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Lithium has become one of the key raw materials for the energy transition due to the central role of lithium-ion batteries in electromobility, energy storage, and the integration of renewable energy sources. However, the rapid increase in demand reveals growing environmental, social, geopolitical, and market tensions. The aim of the paper is a critical synthesis of global lithium utilization from the perspective of challenges, technological innovations, and integrative strategies supporting a more sustainable material–energy system. A broad, systematic literature review covering the entire value chain was applied: resources, extraction, processing, end-use applications, second life of batteries, recycling, and governance. The analysis shows that the strategic importance of lithium arises from the increasing demand pressure from electric vehicles and stationary storage, while the sustainability of the current model is constrained by supply concentration, uneven control over downstream stages, the water–carbon footprint of extraction and processing, social conflicts, and incomplete integration of secondary loops. At the same time, innovations such as direct lithium extraction (DLE), recovery from geothermal brines, design for recycling, second life, and battery passports can partially alleviate these tensions, but they do not eliminate the need for primary supply in the short term. The conclusion of the work is that sustainable global lithium utilization requires simultaneous diversification of sources, development of circular value chains, and multi-level governance integrating resource security, environmental efficiency, and social legitimacy. Full article

The increasing global demand for marine energy resources, coupled with the deployment of offshore oil and gas pipelines and submarine power cables, highlights the requirement for reliable subsea infrastructure. To protect these assets from environmental hazards and anthropogenic disturbances, seabed burial via trenching is widely adopted, with submarine trenchers serving as the main installation equipment. Trenching involves excavating a trench on the seabed to place pipelines, cables, or other subsea infrastructure. These operations involve complex soil–tool interactions that fundamentally govern cutting resistance, trench-wall stability, and overall equipment performance. Specifically, distinct engineering challenges arise across different trencher configurations: plough trenchers often encounter complex seabed structures, jet-type trenchers are prone to trench sidewall collapse, and mechanical trenchers face cutting difficulties in hard clay. A thorough understanding of these interactions is therefore critical for resolving operational challenges and optimizing trencher efficiency in engineering practice. To deeply understand these type-specific issues, this review summarizes the geomechanical problems associated with various trenching technologies, synthesizes recent research advances from analytical frameworks, physical experiments, and numerical simulations, and identifies existing knowledge gaps. By consolidating these findings, the paper provides a reference for addressing trencher-related engineering challenges, supporting equipment optimization, and facilitating the deployment of offshore energy transmission networks. Full article

This study presents a comparative life cycle assessment (LCA) of a conventional wet flue gas desulfurization (FGD) process and two carbonate-melt-based FGD configurations (CMFGD-H and CMFGD-T), based on a functional unit of 1 kg SO₂ removed. Process-level life cycle inventory (LCI) data were generated using process simulation to ensure consistency and comparability across all systems. The results indicate that both CMFGD configurations significantly reduce environmental impacts in terms of global warming potential (GWP), fine particulate matter formation (PM), and terrestrial acidification (TA) compared to the conventional FGD process. Specifically, GWP decreased from 177.75 kg CO₂ eq to 37.47 and 35.68 kg CO₂ eq for CMFGD-H and CMFGD-T, respectively. Similar reductions were observed for PM and TA, primarily due to the elimination of limestone consumption, the absence of gypsum waste generation, and reduced direct process emissions. Hotspot analysis revealed that direct CO₂ emissions dominate GWP across all configurations, whereas PM and TA are influenced by both direct emissions and upstream energy supply. In the CMFGD systems, environmental burdens shift from direct emissions toward upstream processes, particularly electricity and hydrogen production, highlighting the importance of energy system characteristics. However, a clear trade-off was identified in fossil resource scarcity (FRC), which increased significantly for CMFGD configurations (1.858–1.976 kg oil eq) compared to the conventional process (0.128 kg oil eq). This increase is primarily attributed to greater dependence on upstream energy supply chains, including fossil-based electricity, fuel, and hydrogen production. Sensitivity analysis further indicates that FRC is configuration-dependent, with hydrogen consumption dominating in CMFGD-H and CO utilization playing a more significant role in CMFGD-T. Nevertheless, even with reductions in these key parameters, FRC remains substantially higher than that of the conventional process, indicating that this impact is fundamentally governed by upstream energy dependency rather than individual process variables. The results demonstrate that CMFGD technologies offer substantial environmental benefits in terms of emission-related impacts but may increase resource depletion. These findings highlight that achieving sustainable CMFGD systems requires an integrated approach that combines process optimization with low-carbon and resource-efficient energy supply. Full article

Building energy consumption accounts for 30% of global energy use, making building management pivotal to achieving global sustainability. Occupants have profound impacts on the building environment. Incorporating occupant-related factors into the environmental control process is essential for optimizing the efficiency of building management systems (BMSs), which thus gives rise to the concept of occupant-centric control (OCC). Conventional methods rely on simplified models and fixed schedules that fail to satisfy environmental control and occupant requirements, while constructing credible models places strict requirements on the dataset. In this paper, we propose a Model-Aware Predictive Control (MAPC) framework that can construct credible models with limited data and provide room-level control strategies to optimize the trade-off between occupant comfort and energy consumption. The technological innovations of this research are twofold. On the one hand, we design a model construction and fine-tuning method that combines data-driven subspace projection approach with physical priors that can construct credible thermal dynamic models with limited data. On the other hand, to balance the potential conflicts between enhancing occupant comfort and saving energy, we present a hierarchical decision-making mechanism that enables adaptive multi-objective room-level control considering dynamic occupant comfort requirements and energy usage. The experimental results obtained on an EnergyPlus-based simulation dataset and a publicly available dataset demonstrate that MAPC can provide room-level control strategies based on dynamic occupant requirements and user preferences and achieve superior trade-offs between occupant comfort and energy consumption. The ablation experiments also demonstrated the superiority of MAPC in constructing reliable models on limited datasets. MAPC provides pivotal support for the advancement of the intelligent buildings and sustainable indoor environment. Full article

Trajectory optimization during the terminal area energy management (TAEM) phase is pivotal for achieving accurate runway alignment and enhancing landing safety in autonomous aircraft operations. In the presence of initial state uncertainties in TAEM phase, conventional pseudo-spectral methods still suffer from robustness limitations and exhibit a strong dependence on the quality of the initial guess. Therefore, this paper proposes the composite African vulture optimization algorithm (CAVOA), a meta-heuristic framework designed to automate trajectory optimization. An in-depth examination of the heading alignment cone (HAC) trajectory model enables effective heading adjustments prior to landing, augmented by a tailored dynamic pressure profile to ensure safe touchdown velocities. By incorporating dynamic opposition learning, intelligent boundary processing, and composite exploration, CAVOA enhances global search efficiency. These enhancements are substantiated through comparisons with benchmark function optimization, Wilcoxon rank sum tests, and convergence analysis. Numerical simulations validate that CAVOA reliably directs autonomous aircraft to predefined touchdown states, demonstrating superior performance in complex aerial environments. Full article

With the adoption of more ambitious emission reduction strategies in the shipping industry by the International Maritime Organization and the resulting stricter greenhouse gas emission reduction requirements, it is particularly important for all stakeholders in the global maritime shipping industry to assess the energy efficiency of shipping vessels. Forming predictive capabilities for ship fuel consumption and Carbon Intensity Indicator (CII) annual ratings, for example, are two important works. This article adopted 14 different algorithms in three categories of data-driven approaches, i.e., statistics, machine learning and deep learning, including polynomial regression, ridge regression, adaptive boosting, categorical boosting, elastic net, etc., and built the ship fuel consumption prediction model using ship noon report as the data source. The prediction accuracy and computational efficiency of model training were compared based on metrics of coefficient of determination, mean absolute percentage error and floating-point operations per amount of training data. Cross-validations were performed for all 14 algorithms to analyze their sensitivities to their respective tuned parameters. Comparisons indicated that algorithms of the statistics approach were sensitive to the quality of the data source, compared with the machine learning and the deep learning approaches. The accuracy of the elastic net algorithm was sensitive to the tuned parameters. Two algorithms, light gradient boosting machine and random forest, were selected based on their performances of prediction accuracy and computational efficiency of model training. Then, the selected algorithms were separately combined with long short-term memory as the time-series prediction algorithm to form their respective coupled framework. Both of the coupled frameworks achieved successful prediction of the CII annual discriminant and rating of the studied ships. The prediction accuracy was validated to be sufficient. Full article

As the complexity, scale, and nonlinearity of modern engineering optimization problems continue to increase, traditional optimization algorithms face significant challenges in achieving high solution accuracy, fast convergence, and robust performance. To address these issues, this paper proposes a Rural Tourism Migration-based Improved Equilibrium Optimizer (RTM-IEO), aiming to enhance the global search capability and adaptive balance between exploration and exploitation. Specifically, an adaptive lens imaging opposition-based learning strategy is introduced to effectively expand the search space and maintain population diversity. A dynamic elite-guided elimination mechanism is designed to strengthen exploitation capability and accelerate convergence by reconstructing inferior individuals using high-quality solutions. In addition, a multi-stage rural tourism migration strategy is developed to dynamically regulate the search behavior across different optimization phases, enabling a more flexible and efficient search process. The effectiveness of the proposed algorithm is comprehensively validated on the CEC2021 and CEC2022 benchmark suites, where RTM-IEO demonstrates superior performance in terms of convergence accuracy, convergence speed, and robustness compared with several representative state-of-the-art algorithms. The statistical superiority of the proposed method is further confirmed through Friedman mean ranking and Wilcoxon rank-sum tests. To further evaluate its practical applicability, RTM-IEO is applied to the sustainable economic dispatch problem of a microgrid integrating renewable energy sources, including wind power and photovoltaic generation, along with energy storage systems and controllable units. The optimization objective simultaneously considers economic cost minimization and sustainable operation requirements, such as improving renewable energy utilization and reducing dependence on fossil-fuel-based generation. Experimental results indicate that the proposed method achieves a significant reduction in daily operating cost (exceeding 52% compared with benchmark algorithms), while effectively promoting low-carbon energy utilization and enhancing overall system sustainability. Overall, the proposed RTM-IEO provides an efficient and reliable optimization framework for addressing complex global optimization problems, particularly in scenarios requiring a coordinated balance between economic performance and sustainable development. Full article

The rapidly increasing global demand for high-performance thermal insulation materials necessitates a significant shift towards more sustainable and cost-effective solutions. This study unveils a novel and efficient pathway to synthesize engineered cordierite, a highly coveted magnesium aluminosilicate ceramic, by intelligently harnessing biogenic silica extracted directly from rice husk. Rice husk, an abundant agricultural by-product, represents a readily available and often underutilized resource. The methodology involved a precise precipitation method to successfully yield high-purity silica from rice husk ash. This extracted silica was then meticulously combined with commercial magnesium oxide (MgO) and aluminum oxide (Al₂O₃) through a solid-state reaction to synthesize the desired cordierite. The study systematically investigated the profound impact of various sintering temperatures, ranging from 850 °C to 1100 °C, on both the cordierite yield and its crucial physicochemical properties. Our experiments revealed that a sintering temperature of 1100 °C achieved a remarkable 66.5% cordierite yield. Beyond yield, the material processed at 1100 °C exhibited exceptional mechanical and thermal characteristics: a compressive strength of 65 kN/m², a flexural strength of 44 kN/m², a tensile strength of 17.5 kN/m², and a remarkably low thermal conductivity of just 3.2 W/m·K. These attributes match the mechanical requirements for structural insulation, with a thermal conductivity of 3.2 W/m·K. While higher than some high-porosity commercial cordierites (typically 1.2–2.0 W/m·K), the biogenic version offers a 40% reduction in production energy and utilizes 100% recycled silica, balancing thermal performance with superior sustainability. By utilizing agricultural waste, this method reduces CO₂ emissions associated with mineral extraction and minimizes reliance on non-renewable raw materials, providing a practical pathway for the circular economy. Full article

Optoelectronic devices are emerging as a cornerstone of advanced renewable energy technologies, offering innovative routes for energy harvesting, conversion, and management with high efficiency and versatility. This review summarizes recent advances in the semiconductor materials engineering field, device configurations, and light–matter interaction mechanisms that underpin advanced optoelectronic systems for solar energy harvesting, solar-driven chemical conversion, and smart grid integration, among others. Emphasis is placed on the breakthroughs achieved in the perovskite and hybrid photovoltaics, photoelectrochemical energy conversion, and nanostructured optoelectronic platforms that enable much-increased light absorption, reduced recombination losses, and scalable large-scale fabrications. Moreover, the challenges closely linked with long-term stability, environmental durability and benevolence, and worldwide deployment are critically addressed, together with the emerging opportunities in AI design, tandem device technological solutions, integrated energy systems, and machine learning approaches for optimizing device performance, thermal management, and energy storage capabilities. Finally, the present review concludes by outlining the future research directions that could accelerate the transition toward high-performance, cost-effective, and sustainable optoelectronic solutions responsive to global renewable energy requirements. Full article

This study presents a comprehensive bibliometric analysis of research trends in grain cleaning devices from 1998 to 2025. Grain cleaning equipment plays a critical role in post-harvest processing by improving grain quality, reducing losses, and enhancing overall efficiency in agricultural systems. The analysis is based on bibliographic data retrieved from the Scopus database. Various bibliometric tools and indicators, including publication trends, citation analysis, co-authorship networks, and keyword co-occurrence, were employed to identify patterns of development, major contributors, and emerging research themes in this field. The results reveal a significant growth in publications in recent years, reflecting increasing global interest in advanced cleaning technologies, including energy-efficient systems, intelligent sorting, and automation. Key research hotspots include vibration-based separation, pneumatic systems, and smart sensor-based cleaning technologies. This study provides a systematic overview of the intellectual structure and evolution of grain cleaning device research, offering valuable insights for researchers and practitioners. The findings also highlight existing research gaps and suggest future directions for the development of more efficient, sustainable, and intelligent grain processing technologies. Full article

Against the backdrop of the dual-carbon strategy, China’s civil aviation industry, as a high-energy-consumption and high-carbon-emission sector, faces mounting pressure for low-carbon transformation. As the dominant airlines within China’s civil aviation system, Air China, China Eastern Airlines, and China Southern Airlines play a pivotal role in guiding the industry’s high-quality development. Employing the Global Malmquist–Luenberger (GML) index model, this study constructs a global production frontier incorporating undesirable outputs to systematically measure the dynamic evolution of total factor productivity (TFP) for the three major airlines in the period 2005–2023, and further applies a combined static-dynamic regression framework to identify the firm-level heterogeneous mechanisms through which explanatory factors operate. The results reveal significant heterogeneity in TFP trajectories: China Southern Airlines exhibits the most stable efficiency with the lowest volatility; China Eastern Airlines displays the greatest volatility but the strongest post-crisis rebound; and Air China occupies an intermediate position in both efficiency level and volatility. This differentiation stems from fundamental differences in market positioning, strategic orientation, and resource allocation patterns. Market competitiveness exerts a significantly positive effect on TFP for both Air China and China Eastern Airlines. Technological innovation investment generates short-run negative effects across all three airlines, albeit with divergent magnitudes. Human capital accumulation acts as a positive driver for Air China but produces a negative effect for China Southern Airlines, attributable to a structural mismatch between aggressive talent upgrading and organizational absorptive capacity. Shifting the unit of analysis to the firm level, this study identifies three heterogeneous strategic archetypes—market-led, scale-expansion, and regional-deepening—and constructs a differentiated “one firm, one policy” framework to provide targeted policy guidance for improving airline efficiency and facilitating low-carbon transition under carbon constraints. Full article

Agrivoltaic systems, which enable simultaneous crop production and solar photovoltaic (PV) electricity generation on the same land, can support climate mitigation, food security, and rural development. Leguminous crops like beans are globally important, yet there is limited performance studies on diverse agrivoltaic trials. This limits appropriate policy guidance. To overcome these limitations, this study assessed organic green bush bean performance under thirteen PV configurations with varying transparency and spectral properties, comparing both agricultural outcomes against national yields and policy standards. The results in vegetative metrics indicated that blue-spectrum thin-film and intermediate-transparency c-Si modules supported growth near German productivity thresholds. Although no agrivoltaic system matched national average yields, combining crop and energy revenues revealed substantial benefits: the 44%—transparent c-Si configuration generated 340% more total revenue than traditional farming, and the blue 70%—transparent thin-film system achieved 94% of national yield but 164% of conventional farm revenue per acre. Electricity generation gains outweighed modest crop reductions, highlighting strong synergies between food and energy. The results of this study highlights the potential of agrivoltaic systems to enhance land-use efficiency, support renewable energy expansion, and improve rural economic resilience, while underscoring the need for multi-year trials and site-specific controls to validate long-term sustainability outcomes. Full article

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation capability. After a fault occurs near the inverter station, reducing the DC current enables the reactive power from the compensation devices to be released and injected into the receiving-end power grid, thereby providing emergency voltage support for the receiving-end grid. To reduce control costs, an optimization model constrained by transient voltage violation is established, and the DC current modulation is acquired via an online solution. To maintain system stability and meet the requirements of online applications, it is crucial to rapidly solve the optimization model based on the grid operating mode and contingency information to update the emergency control strategy table in the special protection system (SPS). Conventional global orthogonal collocation (GOC) and adaptive orthogonal collocation (AOC)-based solution methods transform the optimization model in the continuous time domain into a nonlinear programming (NLP) problem for solution, which addresses the low efficiency of traditional rolling optimization. However, the GOC- and AOC-based solution methods improve the discretization accuracy of the model by pursuing global uniform densification of collocation points, making it difficult to balance solution accuracy and solution efficiency. To this end, this paper proposes an efficient interval partition dynamic adaptive orthogonal collocation (IP-DAOC)-based solution method. Firstly, the overall optimization time window is interval-partitioned into multiple initial intervals, and an interval-partitioned transient voltage stability emergency control optimization model is established. Furthermore, the interval length and the number of collocation points are dynamically adjusted according to the curvature of interpolation polynomials at collocation points in different intervals. Finally, after interval adjustment, the dynamic equations discretized in adjacent intervals are made continuous by reconstructing the differential matrix. This solution method reduces the total number of collocation points, thereby decreasing the scale of the NLP problem and narrowing the search space, significantly improving solution efficiency while ensuring solution accuracy. To verify the effectiveness of the proposed solution method, simulations are carried out on a modified IEEE 14-bus system. The results are compared with those of the traditional GOC- and AOC-based solution methods, which further demonstrate the superiority of the proposed solution method. Full article

Global demand for sustainability drives interest in bioenergy from sustainable feedstock. Agro-industrial waste such as brewer’s spent grains (BSG) is an important by-product of brewing. This study provides a comprehensive review of the current technologies of BSG for energy recovery and BSG-based materials for energy storage applications. The latest scientific progress, not only from conventional processes on anaerobic digestion, combustion, gasification, pyrolysis, torrefaction, and hydrothermal liquefaction but also from several integrated technologies, pretreatment methods, and additives/catalysts regarding the improvement of energy efficiency and process sustainability, was reviewed. In addition, the co-feedstock practices (co-combustion, anaerobic co-digestion, hydrothermal co-liquefaction, anaerobic co-fermentation) and co-production were examined. AD of BSG yields about 302 NL CH₄/kg COD, generating roughly 0.39 kWh of electricity/kg BSG and 1.71 MJ of thermal energy/kg BSG. Ultrasonic pretreatment enhances methane production up to four times (107 L CH₄/kg TVS) and reduces CO₂ emissions by 0.083 t CO₂eq/t BSG. Anaerobic co-digestion of BSG with other brewery waste increased the yield up to 88 mL CH₄/g TVS, generated approx. 0.348 kWh/kg TVS electricity, and reduced emissions by 0.114 kg CO₂eq/kg TVS. Bioethanol yields can reach 72%, while biohydrogen generation was up to 5154 mL H₂/g glucose. BSG pyrolysis provides up to 71.8% bio-oil, and its calorific value is 18–25 MJ/kg. BSG-derived activated biocarbon has a notable surface area (1792 m²/g) for lithium–sulfur batteries. The assessment showed that BSG’s transformation into bioenergy and energy storage materials aligns with waste reduction and sustainable development goals. However, future research on combined alternative wastes, integrated technologies, green nanotechnology, and artificial intelligence technology could lead to optimal performance and facilitate their industrial application. Full article

Buildings account for nearly 30% of global energy-related carbon emissions. In rapidly developing economies, the operational phase of buildings represents a major and growing source of emissions. However, emission pathways in hot-summer-cold-winter (HSCW) regions remain understudied. This study analyzes carbon emission peaks and influencing factors in the operational phase of existing civilian buildings in Anhui Province. It integrates energy balance tables, the LEAP model, carbon emission factors, and the STIRPAT model. The energy balance table method disaggregates building energy consumption into urban, rural residential and public sectors. It adjusts for transportation energy by deducting specific proportions of gasoline and diesel from industrial, commercial, and residential sectors. Heating energy calculations are simplified because the region has a HSCW climate with limited centralized heating. The LEAP model projects emissions under four scenarios from 2020 to 2060. The STIRPAT model with ridge regression reveals that the permanent population and energy structure negatively influence residential emissions with elasticities of −2.646 and −1.465, respectively. This finding is consistent with the province’s energy transition, where coal use dropped from 28.48% in 2005 to 0.45% in 2020 and electricity use rose from 39.86% to 59.01%. In contrast, per capita GDP, building area, and energy intensity show positive effects. For public buildings, tertiary industry added value and energy structure are key determinants. Scenario analysis identifies the blueprint scenario as optimal, with residential emissions peaking at 34.29 million tons in 2025 and declining to 9.19 million tons by 2060 through measures such as 10% building retrofits by 2025, 75% energy-saving standards for new constructions, 50% retrofits by 2060, and renewable energy integration with building electrification, outperforming the baseline scenario that peaks in 2036 at 49.46 million tons and other intermediate scenarios. The study underscores that energy structure optimization significantly decouples energy consumption from emissions, offering actionable pathways for dual carbon goals through policy synergies in building efficiency, population management, and clean energy adoption to foster sustainable development and the construction industry’s low-carbon transition. Full article