Search Results (587)

This article examines the role of coworking and flexible workspaces in promoting sustainable spatial development in the non-metropolitan areas of Bulgaria. A mixed-method approach was applied, combining inventory enumeration, spatial classification, SDG-based sustainability assessment, and qualitative coding (open, axial, selective). A total of 74 coworking and flexible workspaces were identified across the six national planning regions, evaluated according to six analytical criteria (accessibility, seasonality, specialization, municipal administrative district, urban planning zone, building function) and assessed against five SDG-aligned dimensions (SDG 8, 9, 11, 12, 13). The results reveal uneven territorial distribution, strong concentration in major cities outside the capital, and emerging sustainable models in peripheral areas. Comparative SDG scoring and typological interpretation demonstrate three recurring models—Sustainable Reuse, Nature-Oriented, and Innovative/Experimental—each associated with distinct spatial and environmental characteristics. A metropolitan benchmarking exercise further contextualizes the strongest sustainability profiles. Based on these findings, a conceptual sustainable coworking model is developed for a nationally significant spa and climatic resort, illustrating how coworking can address regional disparities, support green transition policies, and reinforce territorial cohesion. The article concludes by outlining research directions related to digitalization, circular construction, environmental performance indicators, and feasibility assessments for non-metropolitan coworking development. Full article

Spontaneous living spaces are public activity venues within cities that emerge through residents’ autonomous creation and informal planning. Although these spaces may appear disorganized, they serve vital functions: fostering social interaction, enhancing community vitality, improving spatial adaptability, and increasing life satisfaction. However, research on the formation mechanisms, structural logic, resident satisfaction, and the impact of spontaneous living spaces on community vitality is limited, and there is a lack of robust research methodologies. This study aims to explore the formation mechanisms of spontaneous living spaces within historic cultural districts and their influence on community vitality. Using Wuhan’s Tanhualin National Historic and Cultural District as a case study, this research innovatively combines the Mask R-CNN deep learning model with a Random Forest regression model. The Mask R-CNN model was employed to accurately identify and perform pixel-level segmentation of 1249 spontaneous living spaces. Combined with questionnaire surveys and the Random Forest model, this study reveals non-linear relationships between key factors such as community vitality, resident satisfaction with various types of spontaneous living spaces, and crowd density. The findings show that spontaneous living spaces effectively address residents’ unmet needs for emotional connection and dynamic lifestyles—needs often overlooked by official residential planning. This research provides a reliable technical framework and quantitative decision support for regulating the formation of spontaneous living spaces, thereby enhancing residents’ quality of life and urban vitality while preserving historical character. Full article

China’s National Eco-Industrial Parks (NEIPs) represent a significant policy intervention designed to achieve the synergistic reduction in pollution and carbon emissions. While previous studies have examined the impacts of NEIPs on pollution and carbon emissions in isolation, research on their synergistic reduction is still limited. This study constructs a Carbon-Pollution Co-Reduction Index (CPCRI) with weights determined by the entropy weight method (EWM) to capture the joint performance of emission intensities. By applying a staggered difference-in-differences (SDID) model to city-level panel data from the Yangtze River Delta between 2003 and 2021, the study finds that NEIPs significantly improve the CPCRI of cities where NEIPs are located by 2.30 percentage points. This positive effect exhibits a time lag, becoming statistically significant three years after establishment and strengthening thereafter. Mechanism analyses indicate that the synergistic reductions are driven by technological innovation and reduced energy intensity, while heterogeneity analyses reveal that the policy effect is more pronounced in economically developed provinces and larger cities but has diminished in recent years. Then, a coupling coordination degree (CCD) is integrated to construct a new index to capture both joint performance and synergy between reductions. These findings provide robust empirical support for NEIPs as a practical policy tool to achieve sustainable industrial transformation in the Yangtze River Delta. Full article

Using panel data from 273 prefecture-level cities in China from 2008 to 2020, this study employs the Entropy Weight Method -Technique for Order Performance by Similarity to Ideal Solution (EWM-TOPSIS) model to measure people’s well-being and applies a staggered Difference-in-Differences (DID) model to evaluate the impact of the carbon emissions trading system on people’s well-being. The findings indicate that the carbon emissions trading system generally improves people’s well-being. The mechanism analysis reveals that the primary channel through which the carbon emissions trading system improves people’s well-being is the stimulation of green technology innovation. Additionally, fiscal expenditure decentralization negatively moderates the carbon emissions trading system’s impact on people’s well-being, whereas marketization degree does not exert a moderating effect. Further research reveals that fiscal expenditure decentralization exhibits a double threshold effect, while the degree of marketization displays a single threshold effect. The carbon emissions trading system exhibits heterogeneous impacts on people’s well-being. From a regional perspective, the carbon emissions trading system enhances people’s well-being in non-Yangtze River Economic Belt (YREB) regions, whereas it dampens people’s well-being in YREB cities. Regarding resource endowment, the carbon emissions trading system positively influences people’s well-being in non-resource-based cities, but its impact remains statistically insignificant in resource-based cities. Full article

As a pivotal driver of high-quality development, new quality productivity (NQP) forms an indispensable synergistic relationship with land use efficiency (LUE) for achieving regional sustainability. Based on panel data from 29 prefecture-level cities in the Central Plains Urban Agglomeration (CPUA) from 2010 to 2023, this study integrates the entropy-weighted TOPSIS method, super-efficiency Slack-Based Measure (SBM) model, Malmquist index, and fixed-effects models to systematically explore the spatiotemporal evolution of NQP and its underlying impact mechanism on LUE. Key findings reveal: (1) The comprehensive NQP index of the CPUA increased from 0.280 to 0.828, exhibiting a “stepwise rise” trend, with a spatial pattern characterized by a “core–secondary–periphery” three-tier gradient distribution. Zhengzhou, as the core growth pole, played an innovative leading role, while peripheral cities (e.g., Handan, Hebi) remained constrained by resource-dependent economic structures, with NQP indices consistently below 0.2. (2) The average LUE in the study area increased from 0.917 to 1.031. Cities within Henan Province generally performed better than those in Hebei, Shanxi, and Anhui provinces. Total factor productivity grew at an average annual rate of 16.4%, with technological progress serving as the primary driver. (3) NQP exerts a significantly positive impact on LUE, yet with notable heterogeneity: large-scale cities enhanced intensive land use substantially through technological agglomeration and industrial upgrading; cities with scarce arable land and high economic development levels effectively leveraged NQP to boost LUE; in contrast, small cities, regions rich in arable land, and areas with low economic development have not established effective synergistic mechanisms, hindered by limited technological absorption capacity, path dependence, and factor bottlenecks. This study provides empirical support and actionable insights for optimizing land resource allocation and advancing coordinated development between NQP and LUE in similar urban agglomerations. Full article

Against the backdrop of economic recovery and ongoing market restructuring, China’s hotel industry is undergoing a significant shift from being service-based to becoming strategy-driven and competitive. This study aims to identify the key factors driving hotel competitiveness utilizing a multidimensional quantitative perspective. Based on a structured questionnaire survey administered to hotel managers across provinces and cities in China, primary data was collected from a total of 727 valid responses. As such, exploratory factor analysis, comprising 31 ranked items, and multiple regression analysis were performed using SPSS, identifying four dimensions that significantly affect hotel competitiveness: namely, operational characteristics, service quality, customer satisfaction, and competitive performance. The results show that operational characteristics are the primary determinants of hotel competitiveness. Specifically, sustainable development strategy, digital technology adoption, product and service innovation, and pricing strategies were the most significant factors leading to competitive advantage. Unlike previous studies that emphasized service quality and customer satisfaction as the dominant drivers of competitiveness, this study finds that a hotel’s competitive advantage relies heavily on developing strategic operational innovation and resource allocation capabilities. This study’s novelty lies in its use of national empirical data to validate the multidimensional composition and inherent logical relationships for competitive advantage. It also proposes a multidimensional framework for analyzing hotel competitiveness, specifically designed for the developmental characteristics of China’s hotel industry, highlighting the need to transform from a service-oriented to a strategy-driven operational approach. The findings of this paper offer empirical evidence for hotel enterprises to refine management structures, foster innovation, and thereby develop strong capabilities for a sustainable competitive advantage. Full article

With the evolution of social structure and the intensification of population aging, traditional community service centers struggle to meet residents’ complex needs due to their functional singularity and spatial rigidity. In response to the continuously evolving social structure and functional requirements, this research proposes a strategy based on the “Separation of Support and Infill,” distinguishing between the building’s permanent Support Structure and its replaceable Infill Components. These two parts are combined with modularization to achieve long-term spatial adaptability and sustainability throughout the entire life cycle. In terms of functional space, through the combination of vertical stratification, horizontal staggering and spatial permeability, a three-dimensional composite space system is constructed, which not only enhances the functional flexibility but also improves the environmental performance. Taking a design case in Yicheng District, Zhumadian City as an example, through a comparative analysis with the traditional building model, the comparative analysis demonstrates that this framework increases the Floor Area Ratio (FAR) by approximately 0.15 compared to traditional models. Furthermore, the modular characteristics significantly enhance demountability and reusability, reducing construction and demolition waste while lowering life-cycle costs by an estimated 15% to 25%. These studies show that the support structure and the composite functional space system can not only promote social interaction and community cohesion but also reduce the life-cycle cost and carbon emissions. The framework proposed in this paper constructs a theoretical and practical system for sustainable community buildings from the perspectives of functional compounding and low-carbon community development. Its innovation lies in its flexible spatial organization mode and the enhancement of the sustainability of community buildings. Full article

Crowd counting is a significant task in computer vision. By combining the rich texture information from RGB images with the insensitivity to illumination changes offered by thermal imaging, the applicability of models in real-world complex scenarios can be enhanced. Current research on RGB-T crowd counting primarily focuses on feature fusion strategies, multi-scale structures, and the exploration of novel network architectures such as Vision Transformer and Mamba. However, existing approaches face two key challenges: limited robustness to illumination shifts and insufficient handling of scale discrepancies. To address these challenges, this study aims to develop a robust RGB-T crowd counting framework that remains stable under illumination shifts, through introduces two key innovations beyond existing fusion and multi-scale approaches: (1) a cross-modal adaptive fusion module (CMAFM) that actively evaluates and fuses reliable cross-modal features under varying scenarios by simulating a dynamic feature selection and trust allocation mechanism; and (2) a multi-scale aggregation module (MSAM) that unifies features with different receptive fields to an intermediate scale and performs weighted fusion to enhance modeling capability for cross-modal scale variations. The proposed method achieves relative improvements of 1.57% in GAME(0) and 0.78% in RMSE on the DroneRGBT dataset compared to existing methods, and improvements of 2.48% and 1.59% on the RGBT-CC dataset, respectively. It also demonstrates higher stability and robustness under varying lighting conditions. This research provides an effective solution for building stable and reliable all-weather crowd counting systems, with significant application prospects in smart city security and management. Full article

This study explores the digital transformation and public value created through the target city’s smart city Mobile Payment APP and digital city token system within the context of sustainable governance in Taiwan. Adopting a convergent mixed-methods research design, this research integrates quantitative Importance–Performance Analysis (IPA) surveys of 632 users with qualitative in-depth semi-structured interviews involving eight key stakeholders (namely, government officials, system developers, affiliated merchants, and citizen representatives). This methodology assesses service quality, user satisfaction, and cross-sector collaboration effects. The findings reveal that the mobile payment platform significantly enhances digital service delivery; fosters user engagement; and supports sustainable urban development goals, particularly net-zero carbon emissions. However, the IPA results highlight critical service gaps in the “Priority Improvement Zone,” specifically regarding the insufficient number of affiliated merchants and inconvenient information search functions. Qualitative findings attribute these gaps to cross-departmental administrative barriers and security-focused design trade-offs. This study contributes empirical evidence on the integration of financial technology and public service innovation as a means to advance smart governance and sustainable urban ecosystems. The results provide actionable insights for policymakers, city planners, and service designers focused on promoting digital public services that facilitate economic vitality, environmental sustainability, and collaborative governance. Full article

Amidst the paradigm shift in park city development from quantitative metrics to spatial performance, urban complex parks—a novel green space type developed privately yet fulfilling public functions—present an innovative approach to park provision in high-density urban areas. However, systematic empirical evidence on their social value remains scarce. This study characterizes urban complex parks as a new form of green public space that provides key ecosystem services and proposes a three-dimensional evaluation framework integrating “usage vitality, place attractiveness, and user satisfaction.” Analyzing 19 park-equipped complexes among 75 cases in Shanghai using LBS data and online reviews through controlled linear regression and comparative analysis, our results indicate complexes with parks were associated with significantly outperforming others in place attractiveness and user satisfaction. Key findings include associations with a 413.7 m increase in average OD distance, a 3.4–4.0% higher city-level visitor share, and 5.24 percentage points greater median positive review rate. Crucially, spatial location outweighs green ratio and size in determining social value. Ground-level parks, through superior spatial integration, function as effective “social-ecological interfaces,” significantly outperforming rooftop parks in attracting long-distance visitors, stabilizing foot traffic (≈3% lower fluctuation), and enhancing per-store visitation. This demonstrates that green space quality (experiential quality and spatial configuration) matters more than quantity. Our findings suggest that urban complex parks can create social value through perceivable naturalness and restorative environments, providing an empirical basis for optimizing park city implementation in high-density contexts and highlighting the need to reconcile broad attractiveness with equitable local access. Full article

In the context of Industry 4.0, digital transformation is reshaping global energy systems. Among the key enabling technologies, Digital Twin (DT)—a dynamic, virtual replica of physical systems—has emerged as a critical tool for improving the performance, reliability, and safety of clean energy infrastructure. In line with the United Nations Sustainable Development Goals (SDGs)—particularly SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities)—the integration of DTs presents unprecedented opportunities to enhance operational efficiency and support proactive decision making. This state-of-the-art review, focused on studies published in 2020–2025, summarizes applications of DTs across the energy value chain, encompassing a broad spectrum of sectors—including solar, wind, hydropower, hydrogen, geothermal, bioenergy, nuclear, and tidal energy—and their critical role in building-to-grid integration. It synthesizes foundational concepts, assesses the evolution of the DT from a predictive tool to a system-level risk-management platform, and provides a critical analysis of its impact. Furthermore, this review discusses the key challenges hindering widespread adoption, including the critical need for interoperability across systems, ensuring the cybersecurity of socio-technical infrastructure, and addressing the complexities of the human-in-the-loop problem. Key research gaps are identified to guide future innovation. Ultimately, this study underscores the transformative potential of DTs as essential tools for accelerating the digital transformation of the energy sector, offering a robust framework for both methodological development and practical deployment. Full article

The global production of electronic waste (e-waste) has increased due to the quick turnover of electronic devices, creating urgent problems for resource management and environmental sustainability. As a result, e-waste-derived materials (EWDMs) are being explored in pavement engineering research as sustainable substitutes in line with Sustainable Development Goals (SDGs), specifically SDG 9 (Industry, Innovation, and Infrastructure), 11 (Sustainable Cities and Communities), 12 (Responsible Consumption and Production), and 13 (Climate Action). Therefore, to assess global research production and the effectiveness of EWDMs in asphalt applications, this review combines scientometric mapping and systematic evidence synthesis. A total of 276 relevant publications were identified via a thorough search of Web of Science, Scopus, and ScienceDirect (2010–2025). These were examined via coauthorship structures, keyword networks, and contributions at the national level. The review revealed that China, India, and the United States are prominent research hubs. Additionally, experimental studies have shown that EWDMs, such as printed circuit board powder, fluorescent lamp waste glass, high-impact polystyrene, and acrylonitrile–butadiene–styrene, improve the fatigue life, Marshall stability, rutting resistance (up to 35%), and stiffness (up to 28%). However, issues with long-term field durability, microplastic release, heavy metal leaching, and chemical compatibility still exist. These restrictions highlight the necessity for standardised toxicity testing, harmonised mixed-design frameworks, and performance standards unique to EWDMs. Overall, the review shows that e-waste valorisation can lower carbon emissions, landfill build-up, and virgin material extraction, highlighting its potential in the circular pavement industry and promoting sustainable paving practices in accordance with SDGs 9, 11, 12, and 13. This review suggests that further studies on large-scale field trials, life cycles, and technoeconomic assessments are needed to guarantee the safe, long-lasting integration of EWDMs in pavements. It also advocates for coordinated research, supportive policies, and standardised methods. Full article

Green infrastructure and nature-based solutions (NBSs) are, especially in urban areas, one of the key elements in building a friendly living environment that contributes to healthy longevity. This paper presents a novel method for assessing the accessibility of recreational urban green space (RUGS) at the level of individual residential buildings. We designed and piloted a new total accessible recreational urban green space area (TARUGS) index, based on real pedestrian network distances, considering spatial accessibility weighted by the total area of green space available within an approximate 15-min walk. Calculations were carried out individually for each residential building and each individual RUGS, using GIS technologies, including network analysis. The developed methodology allows for the detection of local inequalities in access to all city RUGSs. It enables the inclusion of additional socioeconomic variables in an in-depth spatial equity analysis. The RUGS accessibility ranking of buildings provides a practical tool to support urban intervention planning, as well as the design of solutions that respond to the real needs of residents and environmental challenges. Availability analyses were performed for 108,618 buildings and 146 RUGS. Areas with the highest and clearly insufficient access to RUGS in Warsaw were identified. Over 40,400 buildings were classified as having no access to RUGS (class 0), which accounts for 37% of all residential buildings, while 21,700 buildings were classified as having the best access (class 4), which accounts for 20% of all residential buildings. The districts of Wilanów and Włochy have the worst accessibility, while Wawer and Mokotów have the best. The proposed building-level methodology quantitatively reveals spatial inequalities in access to RUGS, enabling data-driven, equitable planning decisions while highlighting the need to integrate broader accessibility modes, subjective user experiences, and data improvements for a comprehensive assessment of spatial justice. The framework demonstrates how advanced geospatial data analysis, integrating GIS technologies, open data, and network-based innovative solutions, could enhance urban policy-making, improve the design of equitable public spaces, and support resilient land management strategies. Full article

In the context of accelerating global urbanization and sustainable development challenges, impervious surfaces, as a key component of urban land cover, are significantly associated with regional economic development. This study takes Harbin, a typical cold region city, as a research object and constructs a three-level analytical framework of “land surface classification-economic simulation-mechanism analysis.” By innovatively integrating multi-source remote sensing, demographic, and economic data, the research addresses gaps in understanding urban sustainability in cold environments. An enhanced XGBoost algorithm was employed to achieve high-precision classification of ten land surface materials, resulting in a high overall accuracy. Furthermore, a gridded GDP spatialization model developed using high-resolution population data demonstrated superior performance compared to traditional methods. Machine learning-assisted analysis revealed that asphalt and metal surfaces are the most significant impervious materials driving economic output, reflecting the respective influences of transportation infrastructure and industrial agglomeration. Spatial pattern analysis indicates that Harbin’s impervious surfaces exhibit a lower fractal dimension and a distinct grid-like morphology compared to the typical subtropical city of Guangzhou, underscoring urban form adaptations to cold climatic constraints. The strong spatial coupling between gradients of GDP intensity and the attenuation of impervious surface density is quantitatively confirmed. This study provides a quantitative basis and a transferable technical framework for optimizing land use intensity and infrastructure planning in cold cities, thereby offering a scientific foundation for sustainable, intensive land utilization in climate-vulnerable urban systems. Full article

The development of autonomous tunneling is crucial for building the intelligent underground infrastructure that smart cities require. However, in complex urban environments, the need for frequent manual intervention during Tunnel Boring Machine (TBM) operation remains a challenge, hindering overall efficiency and safety. To address the human–machine collaboration gap, this study analyzes practical experiences from six tunnel projects that use autonomous driving systems. Building on this foundation, we develop an intelligent assistant powered by a large language model (LLM). The assistant constructs a complete service architecture and intervention mechanism, proposes a phased intention recognition framework, and uses conversational interaction to achieve efficient human–machine communication. Experimental results demonstrate the strong classification performance of our intention recognition model. Furthermore, engineering case studies validate the assistant’s effectiveness in enhancing operational transparency, increasing user trust, bridging the human–machine information gap, and ultimately ensuring safer and more reliable tunneling. This research provides a feasible and innovative technological path for human–machine collaboration in the construction of critical urban infrastructure. Full article