The transition towards low-carbon energy systems has been increasingly recognised as a critical global priority for mitigating climate change, reducing dependence on fossil resources, and promoting sustainable socioeconomic development. Although Brazil possesses one of the world’s most renewable energy matrices, supported primarily by hydropower, bioenergy, wind, and solar resources, significant challenges remain in the effective integration of waste-to-energy technologies and circular resource management strategies. In this review, the current status of renewable energy deployment in Brazil is critically assessed alongside the generation, management, and valorisation potential of major waste streams, including agricultural biomass, agro-industrial residues, food-processing wastes, municipal solid waste (MSW), wastewater sludge, construction and demolition waste (CDW), pulp and paper residues, and end-of-life tyres. Existing treatment practices and recovery technologies are systematically examined with emphasis on their capacity to convert waste into value-added products such as solid, liquid, and gaseous biofuels, secondary raw materials, and platform chemicals. Particular attention is given to technological limitations, regional disparities in infrastructure, and policy gaps that have constrained the broader implementation of decentralised renewable energy systems, especially in rural and residential sectors. It is observed that despite substantial progress in renewable electricity generation, waste recycling rates, energy recovery efficiency, and integrated waste management practices remain comparatively underdeveloped. The adoption of advanced thermochemical, biochemical, and material recovery technologies is shown to offer significant opportunities for emissions reduction, resource efficiency, and industrial symbiosis. Furthermore, the role of regulatory frameworks, economic incentives, and public investment in accelerating the transition towards a circular and low-carbon economy is highlighted. The findings demonstrate that the strategic integration of renewable energy expansion with waste valorisation could substantially enhance energy security, environmental performance, and economic resilience in Brazil. The technological pathways, policy mechanisms, and management strategies discussed are also considered transferable to other emerging economies facing similar energy and waste management challenges.

Municipal solid waste management remains a critical sustainability challenge in India, while waste-to-energy (WtE) systems increasingly attract attention as a pathway to simultaneously support waste reduction, resource recovery, and sustainable energy development. However, investment participation in WtE projects remains inconsistent despite growing policy interest and deployment targets. This study investigates the factors associated with investment intent toward WtE projects and examines how project perceptions influence early-stage investment attractiveness in the Indian context. A structured questionnaire was administered to 123 respondents, including students and early-career professionals from energy-related and service-oriented sectors. Seven dimensions of project perception—financial, informational, regulatory, market, environmental, socio-cultural, and technological—were evaluated. Fisher’s Exact Test and binary logistic regression were employed to examine factor associations and estimate their relative influence on investment intent. The results showed that all seven factors were significantly associated with investment intent at the 5% significance level. The logistic regression model achieved an overall prediction accuracy of 83.67% on the holdout sample, with a precision of 81.08%, recall of 96.77%, and an F1 score of 0.882 for the investment-positive class. Market acceptance and environmental credibility emerged as the strongest positive predictors of investment willingness, whereas information barriers and perceptions of high capital intensity reduced investment likelihood. The findings indicate that investment decisions related to WtE deployment extend beyond expected financial returns and are strongly influenced by perceptions of market feasibility, environmental reliability, and project transparency. This study demonstrates that improving information quality, strengthening market confidence, and reinforcing environmental assurance can enhance the investment readiness and bankability of sustainable WtE systems. The study contributes empirical evidence for supporting sustainable energy deployment strategies and improving project evaluation practices in emerging economies.

Frequent power outages and an unstable electricity supply continue to affect residential buildings, workshops, and small businesses, especially in developing economies. Conventional backup systems based on lead-acid batteries are often limited by low energy density, poor cycle life, excessive maintenance, and unstable voltage response under heavy loads. Although lithium-based alternatives have emerged as promising substitutes, many low-cost systems still suffer from poor thermal management, weak battery management integration, inaccurate state-of-charge monitoring, and rapid performance degradation during repeated charge-discharge cycles. This study presents the design, fabrication, and experimental validation of a 24 V, 2.5 kWh lithium iron phosphate (LiFePO₄) battery energy storage system integrated with an intelligent monitoring and protection architecture for modern backup power applications. The proposed system employed an 8S1P configuration using 100 Ah LiFePO₄ cells, a smart battery management system (BMS), a 30 A intelligent charger, and an Arduino-based real-time display interface for voltage and current monitoring. Experimental results showed that the battery absorbed approximately 23.5 A at nearly 25% state of charge, indicating excellent charge acceptance and stable regulation. During discharge testing, the system delivered 2.56 kWh while maintaining a gradual voltage reduction from 29.2 V to 23.5 V, demonstrating improved energy retention and output stability. The developed system provides a reliable, safe, and scalable alternative to conventional backup storage technologies. Its practical value includes improved household energy reliability, reduced maintenance requirements, enhanced operational lifespan, and suitability for renewable energy integration in homes, offices, and microgrid applications.

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.

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.

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.