Search Results (671)

This article explores Schipper’s scholarly contributions to the study of dongtian fudi (grotto heavens and blessed lands) and specifically situates this project in its broader intellectual context and Schipper’s own research. While Schipper was not the first to open discussions on this topic, his research in this direction still offers profound insights, such as the coinage of the concept of “Daoist Ecology” and his views on mountain politics. This article argues that Schipper’s work on dongtian fudi is a response to the school of Deep Ecology and its critics, and also a result of critical reflection on the modern dichotomy between nature and culture. In Schipper’s enquiry of dongtian fudi, the “mountain” stands as the central concept: it is not only the essential component of Daoist sacred geography, but a holistic site in which nature and society are interwoven, endowed with both material and sacred significance. Through his analysis of the Daoist practice of abstinence from grain (duangu), Schipper reveals how mountains serve as spaces for retreat from agrarian society and state control, and how they embody “shatter zones” where the reach of centralized power is relatively attenuated. The article also further links Schipper’s project of Beijing as a Holy City to his study of dongtian fudi. For Schipper, the former affirms the universality of the locality (i.e., the unofficial China, the country of people), while the latter envisages the vision of rewriting China from plural localities. Taken together, these efforts point toward a theoretical framework that moves beyond conventional sociological paradigms, one that embraces a total worldly perspective, in which the livelihoods of local societies and their daily lives are truly appreciated as a totality that encompasses both nature and culture. Schipper’s works related to dongtian fudi, though they are rather concise, still significantly broaden the scope of Daoist studies and, moreover, provide novel insights into the complexity of Chinese religion and society. Full article

Accurate estimation of forest carbon stock is critical for understanding ecosystem carbon dynamics and informing climate mitigation strategies. This study presents a deep learning framework that integrates Sentinel-2 multispectral imagery with an enhanced residual neural network for estimating aboveground forest carbon stock in the Liuchong River Basin, Bijie City, Guizhou Province, China. The proposed model incorporates multiscale residual blocks and channel attention mechanisms to improve spatial feature extraction and spectral dependency modeling. A dataset of 150 ground inventory plots was employed for supervised training and validation. Comparative experiments with Random Forest, Gradient Boosting Decision Trees (GBDT), and Vision Transformer (ViT) demonstrate that the enhanced ResNet achieves the best performance, with a root mean square error (RMSE) of 23.02 Mg/ha and a coefficient of determination ($R^2$) of 0.773 on the test set. Spatial mapping results further reveal that the model effectively captures fine-scale carbon stock variations across mountainous forested landscapes. These findings underscore the potential of combining multispectral remote sensing and advanced neural architectures for scalable, high-resolution forest carbon estimation in complex terrain. Full article

This paper examines how artificial intelligence (AI) can be strategically deployed to enhance urban planning and environmental livability in Riyadh by generating data-driven, people-centric design interventions. Unlike previous studies that concentrate primarily on visualization, this research proposes an integrative appraisal framework that combines AI-generated design with site-specific environmental data and native vegetation typologies. This study was conducted across key jurisdictional areas including the Northern Ring Road, King Abdullah Road, Al Rabwa, Al-Malaz, Al-Suwaidi, Al-Batha, and King Fahd Road. Using AI tools, urban scenarios were developed to incorporate expanded pedestrian pathways (up to 3.5 m), dedicated bicycle lanes (up to 3.0 m), and ecologically adaptive green buffer zones featuring native drought-resistant species such as Date Palm, Acacia, and Sidr. The quantitative analysis of post-intervention outcomes revealed surface temperature reductions of 3.2–4.5 °C and significant improvements in urban esthetics, walkability, and perceived safety—measured on a five-point Likert scale with 80–100% increases in user satisfaction. Species selection was validated for ecological adaptability, minimal maintenance needs, and compatibility with Riyadh’s sandy soils. This study directly supports the Kingdom of Saudi Arabia’s Vision 2030 by demonstrating how emerging technologies like AI can drive smart, sustainable urban transformation. It aligns with Vision 2030’s urban development goals under the Quality-of-Life Program and environmental sustainability pillar, promoting healthier, more connected cities with elevated livability standards. The research not only delivers practical design recommendations for planners seeking to embed sustainability and digital innovation in Saudi urbanism but also addresses real-world constraints such as budgetary limitations and infrastructure integration. Full article

The city of Al Ain is a fast-developing area. With building typology varying from low-rise to mid-rise, sustainable design in buildings is needed. As the majority of the city’s population is Emirati Citizens, the percentage of expats is increasing. The expats tend to live in mid-rise buildings. One of the central midrise areas is AL Ain Square. This study aims to investigate how an optimized mashrabiya pattern can impact the energy and the Predicted Mean Vote (PMV) in a 3-bedroom apartment, fully oriented to the south, of an expat family. The methodology is as follows: case study selection, Weather analysis, Modeling/Validation of the base case scenario, Optimization of the mashrabiya pattern, Simulation of various scenarios, and Results. Analyzing the selected case study is the initial step of the methodology. This analysis begins with the district, building typology, and the chosen apartment. The weather analysis is relevant for using the mashrabiya (screen device) and the need to improve energy consumption and thermal comfort. The modeling of the base case shall be performed in Rhino Grasshopper. The validation is based on a one-year electricity bill provided by the owner. The optimization of mashrabiya patterns is an innovative process, where various designs are compared and then optimized to select the most efficient pattern. The solutions to the selected scenarios will then yield the results of the optimal scenario. This study is relevant to industry, academia, and local authorities as an innovative approach to retrofitting buildings. Additionally, the research presents a creative vision that suggests optimized mashrabiya patterns can significantly enhance energy savings, with the hexagonal grid configuration demonstrating the highest efficiency. This finding highlights the potential for geometry-driven shading optimization tailored to specific climatic and building conditions. Contrasting earlier mashrabiya studies that assess one static pattern, we couple a geometry-agnostic evolutionary solver with a utility-calibrated EnergyPlus model to test thousands of square, hexagonal, and triangular permutations. This workflow uncovers a previously undocumented non-linear depth perforation interaction. It validates a hexagonal screen that reduces annual cooling energy by 12.3%, establishing a replicable, grid-specific retrofit method for hot-arid apartments. Full article

This research aims to explore the role of architecture in creating an identifiable brand for Amman. It seeks to put forward a vision through which Amman’s city can formulate a clear model for implementing a successful branding strategy. In doing so, this research studies the concepts associated with the ideas of branding, city image and identity, and the extent to which such ideas are to be implemented in Amman. The study adopted an inductive approach using in-depth, semi-structured interviews with 35 experts with central roles in stating the city’s key values that best reflect the city’s identity. A thematic analysis was conducted in line with theoretical aspects, including the city’s message, strategies for formulating the brand, and communication via architecture. The image of Amman shows an obvious distinction between its historical character and modern global styles as it suffers from disorder within its architectural landscape. Amman needs to rethink its identity in order to create a new brand that keeps pace with time without losing the originality of the place. This calls for re-evaluating the role of the iconic buildings and their associations with the surroundings, enabling them to become of significant presence, both symbolically and operationally, in expressing the city’s personality and promoting its message. Full article

Air pollution caused by CO₂ emissions has become a global issue of vital importance, posing irreversible risks to health and life when concentration of CO₂ becomes too high. This study aims to estimate the CO₂ emissions and carbon footprint of the public transport bus fleet in Bucharest, with a comparative analysis of greenhouse gas (GHG) emissions generated by diesel and electric buses of the Bucharest Public Transport Company (STB S.A.) in the period 2021–2024, after the modernization of the fleet through the introduction of 130 hybrid buses and 58 electric buses. In 2024, the introduction of electric buses and the reduction in diesel bus mileage reduced GHG emissions by almost 13% compared to 2023, saving over 11 kilotons of CO_2e. There was also a 2.68% reduction in the specific carbon footprint compared to the previous year, which is clear evidence of the potential of electric vehicles in achieving decarbonization targets. We have also developed two strategies, one for 2025 and one for the period 2025–2030, replacing the aging fleet with electric vehicles. This demonstrates the relevance of electric transport integrated into the sustainable development strategy for urban mobility systems and alignment with European standards, including improving air quality and living standards. Full article

The increase in garbage generated in modern societies demands the implementation of a more sustainable model as well as new methods for efficient waste management. This article describes the development and implementation of a prototype of a smart bin that automatically sorts waste using a multi-agent system and blockchain integration. The proposed system has sensors that identify the type of waste (organic, plastic, paper, etc.) and uses collaborative intelligent agents to make instant sorting decisions. Blockchain has been implemented as a technology for the immutable and transparent control of waste registration, favoring traceability during the classification process, providing sustainability to the process, and making the audit of data in smart urban environments transparent. For the computer vision algorithm, three versions of YOLO (YOLOv8, YOLOv11, and YOLOv12) were used and evaluated with respect to their performance in automatic detection and classification of waste. The YOLOv12 version was selected due to its overall performance, which is superior to others with mAP@50 values of 86.2%, an overall accuracy of 84.6%, and an average F1 score of 80.1%. Latency was kept below 9 ms per image with YOLOv12, ensuring smooth and lag-free processing, even for utilitarian embedded systems. This allows for efficient deployment in near-real-time applications where speed and immediate response are crucial. These results confirm the viability of the system in both accuracy and computational efficiency. This work provides an innovative solution in the field of ambient intelligence, characterized by low equipment cost and high scalability, laying the foundations for the development of smart waste management infrastructures in sustainable cities. Full article

This paper presents a novel system designed to enhance pedestrian safety in urban environments by utilizing real-time video analysis and machine learning techniques. With a focus on the bustling streets of Macao, known for its high pedestrian traffic and complex road conditions, the proposed model alerts drivers to the presence of pedestrians, significantly reducing the risk of accidents. Leveraging the You Only Look Once algorithm, this research demonstrates how timely alerts can be generated based on risk assessments derived from video footage. The model is rigorously tested against diverse driving scenarios, providing robust accuracy in detecting potential hazards. A comparative analysis of various machine learning algorithms, including Gradient Boosting and Logistic Regression, underscores the effectiveness and reliability of the system. The key finding of this research indicates that dataset refinement and enhanced feature differentiation could lead to improved model performance. Ultimately, this work seeks to contribute to the development of smart city initiatives that prioritize safety through advanced technological solutions. This approach exemplifies a vision for more responsive and responsible urban transport systems. Full article

The observation and analysis of reality have been a human activity for many centuries. Indeed, since the earliest of human cultures, people have been trying to understand their world and to improve its functioning. In the process, they have developed a number of different visions for the future. Urban space is characterized by its dynamism, exhibiting a fascinating blend of heterogeneity and susceptibility to rapid transformation. The primary objective of the present article is to present the urban problems and proposals for their solutions in a historical and global perspective. This work will demonstrate the historical context of diagnosing urban problems, drawing upon the literature from the past century. Depending on the geographical location, the potential exists for the sounding of an alarm or the drawing of attention to aspects that, for some, represent a reality that is difficult to overcome and for others, only a barely noticeable trend. It is asserted that this will ensure that, in the future, cities will function efficiently and be pleasant places to live. In conclusion, the development visions of cities will be presented and discussed. Visions of the future, as a reaction to the world around us, were and are a fundamental category of expectations and considerations, hopes and fears, and science and practice. Full article

In the context of cultural tourism integration, tourists’ spatial perception intention is an important carrier of spatial evaluation. In historic cultural districts represented by Jinan Mingfu City, tourists’ perceptual depth remains underexplored, leading to a misalignment between cultural tourism development and spatial quality needs. Taking Jinan Mingfu City as a representative case of a historic cultural district, while the living heritage model has revitalized local economies, the absence of a tourist perspective has resulted in misalignment between cultural tourism development and spatial quality requirements. This study establishes a technical framework encompassing “data crawling-factor aggregation-human-machine collaborative optimization”. It integrates Python web crawlers, SnowNLP sentiment analysis, and TF-IDF text mining technologies to extract physical elements; constructs a three-dimensional evaluation framework of “visual perception-spatial comfort-cultural experience” through SPSS principal component analysis; and quantifies physical element indicators such as green vision rate and signboard clutter index through street view semantic segmentation (OneFormer framework). A synergistic mechanism of machine scoring and manual double-blind scoring is adopted for correlation analysis to determine the impact degree of indicators and optimization strategies. This study identified that indicators such as green vision rate, shading facility coverage, and street enclosure ratio significantly influence tourist evaluations, with a severe deficiency in cultural spaces. Accordingly, it proposes targeted strategies, including visual landscape optimization, facility layout adjustment, and cultural scenario implementation. By breaking away from traditional qualitative evaluation paradigms, this study provides data-based support for the spatial quality enhancement of historic districts, thereby enabling the transformation of these areas from experience-oriented protection to data-driven intelligent renewal and promoting the sustainable development of cultural tourism. Full article

The rapid urbanization of contemporary cities represents one of the most complex challenges of the 21st century, with profound implications for the environmental, social, and economic sustainability of territories. In this context, urban regeneration emerges as a strategic approach to territorial transformation. The complexity of urban dynamics requires the adoption of innovative paradigms and systemic approaches capable of guiding decision-making processes toward eco-sustainable and resilient solutions. This research develops advanced decision support tools for urban regeneration, using the city of Potenza (Italy) as a case study. The main objective is to identify key indicators to evaluate the effectiveness of urban regeneration interventions in advance (ex-ante). The methodology develops a composite economic-financial risk index capable of providing an accurate picture of existing conditions while adapting to the territorial specificities of the analyzed area. This index, which uses the Analytic Hierarchy Process (AHP) technique to integrate elementary economic-financial indicators in order to assess the sustainability level of urban redevelopment projects, is able to synthesize complex economic variables into a single parameter of immediate comprehension, strategically guiding investments toward a sustainable urban development model. The analysis of results highlights a peculiar territorial configuration: semi-central areas present the greatest criticalities, while there is a progressive decrease in risk both toward the central core and toward peripheral and extra-urban areas. The study represents a significant methodological contribution to future urban regeneration initiatives at the local level, promoting an integrated vision of sustainable urban development for the benefit of current and future generations. Full article

The proliferation of the Internet of Things (IoT) has led to an abundance of data streams and real-time dashboards in domains such as smart cities, healthcare, manufacturing, and agriculture. However, many current IoT dashboards emphasize complex visualizations with minimal textual cues, posing significant barriers to users with visual impairments who rely on screen readers or other assistive technologies. This paper presents AccessiDashboard, a web-based IoT dashboard platform that prioritizes accessible design from the ground up. The system uses semantic HTML5 and WAI-ARIA compliance to ensure that screen readers can accurately interpret and navigate the interface. In addition to standard chart presentations, AccessiDashboard automatically generates long descriptions of graphs and visual elements, offering a text-first alternative interface for non-visual data exploration. The platform supports multi-modal data consumption (visual charts, bullet lists, tables, and narrative descriptions) and leverages Large Language Models (LLMs) to produce context-aware textual representations of sensor data. A privacy-by-design approach is adopted for the AI integration to address ethical and regulatory concerns. Early evaluation suggests that AccessiDashboard reduces cognitive and navigational load for users with vision disabilities, demonstrating its potential as a blueprint for future inclusive IoT monitoring solutions. Full article

Designing physical environments that are safe, functional, and equitable for all users is crucial to understanding the needs and requirements of the local community from a gender perspective, to achieve gender equality and women’s safety in the public realm. In the Saudi context, international assessments of women’s rights still acknowledge the country as one of the most prominent examples of structural gender inequality, both in the world and relative to regional peers within the Middle East and North Africa. This research aims to illuminate women’s mobility behavior as a tool for women’s inclusion in the built environment, supporting policymakers to design projects that build more inclusive cities for women. This research examines the dynamic relationship between women’s mobility and the built environment in Riyadh, Saudi Arabia, within the context of Vision 2030. By employing a mixed-method approach, including literature reviews and a comprehensive survey, the research highlights critical indicators such as safety, cultural norms, and infrastructure. The research concludes that safety, cultural and social norms, and the availability of public facilities significantly impact women’s ease of mobility. The paper reaches an actionable recommendation for policymakers to create more inclusive urban environments that support women’s aspirations and needs, ultimately contributing to a more equitable society that supports the expectations and needs of all women in Riyadh. Full article

The rapid expansion of ride-hailing services has profoundly impacted urban mobility and residents’ travel behavior. This study aims to precisely identify and quantify how the built environment and socioeconomic factors influence spatial variations in ride-hailing demand using multi-source data from Haikou, China. A multi-scale geographically weighted regression (MGWR) model is employed to address spatial scale heterogeneity. To more accurately capture environmental features around sampling points, the DeepLabv3+ model is used to segment street-level imagery, with extracted visual indicators integrated into the regression analysis. By combining multi-scale geospatial data and computer vision techniques, the study provides a refined understanding of the spatial dynamics between ride-hailing demand and urban form. The results indicate notable spatiotemporal imbalances in demand, with varying patterns across workdays and holidays. Key factors, such as distance to the city center, bus stop density, and street-level features like greenery and sidewalk proportions, exert significant but spatially varied impacts on demand. These findings offer actionable insights for urban transportation planning and the design of more adaptive mobility strategies in contemporary cities. Full article

Against the backdrop of increasing urban population and continuous expansion of urban scales, achieving “people-centered” urban sustainable development, namely building livable and sustainable cities, faces formidable challenges. Under the shared global vision of achieving the 2030 Sustainable Development Goals (SDGs), existing research has rarely explored the alignment between the construction of livable cities and the SDGs. This study constructs a scientific and universally applicable evaluation system for urban livability to clarify that building livable cities is a crucial pathway to promoting urban sustainable development. This study integrates the core principles of the three pillars of the United Nations’ sustainable development, the five-dimensional classification logic of the Global Urban Monitoring Framework, and the performance evaluation key points of ISO/TC 268 standards for SC1 “smart community infrastructure” to construct a six-dimensional livable city evaluation system covering society, economy, culture, environment, governance, and infrastructure. Starting from theoretical research on the connotation of livable cities and their alignment with the SDGs, and based on the research team’s evaluation experience and assessment paradigm of SDGs progress at the urban level, this study uses the “Indicator Library for Cities’ Sustainable Development (ILCSD)” as a technical tool to explore the technical methods for establishing an evaluation index system for livable cities. Meanwhile, combining qualitative research and statistical analysis with China’s development strategic needs, it selects 24 sample cities to analyze the level differences among different types of cities under the proposed index system and to identify the key factors and mechanisms influencing the sustainable development of livable cities. Through theoretical research and empirical analysis, this study has derived a set of evaluation indicators for livable cities oriented towards the SDGs, offering urban management stakeholders a reasonable and comprehensive universal evaluation technical tool to enhance urban livability and promote the implementation of the SDGs. Full article