Search Results (623)

Simulation has become a pivotal tool in the design, analysis, and optimization of the built environment, and has been widely adopted by professionals in architecture, engineering, and urban planning. These techniques enable stakeholders to test hypotheses, evaluate design alternatives, and predict performance outcomes prior to construction. Applications span energy consumption, airflow, thermal comfort, lighting, structural behavior, and human interactions within buildings and urban contexts. This study maps the scientific landscape of simulation research in the built environment through a bibliometric analysis of 12,220 publications indexed in Scopus. Using VOSviewer 1.6.20, it conducted citation and keyword co-occurrence analyses to identify key research themes, leading countries and journals, and central publications in the field. The analysis revealed seven primary thematic clusters: (1) human-focused simulation, (2) building-scale energy performance simulation, (3) urban-scale energy performance simulation, (4) sustainable design and simulation, (5) indoor environmental quality simulation, (6) building aerodynamics simulation, and (7) computing in building simulation. By synthesizing these trends and domains, this study provides an overview of the field, facilitating greater accessibility to the simulation literature and informing future interdisciplinary research and practice in the built environment. Full article

Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R² (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals. Full article

The penetration rate of variable energy sources in the current power grid is increasing, with the aim being to expand the use of these energy sources and to replace the traditional black start power supply. This study investigates the black start of a photovoltaic storage joint system based on the system’s short-circuit ratio threshold. Firstly, the principles and control modes of the photovoltaic (PV) system, energy storage system (ESS), and high-voltage direct current (DC) transmission system are studied separately to build an overall model; secondly, computational determinations of the short-circuit ratio under different scenarios are introduced to analyze the strength of the system, and the virtual inertia and virtual damping of the PV system are configured based on this; finally, the change trend of the storage system’s state of charge (SOC) is computed and observed, and the limits of what the system can support in each stage are determined. An electromagnetic transient simulation model of a black start system is constructed in PSCAD/EMTDC, and according to the proposed recovery strategy, the system frequency is maintained in the range of 49.4~50.6 Hz during the entire black start process; the fluctuation in maximum frequency after the recovery of the DC transmission system is no more than 0.1%; and the fluctuation in photovoltaic power at each stage is less than 3%. In addition, all the key indexes meet the requirements for black start technology, which verifies the validity of the strategy and provides theoretical support and a practical reference for the black start of a grid with variable energy sources. Full article

Achieving carbon emission reductions in the residential building sector while maintaining economic growth represents a global challenge, particularly in rapidly developing regions with internal disparities. This study examines Jiangsu Province in eastern China—a economic hub with north-south development gradients—to develop an integrated framework for differentiated carbon reduction pathways. The methodology combines spatial autocorrelation analysis, logarithmic mean Divisia index (LMDI) decomposition, system dynamics modeling, and Tapio decoupling analysis to examine urban residential building emissions across three regions from 2016–2022. Results reveal significant spatial clustering of emissions (Moran’s I peaking at 0.735), with energy consumption per unit area as the dominant driver across all regions (contributing 147.61%, 131.82%, and 147.57% respectively). Scenario analysis demonstrates that energy efficiency policies can reduce emissions by 10.1% while maintaining 99.2% of economic performance, enabling carbon peak achievement by 2030. However, less developed northern regions emerge as binding constraints, requiring technology investments. Decoupling analysis identifies region-specific optimal pathways: conventional development for advanced regions, balanced approaches for transitional areas, and subsidies for lagging regions. These findings challenge assumptions about environment-economy trade-offs and provide a replicable framework for designing differentiated climate policies in heterogeneous territories, offering insights for similar regions worldwide navigating the transition to sustainable development. Full article

Renovating existing buildings is a key strategy for achieving the EU’s climate targets, as over 75% of the current building stock is energy inefficient. This study evaluates the environmental impacts of three façade renovation scenarios for an office building at the Technical University in Zvolen (Slovakia) using a life cycle assessment (LCA) approach. The aim is to quantify and compare these impacts based on material selection and its influence on sustainable construction. The analysis focuses on key environmental indicators, including global warming potential (GWP), abiotic depletion (ADE, ADF), ozone depletion (ODP), toxicity, acidification (AP), eutrophication potential (EP), and primary energy use (PERT, PENRT). The scenarios vary in the use of insulation materials (glass wool, wood fibre, mineral wool), façade finishes (cladding vs. render), and window types (aluminium vs. wood–aluminium). Uncertainty analysis identified GWP, AP, and ODP as robust decision-making categories, while toxicity-related results showed lower reliability. To support integrated and transparent comparison, a composite environmental index (CEI) was developed, aggregating characterisation, normalisation, and mass-based results into a single score. Scenario C–2, featuring an ETICS system with mineral wool insulation and wood–aluminium windows, achieved the lowest environmental impact across all categories. In contrast, scenarios with traditional cladding and aluminium windows showed significantly higher impacts, particularly in fossil fuel use and ecotoxicity. The findings underscore the decisive role of material selection in sustainable renovation and the need for a multi-criteria, context-sensitive approach aligned with architectural, functional, and regional priorities. Full article

Hydrogen is being increasingly integrated into the international trade system as a clean and flexible energy carrier, motivated by the global energy transition and carbon neutrality objectives. The rapid expansion of the global hydrogen trade network has simultaneously exposed several sustainability challenges, including a centralized structure, overdependence on key countries, and limited resilience to external disruptions. Based on this, we develop a risk propagation model that incorporates the absorption capacity of nodes to simulate the propagation of supply shortage risks within the global hydrogen trade network. Furthermore, we propose a composite sustainability index constructed from structural, economic, and environmental resilience indicators, enabling a systematic assessment of the network’s sustainable development capacity under external shock scenarios. Findings indicate the following: (1) The global hydrogen trade network is undergoing a structural shift from a Western Europe-dominated unipolar configuration to a more polycentric pattern. Countries such as China and Singapore are emerging as key hubs linking Eurasian regions, with trade relationships among nations becoming increasingly dense and diversified. (2) Although supply shortage shocks trigger structural disturbances, economic losses, and risks of carbon rebound, their impacts are largely concentrated in a limited number of hub countries, with relatively limited disruption to the overall sustainability of the system. (3) Countries exhibit significant heterogeneity in structural, economic, and environmental resilience. Risk propagation demonstrates an uneven pattern characterized by hub-induced disruptions, chain-like transmission, and localized clustering. Accordingly, policy recommendations are proposed, including the establishment of a polycentric coordination mechanism, the enhancement of regional emergency coordination mechanisms, and the advancement of differentiated capacity-building efforts. Full article

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. Full article

This study provides a comprehensive overview of vertical-axis wind turbines (VAWTs) for emerging energy applications by combining a bibliometric analysis and a thematic mini-review. Scopus-indexed publications from 1979 to 2025 were analyzed using PRISMA guidelines and bibliometric tools (Bibliometrix, CiteSpace, and VOSviewer) to map global research trends, and a parallel mini-review distilled recent advances into five thematic areas: aerodynamic strategies, advanced materials, urban integration, hybrid systems, and floating offshore platforms. The results reveal that VAWT research output has surged since 2006, led by China with strong contributions from Europe and North America, and is concentrated in leading renewable energy journals. Dominant topics include computational fluid dynamics (CFD) simulations, performance optimization, wind–solar hybrid integration, and adaptation to turbulent urban environments. Technologically, active and passive aerodynamic innovations have boosted performance albeit with added complexity, remaining mostly at moderate technology readiness (TRL 3–5), while advanced composite materials are improving durability and fatigue life. Emerging applications in microgrids, building-integrated systems, and offshore floating platforms leverage VAWTs’ omnidirectional, low-noise operation, although challenges persist in scaling up, control integration, and long-term field validation. Overall, VAWTs are gaining relevance as a complement to conventional turbines in the sustainable energy transition, and this study’s integrated approach identifies critical gaps and high-priority research directions to accelerate VAWT development and help transition these turbines from niche prototypes to mainstream renewable solutions. Full article

Mangroves form the dominant coastal plant community of low-energy tropical intertidal habitats and provide critical ecosystem services to humans and the environment. However, more frequent and increasingly powerful hurricanes and storm surges are creating additional pressure on the natural resilience of these threatened coastal ecosystems. Advances in remote sensing techniques and approaches are critical to providing robust quantitative monitoring of post-storm mangrove forest recovery to better prioritize the often-limited resources available for the restoration of these storm-damaged habitats. Here, we build on previously utilized spatial and temporal ranges of European Space Agency (ESA) Sentinel satellite imagery to monitor and map the recovery of the mangrove forests of the British Virgin Islands (BVI) since the occurrence of back-to-back category 5 hurricanes, Irma and Maria, on September 6 and 19 of 2017, respectively. Pre- to post-storm changes in coastal mangrove forest health were assessed annually using the normalized difference vegetation index (NDVI) and moisture stress index (MSI) from 2016 to 2023 using Google Earth Engine. Results reveal a steady trajectory towards forest health recovery on many of the Territory’s islands since the storms’ impacts in 2017. However, some mangrove patches are slower to recover, such as those on the islands of Virgin Gorda and Jost Van Dyke, and, in some cases, have shown a continued decline (e.g., Prickly Pear Island). Our work also uses a linear ANCOVA model to assess a variety of geospatial, environmental, and anthropogenic drivers for mangrove recovery as a function of NDVI pre-storm and post-storm conditions. The model suggests that roughly 58% of the variability in the 7-year difference (2016 to 2023) in NDVI may be related by a positive linear relationship with the variable of population within 0.5 km and a negative linear relationship with the variables of northwest aspect vs. southwest aspect, island size, temperature, and slope. Full article

Smart cities leverage digital marketing to promote sustainability and build a distinctive global branding. Despite its growing significance, the role digital marketing in smart city development remains underexplored. This study aims to fill this gap by employing bibliometric analysis of 1908 articles indexed in the Scopus database (2000–2024), using the Bibliometrix R-Studio (version 1.4.1743) and VOSviewer (version 1.6.20). The analysis reveals two thematic clusters: (1) “Digital Innovation and Sustainability”, which emphasizes technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data for energy efficiency and green urban development; and (2) “Governance and Policy”, which highlights digital marketing’s role in enabling participatory governance, citizen engagement, and inclusive urban policies. Findings underscore that digital marketing is not only a strategic communication channel but also a driver of sustainable urban transformation. By synthesizing insights from urban planning, technology, and sustainability, this paper provides a novel perspective on the intersection of digital marketing and smart cities. The results provide valuable guidance for policymakers, city planners, and researchers to harness digital marketing in promoting sustainability and further develop the smart city concept. Full article

Melamine formaldehyde particle boards (MFPBs), commonly utilized as a wooden decorative material in traditional architecture, demonstrate considerable performance deterioration with extended age, with reductions in essential flame retardancy and structural integrity presenting substantial risks to fire safety in structures. This research examines the impact of thermo-oxidative aging on the flame retardancy of MFPBs. The morphological evolution, surface composition, and flame-retardant characteristics of aged MFPBs were examined via scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TG), limiting oxygen index (LOI), and cone calorimeter (CCT). The results indicate that thermo-oxidative aging (60 °C, 1440 h) markedly reduces the activation energy (E, by 17.05%), pre-exponential factor (A, by 68.52%), LOI value (by 4%, from 27.5 to 26.4), and time to ignition (TTI, by 17.1%, from 41 s to 34 s) while augmenting the peak mass loss rate (MHRR, by 4.7%) and peak heat release rate (pHRR, by 20.1%). Subsequent investigation indicates that aging impairs the char layer structure on MFPB surfaces, hastens the migration and degradation of melamine formaldehyde resin (MFR), and alters the dynamic equilibrium between “MFR surface enrichment” and “thermal decomposition”. The identified degradation thresholds and failure mechanisms provide essential parameters for developing aging-resistant fireproof composites, meeting the pressing demands of building safety requirements and sustainable material design. Full article

The need for integrated governance of water–energy–land–food (WELF) systems has become paramount in achieving sustainable low-carbon transitions, yet policy consistency across these interdependent sectors remains critically underexplored. This study presents the first systematic assessment of policy consistency and synergy within China’s WELF framework, employing an innovative mixed-methods approach that combines a modified Policy Modeling Consistency (PMC) Index with Content Analysis Methodology (CAM). Policy consistency follows a clear hierarchy: energy (PMC = 9.06, ‘Perfect’), water (8.26, ‘Good’), land (7.03, ‘Acceptable’), and food systems (6.91, ‘Acceptable’), with land–food policies exhibiting critical gaps in multifunctional design. Policy synergy metrics further reveal pronounced sectoral disparities: energy (PS = 0.89) and water (0.81) policies demonstrate strong alignment with central government objectives, whereas land (0.68) and food (0.64) systems exhibit constrained integration capacities due to uncoordinated policy architectures and competing sectoral priorities. Building on these findings, we propose three key interventions: (1) institutional restructuring through the establishment of an inter-ministerial coordination body with binding authority to align WELF sector priorities and enforce consistent and synergy targets, (2) the strategic rebalancing of policy instruments by reallocating fiscal incentives toward nexus-optimizing projects while developing innovative market-based mechanisms for cross-sectoral resource exchange, and (3) adaptive governance implementation through regional policy pilots, dynamic feedback systems, and capacity-building networks to enable context-sensitive WELF transitions while maintaining strategic consistency and synergy. These recommendations directly address the structural deficiencies in WELF governance fragmentation and incentive misalignment identified through our rigorous analysis, while simultaneously advancing theoretical discourse and offering implementable policy solutions for achieving integrated low-carbon transition. Full article

The paper is the culmination of research on geometric aspects of assessing the energy demand of a single-family house. In a recent study, two collections of single-family houses were analyzed: (a) a collection of 21 with outlines assumed a priori so that the building area was constant (which is not achievable in practice) and (b) a collection of 33 real buildings, recently designed by the Polish design studio Galeria Domów. These examples show the functioning of the indicators analyzed by the author in earlier papers and indicate the ${RC}_{sq}$ indicator that best reflects the assessment of building compactness in percentage points in relation to the ideal shape of the building plan, which is a square. The ${RC}_{sq}$ index is economically expressed by only two parameters, namely the base area ${\mathrm{A}}_{\mathrm{f}}$ and the building outline perimeter $\mathrm{P}$, and therefore is easy to implement in the BIM system and at the same time covers high-rise buildings. As it turned out, the tested buildings from Galeria Domów have very good geometric and therefore energy efficiency. The above-mentioned indicator also highlights the advisability of analyzing the heated part in addition to the standard full-contour analyses. Full article

This study investigated sustainable activation strategies for residual slag micro powder derived from recycled waste concrete aggregates, aiming to advance circular economy principles in construction materials. An experimental study was carried out to explore the activation mechanisms of slag micro powder from recycled waste concrete aggregates to enhance its utility in building materials. Three methods—mechanical grinding, high-temperature calcination, and mechanical grinding–thermal activation—were evaluated comparatively. The results showed high-temperature calcination at 750 °C for 10 min proved most effective, achieving a 95.85% activity index. High-temperature calcination may contribute to the release of active SiO₂ and Al₂O₃ substances of slag micro powder, thereby improving the hydration performance of slag micro powder and its cement mortar’s compressive strength. The flexural strength of cement mortar after different activation treatments was also analyzed. Mechanical grinding alone showed limited benefits, only achieving a less than 65.59% activity index, while the combined method negatively impacted the mechanical properties of cement mortar samples. An SEM (scanning electron microscope) and EDS (energy dispersive X-ray spectrometer) microstructural analysis supported these findings, highlighting enhanced hydration product formation after calcination at 750 °C for 10 min. This work may contribute to sustainable construction practices through the resource-efficient utilization of industrial by-products. Full article

The aim of this work is to investigate the effect of curing temperature and time on the development of compressive strength in geopolymer mortars produced using ground granulated blast-furnace slag (GGBFS) and fly ash (FA). Considering curing circumstances, both the activation energy and the reference temperature could be used properly to build a reliable anticipated model for predicting the compressive strength of geopolymer-based products (mortar and concrete) using maturity-based techniques. In this study, the compressive strength development of geopolymer mortar made from (FA) and (GGBFS) under varying curing conditions. The mortar was prepared using an alkali solution of sodium hydroxide (NaOH) and sodium silicate (Na₂SiO₃) in a 1:1 ratio, with NaOH molarity of 12. Specimens were cast following ASTM C109 standards, with a binder/sand ratio of 1:2.75, and compacted for full densification. FA-based mortar was cured at 40 °C, 80 °C, and 120 °C, while GGBFS-based mortar was cured at 5 °C, 15 °C, and 40 °C for durations of 0.5 to 32 days. Compressive strength was evaluated at each curing period, and data were analyzed using ASTM C1074 procedures alongside a computational model to determine the best-fit datum temperature and activation energy. The Nurse-Saul maturity method and Arrhenius equation were applied to estimate the equivalent age and maturity index of each mix. A predictive model was developed for geopolymer concrete prepared at an alkali-to-binder ratio of 0.45 and NaOH molarity of 12. The final equation demonstrated high accuracy, offering a reliable tool for predicting geopolymer strength under diverse curing conditions and providing valuable insights for optimizing geopolymer concrete formulations. Full article