Search Results (161)

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations. Full article

Decarbonizing industry, improving urban sustainability, and expanding clean energy use are key global priorities. This study presents a techno-economic analysis (TEA) and life-cycle assessment (LCA) of green hydrogen (GH₂) production via water electrolysis for low-carbon applications in the Central Asian region, with Uzbekistan considered as a representative case study. Solar PV and wind power are used as renewable electricity sources for a 44 MW electrolyzer. The assessment also incorporates recent advances in alkaline water electrolyzers (AWE) enhanced with metal–organic framework (MOF) materials, reflecting improvements in efficiency and hydrogen output. The LCA, performed using SimaPro, evaluates the global warming potential (GWP) across the full hydrogen production chain. Results show that the MOF-enhanced AWE system achieves a lower levelized cost of hydrogen (LCOH) at 5.18 $/kg H₂, compared with 5.90 $/kg H₂ for conventional AWE, with electricity procurement remaining the dominant cost driver. Environmentally, green hydrogen pathways reduce GWP by 80–83% relative to steam methane reforming (SMR), with AWE–MOF delivering the lowest footprint at 1.97 kg CO₂/kg H₂. In transport applications, fuel cell vehicles powered by hydrogen derived from AWE–MOF emit 89% less CO₂ per 100 km than diesel vehicles and 83% less than using SMR-based hydrogen, demonstrating the substantial climate benefits of advanced electrolysis. Overall, the findings confirm that MOF-integrated AWE offers a strong balance of economic viability and environmental performance. The study highlights green hydrogen’s strategic role in the Central Asian region, represented by Uzbekistan’s energy transition, and provides evidence-based insights for guiding low-carbon hydrogen deployment. Full article

Rapid urbanization in developing megacities has exacerbated hydrological imbalances, positioning urban flooding as a major environmental and socio-economic challenge of the twenty-first century. This study investigates the spatial relationship between building density, topography, and flood susceptibility in Jakarta, Indonesia—one of the most flood-prone urban regions globally. Employing geospatial analysis and spatial autocorrelation techniques, the research assesses how variations in land-use concentration and elevation influence the spatial clustering of flood vulnerability. The analytical framework integrates multiple spatial datasets, including Digital Elevation Models (DEMs), building footprint densities, and flood hazard maps, within a Geographic Information System (GIS) environment. Spatial statistical measures, specifically Moran’s I and Local Indicators of Spatial Association (LISA), are utilized to quantify and visualize patterns of flood susceptibility. The findings reveal that zones characterized by high building density and low elevation form statistically significant clusters of heightened flood risk, particularly within the southern and eastern subdistricts of Jakarta. The study concludes that incorporating spatially explicit and statistically rigorous methodologies enhances the accuracy of flood-risk assessments and supports evidence-based strategies for sustainable urban development and resilience planning. Full article

Wildfires increasingly threaten Mediterranean landscapes, particularly in regions like Attica, Greece, where urban sprawl, agricultural abandonment, and climatic conditions heighten the risk at the Wildland–Urban Interface (WUI). The Mediterranean basin, recognized as one of the global wildfire “hotspots”, has witnessed a steady increase in both fire severity, frequency, and burned area during the last four decades, a trend amplified by urban sprawl and agricultural land abandonment. This study represents the first integrated, region-wide mapping of the WUI and associated wildfire risk in Attica, the most densely urbanized area in Greece and one of the most fire-exposed metropolitan regions in Southern Europe, utilizing advanced techniques such as Earth Observation and GIS analysis. For this purpose, various geospatial datasets were coupled, including Copernicus High Resolution Layers, multi-decadal Landsat fire history archive, UCR-STAR building footprints, and CORINE Land Cover, among others. The research delineated WUI zones into 40 interface and intermix categories, revealing that WUI encompasses 26.29% of Attica, predominantly in shrub-dominated areas. An analysis of fire frequency history from 1983 to 2023 indicated that approximately 102,366 hectares have been affected by wildfires. Risk assessments indicate that moderate hazard zones are most prevalent, covering 36.85% of the region, while approximately 25% of Attica is classified as moderate, high, or very high susceptibility zones. The integrated risk map indicates that 37.74% of Attica is situated in high- and very high-risk zones, principally concentrated in peri-urban areas. These findings underscore Attica’s designation as one of the most fire-prone metropolitan regions in Southern Europe and offer a viable methodology for enhancing land-use planning, fuel management, and civil protection efforts. Full article

Amid escalating global water scarcity and growing emphasis on demand-side interventions for sustainable resource use, understanding how consumers’ virtual water cognition can drive food–water resource conservation is critical for strengthening sustainable resource governance. Through a questionnaire survey, this study constructed a Food–Water Behavior Synergy Model to explore the relationship among consumers’ virtual water cognition and food-conservation behavior, water-conservation behavior, and food–water synergistic cognition in China. Results show that virtual water cognition significantly increased food-conservation behavior (β = 0.158, p < 0.001) and WCB (β = 0.064, p < 0.001). Food–water synergistic cognition also positively affected food-conservation behavior (β = 0.099, p < 0.001) and water-conservation behavior (β = 0.035, p < 0.01), consistent with the knowledge–action framework. The magnitudes of these effects differed across subgroups (gender, education level, major, region, and urban–rural residence). Virtual water cognition did not significantly enhance food–water synergistic cognition (β = 0.006, p = 0.758), providing empirical evidence of a knowledge–action gap. There was a strong direct effect of food-conservation behavior on water-conservation behavior (β = 0.498, p < 0.001), and there was evidence that food-conservation behavior mediated the indirect paths from both virtual water cognition and food–water synergistic cognition to water-conservation behavior. Implementing consumer-oriented contextual interventions—such as differentiated educational guidance and water-footprint labeling—would be conducive to translating theoretical knowledge into practical action. Full article

Resource-based regions play an indispensable role as strategic bases for national energy and raw material supply in the global industrialization and urbanization process. However, intensive and large-scale natural resource exploitation—particularly mineral extraction—often triggers dramatic land use/cover changes, leading to a series of problems including cultivated land degradation, ecological function deterioration, and human settlement environment degradation. However, a systematic understanding of the functional transitions within the land use system and their drivers in such regions remains limited. This study takes Shenmu City, a typical resource-based city in the ecologically vulnerable Loess Plateau, as a case study to systematically analyze the transition characteristics and driving mechanisms of land use functions from 2000 to 2020. By constructing an integrated “element–structure–function” analytical framework and employing a suite of methods, including land use transfer matrix, Spearman correlation analysis, and random forest with SHAP interpretation, we reveal the complex spatiotemporal evolution patterns of production–living–ecological functions and their interactions. The results demonstrate that Shenmu City has undergone rapid land use transformation, with the total transition area increasing from 27,394.11 ha during 2000–2010 to 43,890.21 ha during 2010–2020. Grassland served as the primary transition source, accounting for 66.5% of the total transition area, while artificial surfaces became the main transition destination, receiving 38.6% of the transferred area. The human footprint index (SHAP importance: 4.011) and precipitation (2.025) emerged as the dominant factors driving land use functional transitions. Functional interactions exhibited dynamic changes, with synergistic relationships predominating but showing signs of weakening in later periods. The findings provide scientific evidence and a transferable analytical framework for territorial space optimization and ecological restoration management not only in Shenmu but also in analogous resource-based regions facing similar development–environment conflicts. Full article

Accurate forecasting of the ecological footprint (EF) is critical for advancing the Sustainable Development Goals, particularly those related to climate action, responsible consumption and production, and sustainable cities. To address the limitations of conventional machine learning models, such as instability due to random weight initialization and poor generalization, this study proposes a novel hybrid model that integrates the Chinese Pangolin Optimizer (CPO) with the Extreme Learning Machine (ELM). Inspired by the foraging behavior of pangolins, the CPO efficiently optimizes the ELM’s input weights and biases, significantly enhancing prediction accuracy and robustness. Using comprehensive monthly United States data from 1991 to 2020, the model forecasts EF based on key socioeconomic and environmental indicators, including GDP per capita, human capital, financial development, urbanization, globalization, and foreign direct investment. The CPO–ELM model outperforms benchmark models, achieving an R² of 0.9880 and the lowest error metrics across multiple validation schemes. Furthermore, SHAP (Shapley Additive Explanations) analysis reveals that GDP per capita, human capital, and financial development are the most influential drivers of EF, offering policymakers actionable insights. This study demonstrates how interpretable AI-driven forecasting can support evidence-based environmental governance and contribute directly to sustainability targets under the SDG framework. Full article

Accurate and up-to-date data on built-up areas are crucial for urban planning, disaster management, and sustainable development, yet Romania still lacks a unified, official database. In this study we integrated the three widely used global data sources—OpenStreetMap (OSM), Microsoft Building Footprints (MSBFs), and Global Human Settlement Layer Built-up surface (GHS)—onto a 10 m resolution raster grid and applied this consistently at the national scale across 3181 settlement polygons to produce a more accurate, unified ensemble model for Romania. The methodological basis was Triple Collocation Analysis (TCA), extended with ETC/CTC to estimate per-settlement scale factors, enabling the quantification and optimal weighting of the relative errors and accuracy in the absence of independent reference data. Weight patterns vary by settlement type: OSM receives relatively higher weights in smaller rural settlements with less redundant error; in municipalities the stronger OSM–MSBF correlation reduces both of their weights and increases the GHS share; cities exhibit a more balanced weighting. At cell level, the ensemble provides uncertainty quantification via confidence intervals that typically range from 2% to 14% at settlement scale. The resulting model—like any model—does not perfectly reflect reality; however, the ensemble improves the accuracy and timeliness of the available data. The resulting model is replicable and updatable with newer data, making it suitable for numerous practical applications, especially in spatial development and risk analysis. Full article

Waste valorization in construction materials offers a promising pathway to reducing environmental burdens while promoting circular resource strategies in the built environment. This study develops a novel composite mortar formulated with sustainable materials and alternative aggregates, namely polyethylene terephthalate (PET) particles recovered from post-consumer plastic waste and bottom ash from thermal power generation. Natural pumice was incorporated to improve the lightness and the thermal insulation, with cement serving as the binder. The mix design was systematically optimized using the Taguchi method to enhance performance while minimizing carbon emissions. The resulting mortar, produced at both laboratory and small-scale commercial levels, demonstrated favorable technical properties: dry density of 1.3 g/cm³, compressive strength of 5.96 MPa, thermal conductivity of 0.27 W/(m*K), and water absorption of 16.1%. After exposure to 600 °C, it retained 60.6% of its strength and exhibited only a 10.1% mass loss. These findings suggest its suitability for non-load-bearing urban components where sustainability, thermal resistance, and durability are essential. The study contributes to global sustainability goals, particularly Sustainable Development Goal (SDG) 11, 12, and 13, by illustrating how waste valorization can foster resilient construction while reducing the environmental footprint of cities. Full article

Dengue, caused by the dengue virus (DENV), is rapidly expanding its geographical footprint, with increasing incidence not only in over 100 endemic countries in the southern hemisphere but also with more autochthonous transmissions now reported in the northern hemisphere, including regions of Europe and the United States. The clinical presentation of DENV infection ranges from mild febrile illness to severe and potentially fatal conditions, such as dengue hemorrhagic fever (DHF), dengue shock syndrome (DSS), and diverse neurological complications. While vaccine development efforts are underway, significant challenges remain, underscoring the urgent need for a deeper understanding of the virus. This urgency is particularly palpable in Brazil, which has faced an unprecedented surge in dengue cases during the 2024–2025 period. The country has recorded an alarmingly high number of infections and related deaths, stretching its public health infrastructure and highlighting the complex interplay of climate change, urbanization, and viral dynamics in disease propagation. This review provides a global perspective on dengue, systematically exploring its history, morphology, viral cycle, pathogenesis, and epidemiology. By integrating these critical aspects, this article aims to identify pivotal knowledge gaps and guide future research directions essential for developing improved public health interventions against this complex and evolving disease. Full article

According to United Nations projections, the global population is expected to reach 9.7 billion by 2050, with 70% residing in urban areas, while arable land availability continues to decline. Vertical farming (VF) offers a promising pathway for sustainable urban food production by utilizing vertical space and controlled environments. Among emerging approaches, the adaptive vertical farm (AVF) introduces movable shelving systems that adjust to plant growth stages, allowing a higher number of cultivation shelves to be accommodated within the same rack height. In this study, we developed a computational model to quantify and compare the energy consumption of AVF and conventional VF systems under industrial-scale conditions. The reference scenario considered 272 multilevel racks, each hosting 8 shelves in the VF and 15 shelves in the AVF, with Lactuca sativa as the test crop. Energy consumption for thermohygrometric control and lighting was estimated under different sowing schedules, with crop growth dynamics simulated using scheduling algorithms. Plant heat loads were calculated through the Penman–Monteith model, enabling a robust estimation of evapotranspiration and its impact on indoor climate control. Simulation results show that the AVF achieves an average 22% reduction in specific energy consumption for climate control compared to the VF, independently of sowing strategies. Moreover, the AVF nearly doubles the number of cultivation shelves within the same footprint, increasing the cultivable surface area by over 400% compared to traditional flat indoor systems. This work provides the first quantitative assessment of AVF energy performance, demonstrating its potential to simultaneously improve land-use efficiency and reduce energy intensity, thereby supporting the sustainable integration of vertical farming in urban food systems. Full article

Accurate extraction of building features from remotely sensed data is essential for supporting research and applications in urban planning, land management, transportation infrastructure development, and disaster monitoring. Despite the prominence of deep learning as the state-of-the-art (SOTA) methodology for building extraction, substantial challenges remain, largely stemming from the diversity of building structures and the complexity of background features. To mitigate these issues, this study introduces DeepSwinLite, a lightweight architecture based on the Swin Transformer, designed to extract building footprints from very high-resolution (VHR) imagery. The model integrates a novel local-global attention module to enhance the interpretation of objects across varying spatial resolutions and facilitate effective information exchange between different feature abstraction levels. It comprises three modules: multi-scale feature aggregation (MSFA), improving recognition across varying object sizes; multi-level feature pyramid (MLFP), fusing detailed and semantic features; and AuxHead, providing auxiliary supervision to stabilize and enhance learning. Experimental evaluations on the Massachusetts and WHU Building Datasets reveal the superior performance of DeepSwinLite architecture when compared to existing SOTA models. On the Massachusetts dataset, the model attained an OA of 92.54% and an IoU of 77.94%, while on the WHU dataset, it achieved an OA of 98.32% and an IoU of 92.02%. Following the correction of errors identified in the Massachusetts ground truth and iterative enhancement, the model’s performance further improved, reaching 94.63% OA and 79.86% IoU. A key advantage of the DeepSwinLite model is its computational efficiency, requiring fewer floating-point operations (FLOPs) and parameters compared to other SOTA models. This efficiency makes the model particularly suitable for deployment in mobile and resource-constrained systems. Full article

At present, the decarbonization of the public transport sector plays a key role in international and regional policies. Among the various energy vectors being considered for future clean bus fleets, green hydrogen and electricity are gaining significant attention thanks to their minimal carbon footprint. However, a comprehensive Life Cycle Assessment (LCA) is essential to compare the most viable solutions for public mobility, accounting for variations in weather conditions, geographic locations, and time horizons. Therefore, the present work compares the life cycle environmental impact of different powertrain configurations for urban buses. In particular, a series hybrid architecture featuring two possible hydrogen-fueled Auxiliary Power Units (APUs) is considered: an H2-Internal Combustion Engine (ICE) and a Fuel Cell (FC). Furthermore, a Battery Electric Vehicle (BEV) is considered for the same application. The global warming potential of these powertrains is assessed in comparison to both conventional and hybrid diesel over a typical urban mission profile and in a wide range of external ambient conditions. Given that cabin and battery conditioning significantly influence energy consumption, their impact varies considerably between powertrain options. A sensitivity analysis of the BEV battery size is conducted, considering the effect of battery preconditioning strategies as well. Furthermore, to evaluate the potential of hydrogen and electricity in achieving cleaner public mobility throughout Europe, this study examines the effect of different grid carbon intensities on overall emissions, based also on a seasonal variability and future projections. Finally, the present study demonstrates the strong dependence of the carbon footprint of various technologies on both current and future scenarios, identifying a range of boundary conditions suitable for each analysed powertrain option. Full article

Global warming, anthropogenic pressure, and urban expansion at the expense of green spaces are leading to an increase in the incidence of urban heat islands, creating discomfort and health issue for citizens. This present research aimed at quantifying the impact of nature-based solutions to support decision-making processes in sustainable energy action plans. A simple method is provided, linking applied thermodynamics to physics-informed modeling of urban built-up and green areas, high-resolution climate models at urban scale, greenery modeling, spatial georeferencing techniques for energy, and entropy exchanges evaluation in urban built-up areas, with and without vegetation. This allows the outdoor climate conditions and thermo-hygrometric well-being to improve, reducing the workload of cooling plant-systems in buildings and entropy flux to the environment. The finalization and post-processing of obtained results allows the definition of entropy footprints. The main findings show a decrease in greenery’s contribution for different scenarios, referring to a different climatological dataset, but an increase in entropy that becomes higher for the scenario with higher emissions. The comparison between the entropy footprint values for different urban zones can be a useful support to public administrations, stakeholders, and local governments for planning proactive resilient cities and anthropogenic impact reduction and climate change mitigation. Full article

The building sector is a pivotal driver of global resource depletion and environmental deterioration, being responsible for 40% of raw material consumption, 16% of water usage, 25% of timber utilization, and 40% of total energy demand. It also accounts for 30% of worldwide greenhouse gas (GHG) emissions, predominantly CO₂. The operational phase of buildings is the most energy-intensive and emission-heavy stage, accounting for 85–95% of their total life-cycle energy consumption. This energy is primarily expended on heating, cooling, ventilation, and hot water systems, which are largely dependent on fossil fuels. Furthermore, embodied energy, the cumulative energy expended from the extraction of materials through construction, operation, and eventual demolition, plays a substantial role in a building’s overall environmental footprint. To address these pressing challenges, this study discusses sustainable innovations within green architecture and biomimicry. Our topic supports the 2030 vision Sustainable Development Goals (SDGs), both directly and indirectly (SDGs 7, 9, 11, 12, and 13). This study also explores cutting-edge applications, such as algae- and slime mold-inspired decentralized urban planning, which offer innovative pathways toward energy efficiency and sustainability. Considering the integration of renewable energy sources, passive design methodologies, and eco-friendly materials, this research emphasizes the transformative potential of biomimicry and green architecture in fostering a sustainable built environment, mitigating climate change, and cultivating a regenerative coexistence between human habitats and the natural world. Full article