Search Results (22,787)

In people-centered urban planning, enhancing the well-being of residents and tourists is one of the core objectives. Tourist emotion serves not only as a key indicator of the tourism experience but also indirectly reflects the quality of a city’s public spaces and built environment. In recent years, user-generated content has provided abundant data for understanding human emotional responses in urban environments, while deep learning models offer new technological pathways for extracting spatial–emotional associations from such data. However, existing research lacks a systematic evaluation of emotion analysis models from an urban spatial perspective and their application to uncover the relationship between emotional distribution and spatial characteristics in specific urban contexts. Based on a dataset of 9419 manually annotated travel reviews from Harbin, this study developed a multi-level evaluation framework and conducted a systematic comparison of seven emotion analysis models. This study then screened for the optimal model combinations based on two dimensions—spatial location and emotion polarity—to create a model matching matrix for mapping Harbin’s emotion map. Subsequently, a regression analysis was performed to examine the relationship between emotions and built environment elements. The results show that the ERNIE model demonstrated the best overall performance. Road density, green space density, and accommodation facility density were positively correlated with emotion, while POI diversity showed a negative correlation. This study demonstrates that emotion analysis technology can serve as a valuable analytical tool for identifying spatial patterns of sentiment, thereby offering empirical support for optimizing spatial design parameters and advancing a more people-centered approach to urban development. Full article

Using panel data from 11 prefecture-level cities in Zhejiang Province (2014–2023), this study applies the entropy method, spatial autocorrelation analysis, and an obstacle-factor diagnosis model to examine the spatiotemporal evolution, regional disparities, and constraints on urban–rural integration. The results show a steady upward trend in urban–rural integration alongside significant regional disparities. This reveals a complex pattern marked by the coexistence of convergence and divergence. Spatially, a clear “northeast–high, southwest–low” pattern is observed, with local adjustments within a stable framework, reflecting a “stable core and entrenched low-value areas.” Spatial agglomeration is characterized by “dual-core agglomeration with a predominantly non-significant periphery,” dominated by homogeneous “high–high” and “low–low” clusters, with no statistically significant spatial outliers. Obstacle factor diagnosis indicates markedly uneven constraining effects across subsystems, with spatial integration exhibiting the highest degree of obstacles. The composition of primary obstacle factors is highly stable, and obstacle structures differ significantly across city tiers. These findings elucidate the spatiotemporal evolution and core constraints of urban–rural integration in Zhejiang, offering a theoretical and decision-making basis for advancing high-quality urban–rural integration in the region. Full article

Objectives: This study examines how different benefit payment modes under China’s long-term care insurance (LTCI) program influence healthcare utilization and medical expenditures among middle-aged and older adults. Specifically, it compares the effects of in-kind benefits and mixed benefits on healthcare service use and financial burden. Methods: This study uses data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018, focusing on middle-aged and older adults with functional limitations. Exploiting the staggered implementation of LTCI pilot programs across 14 cities, a difference-in-differences (DID) approach is employed to estimate the causal effects of different benefit payment modes on healthcare utilization and expenditures. Heterogeneity analyses are conducted to explore differences between rural and urban populations. Results: The results indicate that the in-kind benefit mode significantly reduces inpatient visits, total medical costs, and out-of-pocket expenditures. By contrast, the mixed benefit mode shows only a modest reduction observed mainly in outpatient visits. Heterogeneity analysis further reveals that in-kind benefits are particularly effective in reducing healthcare utilization and medical expenditures among rural residents, while urban residents experience higher reductions in out-of-pocket spending. Conclusions: These findings highlight the importance of benefit design in shaping the effectiveness of LTCI policies. Prioritizing service-based benefits may improve healthcare system efficiency and reduce financial burdens among older adults. The results provide policy-relevant insights for optimizing LTCI benefit design in China and other aging societies. Full article

Urban agglomerations have become the dominant spatial platform of urbanization, regional coordination, and economic transformation in China. Yet whether the expansion of agglomeration scale at the urban-agglomeration level alleviates or intensifies energy use remains insufficiently understood. Extending the scale of analysis from individual cities to integrated urban agglomerations, this study investigates 64 cities in four major Chinese urban agglomerations, including Beijing–Tianjin–Hebei, the Yangtze River Delta, the Pearl River Delta, and Chengdu–Chongqing, over the period 2006–2023. Using panel data models, this study examines the impact of the scale agglomeration within urban agglomeration on urban energy intensity. The results show that the overall agglomeration scale generated by urban agglomeration formation significantly suppresses energy intensity while indicating a robust energy-saving effect: every 10% increase in agglomeration scale is associated with a decline of approximately 0.0893 million tons of standard coal per CNY 100 million of GDP. This finding remains stable after addressing endogeneity concerns and performing a series of robustness checks. Mechanism analyses further suggest that this effect operates primarily through talent agglomeration, technological progress, and public transportation expansion. In addition, the energy-saving effect is more pronounced in smaller cities, cities with lower administrative rank, cities with weaker factor mobility, and cities characterized by poorer air quality but stronger public environmental attention. These findings contribute to the literature on urban agglomeration and green development by showing that the agglomeration scale within urban agglomerations can generate inclusive energy-efficiency gains, especially for relatively disadvantaged cities, thereby offering important implications for spatial governance and low-carbon transition in rapidly urbanizing economies. Full article

As rural revitalization advances, the age-friendliness of public spaces directly impacts the well-being of left-behind elderly populations. However, the spatial and social marginalization of these vulnerable groups in tourism-driven peri-urban villages remains critically underexplored. To bridge this gap, this study proposes a quantitative evaluation framework integrating space syntax and multiple linear regression to investigate the matching mechanism between physical spatial layout and elderly activity needs. Focusing on Shixia Village in Beijing, surveys and satisfaction assessments were conducted with 30 elderly residents (representing a rigorous 27.3% of the permanent population). Space syntax analysis revealed a distinct “core-periphery” spatial differentiation. Despite a moderate spatial intelligibility (0.586), the rapid decay of integration in peripheral clusters acts as the primary physical bottleneck restricting the elderly’s social radius. Furthermore, regression results indicate that public facility accessibility (β = 0.703) and residential environment quality (β = 0.779) are the core positive drivers of satisfaction (p < 0.001). Conversely, road connectivity exhibited an unexpected negative correlation (β = −0.308). This highlights a crucial “double-edged sword” effect: in traditional villages with tourism development, excessive spatial permeability diminishes the elderly’s territorial sense of security due to external traffic interference. Finally, targeted optimization strategies—including traffic-calming interventions and hierarchical node layouts—are proposed, providing an operational evaluation model and design reference for age-friendly environmental construction in similar peri-urban villages. Full article

This study investigates the rapid, often uncontrolled urban expansion in Khenchela, a medium-sized city in Algeria’s eastern High Plains, and its profound environmental repercussions amid semi-arid fragility. Drawing on sustainable urban development and resilience frameworks, it dissects pressures such as green space reduction (from 45 ha in 1998 to 33 ha in 2023, dropping per capita from 6.1 m² to 3 m² below WHO standards), water scarcity with 35% leakage losses waste mismanagement, informal settlements on hazardous lands, air/soil pollution, and climate vulnerabilities like heat waves and flooding. Employing a mixed-methods approach documentary analysis of (MPLUUP, LUP and MDP) plans, GIS cartography of spatial evolution (2000–2025), statistical demographics, field observations, and institutional critiques, the research exposes governance gaps: fragmented coordination, weak ecological integration, and resource shortages. It reveals socio-spatial disparities across functional zones, underscoring the need for adaptive, participatory strategies that promote polycentric and compact urban forms, enhanced biodiversity, efficient infrastructure, and inclusive governance to strengthen urban resilience. Full article

This study employs panel data for 275 Chinese cities from 2011 to 2021. Water use efficiency is measured as an aggregate city-level indicator via stochastic frontier analysis, while the level of digital economy development is quantified using principal component analysis. We then employ the system generalized method of moments to investigate the causal relationship between the digital economy and urban water use efficiency, and further identify industrial structure upgrading as the mediating role through which the digital economy affects water efficiency. The main findings are as follows: (1) The digital economy has a significant positive impact on urban water use efficiency. (2) Regional heterogeneity analysis shows that the digital economy presents a stronger positive effect on water use efficiency in eastern regions than in central and western regions. (3) Exploratory mechanism analysis indicates that industrial structure upgrading serves as the mediating role through which the digital economy improves urban water use efficiency. Based on the empirical findings, this paper draws targeted policy implications. Full article

Background/Objectives: Cardiovascular diseases remain one of the major public health challenges in Croatia, with coronary artery disease (CAD) as the most prevalent. The uneven development and concentration of healthcare facilities in urban areas suggest that rural regions lag in providing adequate healthcare. Methods: This quantitative, retrospective study included 732 patients diagnosed with acute coronary syndrome (ACS) who were treated between 1 January 2014 and 31 December 2023 at Ogulin General Hospital, Croatia. Data were extracted from the hospital information system, and statistical analyses were performed, with p < 0.05 considered significant. Results: The analysis showed a decreasing trend in hospitalizations of patients with STEMI (Z = −3.574; p < 0.001) and an increase in hospitalizations of patients with NSTEMI (Z = 3.124; p = 0.002). No seasonal (χ² = 26.33; p = 0.238) or gender differences (χ² = 3.348; p = 0.188) were observed. A significant association between age and ACS occurrence was observed (χ² = 57.35; p < 0.001). The proportion of patients transferred to another health institution for further treatment was low (39.21%), particularly among patients with STEMI (12.89%). Conclusions: The results of this study indicate changes in the dynamics of ACS occurrence during the observed ten-year period. The number of hospitalizations is decreasing significantly, with a very low number of transfers of patients with STEMI; at the same time, there is an increase in hospitalizations of patients with NSTEMI. The study did not show statistically significant seasonal or gender differences in the incidence of ACS, while the incidence of NSTEMI increases with the age of the patients. These results emphasize the need for further improvement in the organization of healthcare for patients with ACS in the rural area served by Ogulin General Hospital. Full article

Urban green spaces are increasingly threatened by rapid urban expansion, making their continuous monitoring and prediction essential for sustainable urban management. This study investigates the spatiotemporal dynamics of urban garden landscapes in Shiraz, Iran, by integrating multi-temporal Landsat imagery, GIS analysis, and CA–Markov modeling. Landsat data from 2003, 2013, and 2023 were processed to derive the Normalized Difference Vegetation Index (NDVI), which was classified into four vegetation-density categories to quantify land-cover transitions. A CA–Markov framework implemented in IDRISI TerrSet (Version 20.0) was then employed to simulate spatial dynamics and predict vegetation changes for 2033. Results reveal a significant expansion of non-vegetated areas from 711.93 ha in 2003 to 976.66 ha in 2023, accompanied by a decline in dense vegetation from 403.68 ha to 382.64 ha. Model projections indicate a further reduction in dense vegetation to 239.35 ha by 2033, suggesting ongoing fragmentation of urban green infrastructure driven by development pressures. By combining time-series remote sensing, GIS-based spatial analysis, and predictive modeling, this study provides an integrative framework for detecting, interpreting, and forecasting urban land-cover change. The findings offer evidence-based insights to support sustainable urban planning, green infrastructure protection, and climate-resilient city management in rapidly growing urban environments. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. Full article

Rail-trail corridors in the agricultural Midwest exhibit layered ecological conditions influenced by the material legacy of railroad infrastructure and contemporary land use pressures. This study uses a mixed-methods approach integrating GIS analysis, field documentation, and open-response surveys with trail managers to characterize the structural and ecological heterogeneity of two rail-trails within the Corn Belt. Spatial methods quantify variation in right of way width, land cover context, connectivity, and patterns of fragmentation, revealing that corridors shift in response to agricultural edges, successional woodlands, riparian zones, and urban conditions. Field visits and on-site sketching provide fine-grained insight into vegetative structure, topography, and edge dynamics, while the thematic analysis of survey responses highlights how management regimes, resource limitations, invasive species, and adjacent land uses shape ecological patterns along the trail. Together, these methods support the development of a typology of rail-trails based on their vegetative, hydrological, and disturbance patterns. We argue that design and management should work with the nuance of the corridors, noting the potential for landscape experimentation. Novel design approaches can support the performance of rail-trails as ecological infrastructure while enabling meaningful human–environment interactions within the right of way. Full article

Greening, as a concept, is becoming an essential component of contemporary urban planning worldwide, and universities have begun adopting green policies. While there are numerous studies on climate change, green infrastructure, ecology, and sustainability in planning practice, limited scientific research explores how these concepts are embedded within the educational landscape. This paper aims to develop a methodological framework for mapping the “educational greenscape” by evaluating three levels of higher education in a top-down manner: (01) university, (02) faculty, and (03) subject. The research methodology relies on: an extensive literature review and content analysis; a multi-level case study of the University of Belgrade, focusing on an expert survey based on the European University Association framework; curriculum content evaluation at the Faculty of Architecture, using predefined keywords; and the identification of green interventions and their implementation within the subject “Sustainable Territorial Development,” at the Faculty of Architecture. The specific findings indicate that green activities at the institutional level lack resources, communication, and governance. At the faculty level, there is an apparent need for a more even distribution of green urban planning approaches across different faculty courses. However, subject-level assessment showed the successful implementation of the green urban planning concept into teaching and learning methodologies, with it showing transformative potential and providing a universally applicable methodological framework for mapping the educational greenscape. Full article

This study systematically examines the impacts of extreme temperatures on the digital economy development index and the underlying mechanisms based on panel data from 281 prefecture-level cities in China from 2012 to 2023. This study explicitly distinguishes the distinctive adaptive capacity of the digital economy in responding to climate risks. Through global and local spatial autocorrelation analysis, the study finds that both extreme temperatures and the digital economy exhibit significant spatial clustering. This study employs the spatial Durbin model (SDM) and effect decomposition and further incorporates the GS2SLS estimator alongside dual instrumental variables constructed from historical geographic characteristics to address endogeneity, thereby identifying the asymmetrical impacts of extreme heat and extreme cold on the digital economy with great rigor. Specifically, extreme heat fosters short-term local digital demand that is subsequently translated into long-term growth in IT human capital and infrastructure, thereby increasing the DEDI. However, its net spatial effect is inhibitory due to energy crowding out. Extreme cold, by contrast, primarily disrupts supply chains and intensifies energy consumption, with its impact largely confined to the local scope. Green technological innovation mitigates the impact of extreme heat on the digital economy through demand substitution, while, under extreme cold, it manifests as the physical protection of infrastructure. Meanwhile, an optimized industrial structure substantially reduces the economy’s dependence on supply chains, amplifying the promotional effect of extreme temperatures on the digital economy and reflecting the transformation capacity of regions under complex environmental conditions. Heterogeneity analysis demonstrates that the effects of extreme temperatures vary significantly across different urban agglomerations, economic zones, geographic regions and city types. This study not only extends the theoretical framework for the economic assessment of climate risks and spatial econometric analysis to the climate sensitivity of the digital economy but also provides empirical evidence for understanding the complex relationship between climate change and digital economy development and offers references for differentiated policies in a coordinated regional digital economy. Full article

Land subsidence (LS) is a widespread geoenvironmental problem driven by both natural processes and human activities. Identifying the main factors controlling LS susceptibility and their spatial contribution patterns is essential for LS management and mitigation. In this study, an interpretable earth observation framework was developed for the North China Plain (NCP) to quantify both spatial and non-spatial contributions of dominant LS drivers. Land displacement was derived from Sentinel-1A SAR images using Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) processing. The displacement map was then combined with nine geoenvironmental variables to construct an LS susceptibility model using the eXtreme Gradient-Boosting (XGBoost) algorithm. The model performed well, with an R² of 0.96, an EVS of 0.96, and an MAE of 2.25 mm/yr. SHapley Additive exPlanations (SHAP) analysis was employed to quantify feature contributions and their effects on LS susceptibility. The results show that a deep groundwater level (DGL) was the dominant factor, followed by elevation and a shallow groundwater level (SGL). The effect of DGL was strongest when it ranged from −75 to 20 m. Elevation showed a clear effect on LS occurrence when values fall between 30 and 50 m. Relatively high subsidence sensitivity was mainly observed in areas where SGL was below −7 m. Interaction effects, particularly those between DGL and elevation and between DGL and SGL, further increased LS susceptibility in specific areas. The highest predicted susceptibility occurred in areas with DGL below −20 m and elevations below 30 m. Higher susceptibility was also identified where DGL was high and SGL ranged between −20 and −10 m, and where DGL was low and SGL ranged from 15 to 20 m. In contrast, factors such as slope and aspect had limited influence at the regional scale. The contributions of the predominant factors show obvious marginal effects and significant spatial heterogeneity to LS susceptibility. The results clarify where and how key factors shape subsidence and can inform targeted mitigation measures and urban planning by local authorities. Full article

Anthropogenic polymers have become an increasingly important class of emerging contaminants in terrestrial ecosystems. While extensive research has focused on microplastics in aquatic environments, their interactions with soil systems and particularly with soil organic matter (SOM) remain insufficiently understood. Soil represents a major environmental sink for polymer residues originating from agricultural practices, urban activities, and atmospheric deposition. Accordingly, associations between polymers and SOM, including humic substances, may significantly influence the retention, mobility, and transformation of carbon in soil systems. This review synthesizes current knowledge on the influence of synthetic polymers on soil organic matter dynamics. A bibliometric and qualitative literature analysis based on publications indexed in Web of Science and Scopus from 1979 to 2025 was conducted to identify major research trends and knowledge gaps. The results indicate that polymer particles can alter soil structure, microbial activity, and sorption processes, thereby affecting the stability and cycling of soil organic carbon. Interactions between polymer surfaces and humic substances may modify aggregation processes and influence the persistence and mobility of both polymers and organic carbon compounds. Despite the rapid growth of research on microplastics, studies addressing polymer–SOM interactions remain limited and methodologically heterogeneous. Greater integration between polymer research, soil science, and land use studies is necessary to better understand the implications of polymer contamination for soil quality and carbon cycling. The findings highlight the need for standardized analytical approaches and interdisciplinary research frameworks to assess the long-term effects of polymers in soil ecosystems. Full article