Search Results (721)

With the global economic restructuring and the consequent population mobility, urban shrinkage has become a common phenomenon. The Qinling–Daba Mountains, a zone with a key ecological function in China, have long experienced population decline and functional degradation. Clarifying the dynamics and influencing factors of urban shrinkage plays a vital role in supporting the sustainable development of the region. This study, using permanent resident population growth rates and nighttime light data, classified cities in the region into four spatial patterns: expansion–growth, intensive growth, expansion–shrinkage, and intensive shrinkage. It further examined the spatial characteristics of shrinkage across four periods (2005–2010, 2010–2015, 2015–2020, and 2020–2022). A Geographically and Temporally Weighted Regression (GTWR) model was applied to examine core influencing factors and their spatiotemporal heterogeneity. The results indicated the following: (1) The dominant pattern of urban shrinkage in the Qinling–Daba Mountains shifted from expansion–growth to expansion–shrinkage, highlighting the paradox of population decline alongside continued spatial expansion. (2) Three critical indicators significantly influenced urban shrinkage: the number of students enrolled in general secondary schools (X₅), the per capita disposable income of urban residents (X₇), and the number of commercial and residential service facilities (X₁₂), with their effects exhibiting significant spatiotemporal heterogeneity. Temporally, X₁₂ was the most influential factor in 2005 and 2010, while in 2015, 2020, and 2022, X₅ and X₇ became the dominant factors. Spatially, X₇ significantly affected both eastern and western areas; X₅’s influence was most pronounced in the west; and X₁₂ had the greatest impact in the east. This study explored the patterns and underlying drivers of urban shrinkage in underdeveloped areas, aiming to inform sustainable development practices in regions facing comparable challenges. Full article

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

Understanding the carbon emissions of multimodal travel—comprising walking, metro, bus, cycling, and ride-hailing—is essential for promoting sustainable urban mobility. However, most existing studies focus on single-mode travel, while underlying spatiotemporal and behavioral determinants remain insufficiently explored due to the lack of fine-grained data and interpretable analytical frameworks. This study proposes a novel integration of high-frequency, real-world mobility trajectory data with interpretable machine learning to systematically identify the key drivers of carbon emissions at the individual trip level. Firstly, multimodal travel chains are reconstructed using continuous GPS trajectory data collected in Beijing. Secondly, a model based on Calculate Emissions from Road Transport (COPERT) is developed to quantify trip-level CO₂ emissions. Thirdly, four interpretable machine learning models based on gradient boosting—XGBoost, GBDT, LightGBM, and CatBoost—are trained using transportation and built environment features to model the relationship between CO₂ emissions and a set of explanatory variables; finally, Shapley Additive exPlanations (SHAP) and partial dependence plots (PDPs) are used to interpret the model outputs, revealing key determinants and their non-linear interaction effects. The results show that transportation-related features account for 75.1% of the explained variance in emissions, with bus usage being the most influential single factor (contributing 22.6%). Built environment features explain the remaining 24.9%. The PDP analysis reveals that substantial emission reductions occur only when the shares of bus, metro, and cycling surpass threshold levels of approximately 40%, 40%, and 30%, respectively. Additionally, travel carbon emissions are minimized when trip origins and destinations are located within a 10 to 11 km radius of the central business district (CBD). This study advances the field by establishing a scalable, interpretable, and behaviorally grounded framework to assess carbon emissions from multimodal travel, providing actionable insights for low-carbon transport planning and policy design. Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

Dark skies, a vital natural and cultural resource, have been increasingly threatened by light pollution due to rapid urbanization, leading to ecological degradation and biodiversity loss. As a key strategy for sustainable regional development, dark sky parks (DSPs) not only preserve nocturnal environments but also enhance livability by balancing urban expansion and ecological conservation. This study develops a novel framework for evaluating DSP suitability, integrating ecological and socio-economic dimensions, including the resource base (e.g., nighttime light levels, meteorological conditions, and air quality) and development conditions (e.g., population density, transportation accessibility, and tourism infrastructure). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, we employ Delphi expert consultation, GIS spatial analysis, and multi-criteria decision-making to identify optimal DSP locations and prioritize conservation zones. Our key findings reveal the following: (1) spatial heterogeneity in suitability, with high-potential zones being concentrated in the GBA’s northeastern, central–western, and southern regions; (2) ecosystem advantages of forests, wetlands, and high-elevation areas for minimizing light pollution; (3) coastal and island regions as ideal DSP sites due to the low light interference and high ecotourism potential. By bridging environmental assessments and spatial planning, this study provides a replicable model for DSP site selection, offering policymakers actionable insights to integrate dark sky preservation into sustainable urban–regional development strategies. Our results underscore the importance of DSPs in fostering ecological resilience, nighttime tourism, and regional livability, contributing to the broader discourse on sustainable landscape planning in high-urbanization contexts. Full article

Urban expansion will squeeze the green space system and cause ecological fragmentation. The question of how to expand cities more scientifically and build eco-cities has become an important topic of sustainable urban construction. This paper takes Shanghai as a research case. A deep neural network combined with an attention mechanism model measures the comprehensive level of the built environment and green space pattern of urbanization and quantitatively analyzes the coordinated relationship between the two using the coupled degree of coordination model. Subsequently, the K-Means clustering model was used for spatial clustering to determine the governance and construction directions for different spatial areas and was, finally, combined with the LightGBM model plus SHAP to analyze the importance and threshold effect of the indicators on the degree of coupled coordination. The results of the study show that (1) the core area of the city shows a high state of coordination, indicating that Shanghai has a better green space construction in the central city, but the periphery shows different imbalances; (2) three different kinds of areas are identified, and different governance measures as well as the direction of urbanization are proposed according to the characteristics of the different areas; and (3) this study finds that the structural indicators of the built environment, such as Average Compactness, Weighted Average Height, and Land Use Diversity, have a significant influence on the coupling coordination degree and have different response thresholds. The results of the study provide theoretical support for regional governance and suggestions for the direction of urban expansion for sustainable urbanization. Full article

Autonomous earthwork machinery is gaining traction as a means to boost productivity and safety on space-constrained urban sites, yet the fast-growing literature has not been fully integrated. To clarify current knowledge, we systematically searched Scopus and screened 597 records, retaining 157 peer-reviewed papers (2015–March 2025) that address autonomy, integrated control, or risk mitigation for excavators, bulldozers, and loaders. Descriptive statistics, VOSviewer mapping, and qualitative synthesis show the output rising rapidly and peaking at 30 papers in 2024, led by China, Korea, and the USA. Four tightly linked themes dominate: perception-driven machine autonomy, IoT-enabled integrated control systems, multi-sensor safety strategies, and the first demonstrations of fleet-level collaboration (e.g., coordinated excavator clusters and unmanned aerial vehicle and unmanned ground vehicle (UAV–UGV) site preparation). Advances include centimeter-scale path tracking, real-time vision-light detection and ranging (LiDAR) fusion and geofenced safety envelopes, but formal validation protocols and robust inter-machine communication remain open challenges. The review distils five research priorities, including adaptive perception and artificial intelligence (AI), digital-twin integration with building information modeling (BIM), cooperative multi-robot planning, rigorous safety assurance, and human–automation partnership that must be addressed to transform isolated prototypes into connected, self-optimizing fleets capable of delivering safer, faster, and more sustainable urban construction. Full article

Tourist satisfaction of nighttime urban lakes as scenic areas, such as the Daming Lake, is influenced by multiple factors, which are crucial for tourists’ experiences and the sustainable development of these areas. This paper explores the factors impacting nighttime visitor satisfaction at the Daming Lake Scenic Area. Basing our studies on analysis of the literature and questionnaire surveys, the study constructs a visitor satisfaction evaluation index system based on the Expectancy-Disconfirmation Theory. Utilizing the revised importance-performance analysis method, the study identifies several significant influencing factors including the distinctive features of nighttime shopping products, the rich variety of nighttime tourscape and entertainment products, the aesthetically pleasing design of nighttime lighting products, the affordable price of nighttime dining products, and the diverse methods, reasonable pricing, and multimodal transit options of nighttime transportation. Furthermore, it finds the main factors that reduce tourists’ satisfaction in nighttime urban lakes include: premium pricing of nighttime shopping and dining products, transport infrastructure deficiencies, the cultural connotation of tourism products, and the safety of nighttime tourscape and entertainment products. This research provides insights to enhance satisfaction in urban lake scenic areas and expands the application of the tourist satisfaction theory. Full article

In pursuit of the Sustainable Development Goals (SDGs)’s objective of eliminating slum cities, the government of Indonesia has initiated a survey-based slum mapping program. Unfortunately, recent observations have highlighted considerable inconsistencies in the mapping process. These inconsistencies can be attributed to various factors, including variations in the expertise of surveyors and the intricacies of the indicators employed to characterize slum conditions. Consequently, reliable data is lacking, which poses a significant barrier to effective monitoring of slum upgrading programs. Remote sensing (RS)-based approaches, particularly those employing deep learning (DL) techniques, have emerged as a highly effective and accurate method for identifying slum areas. However, the reliance on RS alone is likely to encounter challenges in complex urban environments. A substantial body of research has previously identified the merits of integrating land surface data with RS. Therefore, this study seeks to combine remote sensing imagery (RSI) with street view imagery (SVI) for the purpose of slum mapping and compare its accuracy with a field survey conducted in 2024. The city of Bandung is a pertinent case study, as it is facing a considerable increase in population density. These slums collectively encompass approximately one-tenth of Bandung City’s population as of 2020. The present investigation evaluates the mapping results obtained from four distinct deep learning (DL) networks: The first category comprises FCN, which utilizes RSI exclusively, and FCN-DK, which also employs RSI as its sole input. The second category consists of two networks that integrate RSI and SVI, namely FCN and FCN-DK. The findings indicate that the integration of RSI and SVI enhances the precision of slum mapping in Bandung City, particularly when employing the FCN-DK network, achieving an accuracy of 86.25%. The results of the mapping process employing a combination of the FCN-DK network, which utilizes the RSI and SVI, indicate the presence of 2294 light slum points and 29 medium slum points. It should be noted that the outcomes are contingent upon the methodological approach employed, the accessibility of the dataset, and the training data that mirrors the distribution of slums in 2020 and the specific degree of its integration within the FCN network. The FCN-DK model, which integrates RSI and SVI, demonstrates enhanced performance in comparison to the other models examined in this study. Full article

Urban electricity power consumption (EPC) prediction plays a crucial role in urban management and sustainable development. Nighttime light (NTL) remote sensing imagery has demonstrated significant potential in estimating urban EPC due to its strong correlation with human activities and energy use. However, most existing models focus on annual-scale estimations, limiting their ability to capture month-scale EPC. To address this limitation, a novel monthly EPC prediction model that incorporates monthly average temperature, and the interaction between NTL data and temperature was proposed in this study. The proposed method was applied to cities within the Yangtze River Delta (YRD) urban agglomeration, and was validated using datasets constructed from NPP/VIIRS and SDGSAT-1 satellite imageries, respectively. For the NPP/VIIRS dataset, the proposed method achieved a Mean Absolute Relative Error (MARE) of 7.96% during the training phase (2017–2022) and of 10.38% during the prediction phase (2023), outperforming the comparative methods. Monthly EPC spatial distribution maps from VPP/VIIRS data were generated, which not only reflect the spatial patterns of EPC but also clearly illustrate the temporal evolution of EPC at the spatial level. Annual EPC estimates also showed superior accuracy compared to three comparative methods, achieving a MARE of 7.13%. For the SDGSAT-1 dataset, leave-one-out cross-validation confirmed the robustness of the model, and high-resolution (40 m) monthly EPC maps were generated, enabling the identification of power consumption zones and their spatial characteristics. The proposed method provides a timely and accurate means for capturing monthly EPC dynamics, effectively supporting the dynamic monitoring of urban EPC at the monthly scale in the YRD urban agglomeration. Full article

Urban road traffic congestion poses significant challenges to sustainable mobility in smart cities. Traditional traffic light systems, reliant on static or semi-fixed timers, fail to adapt to dynamic traffic conditions, exacerbating congestion and limiting inclusivity. To address these limitations, this paper proposes H-ATLM (a hybrid adaptive traffic lights management), a system utilizing the deep deterministic policy gradient (DDPG) reinforcement learning algorithm to optimize traffic light timings dynamically based on real-time data. The system integrates advanced sensing technologies, such as cameras and inductive loops, to monitor traffic conditions and adaptively adjust signal phases. Experimental results demonstrate significant improvements, including reductions in congestion (up to 50%), increases in throughput (up to 149%), and decreases in clearance times (up to 84%). These findings open the door for integrating accessibility-focused features such as adaptive signaling for accessible vehicles, dedicated lanes for paratransit services, and prioritized traffic flows for inclusive mobility. Full article

The rapid expansion of urban areas necessitates effective monitoring systems for sustainable development planning. Time-series change detection algorithms applied to satellite imagery offer promising solutions, but their comparative effectiveness specifically for urban land cover monitoring remains poorly understood. This study aims to systematically evaluate and optimize three widely used algorithms—LandTrendr, CCDC, and BFAST—selected for their proven capabilities in different land cover change contexts and distinct algorithmic approaches. Using Landsat 5/7/8 (TM/ETM+/OLI) time-series data from 2000 to 2020 and a globally distributed dataset of 200 sample locations spanning six continents, we assess these algorithms across multiple spectral bands and parameter settings for land cover change detection in urban areas. Our analysis reveals that CCDC achieves the highest accuracy (78.14% F1 score) when utilizing complete spectral information (bands B1–B7), outperforming both BFAST (74.32% F1 score with NDVI) and LandTrendr (71.29% F1 score with B1). We demonstrated that, contrary to conventional approaches that prioritize vegetation indices, visible light bands—particularly B1 and B2—achieve higher performance across multiple algorithms. For instance, in LandTrendr, B1 yielded an F1 score of 71.29%, whereas NDVI and EVI produced 56.19% and 53.16%, respectively. Similarly, in CCDC, B2 achieved an F1 score of 72.19%, while NDVI and EVI resulted in 68.57% and 65.33%, respectively. Our findings underscore that parameter optimization and band selection significantly impact detection accuracy, with variations up to 30% observed across different configurations. This comprehensive evaluation provides critical methodological guidance for satellite-based urban expansion monitoring and identifies specific optimization strategies to enhance the application of existing algorithms for urban land cover change detection. Full article

Spice by-products, often discarded as waste, represent an untapped resource for sustainable packaging solutions due to their unique, multifunctional, and bioactive profiles. Unlike typical plant residues, these materials retain diverse phytochemicals—including phenolics, polysaccharides, and other compounds, such as essential oils and vitamins—that exhibit controlled release antimicrobial and antioxidant effects with environmental responsiveness to pH, humidity, and temperature changes. Their distinctive advantage is in preserving volatile bioactives, demonstrating enzyme-inhibiting properties, and maintaining thermal stability during processing. This review encompasses a comprehensive characterization of phytochemicals, an assessment of the re-utilization pathway from waste to active materials, and an investigation of processing methods for transforming by-products into films, coatings, and nanoemulsions through green extraction and packaging film development technologies. It also involves the evaluation of their mechanical strength, barrier performance, controlled release mechanism behavior, and effectiveness of food preservation. Key findings demonstrate that ginger and onion residues significantly enhance antioxidant and antimicrobial properties due to high phenolic acid and sulfur-containing compound concentrations, while cinnamon and garlic waste effectively improve mechanical strength and barrier attributes owing to their dense fiber matrix and bioactive aldehyde content. However, re-using these residues faces challenges, including the long-term storage stability of certain bioactive compounds, mechanical durability during scale-up, natural variability that affects standardization, and cost competitiveness with conventional packaging. Innovative solutions, including encapsulation, nano-reinforcement strategies, intelligent polymeric systems, and agro-biorefinery approaches, show promise for overcoming these barriers. By utilizing these spice by-products, the packaging industry can advance toward a circular bio-economy, depending less on traditional plastics and promoting environmental sustainability in light of growing global population and urbanization trends. Full article

Objectives: An aging population and declining birth rates are among the challenges that smart cities currently face and will continue to face in the near future. In light of the above, this article seeks to answer the following question: Are older people (seniors) taken into account and described in the literature on smart cities, and if so, how? Methods: To answer this research question, a systematic literature review was conducted using the Bibliometrix package in R. In the process of systematizing the publications, the authors additionally used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method and qualitative text analysis. Findings: The research shows that relatively little attention is paid to seniors in smart cities in the literature on the subject. Among the few publications on smart aging, the technological trend dominates, in which researchers present the possibilities of using IT and ICT to improve medical and social care for seniors, and to improve their quality of life (Smart Living, Smart Mobility). In the non-technological trend, most analyses focus on the determinants of quality of life and the distinguishing features of senior-friendly cities. Implications: There is a clear lack of a “human” perspective on aging in smart cities and publications on Smart Governance and Smart People that would provide guidelines for making elderly people full and equal stakeholders in smart cities. It is also necessary to develop practical documents and procedures that define a comprehensive and long-term urban policy for elderly adults. The analyses contribute to diagnosing current and determining further directions of research on smart aging in smart cities. The results clearly imply the need to intensify social, humanistic, and governance research on the role of seniors in smart cities. Full article

Background: Trauma-related injuries are among the most common reasons for paediatric hospital presentations and represent a substantial component of orthopaedic care. Their management poses unique challenges due to ongoing skeletal development in children. While most reported fractures occur at home or during sports, prior studies have primarily used data from urban European populations, limiting the relevance of their findings for rural and regional settings. Urban-centred research often informs public healthcare guidelines, treatment algorithms, and infrastructure planning, introducing a bias when findings are generalised outside of metropolitan populations. This study addresses that gap by analysing fracture data from two rural trauma centres in New South Wales, Australia. This study assesses paediatric fractures resulting from severe injury mechanisms in rural areas, identifying common fracture types, underlying mechanisms, and treatment approaches to highlight differences in demographics. These findings aim to cast a light on healthcare challenges that regional areas face and to improve the overall cultural safety of children who live and grow up outside of the metropolitan trauma networks. Methods: We analysed data from two major rural referral hospitals in New South Wales (NSW) for paediatric injuries presenting between 1 January 2018 and 31 December 2022. This study included 150 patients presenting with fractures following severe mechanisms of injury, triaged into Australasian Triage Scale (ATS) categories 1 and 2 upon initial presentation. Results: A total of 150 severe fractures were identified, primarily affecting the upper and lower limbs. Males presented more frequently than females, and children aged 10–14 years old were most commonly affected. High-energy trauma from motorcycle (dirt bike) accidents was the leading mechanism of injury among all patients, and accounted for >50% of injuries among 10–14-year-old patients. The most common fractures sustained in these events were upper limb fractures, notably of the clavicle (n = 26, 17.3%) and combined radius/ulna fractures (n = 26, 17.3%). Conclusions: Paediatric trauma in regional Australia presents a unique and under-reported challenge, with high-energy injuries frequently linked to unregulated underage dirt bike use. Unlike urban centres where low-energy mechanisms dominate, rural areas require targeted prevention strategies. While most cases were appropriately managed locally, some were transferred to tertiary centres. These findings lay the groundwork for multi-centre research, and support the need for region-specific policy reform in the form of improved formal injury surveillance, injury prevention initiatives, and the regulation of under-aged off-road vehicular usage. Full article