Search Results (2,883)

This article provides a critical review of the current design and research challenges in contemporary timber architecture. Conducted from the perspective of a designer-researcher, the review focuses on the role of wood as a material at the intersection of environmental performance, cultural meaning, and spatial practice. The study adopts a conceptual, problem-oriented approach, eschewing the conventional systematic aggregation of existing data. The objective of this study is to identify, interpret and categorise the key issues that are shaping the evolving discourse on timber architecture. The analysis is based on peer-reviewed literature published between 2020 and 2025, sourced from the Scopus and Web of Science Core Collection databases. Fifteen thematic challenges have been identified and classified according to their recognition level in academic and design contexts. The subjects under discussion include well-established topics, such as life cycle assessment and carbon storage, as well as less commonly explored areas, such as symbolic durability, social acceptance, traceability, and the upcycling of low-grade wood. The review under consideration places significant emphasis on the importance of integrating technical, cultural, and perceptual dimensions when evaluating timber architecture. The article proposes an interpretive framework combining design thinking and transdisciplinary insights. This framework aims to bridge disciplinary gaps and provide a coherent structure for understanding the complexity of timber-related challenges. The framework under discussion here encourages a broader understanding of wood as not only a sustainable building material but also a vehicle for systemic transformation in architectural culture and practice. The study’s insights may support designers, educators, and policymakers in identifying strategic priorities for the development of future-proof timber-based design practices. Full article

Micronutrients play a prominent role in plant growth and development, and their bioavailability is a growing global concern. Zinc is one of the most important micronutrients in the plant life cycle, acting as a metallic cofactor for numerous biochemical reactions within plant cells. Zinc deficiency in plants leads to various physiological abnormalities, ultimately affecting nutritional quality and posing challenges to food security. Biofortification methods have been adopted by agronomists to increase Zn concentrations in crops through optimal foliar and soil applications. Changing climatic conditions and conventional agricultural practices alter edaphic factors, reducing zinc bioavailability in soils due to abrupt weather changes. Precision agriculture emphasizes need-based and site-specific technologies to address these nutritional deficiencies. Nanoscience, a multidimensional approach, reduces particle size to the nanometer (nm) scale to enhance their efficiency in precise amounts. Nanoscale forms of Zn⁺² and their broad applications across crops are gaining attention in agriculture under varied application methods. This review focuses on the significance of Zn oxide (ZnO) nanoparticles (ZnONPs) and their extensive application in crop production. We also discuss optimum dosage levels, ZnONPs synthesis, application methods, toxicity, and promising future strategies in this field. Full article

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article

The sharing economy is considered to be a potentially efficacious approach for promoting sustainable production and consumption (SPC). This study utilizes dockless bike sharing (DBS) in Beijing as a case study to examine how sharing economy policies and business practices advance SPC. It also dynamically quantifies the environmental and economic performance of DBS practices from a life cycle perspective. The findings indicate that effective SPC practices can be achieved through the collaborative efforts of multiple stakeholders, including the government, operators, manufacturers, consumers, recycling agencies, and other business partners, supported by regulatory systems and advanced technologies. The SPC practices markedly improved the sustainability of DBS promotion in Beijing. This is evidenced by the increase in greenhouse gas (GHG) emission reduction benefits, which have risen from approximately 35.81 g CO₂-eq to 124.40 g CO₂-eq per kilometer of DBS travel. Considering changes in private bicycle ownership, this value could reach approximately 150.60 g CO₂-eq. Although the economic performance of DBS operators has also improved, it remains challenging to achieve profitability, even when considering the economic value of the emission reduction benefits. In certain scenarios, DBS can maximize profits by optimizing fleet size and efficiency, without compromising the benefits of emission reductions. The framework of stakeholder interaction proposed in this study and the results of empirical analysis not only assist regulators, businesses, and the public in better understanding and promoting sustainable production and consumption practices in the sharing economy but also provide valuable insights for achieving a win-win situation of platform profitability and environmental benefits in the SPC practice process. Full article

Silvopastoral systems, though ecologically beneficial, remain underrepresented in the European Union’s Common Agricultural Policy and are seldom studied in Mediterranean contexts. The current study assesses both the environmental and economic aspects of five typical silvopastoral systems in central Greece, encompassing cattle, sheep, and goat farming. A Life Cycle Assessment approach was implemented to quantify greenhouse gas emissions using economic allocation, distributing impacts between milk and meat outputs. Enteric fermentation was the major emission source, accounting for up to 65.14% of total emissions in beef-based systems, while feeding and soil emissions were more prominent in mixed and small ruminant systems. Total farm-level emissions ranged from 60,609 to 273,579 kg CO₂eq per year. Economically, only beef-integrated systems achieved an average annual profitability above EUR 20,000 per farm, based on financial data averaged over the last five years (2020–2024) from selected case studies in central Greece, while the remaining systems fell below the national poverty threshold for an average household, underscoring concerns about their economic viability. The findings underline the dual challenges of economic viability and policy neglect, stressing the need for targeted support if these multifunctional systems are to add value to EU climate goals and rural sustainability. Full article

In response to the escalating climate crisis, reducing greenhouse gas emissions (GHG) has become a top priority for both the public and private sectors. The pavement industry plays a key role in this transition, offering innovative technologies that minimize environmental impacts without compromising performance. Among these, the incorporation of recycled tire rubber and warm-mix asphalt (WMA) additives represents a promising strategy to reduce energy consumption and resource depletion in road construction. This study conducts a comparative life cycle assessment (LCA) to evaluate the environmental performance of an asphalt pavement incorporating recycled rubber and a WMA additive—referred to as R-W asphalt—against a conventional hot-mix asphalt (HMA) pavement. The analysis follows the ISO 14040/44 standards, covering material production, transport, construction, and maintenance. Two service-life scenarios are considered: one assuming equivalent durability and another with a five-year extension for the R-W pavement. The results demonstrate environmental impact reductions of up to 57%, with average savings ranging from 32% to 52% across key impact categories such as climate change, land use, and resource use. These benefits are primarily attributed to lower production temperatures and extended maintenance intervals. The findings underscore the potential of R-W asphalt as a cleaner engineering solution aligned with circular economy principles and climate mitigation goals. Full article

Hydrogen production is increasingly vital for global decarbonization but remains a water- and energy-intensive process, especially in arid regions. Despite growing attention to its climate benefits, limited research has addressed the environmental impacts of water sourcing. This study employs a life cycle assessment (LCA) approach to evaluate three water supply strategies for hydrogen production: (1) seawater desalination without brine treatment (BT), (2) desalination with partial BT, and (3) freshwater purification. Scenarios are modeled for the United Arab Emirates (UAE), Australia, and Spain, representing diverse electricity mixes and water stress conditions. Both electrolysis and steam methane reforming (SMR) are evaluated as hydrogen production methods. Results show that desalination scenarios contribute substantially to human health and ecosystem impacts due to high energy use and brine discharge. Although partial BT aims to reduce direct marine discharge impacts, its substantial energy demand can offset these benefits by increasing other environmental burdens, such as marine eutrophication, especially in regions reliant on carbon-intensive electricity grids. Freshwater scenarios offer lower environmental impact overall but raise water availability concerns. Across all regions, feedwater for SMR shows nearly 50% lower impacts than for electrolysis. This study focuses solely on the environmental impacts associated with water sourcing and treatment for hydrogen production, excluding the downstream impacts of the hydrogen generation process itself. This study highlights the trade-offs between water sourcing, brine treatment, and freshwater purification for hydrogen production, offering insights for optimizing sustainable hydrogen systems in water-stressed regions. Full article

Flocculants are indispensable in water and wastewater treatment, enabling the aggregation and removal of suspended particles, colloids, and emulsions. However, the conventional development and application of flocculants rely heavily on empirical methods, which are time-consuming, resource-intensive, and environmentally problematic due to issues such as sludge production and chemical residues. Recent advances in machine learning (ML) have opened transformative avenues for the design, optimization, and intelligent application of flocculants. This review systematically examines the integration of ML into flocculant research, covering algorithmic approaches, data-driven structure–property modeling, high-throughput formulation screening, and smart process control. ML models—including random forests, neural networks, and Gaussian processes—have successfully predicted flocculation performance, guided synthesis optimization, and enabled real-time dosing control. Applications extend to both synthetic and bioflocculants, with ML facilitating strain engineering, fermentation yield prediction, and polymer degradability assessments. Furthermore, the convergence of ML with IoT, digital twins, and life cycle assessment tools has accelerated the transition toward sustainable, adaptive, and low-impact treatment technologies. Despite its potential, challenges remain in data standardization, model interpretability, and real-world implementation. This review concludes by outlining strategic pathways for future research, including the development of open datasets, hybrid physics–ML frameworks, and interdisciplinary collaborations. By leveraging ML, the next generation of flocculant systems can be more effective, environmentally benign, and intelligently controlled, contributing to global water sustainability goals. Full article

Accelerated industrialization and surging energy demands have led to continuously rising atmospheric CO₂ concentrations. Developing sustainable methods to reduce atmospheric CO₂ levels is crucial for achieving carbon neutrality. Concurrently, the rapid development of new energy vehicles has driven a significant increase in demand for lithium-ion batteries (LIBs), which are now approaching an end-of-life peak. Efficient recycling of valuable metals from spent LIBs represents a critical challenge. This study employs conventional hydrometallurgical processing to recover valuable metals from spent LIBs. Subsequently, Ni-doped Co₃O₄ (Ni:Co₃O₄) supported on the natural mineral attapulgite (ATP) was synthesized via a sol–gel method. The incorporation of a small amount of Ni into the Co₃O₄ lattice generates oxygen vacancies, inducing a localized surface plasmon resonance (LSPR) effect, which significantly enhances charge carrier transport and separation efficiency. During the photocatalytic reduction of CO₂, the primary product CO generated by the Ni:Co₃O₄/ATP composite achieved a high production rate of 30.1 μmol·g⁻¹·h⁻¹. Furthermore, the composite maintains robust catalytic activity even after five consecutive reaction cycles. Full article

Plastic waste accumulation is one of the most pressing environmental challenges of the 21st century, owing to the widespread use of synthetic polymers and the limitations of conventional recycling methods. Among available strategies, chemical upcycling via depolymerization has emerged as a promising circular approach that converts plastic waste back into valuable monomers and chemical feedstocks. This article provides an in-depth narrative review of recent progress in the upcycling of major plastic types such as PET, PU, PS, and engineering plastics through thermal, chemical, catalytic, biological, and mechanochemical depolymerization methods. Each method is critically assessed in terms of efficiency, scalability, energy input, and environmental impact. Special attention is given to innovative catalyst systems, such as microsized MgO/SiO₂ and Co/CaO composites, and emerging enzymatic systems like engineered PETases and whole-cell biocatalysts that enable low-temperature, selective depolymerization. Furthermore, the conversion pathways of depolymerized products into high-purity monomers such as BHET, TPA, vanillin, and bisphenols are discussed with supporting case studies. The review also examines life cycle assessment (LCA) data, techno-economic analyses, and policy frameworks supporting the adoption of depolymerization-based recycling systems. Collectively, this work outlines the technical viability and sustainability benefits of depolymerization as a core pillar of plastic circularity and monomer recovery, offering a path forward for high-value material recirculation and waste minimization. Full article

The integration of Life Cycle Assessment (LCA) into Building Information Modeling (BIM) processes is becoming increasingly important for enhancing the environmental performance of construction projects. This scoping review examines how LCA methods and environmental data are currently integrated into BIM workflows, focusing on automation, data standardization, and visualization strategies. We selected 43 peer-reviewed studies (January 2010–May 2025) via structured searches in five major academic databases. The review identifies five main types of BIM–LCA integration workflows; the most common approach involves exporting quantity data from BIM models to external LCA tools. More recent studies explore the use of artificial intelligence for improving automation and accuracy in data mapping between BIM objects and LCA databases. Key challenges include inconsistent levels of data granularity, a lack of harmonized EPD formats, and limited interoperability between BIM and LCA software environments. Visualization methods such as color-coded 3D models are used to support early-stage decision-making, although uncertainty representation remains limited. To address these issues, future research should focus on standardizing EPD data structures, enriching BIM objects with validated environmental information, and developing explainable AI solutions for automated classification and matching. These advancements would improve the reliability and usability of LCA in BIM-based design, contributing to more informed decisions in sustainable construction. Full article

The life-cycle carbon emissions (LCCE) assessment of dairy barns is crucial for identifying low-carbon transition pathways and promoting the sustainable development of the dairy industry. We applied a life cycle assessment approach integrated with building information modeling and EnergyPlus to establish a full life cycle inventory of the material quantities and energy consumption for dairy barns. The LCCE was quantified from the production to end-of-life stages using the carbon equivalent of dairy barns (CEDB) as the functional unit, expressed in kg CO₂e head⁻¹ year⁻¹. A carbon emission assessment model was developed based on the “building–process–energy” framework. The LCCE of the open barn and the lower profile cross-ventilated (LPCV) barn were 152 kg CO₂e head⁻¹ year⁻¹ and 229 kg CO₂e head⁻¹ year⁻¹, respectively. Operational carbon emissions (OCE) accounted for the largest share of LCCE, contributing 57% and 74%, respectively. For embodied carbon emissions (ECE), the production of building materials dominated, representing 91% and 87% of the ECE, respectively. Regarding carbon mitigation strategies, the use of extruded polystyrene boards reduced carbon emissions by 45.67% compared with stone wool boards and by 36% compared with polyurethane boards. Employing a manure pit emptying system reduced carbon emissions by 76% and 74% compared to manure scraping systems. Additionally, the adoption of clean electricity resulted in a 33% reduction in OCE, leading to an overall LCCE reduction of 22% for the open barn and 26% for the LPCV barn. This study introduces the CEDB to evaluate low-carbon design strategies for dairy barns, integrating building layout, ventilation systems, and energy sources in a unified assessment approach, providing valuable insights for the low-carbon transition of agricultural buildings. Full article

To address the wear life prediction challenge of Guide Cones in passive compliant connectors under dynamic loads within specialized equipment, this study proposes a dynamic wear modeling and life assessment method based on the improved Archard model. Through integrated theoretical modeling, finite element simulation, and experimental validation, we establish a bidirectional coupling framework analyzing dynamic contact mechanics and wear evolution. By developing phased contact state identification criteria and geometric constraints, a transient load calculation model is established, revealing dynamic load characteristics with peak contact forces reaching 206.34 N. A dynamic contact stress integration algorithm is proposed by combining Archard’s theory with ABAQUS finite element simulation and ALE adaptive meshing technology, enabling real-time iterative updates of wear morphology and contact stress. This approach constructs an exponential model correlating cumulative wear depth with docking cycles (R² = 0.997). Prototype experiments demonstrate a mean absolute percentage error (MAPE) of 14.6% between simulated and measured wear depths, confirming model validity. With a critical wear threshold of 0.8 mm, the predicted service life reaches 45,270 cycles, meeting 50-year operational requirements (safety margin: 50.9%). This research provides theoretical frameworks and engineering guidelines for wear-resistant design, material selection, and life evaluation in high-reliability automatic docking systems. Full article

The environmental impact of milk production, particularly its share of greenhouse gas (GHG) emissions, is a topic under investigation in various parts of the world. This paper presents an overview of current knowledge on the carbon footprint (CF) of milk production at the farm level, with a particular focus on technological, environmental and organisational factors affecting emission levels. The analysis is based on a review of, inter alia, 46 peer-reviewed publications and 11 environmental reports, legal acts and databases concerning the CF in different regions and under various production systems. This study identifies the main sources of emissions, including enteric fermentation, manure management, and the production and use of feed and fertiliser. It also demonstrates the significant variability of the CF values, which range, on average, from 0.78 to 3.20 kg CO₂ eq kg⁻¹ of milk, determined by the farm scale, nutritional strategies, local environmental and economic determinants, and the methodology applied. Moreover, this study stresses that higher production efficiency and integrated farm management could reduce the CF per milk unit, with further intensification having, however, diminishing effects. The application of life cycle assessment (LCA) methods is essential for a reliable assessment and comparison of the CF between systems. Ultimately, an effective CF reduction requires a comprehensive approach that combines improved nutritional practices, efficient use of resources, and implementation of technological innovations adjusted to regional and farm-specific determinants. The solutions presented in this paper may serve as guidelines for practitioners and decision-makers with regard to reducing GHG emissions. 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