities and injuries. Descriptive analyses revealed clear seasonal and weekly patterns, as well as substantial differences by participant type and driving licence status. Pedestrians and passengers faced the highest risk, with fatality rates more than five times higher than those of drivers. An XGBoost classifier was trained to predict injury severity, and SHAP analysis was applied to interpret the model’s outputs at the feature level. Participant role emerged as the most important predictor, followed by driving licence status, vehicle type, lighting conditions, and road geometry. These findings provide actionable insights for sustainable road safety interventions, including stronger protection for pedestrians and passengers, stricter enforcement against unlicensed driving, and infrastructural improvements such as better lighting and safer road design. By combining machine learning with interpretability tools, this study offers an analytical framework that can inform evidence-based policies aimed at reducing crash-related harm and advancing sustainable transport development.

Traditional transportation systems are increasingly scrutinized for their environmental footprint, driving the need for sustainable alternatives. Recent developments in renewable energy technologies have paved the way for Electric Vehicles, Hydrogen Fuel Cells, Biofuels, Solar-Powered Vehicles, and Hybrid Systems, all of which offer environmentally-friendly solutions. However, the successful integration of these technologies into existing transport systems presents challenges such as infrastructure development, policymaking, public perception, and societal acceptance. Sustainable transportation encompasses more than technology and economics. It addresses societal inclusivity, environmental conservation, and long-term viability. Social equality ensures equitable access to transport, economic considerations highlight short-term costs and long-term benefits, and health and well-being are affected by sustainable transportation patterns. Community engagement, though challenging, is essential to craft locally tailored solutions and foster a collective sense of ownership. These facets form the foundation for a greener, healthier, and more inclusive transport system. The global shift towards renewable transport technologies is multifaceted, integrating political, economic, social, technological, environmental, and legal dimensions. Using the PESTEL method, the complex transition towards renewable transport technologies can be understood, especially in contexts like Ukraine. Given its unique socio-political landscape, Ukraine's move towards sustainable transport is influenced by multiple factors, including international climate agreements, economic restructuring, public perception, and technological advancements. Ukraine's transport sector, heavily reliant on fossil fuels, has the potential to transform using its abundant renewable resources. Tapping into these resources can offer numerous benefits, though technological and infrastructural challenges persist. Strategic investments and embracing renewable technologies are key for Ukraine to build a sustainable transport and societal framework.

For effective logistics planning and pricing strategies, it is essential to predict road freight transportation costs accurately. Using a real-world dataset with 45,569 freight offers and 52 different variables, including financial, logistical, geographical, and temporal characteristics, this study presents a data-driven method for forecasting transport prices. To create a strong predictive model, the approach combines hyperparameter optimization, evolutionary feature selection, and extensive feature engineering. Because gradient boosting works well for modelling intricate, nonlinear relationships, it was used as the main algorithm. Temporal dependencies were maintained through a nested cross-validation framework with a time-series split, which improved the generalizability of the model. With a mean absolute percentage error (MAPE) of 6.27%, the model showed excellent predictive accuracy. Key predictive factors included total transport distance, load and delivery quantities, temperature constraints, and aggregated categorical features such as route and vehicle type. The results confirm that evolutionary algorithms are capable of efficiently optimizing model parameters, as well as feature subsets, greatly enhancing interpretability and performance. In the freight logistics industry, this method offers useful insights for operational and dynamic pricing decision-making. This model may be expanded in future research to include external data sources and investigate its suitability for use in various geographic locations and modes of transportation.