Search Results (674)

Remote sensing semantic segmentation is critical for extracting fine-grained spatial information in applications such as urban planning and environmental monitoring. However, existing methods face significant challenges in modeling heterogeneous spatial relationships within complex urban scenes, where semantically related regions are spatially dispersed yet functionally interdependent. Conventional convolutional neural networks exhibit limited receptive fields that fail to capture long-range dependencies, while Transformer-based approaches capture global dependencies but do not explicitly model regional heterogeneity, leading to blurred boundaries and category confusion. To address these limitations, this paper proposes a novel multi-relational-aware segmentation framework that leverages hypergraph theory to dynamically model higher-order semantic groupings across non-adjacent regions. The core innovation lies in a hypergraph structure learning unit that employs fuzzy clustering to partition multi-scale features into adaptive hyperedge sets, enabling joint representation of topological associations and functional dependencies among spatially distributed entities. Additionally, a multi-scale co-modeling strategy integrates stochastic feature masking with weighted fusion to bridge semantic abstraction and spatial localization. Experiments demonstrate that the proposed method achieves state-of-the-art mIoU performance on the LoveDA, Vaihingen, and Potsdam datasets, obtaining mIoU scores of 54.70%, 85.01%, and 87.64%, with improvements of 0.30%, 0.91%, and 0.08%, respectively, over the best existing methods. Full article

Emerging industrial agglomeration drives green transformation through knowledge spillovers and economies of scale. However, its effects exhibit pronounced nonlinearity and heterogeneity, shaped by spatial externalities and development stages. This paper investigates 19 Chinese urban agglomerations over the period 2014 to 2023. Kernel density estimation based on enterprise-level Point-of-Interest (POI) data is used to characterize spatial agglomeration patterns across eight emerging sectors. A two-stage dynamic network super-efficiency SBM model decomposes Green Transition Efficiency (GTE) into resource utilization and pollution control sub-stages. An XGBoost-SHAP-GEO analytical framework, combined with partial dependence analysis, then identifies nonlinear driving mechanisms. The main findings are as follows: First, emerging industrial agglomeration intensifies and polarizes toward the eastern coast, whereas GTE displays a “high-west, low-east” pattern. This produces a significant spatial mismatch, rooted in the near-saturation of environmental carrying capacity in eastern regions, where congestion effects exceed knowledge spillover dividends. Second, geographic characteristics constitute the primary factor shaping GTE and operate through nonlinear interactions with industrial agglomeration and R&D investment. Notably, their moderation direction is reversible, suggesting that geographic endowments should be understood as “conditional assets” rather than fixed advantages. Third, nonlinear patterns across sectors are highly heterogeneous. The bio-industry is the only sector to achieve a J-shaped positive breakthrough. Information technology and new materials exhibit persistent inhibition, while related services display an extremely narrow threshold window with the deepest negative reversal. Thus, “moderate agglomeration” is a multidimensional concept that shifts dynamically with industry type and regional endowment. Fourth, driving mechanisms display stage-dependent evolution. The incubation stage relies on natural endowments and basic industrial pull, with the green bottleneck residing in resource utilization efficiency. The growth stage faces multiple tensions from coexisting positive and negative effects. The optimization stage shifts toward R&D innovation and industrial greening, marking a qualitative transformation from MAR externalities to Jacobs externalities. In addition, the non-significant linear coefficient in the 2SLS instrumental variable test is consistent with the inverted U-shaped nonlinear finding, further validating the necessity of a nonlinear analytical framework. These findings provide differentiated governance evidence for balancing industrial agglomeration with green sustainable development. Full article

Health systems are fundamental to ensuring the right to health and maintaining the stability of welfare states. In this broad framework, this narrative review takes a look at Italy and China—two countries with very different historical backgrounds and institutional paths—as they deal with challenges like demographic aging, increasing chronic illnesses, and ongoing disparities in access to care. This paper aims to examine the most relevant peer-reviewed studies, as well as current legal and policy documents, in order to outline a comparative overview of the approaches to financing, coverage, and service provision in Italy and China, two very different countries. The Italian National Health Service, financed through general taxation and designed to be universal, aims to ensure equitable access to healthcare while having to contend with significant regional differences and financial difficulties. On the other hand, China, which operates with a mixed model, has rapidly expanded its insurance coverage using a variety of schemes and embracing technological advances, which has enabled large-scale access to healthcare, although considerable differences remain between urban and rural areas and provinces. When compared, each system shows particular strengths: Italy’s local care networks and focus on continuous services offer a model for inclusive welfare, while China’s use of digital tools demonstrates how innovation might help overcome obstacles in settings with limited resources and maintain continuity of care. There is a noticeable overlap in how both countries handle chronic diseases and their shared concern for equity in health policy. Considering these points, mixed approaches that combine universal coverage, financial viability, and flexible use of technology could provide useful ideas for crafting more fair, effective, and resilient health policies in various settings. Rather than proposing direct policy transfer, the comparison identifies context-dependent strategies that may inform future healthcare reforms. Full article

Against the background of global production-network restructuring, low-carbon transition, and rising external uncertainty, this study examines the effect of green infrastructure investment on urban industrial chain resilience. Using panel data for 285 Chinese prefecture-level cities from 2012 to 2024, industrial chain resilience is measured from the dimensions of industrial diversification and urban innovation capacity. Double machine learning is employed for baseline estimation, supplemented by mediation analysis, threshold regression, spatial econometric analysis, and a series of robustness tests. The results show that green infrastructure investment significantly enhances industrial chain resilience, and the finding remains robust to alternative model specifications, cross-fitting settings, generalized propensity score weighting, continuous-treatment entropy balancing, winsorization, and the exclusion of pandemic-period observations. Resource allocation efficiency plays a partial mediating role in this relationship. The threshold analysis identifies a significant nonlinear effect associated with energy consumption intensity, with the positive effect of green infrastructure investment being stronger below the estimated threshold and weakening above it. Spatial analysis further shows significant spatial dependence in both green infrastructure investment and industrial chain resilience, together with positive spillover effects on neighboring cities. These findings highlight the importance of improving green infrastructure investment efficiency, strengthening factor allocation, and promoting regional coordination in enhancing urban industrial chain resilience. Full article

Reliable operating-state recognition of gas pressure regulators is essential for pressure stability, operational safety, and supply continuity in urban natural gas distribution networks. However, SCADA pressure–flow signals from regulating stations are often affected by non-stationary noise, impulsive disturbances, limited fault-state samples, and short-window temporal fluctuations, which reduce the reliability of data-driven recognition. To address these issues, this study proposes K²-TLNet, a noise-state-guided fault-state recognition framework for gas pressure regulation processes. The framework integrates adaptive Kalman filtering, training-only KMeans-SMOTE, parallel temporal convolutional network–long short-term memory feature extraction, and a Noise-Aware Gated Fusion mechanism. Adaptive Kalman filtering is used to generate denoised pressure–flow sequences and extract innovation-residual-based noise descriptors. These descriptors guide the fusion module to adaptively balance local transient features from the temporal convolutional network and contextual temporal features from long short-term memory. A field-SCADA-background-based semi-synthetic dataset was constructed using real operating records and mechanism-informed fault-state emulation rules. Experimental results demonstrate that K²-TLNet achieves 98.50% accuracy and 98.20% Macro-F1, while maintaining strong robustness under Gaussian and impulsive noise disturbances. Full article

Cities, being elementary concentrations of socio-economic activities and resource-environmental pressures, confront significant challenges in promoting new quality productive force (NQPF) development because technology, resource, and information conditions may be spatially associated across cities. Although the nexus among cities has gained widespread recognition in China’s high-quality development such as in innovations and low-carbon transformation, a critical gap exists in quantitatively assessing how model-estimated inter-city spatial correlations relate to high-quality development within an integrated framework. To bridge this gap, this study constructs an urban social correlation network (SCN) analysis framework, integrates spatial correlation methods to overcome the limitations of traditional heterogeneity assessment, and applies it to 283 Chinese cities from 2010 to 2023 to measure network characteristics and structural resilience related to NQPF. The results show that the network density rose from 0.0608 in 2010 to 0.1577 in 2023. By 2023, the SCN featured higher connectivity and reciprocity, comparatively high but lower efficiency, low hierarchy, and no obvious core–periphery structure. The 283 cities were divided into four blocks, exhibiting denser intra-regional than inter-regional links, and strengthened inter-regional interactions over time. The displayed node-removal trajectories declined faster under centrality-ordered removal than under one reported random-removal sequence. The network was more vulnerable to intentional attacks than to random attacks. Motif analysis indicates that M1 and M2 dominated and contributed to low network density, while the shift from open structural holes toward a mix of open and closed structures was associated with network evolution. These findings indicate that cultivating key node cities and improving inter-city coordination mechanisms to enhance network resilience are critical pathways for advancing NQPF development with Chinese characteristics, providing quantitative evidence for targeted inter-city coordination policymaking. Full article

As tourism shifts from sightseeing to experience-oriented consumption, understanding how tourist perception differs across heterogeneous destination types and spatial scales remains challenging. Using 23,439 Volunteered Geographic Information (VGI) reviews archived for six destinations in Shangrao, China, this study compares mountain-type and rural comprehensive destination products. These are operational dominant-function categories rather than mutually exclusive geomorphological classes. The archive supports fine-grained sentiment, topic, and semantic-network analyses; annual temporal comparisons use the full 23,439-review corpus covering 2019–2025, whereas a separate subset of reviews posted from 1 August 2022 with official IP labels, aggregated into 2022–2024 province–year observations, supports Pooled Ordinary Least Squares (Pooled OLS) estimation. A hybrid lexicon–XLM-RoBERTa workflow, BERTopic, semantic co-occurrence analysis, and Pooled OLS are integrated in a cross-scale framework. Static results reveal shared strengths and weaknesses—high scenery and overall-experience evaluations but low price evaluations—alongside type-specific structures: mountain reviews concentrate on natural scenery, climbing effort, and accessibility, whereas rural reviews span village landscapes, cultural activities, accommodation, and nighttime experiences. Temporally, mountain demand retains a stable scenic core while accessibility concerns become more salient; rural demand shifts from traditional agricultural landscapes toward nighttime performances and other experience-oriented products. Cross-scale regressions identify destination- and dimension-specific correlates rather than causal drivers: urbanization is positively associated with several rural evaluations, while ecological contrast, climatic difference, and competing scenic resources are associated with more critical assessments in selected dimensions. The findings show that perception differences arise from the interaction of destination product structures and origin-region contexts, supporting differentiated accessibility management for mountain destinations and balanced product innovation, service improvement, and commercialization control for rural destinations. Full article

This paper presents a resilience-driven probabilistic multiobjective framework for reactive power planning in islanded microgrids under extreme contingencies, explicitly integrating sustainability objectives and alignment with the United Nations Sustainable Development Goals (SDGs). The proposed planning framework simultaneously optimizes technical reliability, economic viability, environmental sustainability, and social resilience using the IEEE 33-bus distribution system as a representative test network. Uncertainties associated with solar irradiance, wind speed, and load demand are modeled using the Two-Point Estimation Method (2PEM), while the Non-dominated Sorting Genetic Algorithm II (NSGA-II) determines Pareto optimal planning solutions for five reactive power support strategies. The results demonstrate that planning solutions optimized for grid-connected operation are not necessarily the most effective under islanded conditions. Within the adopted multi-criteria evaluation framework, the dedicated D-STATCOM strategy achieves the highest overall normalized performance, providing 87.2% load preservation, 93.7% critical-load protection, and an 8.7 h representative survival time, while reducing total load shedding to 12.8% and eliminating high-risk shedding events (>30%). Furthermore, it decreases event-related economic losses by more than 75% and achieves the lowest environmental impact, with a 62.5% reduction in life-cycle CO₂ emission intensity relative to the conventional grid baseline. A normalization sensitivity analysis confirms that the comparative ranking of the investigated strategies remains unchanged under alternative normalization methods, demonstrating the robustness of the proposed evaluation framework. From a sustainability perspective, the proposed framework contributes to SDG 7 (Affordable and Clean Energy) through reliable low-carbon microgrid operation, SDG 9 (Industry, Innovation and Infrastructure) through resilient power system planning, SDG 11 (Sustainable Cities and Communities) by enhancing the continuity of critical urban services, SDG 13 (Climate Action) through reduced life-cycle emissions, and SDG 8 (Decent Work and Economic Growth) by supporting local employment associated with distributed energy deployment. Full article

With the rapid advancement of urbanization, the density and vulnerability of urban lifeline networks are increasing, and urban lifeline security risks have become a major challenge to urban public security governance. The backward governance means and fragmented governance mechanisms cannot adapt to the complex emerging urban public security risks. Digital empowerment is considered to be a new solution for the holistic governance of urban lifeline security, but related research has only focused on a single scenario, a single risk type or a single risk management link. This study shifted from a single perspective to a holistic perspective and explored how to use digital technology to develop the urban lifeline security project from three levels, that is, overall methods, key supporting technology, and governance mechanism innovation, so as to enable a holistic governance model for lifeline security. Specifically, this study constructed the main processes and methods for constructing urban lifeline security projects, the key supporting technology system for the scenario-driven urban lifeline security project, and the overall governance mechanism for the urban lifeline security project. The case from Hefei, China, further verifies the effectiveness of urban lifeline security engineering. The contribution of this study is to promote the collaborative innovation and integrated application of engineering technology and governance mechanisms in the field of holistic governance of urban lifeline security. Full article

Digital technologies are reshaping the spatial organization of economic activities, expanding cities’ access to external innovation resources across geographical boundaries, and creating new pathways for sustainable urban development. Using panel data for 279 prefecture-level cities in China from 2011 to 2022, this study constructs an urban digital network based on the cross-city organizational linkages of listed digital firms and their subsidiaries and measures virtual agglomeration by combining network linkages with urban digital foundations. The results show that virtual agglomeration significantly promotes urban innovation, and this finding remains robust to instrumental-variable estimation, alternative measures, and lagged specifications. Virtual agglomeration also strengthens the ability of latecomer cities to translate innovation gaps into subsequent growth. Both local digital foundations and external digital linkages contribute to this effect, while knowledge-related variety serves as an important transmission channel. The positive relationship is more pronounced in resource-based, third-tier, non-urban-agglomeration, and western cities. Further analysis shows that virtual agglomeration significantly promotes urban green innovation, indicating that its innovation effect extends to environmentally oriented technological development. Overall, virtual agglomeration improves access to innovation resources, supports innovation catch-up in less-developed cities, and, by promoting green technological innovation, offers a digital-network-based pathway toward more coordinated and sustainable urban development. Full article

Cross-border regions have become key arenas for experimenting with new territorial configurations, particularly where functional interdependencies challenge inherited administrative boundaries. This article examines how cross-border cooperation contributes to innovative territorial construction along the French–Italian Alpine border, focusing on how territorial actors mobilise the border and how these dynamics relate to the emerging notion of transfrontierisation. The study centres on the urban–mountain system linking Savoie, the Aosta Valley, and the Metropolitan City of Turin, using the A-MONT project (Interreg ALCOTRA 2021–2027) as an empirical entry point. A mixed methodology was applied, combining quantitative analysis of ALCOTRA projects from the 2014 to 2020 and 2021 to 2027 programmes, qualitative fieldwork to local and supra-local actors, and a critical review of recent Integrated Territorial Plans (PITER). The results reveal a pronounced centre–periphery pattern in project participation, persistent disparities in administrative capacity, the development of informal networks that mitigate institutional fragmentation, and the emergence of shared territorial diagnoses. The article argues that these dynamics extend beyond project-based interaction and proposes the concept of transfrontierisation to describe the process through which cross-border cooperation reshapes supra-local networks, stabilises governance arrangements, and fosters shared territorial referents without replacing existing institutional frameworks. Full article

Identifying the spatio-temporal evolutionary patterns and driving mechanisms of rural tourism network attention is essential for predicting the development trends of the rural tourism industry and delivering refined industrial governance. Taking 356 prefecture-level cities in China from 2015 to 2024 as basic research units, this paper constructs a comprehensive evaluation system for rural tourism network attention based on multi-source data. Furthermore, its spatio-temporal evolution characteristics and internal influencing factors are systematically investigated by means of spatial autocorrelation analysis and geographically weighted regression. The results indicate that the overall level of rural tourism network attention in China shows an obvious fluctuating growth trend, which can be divided into three successive stages, namely steady growth (from 0.8530 in 2015 to 1.2028 in 2019), explosive growth (from 1.9563 in 2020 to 3.7471 in 2021) and high-level fluctuation (maintained in the high range of 2.4–3.4). In addition, with the continuous iteration of internet communication media, the guiding influence of traditional search platforms has gradually weakened, while emerging social media and short-video platforms have become the core carriers of online tourism traffic. Correspondingly, media innovation persistently reshapes the spatial distribution pattern of rural tourism network attention. In terms of spatial characteristics, rural tourism network attention has undergone a significant transformation from geographical gradient polarization to overall regional equilibrium. Specifically, from 2015 to 2024, the overall Moran’s I index remained positive, with values ranging from 0.0116 to 0.1358, indicating an overall trend of gradual decline. High-attention areas are predominantly concentrated in economically developed urban agglomerations, whereas remote and economically underdeveloped regions exhibit contiguous low-value aggregation characteristics, which reveals a remarkable trend of balanced development nationwide. In view of driving mechanisms, highway network density, tourism income, rural tourism resource and enrollment of university students are identified as the core driving factors dominating the spatio-temporal evolution of rural tourism network attention. Moreover, the intensity of the influence of each factor presents distinct spatial heterogeneity. This study further reveals that the spatial heterogeneity of rural tourism network attention calculated using multi-source fused data shows a remarkable convergent characteristic, which can reflect the actual distribution of the rural tourism market more objectively and accurately. Meanwhile, rural tourism network attention is typically characterized by scale-dependent with the spatial distribution at the macro-scale being more balanced than that at the meso- and micro-scales. Full article

Enhancing green total-factor energy efficiency (GTFEE) is a critical pathway for promoting the green transformation of economic and social development. However, the potential influence of the construction and development of urban functional networks (UFNs) on this process has received limited scholarly attention. Using panel data from 148 cities across nine major Chinese urban agglomerations from 2010 to 2023, this study finds the following: First, the development of UFNs at the urban agglomeration scale significantly enhances GTFEE. Second, UFNs improve GTFEE through two distinct mechanisms: “government action” and “market efficiency.” The “government action” mechanism primarily operates by promoting market integration, reducing land resource misallocation, and curbing urban sprawl. The “market efficiency” mechanism functions by enhancing factor mobility, optimizing the allocation of production factors, and accelerating the diffusion of green technological innovations. Third, the effect of UFNs on GTFEE varies according to local development conditions, infrastructure connectivity, and policy continuity and coordination. The enhancement effect is more pronounced in cities located near coastlines, those connected to other cities within the agglomeration by high-speed rail, and cities with longer-serving municipal Party secretaries. Fourth, urban agglomerations characterized by polycentric spatial structures and stronger agglomeration externalities are better able to leverage the functional node effects of their constituent cities, thereby further enhancing GTFEE. Full article

Construction waste management (CWM) is vital for enhancing resource utilization, ecological civilization, and high-quality development. Technological innovation is the cornerstone of efficient CWM, driving waste reduction, reuse, and recycling practices. Understanding the relevance of CWM technological innovation across different regions is an important foundation for the scientific formulation of CWM policies and a key entry point for achieving a circular economy. This study employs patents to reflect CWM technological innovation, uses social network analysis (SNA) to explore its spatial network characteristics, and adopts a quadratic assignment procedure (QAP) to verify its influencing factors across Chinese provinces. Results demonstrate significant overall network characteristics in CWM technological innovation, though technological innovation in CWM exhibits low spatial correlation among provincial regions of China. The analysis reveals a pronounced polarization in the CWM innovation networks across Chinese provinces, demonstrating a distinct core–periphery structure. Although spatial clustering in China’s CWM technological innovation is evident, correlations mostly exist within blocks, indicating a need to enhance inter-block correlations. The greater the disparity in living standards and the smaller the differences in economic structure, educational development, and urban construction, the more likely provinces are to establish spatial associations, thereby increasing the probability of forming a spatial correlation network of CWM technological innovation. This study contributes to the growing literature on construction waste management by providing a spatial network perspective for understanding regional technological innovation and its driving mechanisms. The findings inform regional coordinated strategies for sustainable CWM practices. Full article

The insufficient connectivity between human activity spaces and the natural environment impairs ecosystem connectivity and leads to the gradual compression of the natural environment by human activity spaces. In highly urbanized areas, human activity spaces and natural environments are often disconnected, yet few existing ecological network studies have actively incorporated human activity spaces into the an optimization framework to promote their integration with natural ecosystems. Taking Guangzhou as a case study, this research integrates MSPA-InVEST (the Morphological Spatial Pattern Analysis—Integrated Valuation of Ecosystem Services and Trade-offs model), landscape connectivity assessment, the MCR model (Minimum Cumulative Resistance model), and the gravity model to identify ecological sources and potential corridors. It innovatively introduces enterprise kernel density to add planned sources and planned corridors, thereby developing an ecological network optimization scheme that achieves spatial integration between human activity spaces and the natural environment. The results identified 38 ecological sources (total area ~2907.65 km², accounting for 39.07% of the study area) and 46 basic ecological corridors, showing a spatial pattern of “dense in the north, sparse in the central-south”. The addition of 7 planned sources and 22 planned corridors increased the network’s closure, connectivity, and linkage rate by 115.38%, 24.79%, and 23.26%, respectively, with the overall connectivity reaching 0.53. The spatial pattern shifted from an uneven distribution to a “north–south linkage and full coverage” layout. The optimized ecological network effectively connects the major ecological source areas in Guangzhou and, in combination with natural corridors such as rivers, forms a comprehensive ecological security pattern of “mountains–water–forests–city”, thereby constructing a comprehensive ecological network framework for highly urbanized areas. This method takes into account both ecological integrity and economic spatial demands, providing a scientific reference for the construction of ecological networks and the optimization of territorial space in high-density megacities, with the ultimate goal of integrating human activity spaces with natural ecosystems and fostering sustainable development. Full article