Search Results (488)

Food loss and waste from the European Union’s dairy supply chain, particularly in the management of fresh milk, imposes significant environmental burdens. This study demonstrates that implementing Radio Frequency Identification (RFID)-enabled digital decision-support tools can substantially reduce these impacts across the region. A cradle-to-grave life cycle assessment (LCA) was used to quantify both the additional environmental burdens from RFID (tag production, usage, and disposal) and the avoided burdens due to reduced milk losses in the farm, processing, and distribution stages. Within the EU’s fresh milk supply chain, the implementation of digital tools could result in annual net reductions of up to 80,000 tonnes of CO₂-equivalent greenhouse gas emissions, 81,083 tonnes of PM_2.5-equivalent particulate matter, 84,326 tonnes of land use–related carbon deficit, and 80,000 cubic meters of freshwater-equivalent consumption. Spatial analysis indicates that regions with historically high spoilage rates, particularly in Southern and Eastern Europe, see the greatest benefits from RFID enabled digital-decision support tools. These environmental savings are most pronounced during the peak months of milk production. Overall, the study demonstrates that despite the environmental footprint of RFID systems, their integration into the EU’S dairy supply chain enhances transparency, reduces waste, and improves resource efficiency—supporting their strategic value. Full article

Large-scale battery datasets often contain anomalous data due to sensor noise, communication errors, and operational inconsistencies, which degrade the accuracy of data-driven prognostics. However, many existing studies overlook the impact of such anomalies or apply filtering heuristically without rigorous benchmarking, which can potentially introduce biases into training and evaluation pipelines. This study presents a deep learning framework that integrates autoencoder-based anomaly detection with a residual neural network (ResNet) to achieve state-of-the-art prediction of remaining useful life at the cycle level using only a single-cycle input. The framework systematically filters out anomalous samples using multiple variants of convolutional and sequence-to-sequence autoencoders, thereby enhancing data integrity before optimizing and training the ResNet-based models. Benchmarking against existing deep learning approaches demonstrates a significant performance improvement, with the best model achieving a mean absolute percentage error of 2.85% and a root mean square error of 40.87 cycles, surpassing prior studies. These results indicate that autoencoder-based anomaly filtering significantly enhances prediction accuracy, reinforcing the importance of systematic anomaly detection in battery prognostics. The proposed method provides a scalable and interpretable solution for intelligent battery management in electric vehicles and energy storage systems. Full article

The increasing integration of wind and photovoltaic energy into power systems brings about large fluctuations and significant challenges for power absorption. Wind–solar–hydro–storage multi-energy complementary systems, especially joint dispatching strategies, have attracted wide attention due to their ability to coordinate the advantages of different resources and enhance both flexibility and economic efficiency. This paper develops a capacity optimization model for a wind–solar–hydro–storage multi-energy complementary system. The objectives are to improve net system income, reduce wind and solar curtailment, and mitigate intraday fluctuations. We adopt the quantum particle swarm algorithm (QPSO) for outer-layer global optimization, combined with an inner-layer stepwise simulation to maximize life cycle benefits under multi-dimensional constraints. The simulation is based on the output and load data of typical wind, solar, water, and storage in Yunnan Province, and verifies the effectiveness of the proposed model. The results show that after the wind–solar–hydro–storage multi-energy complementary system is optimized, the utilization rate of new energy and the system economy are significantly improved, which has a wide range of engineering promotion value. The research results of this paper have important reference significance for the construction of new power systems and the engineering design of multi-energy complementary projects. Full article

This study experimentally validates a solar-thermal desalination system equipped with predictive feedwater control guided by real-time solar forecasting. Unlike conventional systems that react to temperature changes, the proposed approach proactively adjusts feedwater flow in anticipation of solar variability. To assess environmental and financial sustainability, the study integrates this control logic with a full Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA). Field testing in a high-temperature, arid region demonstrated strong performance, achieving a Global Warming Potential (GWP) of 1.80 kg CO₂-eq/m³ and a Levelized Cost of Water (LCOW) of $0.88/m³. Environmental impacts were quantified using OpenLCA and ecoinvent datasets, covering climate change, acidification, and eutrophication categories. The TEA confirmed economic feasibility, reporting a positive Net Present Value (NPV) and an Internal Rate of Return (IRR) exceeding 11.5% over a 20-year lifespan. Sensitivity analysis showed that forecast precision and TES design strongly influence both environmental and economic outcomes. The integration of intelligent control with simplified thermal storage offers a scalable, cost-effective solution for off-grid freshwater production in solar-rich regions. Full article

The widespread integration of electric vehicles (EVs) into smart grid infrastructures necessitates intelligent and robust battery health diagnostics to ensure system resilience and performance longevity. While numerous studies have addressed the estimation of State of Health (SOH) and the prediction of remaining useful life (RUL) using machine and deep learning, most existing models fail to capture both short-term degradation trends and long-range contextual dependencies jointly. In this study, we introduce V2G-HealthNet, a novel hybrid deep learning framework that uniquely combines Long Short-Term Memory (LSTM) networks with Transformer-based attention mechanisms to model battery degradation under dynamic vehicle-to-grid (V2G) scenarios. Unlike prior approaches that treat SOH estimation in isolation, our method directly links health prediction to operational decisions by enabling SOH-informed adaptive load scheduling and predictive maintenance across EV fleets. Trained on over 3400 proxy charge-discharge cycles derived from 1 million telemetry samples, V2G-HealthNet achieved state-of-the-art performance (SOH RMSE: 0.015, MAE: 0.012, $R^2$: 0.97), outperforming leading baselines including XGBoost and Random Forest. For RUL prediction, the model maintained an MAE of 0.42 cycles over a five-cycle horizon. Importantly, deployment simulations revealed that V2G-HealthNet triggered maintenance alerts at least three cycles ahead of critical degradation thresholds and redistributed high-load tasks away from ageing batteries—capabilities not demonstrated in previous works. These findings establish V2G-HealthNet as a deployable, health-aware control layer for smart city electrification strategies. Full article

Tin is an indispensable metal for contemporary society owing to its extensive application. China is a major tin manufacturer and consumer worldwide. Nonetheless, the crucial characteristics of its tin metabolism remain limited. Therefore, a dynamic material flow analysis (MFA) from 2001 to 2022 was performed in this study to trace China’s tin flows and stocks. Findings show that China became a net tin exporter from a life cycle perspective, and annual tin consumption embodied in various final products varied between 49.3 kilo tons (Kt) in 2001 and 161.5 Kt in 2022, with home appliances and electronics being the dominant consumption sectors. A total of 913.3 Kt of tin became in-use stocks. In addition, the imported tin embodied in various final products varied between 13.9 Kt in 2001 and 21.6 Kt in 2022, with machinery being the dominant consumption sector. The exported tin embodied in various final products varied between 12.0 Kt in 2001 and 76.3 Kt in 2022, with machinery being the dominant consumption sector. Finally, this study proposes some suggestions, in view of the Chinese reality, like enhancing tin recycling, promoting tin geological prospecting, optimizing the structure of the tin trade, and promoting regional cooperation, to improve the supply security of tin resources. Full article

The global escalation of organic waste generation, coupled with rising protein demand and environmental pressure, necessitates innovative, circular approaches to resource management. Hermetia illucens (Black Soldier Fly, BSF) has emerged as a leading candidate for integrated waste-to-resource systems. This review examines BSF biological and genomic adaptations underpinning waste conversion efficiency, comparative performance of BSF bioconversion versus traditional treatments, nutritional and functional attributes, techno-economic, regulatory, and safety barriers to industrial scale-up. Peer-reviewed studies were screened for methodological rigor, and data on life cycle traits, conversion metrics, and product compositions were synthesized. BSF larvae achieve high waste reductions, feed-conversion efficiencies and redirect substrate carbon into biomass, yielding net CO₂ emissions as low as 12–17 kg CO₂ eq ton⁻¹, an order of magnitude below composting or vermicomposting. Larval biomass offers protein, lipids (notably lauric acid), micronutrients, chitin, and antimicrobial peptides, with frass serving as a nutrient-rich fertilizer. Pathogen and antibiotic resistance gene loads decrease during bioconversion. Key constraints include substrate heterogeneity, heavy metal accumulation, fragmented regulatory landscapes, and high energy and capital demands. BSF systems demonstrate superior environmental and nutritional performance compared to conventional waste treatments. Harmonized safety standards, feedstock pretreatment, automation, and green extraction methods are critical to overcoming scale-up barriers. Interdisciplinary innovation and policy alignment will enable BSF platforms to realize their full potential within circular bio-economies. Full article

Direct Air Capture (DAC) is emerging as a critical climate change mitigation strategy, offering a pathway to actively remove atmospheric CO₂. This comprehensive review synthesizes advancements in DAC technologies, with a particular emphasis on the pivotal role of nanostructured solid sorbent materials. The work critically evaluates the characteristics, performance, and limitations of key nanomaterial classes, including metal–organic frameworks (MOFs), covalent organic frameworks (COFs), zeolites, amine-functionalized polymers, porous carbons, and layered double hydroxides (LDHs), alongside solid-supported ionic liquids, highlighting their varied CO₂ uptake capacities, regeneration energy requirements, and crucial water sensitivities. Beyond traditional temperature/pressure swing adsorption, the review delves into innovative DAC methodologies such as Moisture Swing Adsorption (MSA), Electro Swing Adsorption (ESA), Passive DAC, and CO₂-Binding Organic Liquids (CO₂ BOLs), detailing their unique mechanisms and potential for reduced energy footprints. Despite significant progress, the widespread deployment of DAC faces formidable challenges, notably high capital and operational costs (currently USD 300–USD 1000/tCO₂), substantial energy demands (1500–2400 kWh/tCO₂), water interference, scalability hurdles, and sorbent degradation. Furthermore, this review comprehensively examines the burgeoning global DAC market, its diverse applications, and the critical socio-economic barriers to adoption, particularly in developing countries. A comparative analysis of DAC within the broader carbon removal landscape (e.g., CCS, BECCS, afforestation) is also provided, alongside an address to the essential, often overlooked, environmental considerations for the sustainable production, regeneration, and disposal of spent nanomaterials, including insights from Life Cycle Assessments. The nuanced techno-economic landscape has been thoroughly summarized, highlighting that commercial viability is a multi-faceted challenge involving material performance, synthesis cost, regeneration energy, scalability, and long-term stability. It has been reiterated that no single ‘best’ material exists, but rather a portfolio of technologies will be necessary, with the ultimate success dependent on system-level integration and the availability of low-carbon energy. The review paper contributes to a holistic understanding of cutting-edge DAC technologies, bridging material science innovations with real-world implementation challenges and opportunities, thereby identifying critical knowledge gaps and pathways toward a net-zero carbon future. Full article

The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive pest that poses serious threats and causes significant losses to the production of maize in China. This study evaluated the feeding and oviposition preferences of S. frugiperda when reared on four maize cultivars—sweet, waxy, common, and silage—across three consecutive generations. It also compared population adaptability among these cultivars and analyzed population parameters between the F1 and F3 generations. The findings revealed that all four F1 generation populations showed a preference for feeding and oviposition on sweet maize. However, over time, S. frugiperda exhibited a stronger preference, in terms of feeding and oviposition behaviors, for the natal host plant across three consecutive generations of rearing. The fall armyworm completed its life cycle and oviposited on all four maize varieties over three generations. The sweet cultivar population had the highest intrinsic rate of increase, finite rate of increase, net reproductive rate, larval survival rate, pupation rate, eclosion rate, fecundity, and pupal weight, while the silage cultivar population had the shortest larval stage, pre-adult stage, and adult lifespan and the pupal weight and the fecundity were the lowest. Overall, the population fitness was the highest on the sweet cultivar, and the lowest on the silage cultivar. Compared with F1, the F3 generation of the FAW had a significantly shorter developmental duration in four maize cultivars. Except for the waxy maize cultivars, the fecundity of the other three cultivars did not differ significantly between F1 and F3. This study provides fundamental information on the trend of fall armyworm population changes in maize fields and serves as a reference for rational maize cultivar planting decisions. Full article

Priophorus fulvostigmatus, a species of leaf-cutter wasp, is an important leaf-feeding pest on strawberries. We investigated the effects of temperature and soil moisture content on key life cycle parameters of P. fulvostigmatus. The development time, survival, fecundity, and life table parameters of P. fulvostigmatus were observed at five temperatures. Pupal development and survival under five moisture contents (8%, 12%, 16%, 20%, and 24%) and four durations of water immersion (0, 1, 3, and 5 d) were recorded. P. fulvostigmatus could complete its life cycle at a constant temperature range of 16–28 °C. The duration of the immature stage first decreased and then increased with rising temperature, being longest at 16 °C and shortest at 25 °C. Female longevity and female fecundity did not differ between the temperature range of 16–25 °C. However, survival rates at all developmental stages decreased with increasing temperature. At 28 °C, both fecundity and survival rates of P. fulvostigmatus were significantly reduced compared to other temperatures. Compared with that at the constant temperature of 22 °C, the developmental duration of each stage was similar at a fluctuating temperature of 22 °C. The number of eggs laid per female, the longevity of male adults, and the eclosion rate were all significantly reduced. The net reproductive rate (R₀) under constant temperature conditions was significantly higher than under fluctuating temperature conditions, and the mean generation period (T), intrinsic rate of increase (r), and finite rate of increase (λ) differed significantly. The soil moisture content significantly impacted the pupation and eclosion of P. fulvostigmatus. Differences in soil moisture content had no significant effect on the duration of development; a moisture content of 8–16% was more suitable for their pupation and eclosion. Pupal development differed significantly between different periods of water immersion after the mature larvae were immersed in the soil. The longer the larvae remained in the soil, the lower their emergence rate after immersion. Thus, environmental temperature affected the growth, reproduction, and survival of P. fulvostigmatus. The optimal soil moisture for pupation of mature larvae was 12% to 16%. After the larvae were immersed in soil, the emergence rate was significantly reduced. These findings expand our understanding of the biological characteristics of P. fulvostigmatus and can facilitate the development of prevention and control strategies. Full article

The building sector holds a significant position in the global energy consumption share, and its environmental impact continues to intensify, making the construction industry a key player in sustainable development. The application of life cycle assessment on buildings (LCA-B) is widely employed to evaluate building energy and environment performance, and thus is of great significance for ensuring the sustainability of the project. This work aims to provide a systematic overview of LCA-B development based on reviewed literature. A three-stage mixed research method is adopted in this study: Firstly, an overall analysis framework is constructed, and 327 papers related to building life cycle assessment published between 2009 and 2025 are screened out by using the bibliometric method; Then, through scientometrics analysis, the journal regions, sources, scholars, and keyword evolution are revealed and analyzed using VOSviewer tool, and the hotspots in the field of LCA-B (e.g., integration of building information modeling (BIM) in LCA-B, multi-dimensional framework of environment–society–culture) are preliminarily explored based on the selected highly cited papers. The research finds that: (1) the performance of low energy buildings is better than that of net zero energy buildings from the perspective of LCA; (2) software compatibility and data exchange are the main obstacles in the integration of BIM-LCA; (3) a multi-dimensional LCA framework covering the social or cultural aspects is expected for a comprehensive assessment of building performance. This study provides a systematic analysis and elaboration of review articles related to LCA-B and thereby provides researchers with in-depth insight into this field. Full article

Food waste management is a major global issue, and alternative protein sources like insect farming offer a sustainable solution. This study investigated the environmental impacts of black soldier fly larvae (BSFL) production using a Life Cycle Assessment (LCA), evaluating its role in both protein production and food waste treatment. The assessment considered three functional units: FU₁ (1 kg of dried larvae), FU₂ (per kg of protein), and FU₃ (treatment of 1 ton of food waste). The results indicate that larvae rearing is the largest contributor to emissions in FU₁ (46% of 18.51 kg CO₂ eq). In FU₂, BSFL protein shows a higher climate impact (49.41 kg CO₂ eq) than fishmeal or soybean meal but requires significantly less land. FU₃ demonstrates that BSFL-based composting can achieve net negative emissions (~−24.8 kg CO₂ eq), outperforming conventional waste treatment. An optimized scenario (Scenario A) shows marked improvements across all units compared to a Business-as-Usual case, including a 79% reduction in FU₁ emissions and a 577% increase in FU₃ carbon savings. These findings underline the environmental advantages of BSFL systems, especially in Singapore, and support their potential as sustainable alternatives for protein production and food waste management. Full article

This review article examines the role of additive manufacturing (AM) in increasing energy efficiency and sustainability within the evolving framework of Industry 5.0 and 6.0. This review highlights the unique ability of additive manufacturing to deliver mass-customized products while minimizing material waste and reducing energy consumption. The integration of smart technologies such as AI and IoT is explored to optimize AM processes and support decentralized, on-demand manufacturing. Thisarticle discusses different AM techniques and materials from an environmental and life-cycle perspective, identifying key benefits and constraints. This review also examines the potential of AM to support circular economy practices through local repair, remanufacturing, and material recycling. The net energy efficiency of AM depends on the type of process, part complexity, and production scale, but the energy savings per component can be significant if implemented strategically.AM significantly improves energy efficiency in certain manufacturing contexts, often reducing energy consumption by 25–50% compared to traditional subtractive methods. The results emphasize the importance of innovation in both hardware and software to overcome current energy and sustainability challenges. This review highlights AM as a key tool in achieving a human-centric, intelligent, and ecological manufacturing paradigm. Full article

In the context of agricultural green transformation, the balance between the environmental footprint and economic return is a key indicator for measuring the synergy of high yields, high efficiency, and environmental friendliness in agricultural systems. However, the pathways and mechanisms for achieving this synergy in orchard systems remain unclear. Based on a three-year field experiment in Pinghe County, Fujian Province, a comprehensive evaluation framework integrating life cycle assessment (LCA) was constructed. This framework was used to systematically analyze the differences in the net ecosystem economic benefit (EEB) and environmental impact of four fertilization regimes: the conventional farming regime with no mulching (A; 1084 kg N ha⁻¹, 914 kg P₂O₅ ha⁻¹, and 906 kg K₂O ha⁻¹), the conventional farming regime with mulching (B), the optimized fertilization regime with water–fertilizer integration (C; 250 kg N ha⁻¹, 200 kg K₂O ha⁻¹, 100 kg MgO ha⁻¹, and 400 kg CaO ha⁻¹), and the optimized fertilization regime with controlled-release fertilizers (D). The results showed that regime D performed best in terms of yield, nutrient-use efficiency, and EEB, which increased by 220.5% and 297.5% compared with regime A, and reduced the input cost by CNY 63,100~69,000 hm⁻². Moreover, compared with regime A, regimes B, C, and D significantly reduced the carbon, nitrogen, and phosphorus footprints, respectively, with the carbon footprint reduced by 6.7~21.7%, 72.4~74.8%, and 71.6~76.5%; the nitrogen footprint reduced by 2.6~19.0%, 80.7~82.2%, and 80.1~83.4%; and the phosphorus footprint reduced by 15.3%, 100%, and 100%. Furthermore, the comprehensive evaluation index (CEI) is D > C > B > A. In total, the three optimized regimes balanced high yield with environmental sustainability, with the D regime showing the best performance, offering scientific support for transitioning to low-carbon, high-value orchards in smallholder systems. Full article

The growing global demand for sustainable energy solutions has spurred interest in hybrid renewable energy systems, particularly those combining photovoltaic (PV) solar and wind power. This study records the technical and financial feasibility of establishing hybrid solar photovoltaic and wind power stations in Iraq, Al-Rutbah and Al-Nasiriya, with a total power of 60 MW for each, focusing on optimizing energy output and cost-efficiency. The analysis evaluates key technical factors, such as resource availability, system design, and integration challenges, alongside financial considerations, including capital costs, operational expenses, and return on investment (ROI). Using the RETScreen program, the research explores potential locations and configurations for maximizing energy production and minimizing costs, and the evaluation is performed through the calculation Internal Rate of Return (IRR) on equity (%), the Simple Payback (year), the Net Present Value (NPV), and the Annual Life Cycle Savings (ALCSs). The results show that both PV and wind technologies demonstrate significant energy export potential, with PV plants exporting slightly more electricity than their wind counterparts. Al Nasiriya Wind had the highest output, indicating favorable wind conditions or better system performance at that site. The results show that the analysis of the proposed hybrid system has a standardizing effect on emissions, reducing variability and environmental impact regardless of location. The results demonstrate that solar PV is significantly more financially favorable in terms of capital recovery time at both sites, and that financial incentives, especially grants, are essential to improve project attractiveness, particularly for wind power. The analysis underscores the superior financial viability of solar PV projects in both regions. It highlights the critical role of financial support, particularly capital grants, in turning renewable energy investments into economically attractive opportunities. Full article