This study examines the implications of replacing the Italian vehicle fleet with electric vehicles powered exclusively through fast and slow charging. The purpose is to quantify the additional electrical energy and peak charging power required, and to assess their compatibility with the present characteristics of major European electricity systems. The methodology combines national mobility statistics, estimated charging demand profiles, and empirical scaling factors derived from refuelling infrastructure to determine both annual energy requirements and instantaneous power needs. The analysis indicates that full fleet electrification for night-only charging would increase national electricity consumption by approximately 40–50%, a substantial yet potentially manageable rise in annual energy consumption. By contrast, the charging power needed to support large-scale fast charging reaches values close to 280 gigawatts, far exceeding the peak loads currently managed by existing transmission networks. This peak requirement is nearly five times higher than the present Italian maximum demand and surpasses, by large margins, the peak values recorded in comparable European systems. The results indicate that the principal challenge of transport electrification lies in accommodating extremely concentrated power demand within limited temporal windows. The conclusions emphasize the need for substantial upgrades to transmission and distribution networks, complemented by the widespread adoption of controlled slow charging and demand-shifting strategies that can help reduce peak loads. These findings suggest that the feasibility of large-scale vehicle electrification hinges critically on managing instantaneous power rather than total energy, underscoring the importance of coordinating infrastructure planning, regulatory frameworks, and charging behavior to ensure that electric mobility can be integrated into existing power systems without compromising stability or reliability.

The goal of the present work is to evaluate and select the optimum chitosan-based biodegradable biopolymer composite for energy storage devices for sustainable planning. In this study, “sustainable planning” is specifically addressed at the material selection stage, focusing on the identification of biodegradable and environmentally benign polymer composites that reduce long-term ecological impact and electronic waste generation. The proposed model therefore supports early-stage sustainable design decisions without requiring a full life-cycle assessment. To assess the options—pure chitosan and chitosan modified with different weight percent (10%, 20% and 30%) of 2,6-pyridinedicarboxylic acid; the study offers an integrated multi-criteria decision-making (MCDM) approach called TOPSIS. The entropy approach is used to overcome the impreciseness of eliciting judgments in the preferences of criteria since information pertaining to material attributes is always imprecise. The best sources are then chosen using the TOPSIS approach. According to the results, alternating current (AC) conductivity (40 Hz) is the most important criterion, and chitosan–2,6-pyridinedicarboxylic acid (CPCA) 20 is the best option with the greatest score value. The robustness of the proposed methodology is further demonstrated by sensitivity analysis.

Reliable characterisation of wind speed variability is essential for assessing wind energy potential, particularly in regions where low-speed regimes dominate and resource uncertainty is high. In this study, long-term near-surface wind speed behaviour in Abuja, Nigeria, was statistically modelled using 46 years (1980–2025) of monthly mean Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalysis data at a height of 10 m above ground level. Descriptive statistical properties, including mean, standard deviation, skewness, and kurtosis, were first evaluated to characterise distributional features and deviations from Gaussian behaviour. Three skewed probability density functions (PDFs)—Weibull, Gamma, and Lognormal distributions—were subsequently fitted using Maximum Likelihood Estimation (MLE) and the Method of Moments (MOM). Model performance was assessed through graphical and statistical diagnostics, including probability density histograms, quantile–quantile (Q-Q) plots, and Cullen–Frey skewness–kurtosis analysis, enabling comparative evaluation of tail behaviour and modal structure. The wind regime in Abuja was found to be relatively stable and dominated by low wind speeds, with the principal mode located between 1.5 and 2.0 m/s. Approximately 80% of observed wind speeds were below 2.2 m/s, indicating a persistent low-energy environment. The Weibull and Gamma distributions provided the most accurate representation of the empirical data, successfully capturing the moderate positive skewness, limited tail extent, and weak bimodal tendency. In contrast, the Lognormal distribution systematically overestimated probability density at lower wind speed intervals and exhibited poorer agreement in upper quantiles. These findings demonstrate that skewed distribution modelling significantly improves representation of low-speed wind regimes and highlight the importance of site-specific statistical parameterisation for wind resource assessment in semi-arid Sub-Saharan environments. The results provide a robust statistical basis for wind energy feasibility analysis, micro-siting considerations, and hybrid renewable system design in regions characterised by marginal wind resources.