Search Results (3,298)

The Loess Plateau in China is highly susceptible to frequent landslides and other geological disasters, which have led to substantial losses of natural and human resources and are frequently reported in the news media. Yuzhong County, located east of Lanzhou City, is a mountainous region with considerable development potential. On 7 August 2025, this area experienced a large-scale geological disaster characterized by a compound event involving both landslides and debris flows, resulting in nearly several hundred casualties. With the ongoing urban expansion of Yuzhong County in recent years, the prediction and prevention of geological disasters have become increasingly critical. This study employed three machine learning algorithms—Multiple Logistic Regression (LR), Random Forest (RF), and XGBoost (XG)—to assess landslide susceptibility in Yuzhong County. A total of 169 historical landslide points, supplemented by additional sites identified through field investigations, were compiled, along with 200 non-landslide locations. Multiple environmental factors were incorporated into the models to analyze landslide susceptibility across different areas. Because LR can effectively capture the generalized influence of precipitation variability, it was selected as the primary model for the final susceptibility mapping. To more accurately evaluate the impact of precipitation on landslide occurrence, average seasonal precipitation across the four seasons was used as a predictive factor. To refine the risk assessment at the township level, both raster-based and landslide-unit-based evaluation approaches were adopted. Overlay analyses were then performed by integrating urban infrastructure, population distribution, and predicted landslide hazard zones, while also accounting for the potential influence of extreme precipitation events. The results reveal that the mountainous areas in eastern Mapo Township, southern Xiaokangying Township, southern Xiaguanying Town, and the south-central part of Qingshuiyi Township are high-risk zones prone to group-occurrence landslide disasters. Full article

Amid growing global economic uncertainty, strengthening tourism economic resilience is critical to the economic sustainability of tourism destinations. Using panel data for 288 Chinese prefecture-level cities from 2010 to 2024, this study uses the staggered designation of the National New-Generation Artificial Intelligence Innovation and Development Pilot Zones as a quasi-natural experiment and applies a staggered difference-in-differences (DID) model to examine whether artificial intelligence (AI) development promoted by the pilot-zone initiative enhances tourism economic resilience. Results show that the pilot-zone initiative significantly enhances city-level tourism economic resilience, and this finding remains robust across a series of endogeneity and robustness checks. Mechanism analysis identifies data factor utilization, technological innovation, and industrial structure upgrading as three parallel channels. Moderation analysis shows that the resilience-enhancing effect of the pilot-zone initiative is stronger in cities with more developed digital infrastructure, higher levels of marketization, and greater human resources. Heterogeneity analysis reveals overall regional heterogeneity and a stronger effect in resource-based cities. These findings clarify the mechanisms and boundary conditions linking AI development promoted by the pilot-zone initiative to tourism economic resilience and provide implications for technology-enabled sustainable tourism development. Full article

Surface ozone (O₃) pollution has become an increasingly important constraint on further improvements in urban air quality, particularly in industrial cities where complex terrain, local emissions, and meteorological conditions interact. In this study, hourly air pollutants, meteorological parameters, and high-time-resolution volatile organic compound (VOC) observations collected at a single urban-core site during September from 2021 to 2024 were used to investigate O₃ pollution characteristics, VOC reactivity, source contributions, and driving mechanisms in a resource-based heavy-industrial city in northwestern Henan Province, China. Ozone formation potential (OFP), diagnostic ratios, positive matrix factorization (PMF), meteorological normalization, and extreme gradient boosting combined with Shapley additive explanations (XGBoost-SHAP) were integrated to identify key reactive species, major sources, and meteorological–precursor interactions. The mean maximum daily 8 h average O₃ concentrations were 113.44, 150.32, 123.59, and 154.18 μg·m⁻³ from 2021 to 2024, respectively, with the highest level observed in 2024 despite the lowest nitrogen dioxide (NO₂) and carbon monoxide (CO) concentrations. O₃ was positively correlated with temperature and negatively correlated with relative humidity, indicating the importance of hot and relatively dry conditions. Total VOC OFP first increased and then declined, with alkenes dominating in 2021 and aromatics exceeding alkenes after 2022. Ethene, m/p-xylene, toluene, and vinyl chloride were identified as priority reactive species. PMF results showed that mixed industrial processes and vehicle exhaust were the dominant VOC sources, contributing 32.3% and 23.8%, respectively. Under the original meteorological-normalization specification, represented meteorological features accounted for 64.7% of the modeled O₃ increase during the study period. Sensitivity specifications retained meteorological dominance but showed that the exact share was model dependent. SHAP analysis further identified temperature, short-term temperature variation, relative humidity, alkenes, and NO₂ as key drivers. These results suggest that O₃ pollution in this heavy-industrial city is jointly shaped by favorable meteorological conditions, reactive VOCs, nitrogen oxides (NOx) chemistry, and combined industrial and traffic emissions. Accordingly, industrial processes, vehicle exhaust, and highly reactive VOC species are likely priority targets for mitigation, while the effectiveness of coordinated VOC–NOx control still warrants further regime-specific evaluation. Full article

Coordinating air pollution control with carbon mitigation is a central challenge for urban sustainability. However, there is currently a lack of sufficient research on whether digital infrastructure can complement environmental regulations to enhance the synergy of pollution and carbon emission reduction (SPCER). Using the overlapping implementation of China’s Key Air Pollution Control Zones (KAPCZs) and Broadband China (BC) policies as an empirical policy setting, this study applies a partially linear Double Machine Learning framework to panel data for 282 Chinese cities from 2006 to 2023. The results show that the dual-policy pilots significantly improve urban SPCER, while a unified interaction test further provides statistical evidence of a positive synergistic effect between the two policies. Mediation analyses provide evidence consistent with Green Technological Innovation, industrial structure upgrading, and green finance development as potential transmission mechanisms. Formal cross-group tests reveal significant heterogeneity across regions and city characteristics, with larger effects in highly urbanized, low industrial development, non-old industrial base, central, and non-resource-based cities. Spatial Durbin Model estimates further indicate positive spillovers to neighboring cities. These findings demonstrate the potential value of coordinating environmental regulation with digital infrastructure and provide evidence for differentiated policy design and cross-regional collaboration in urban digital–green transitions. Full article

Collaborative intelligent sensing in the Social Internet of Things (Social IoT) relies on distributed AI-enabled sensors to support complementary information sharing, multimodal perception, and real-time autonomous decision-making. Under high-load conditions, mismatches between resource provisioning and sensing quality of experience (QoE) can significantly degrade system performance in applications such as smart cities. To address this issue, this paper proposes a service priority-aware collaborative sensing support framework based on a joint next-generation passive optical network (NG-PON) and cooperative intelligent service-based radio access network (CIS-RAN) architecture. The framework enables edge AI-driven inference and distributed sensor collaboration in heterogeneous Social IoT environments. Service-slice-specific priority weights are assigned to optical network units (ONUs) and wavelengths according to the QoE requirements and latency sensitivity of sensing tasks, allowing dynamic wavelength tuning that prioritizes high-impact collaborative services. The utility of a centralized intelligent processing pool is formulated to achieve priority-consistent and efficient resource coordination under collaborative constraints. In addition, a multi-agent AI-driven optimization framework is employed to derive adaptive resource allocation strategies that incorporate service priorities while satisfying stringent service-level agreements (SLAs). Simulation results show that the proposed framework improves system-level proxy metrics, including total utility, wavelength satisfaction, and resource utilization, compared with representative baseline schemes. Full article

In an era prioritizing high-quality development and ecological civilization, optimal land resource allocation is vital for overcoming resource constraints and fostering new quality productive forces, with green technology innovation (GTI) serving as a key driver. Using data from 41 Yangtze River Delta cities (2003–2022), this study measures urban land resource misallocation (ULM) via the factor relative distortion coefficient and employs fixed effect, mediating effect, and spatial Durbin models to explore GTI’s impact and spatial spillovers on ULM. Results show a fluctuating yet rising ULM trend with significant spatial disparities. Land allocation efficiency is higher in central and southeastern coastal cities, while western and northern areas exhibit high-misallocation agglomeration. GTI significantly reduces ULM, particularly in non-resource-based cities, mainly through industrial structure optimization and agglomeration. Spatial spillover analysis reveals that GTI not only improves local land allocation but also positively influences neighboring regions via technology spillovers and industrial synergy. These findings suggest that incentive policies should strengthen GTI to enhance region-wide land utilization efficiency. Full article

Objective: This study aimed to develop machine learning (ML)-based models for the early prediction of macrosomia using only maternal sociodemographic and obstetric data. Methods: This retrospective study included 100 pregnant women who delivered at the Obstetric Clinic of Eskisehir City Hospital between January 2022 and December 2023. Participants were classified as nulliparous (n = 48) or parous (n = 52) and further categorized according to the presence or absence of fetal macrosomia (birth weight > 4000 g). Predictor variables included maternal age, body mass index (BMI), gravida, smoking status, history of diabetes mellitus, and hypertension; previous birth weight was additionally included in the parous model. Separate machine learning models were developed for nulliparous and parous women using Extra Trees Classifier, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting (XGB) Classifier, Random Forest, and Logistic Regression. The dataset was randomly divided into training (80%) and testing (20%) subsets. Internal validation was performed using 10-fold cross-validation within the training dataset to optimize model performance and reduce overfitting. Given the relatively small sample size, this study was designed as a pilot exploratory investigation. Results: Overall, 48 nulliparous and 52 parous mothers were included in the study. Among the nulliparous women, 22 (45.8%) had macrosomic newborns, whereas 26 (54.2%) had normal birthweight newborns. Among parous women, 28 (53.8%) had macrosomic newborns, while 24 (46.2%) had normal birthweight newborns. For nulliparous mothers, the XGB Classifier achieved the highest accuracy (80%) and AUC-ROC (82.2%), demonstrating robust predictive performance. For parous mothers, the XGB Classifier again outperformed other ML models, achieving an accuracy of 72.7% and an AUC-ROC of 83.3% in predicting macrosomia. Conclusions: This study highlights the feasibility of ML-based decision support systems in obstetrics, particularly in low-resource settings, to predict macrosomia using readily available maternal characteristics. As a pilot study, these findings should be interpreted cautiously and require validation in larger multicenter cohorts before clinical implementation. Full article

Sustainable aviation fuel (SAF) deployment requires simultaneous coordination of spatially distributed biomass supply chains and nonlinear conversion technologies. This study develops an integrated multiscale optimization framework for SAF production in Mexico using second-generation sugarcane bagasse as the lignocellulosic resource for alcohol-intermediate production, followed by an intensified Alcohol-to-Jet (ATJ) upgrading stage targeting Mexico City Airport. A multi-period mixed-integer linear programming model optimizes harvest-area selection, biorefinery location, biomass allocation, inventory, production, and distribution, while an Aspen Plus model of an intensified Alcohol-to-Jet (ATJ) process with reactive distillation is optimized using Differential Evolution with Tabu List. In this framework, the ATJ block is not modeled as direct biomass-to-jet conversion. In the base case, ethanol is used as the representative alcohol intermediate. Therefore, the upstream biomass-to-alcohol section is represented through an effective bagasse-to-ethanol coefficient, whereas the Aspen Plus–DETL model explicitly describes the downstream ethanol-to-ATJ-range hydrocarbon blendstock upgrading section through dehydration, ethylene oligomerization, hydrogenation, and fractionation. Process yield, production cost, environmental impact, and feasible capacity are fed back into the supply chain model, making process performance endogenous rather than fixed. Results show that the decoupled supply chain baseline favors large production capacities, reducing TAC from approximately 1010 to 340 USD/tSAF and system-level EI99 from 1.110 to 1.059 kPt EI99/tSAF as capacity increases from 80,000 to 490,000 tSAF/year. This corresponds to an environmental reduction of approximately 5.2%. In contrast, isolated ATJ optimization exhibits nonlinear scale-dependent behavior, with process-only EI99 decreasing from approximately 1.70 to 0.55 kPt EI99/tSAF. The integrated framework identifies an intermediate capacity region of 120,000–300,000 tSAF/year, with TAC values of approximately 3400–6800 USD/tSAF and total EI99 values of approximately 0.78–1.31 kPt EI99/tSAF. The integrated base case at 200,000 tSAF/year has an EI99 value of approximately 1.063 kPt EI99/tSAF. Full article

Promoting urban green and low-carbon transformation is essential for achieving carbon peaking and carbon neutrality, yet cities continue to face financing constraints, insufficient project identification, and weak incentives for green innovation. This study treats China’s climate investment and financing pilot program as a quasi-natural experiment. It uses panel data for 242 prefecture-level and above cities from 2016 to 2023 to estimate its effect on urban green transformation with a difference-in-differences (DID) specification. The results show that the pilot significantly promotes urban green transformation, and the policy effect is stronger in coastal cities, large and medium-sized cities, major urban agglomerations, and non-resource-based cities. Mechanism analysis shows that the pilot promotes urban green transformation by increasing local governments’ focus on carbon reduction, advancing green finance development, and stimulating green technological innovation. These findings support improving pilot evaluation and implementation, strengthening climate-project development and green financial instruments, and tailoring policy support to local conditions. Full article

Land transport infrastructure (LTI) is a core component of sustainable transport systems, shaping mobility, efficiency, and the spatial distribution of development gains. Existing studies of urban–rural income inequality mainly focus on individual transport modes or aggregate infrastructure scale, with limited attention to transport-system composition and its contextual dependence. This study addresses this gap by conceptualizing LTI as a layered system and examining how its internal composition is associated with urban–rural income inequality across different levels of urbanization and economic development. Using a balanced panel of 286 prefecture-level cities from 2013 to 2023, the study constructs ratio-based indicators of compositional shifts within road systems, within rail systems, and between rail and road infrastructure. Two-way fixed-effects models incorporate interactions with urbanization and economic development. Conditional marginal-effect maps are then used to identify how these associations change across development contexts. The results reveal a clear stage-dependent pattern. Urbanization generally attenuates the inequality-widening association of mobility-oriented upgrading, whereas economic development influences whether such upgrading reinforces spatial polarization or supports wider diffusion. When urbanization and development are both sufficiently advanced, the marginal association may shift toward inequality reduction. At earlier stages, accessibility-oriented roads and conventional rail tend to show stronger equalizing associations. Mobility-oriented roads and high-speed rail are more likely to be associated with narrower inequality in more advanced settings. Mechanism-oriented analyses yield evidence consistent with two potential channels: the agricultural–non-agricultural labor-productivity gap and the non-agricultural employment share. The extended analyses and robustness checks broadly support the main findings. These findings indicate that transport infrastructure upgrading should be evaluated not only in terms of efficiency, but also according to whether the resulting infrastructure mix broadens access to opportunities, improves resource allocation, and supports inclusive regional development. Full article

Urban agglomerations concentrate human populations, economic activities, and land development, but gains in land-use efficiency do not necessarily progress synchronously with improvements in human-development capability. This study examines the coordination and mismatch between population high-quality development (PHQD)—referring specifically to the high-quality development of the human population—and land green use efficiency (LGUE) across 131 cities in six major Chinese urban agglomerations from 2011 to 2023. PHQD is measured using the entropy-weighted TOPSIS method. LGUE is measured using a non-oriented global super-efficiency slacks-based measure (SBM) model with undesirable outputs. People–land coordination is assessed using a modified coupling coordination degree (MCCD) model combined with a relative development index (RDI). Kernel density estimation, Dagum Gini decomposition, and Geodetector are applied to analyze distribution dynamics, disparities, and spatially stratified associations. MCCD rose from 0.311 to 0.419, although most cities remained at relatively low coordination levels. PHQD-lagging was the dominant mismatch type in 2023, involving 98 of 131 cities. Overall MCCD inequality declined from 0.138 to 0.103, while transvariation replaced between-group differences as the largest disparity component. Economic development had the strongest explanatory power, and all pairwise interaction q values exceeded the corresponding single-factor q values. Sustainable urban transformation therefore requires coordinated investment in human capital and public services alongside continued improvements in resource- and environmentally constrained land-use efficiency. Full article

Geospatial heterogeneity plays a critical role in shaping housing tenure among China’s floating population. Using three binary indicators—homeownership, social housing, and informal housing—this study measures migrants’ housing affordability, access to public housing resources, and residential stability. Based on nationally representative data from the 2017 China Migrants Dynamic Survey (CMDS), we examine how geospatial characteristics influence migrants’ housing tenure. The results show that both origin and destination characteristics significantly affect housing outcomes. Migrants moving to higher-tier cities are more likely to obtain social housing because these cities provide better public services and greater opportunities to accumulate human and social capital. However, high housing prices in megacities substantially reduce the likelihood of homeownership. A clear birthplace effect is also observed: migrants originating from eastern China and megacities have significantly higher probabilities of obtaining both social and owner-occupied housing, reflecting the advantages associated with more developed places of origin. In addition, marital status, duration of migration, educational attainment, and hukou status consistently influence housing tenure. These findings highlight the importance of geospatial heterogeneity in explaining housing differentiation among China’s floating population and provide evidence for improving housing policies during rapid urbanization. Geospatial factors shape both the housing choices and associated stratification patterns of China’s internal migrants, thereby exerting a discernible influence on the national trajectory toward sustainable development. Accordingly, policymakers must prioritize the pursuit of balanced regional development—with particular emphasis on aligning regional economic performance, infrastructure provision, public service accessibility, and the spatial distribution of population. Such measures would help attenuate the impact of geospatial disparities on migrant housing differentiation and, in turn, advance the country’s broader agenda for sustainable development. Full article

The smart city concept promotes the use of innovative solutions to improve residents’ quality of life while supporting sustainable urban development. In the context of climate change and rapid technological advancement, there is a growing demand for energy-efficient cooling systems that use natural resources. This study evaluates the influence of the hydrological regime of the Warta River on its cooling potential in Poznań, one of the largest cities in Poland. Based on hydrological data collected between 1971 and 2024, the distributions of river discharge and water temperature were analysed, as these represent the two key parameters determining the feasibility of river-based free-cooling systems. Considering environmental flow requirements and water temperature thresholds, several operating scenarios were developed to simulate cooling capacities of 100, 150, and 200 MW at temperature differences (ΔT) of 3 and 5 K. Among the analysed variants, the lowest cooling demand scenario (100 MW, ΔT = 3) provided suitable operating conditions for a river-based free-cooling system during 10,582 days, corresponding to 53.6% of the study period. In contrast, the highest cooling demand scenario (200 MW, ΔT = 5) was feasible during 43.9% of the analysed period. The results indicate that the Warta River has considerable potential as a natural cooling source for free-cooling applications, although this potential exhibits pronounced seasonal variability. The highest cooling capacity can be achieved during spring and autumn, while lower capacities are available in summer and the lowest in winter. River water temperature was identified as the dominant limiting factor, accounting for approximately 96% of all cases in which free-cooling operation was not feasible. Furthermore, the observed increase in river water temperature has reduced the number of summer days during which the required cooling capacity can be achieved. The findings enable the identification of periods when river water can fully or partially replace conventional mechanical cooling systems. They also provide a framework for assessing the seasonal and operational potential of surface waters in support of future investments integrating rivers into urban cooling infrastructure. Full article

Urban shrinkage in resource-based cities manifests not only as population decline but also as spatial mismatches between current human activities and inherited urban systems. Existing studies have mainly focused on single dimensions such as service accessibility or land-use efficiency, with limited attention to the multidimensional incompatibility between human activities, service systems, built environment, and industrial risks. This study develops a human activity-centered spatial mismatch framework to examine the spatial consistency between human activities and urban systems in shrinking resource-based cities. Taking the central urban area of Daqing as a case study, this study integrates multi-source urban big data across four spatial dimensions—human activity, public service provision, built environment, and industrial risk exposure—and identifies three types of spatial mismatch: service supply–demand mismatch, built environment mismatch, and industrial risk exposure conflict. The results show that human activities in central Daqing exhibit a polycentric pattern, while their spatial distribution is not fully consistent with existing urban systems. Specifically, service accessibility mismatch, built environment underutilization, and human activity–industrial risk overlap coexist across different areas, reflecting asynchronous adjustment between human activities and inherited urban structures. This study provides a human activity-centered framework for diagnosing multidimensional spatial conflicts in shrinking resource-based cities and supports a transition from expansion-oriented planning toward differentiated adaptive spatial governance. Full article

Rapid growth in photovoltaic (PV) development has intensified competition between renewable energy expansion and land resources, highlighting the importance of integrating PV growth with spatial planning toward carbon neutrality. This study addresses the lack of township-level evidence by integrating spatio-temporal analysis, the Optimal Parameter Geodetector (OPGD), and the Patch-generating Land Use Simulation (PLUS) model to investigate the evolution, driving mechanisms, and future spatial patterns of PV development. Taking Xintai City as a case study, this research examines PV development from 2010 to 2022 and simulates future spatial patterns under natural development (ND) and carbon neutrality (CN) scenarios. Results show that PV development entered a rapid expansion stage after 2015, characterized by leapfrog growth and increasing spatial agglomeration. The centroid of PV patches shifted eastward, whereas the centroid of PV area migrated westward, indicating differentiated spatial evolution between the number and scale of PV facilities. PV development was jointly shaped by climatic conditions, socioeconomic factors, and land availability, with interactions among multiple factors substantially enhancing spatial heterogeneity. Scenario simulations revealed that PV development continued under both scenarios, while the CN scenario promoted more concentrated expansion in coal subsidence areas and inefficient industrial and mining lands, thereby helping alleviate potential conflicts with cultivated land protection. These findings highlight the importance of prioritizing low-conflict land resources and strengthening spatial coordination between renewable energy development and land management to support sustainable low-carbon development in resource-based cities. Full article