RePec

Cryptocurrency markets are highly volatile, creating challenges for accurate risk management and forecasting. As digital assets become more integrated into financial systems, understanding their volatility dynamics is essential for investors and policymakers. Previous research has primarily applied standard GARCH models to cryptocurrencies, often neglecting advanced specifications that capture asymmetry, regime-switching, and long-memory effects. This limits the accuracy of volatility forecasts and fails to reflect the unique behaviour of digital assets. This study aims to identify the most effective GARCH-class models for forecasting volatility in Bitcoin, Ethereum, Binance Coin, and Ripple. We analyse daily returns from August 2017 to December 2024, applying eight advanced GARCH specifications: EGARCH, GJR-GARCH, FIGARCH, HYGARCH, MSGARCH, CS-GARCH, and Log-GARCH. Hyperparameter tuning is conducted via grid search across lag orders (p, q ∈ [1, 5]), mean equations, and error distributions. Model performance is evaluated using AIC, BIC, RMSE, and MAE. Results show that MSGARCH and EGARCH outperform symmetric and short-memory models, highlighting the importance of regime-switching and leverage effects. FIGARCH provides the best fit for Bitcoin and Ethereum, confirming long-memory persistence. Skewed Student’s t and GED distributions improve accuracy by capturing heavy tails and asymmetry. These findings demonstrate the limitations of standard GARCH models and underscore the value of advanced specifications in modelling cryptocurrency volatility. The study offers practical insights for traders and risk managers, contributing to more robust forecasting in non-stationary markets. Advanced GARCH models significantly enhance volatility prediction for digital assets. Future research could extend this framework to other speculative instruments or integrate machine learning techniques to further improve performance.

The growing depletion of fossil fuel resources and rising energy costs underscore the need for efficient renewable energy technologies, such as unglazed transpired solar collectors (UTSCs). UTSCs harness solar energy to preheat outdoor air, thereby improving building energy efficiency and reducing reliance on conventional heating systems. This study presents a computational fluid dynamics (CFD) analysis of UTSC performance under Lithuanian winter conditions (ambient air temperature −2.64 °C, solar irradiance 733.45 W/m2, wind speed 1.93 m/s) using two- and three-dimensional models developed in ANSYS FLUENT. The 3D model simulates a realistic wall fragment with multiple repeating sheet metal profiles and an air gap, while the 2D model represents a longitudinal section applicable to generic UTSC configurations. Both models were validated against experimental data and used to evaluate airflow velocity, pressure distribution, and air temperature rise. The results indicate overall thermal efficiencies of 54.32% for the 3D model and 54.07% for the 2D model, demonstrating that simplified 2D models can achieve comparable accuracy while significantly reducing computational cost. These findings highlight the potential of high-resolution CFD modelling for optimizing UTSC design and enabling faster, more reliable assessments for integration in industrial and commercial building applications.

The rapid development of electromobility increases the need for fast, accessible and robust charging stations devoted to EVs (electric vehicles). Planning a network of such stations poses new challenges—amongst others, a power supply that may power such chargers. One major concept is to utilise wind energy as a power source. The paper analyses meteorological data gathered since 2001 in several stations across Poland to achieve quantitative indexes, which summarise (a) wind power density (WPD) as a metric of energy amount, (b) long-term (multiannual) time trends of amount of energy, (c) short-term stability (and thus predictability) of the wind power. The indexes that cover the abovementioned factors allow the authors to answer the research questions, where the local wind conditions allow the authors to consider the integration of a wind powerplant and a network of EV chargers. Additionally, we investigated locations where the amount of available energy is sufficient, but the variability of wind power impedes its practical exploitation. In such cases, the power system may be extended by an energy storage system that acts as a buffer, smoothing power fluctuations and thereby improving the robustness and reliability of downstream charging systems.

This editorial review presents five scientific articles published in the Special Issue “Sustainable Transportation: Logistics and Route Network Aspects”, which explores the areas of transport and logistics from different—but interrelated—perspectives [...].

This article presents a thorough methodology for the planning of new urban metro systems by incorporating multi-regional empirical data alongside city-specific correction factors. This approach facilitates a more precise estimation of the necessary line length and the number of stations required. This research presents a systematic algorithm that produces various design alternatives, taking into account factors such as population, geographical area, and transportation demand, thereby tackling the significant variability noted among current global metro systems. A notable aspect of this work is the incorporation of operational performance forecasting, which includes elements such as crime rates, emergency response times, passenger density, and renewable energy utilization. This illustrates that these indicators are influenced not solely by the physical parameters of the system but also by wider social and infrastructural contexts. The proposed methodology offers urban planners a versatile, data-informed instrument that can be utilized across various urban contexts, thereby improving initial decision-making processes that promote sustainable metro development.

This research examines the relationship between Self-Determination Theory (SDT) constructs and objective measures of worker performance in an international call center environment. It analyzes workplace learning as a continuous process where formal training intersects with on-the-job skill application, investigating why employees exposed to identical learning experiences demonstrate different levels of knowledge transfer in their daily work. It focuses on the relationship between satisfaction of psychological needs (autonomy, competence, and relatedness) and seven aspects of performance. With the high training intensity needed in call center operations, the research gains an insight into adult learning motivation and its linkage to post-training performance results in diverse competency areas. Using confirmatory factor analysis and structural equation modeling, this research tests the hypothesis that self-determination-oriented workers will exhibit superior performance results. Outcomes are useful in explaining quality of motivation in work environments and organizational performance implications.  .

This study explores the factors that influence Gen Z consumers’ green purchasing behavior, addressing a gap in current research by combining the Theory of Planned Behavior, the Theory of Consumption Values, and Generational Theory, and applying them to the touristic accommodation context. Based on a quantitative survey of Gen Z tourists from Spain, Norway, and Lithuania, the study examined traditional constructs of the Theory of Planned Behavior alongside subjective knowledge, environmental consciousness, perceived value, and green trust. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings reveal a significant role for functional value (price) and emotional value (quality) in shaping Gen Z’s attitudes toward sustainable accommodation. Furthermore, subjective knowledge and environmental consciousness strengthen green trust, which in turn contributes to a more positive attitude toward sustainable touristic options. Attitude, subjective norms, and perceived behavioral control significantly predict behavioral intention, while both perceived behavioral control and behavioral intention directly influence actual purchasing behavior. The findings suggest that tourism marketers should focus on enhancing price-related and emotionally meaningful value propositions, while also fostering trust through clear communication of environmental performance. Overall, the study offers a comprehensive understanding of the drivers behind Gen Z’s sustainable accommodation choices and provides practical implications for promoting environmentally responsible tourism.

The widespread use of gasoline blended with 10% ethanol (E10) has raised questions regarding engine performance and emissions under conditions where ethanol supply may be disrupted, and pure gasoline (E0) is temporarily used instead. This study experimentally investigates the effects of E0 and E10 fuels on fuel consumption and exhaust emissions in spark-ignition engines equipped with two different fuel supply systems: multi-point fuel injection (MPI) and carburetion (CARB). Chassis dynamometer tests were performed on two passenger vehicles under steady-state part-load conditions at vehicle speeds of 60, 90, and 120 km/h, as well as during full-throttle operation. E0 and E10 were tested separately under identical operating points. Fuel consumption, brake-specific fuel consumption, air–fuel ratio, and exhaust gas components (CO, CO2, HC, O2) were measured and analysed. The results show that the MPI-equipped vehicle exhibited consistently lower fuel consumption when operating on E0 compared to E10, primarily due to the lower volumetric heating value of ethanol. In contrast, the carbureted engine demonstrated a stronger sensitivity to fuel composition, with E10 leading to leaner mixture formation and pronounced changes in fuel consumption and emissions. CO and HC emissions were significantly lower in the MPI engine, mainly due to closed-loop stoichiometric control combined with the presence of a three-way catalytic converter, while E10 substantially reduced these emissions in the carbureted engine. CO and HC emissions were significantly lower in the MPI configuration, mainly due to closed-loop stoichiometric control combined with the presence of a three-way catalytic converter. In the carbureted configuration, E10 substantially reduced CO and HC emissions compared to E0, primarily as a result of leaner mixture formation. Overall, the findings indicate that modern MPI engines are less sensitive to whether the supplied fuel is E10 and E0, whereas carbureted engines may show notable changes in performance and emissions under the same operating conditions.

Transport decarbonization and electrification are the current concepts of sustainable logistics. The European Green Deal aims to remove internal combustion engine vehicles from the roads and make the continent climate neutral by 2050. However, there is much debate about the means to achieve this goal and the rivalry between diesel and electric vehicles. This article aims to analyze the impact of the energy efficiency of diesel and electric vehicles on the sustainability of urban logistics and the benefits for the average transport user—the driver. The study uses scientific literature, statistical, comparative, SWOT analysis methods, and experimental research methods. In addition, a qualitative study was conducted with the help of experts, and the problematic relationships between diesel and electric vehicles were analyzed. The results of the study showed that even an old diesel vehicle is not inferior to a new electric vehicle in terms of energy efficiency and operation for the average user but does not meet the theoretical sustainability standards for urban logistics. Therefore, broader apolitical discussion and practical experiments are needed to ensure that the results of future research are unbiased.

Air pollution remains a major public-health concern, and exposure to particulate matter (PM), particularly PM10 (with a diameter ≤ 10 µm), is associated with adverse respiratory and cardiovascular outcomes. Most research relies on a singular model for PM10 surface estimation. This study is an assessment of a national-scale, daily PM10 estimation framework for Lithuania (2019–2024), using a hybrid machine-learning method that combines Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms. Hourly PM10 observations were aggregated from 18 monitoring stations to obtain daily means and temporal means. The predictors integrated meteorological factors, such as temperature, wind, humidity, and precipitation, to determine satellite-based atmospheric composition from Sentinel-5P Tropospheric Monitoring Instruments (TROPOMI). Atmospheric components include nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), formaldehyde (HCHO), and the absorbing aerosol index (AI). Moderate-Resolution Imaging Spectroradiometers (MODIS) were used to record land-surface temperature and static spatial descriptors, such as elevation, land cover, Normalized Difference Vegetation Index (NDVI), population, and road proximity. The dataset was partitioned temporally into training (70%), validation (20%), and testing (10%). The hybrid model achieved an improved accuracy, compared with single-model baselines, reaching a coefficient of determination (R2) of 0.739 in validation and R2 = 0.75 in the tested dataset. Mean absolute error (MAE) was 3.15 µg/m3, and root mean square error (RMSE) was 3.98 µg/m3. The results indicate a slight tendency to overestimate PM10 concentrations at lower concentration levels. Feature-importance analysis revealed that short-term temporal persistence is the key to daily PM10 prediction, while meteorological variables provide secondary contributions. Temporal evaluation, using consecutive two-year windows, revealed a consistent improvement in predictive performance from 2019–2020 to 2023–2024, while station-level analysis showed moderate-to-strong agreement between the predicted and observed PM10 concentrations across monitoring stations, with R2 ranging from 0.455 to 0.760. This provides decision-support capabilities for air-quality management, the evaluation of mitigation measures, and integration of air-pollution considerations into sustainable urban planning strategies assessing public-health protection.