Search Results (1,499)

An in-depth understanding of the spatiotemporal heterogeneity of ecosystem service (ES) trade-offs and synergies, along with their driving factors, is crucial for formulating key ecological restoration strategies and effectively allocating ecological environmental resources in the Hulunbuir region. This study employed an integrated analytical approach combining the InVEST model, ArcGIS geospatial processing, R software environment, and Optimal Parameter Geographical Detector (OPGD). The spatiotemporal patterns and driving factors of the interaction of four major ES functions in Hulunbuir area from 2000 to 2020 were studied. The research findings are as follows: (1) carbon storage (CS) and soil conservation (SC) services in the Hulunbuir region mainly show a distribution pattern of high values in the central and northeast areas, with low values in the west and southeast. Water yield (WY) exhibits a distribution pattern characterized by high values in the central–western transition zone and southeast and low values in the west. For forage supply (FS), the overall pattern is higher in the west and lower in the east. (2) The trade-off relationships between CS and WY, CS and SC, and SC and WY are primarily concentrated in the western part of Hulunbuir, while the synergistic relationships are mainly observed in the central and eastern regions. In contrast, the trade-off relationships between CS and FS, as well as FS and WY, are predominantly located in the central and eastern parts of Hulunbuir, with the intensity of these trade-offs steadily increasing. The trade-off relationship between SC and FS is almost widespread throughout HulunBuir. (3) Fractional vegetation cover, mean annual precipitation, and land use type were the primary drivers affecting ESs. Among these factors, fractional vegetation cover demonstrates the highest explanatory power, with a q-value between 0.6 and 0.9. The slope and population density exhibit relatively weak explanatory power, with q-values ranging from 0.001 to 0.2. (4) The interactions between factors have a greater impact on the inter-relationships of ESs in the Hulunbuir region than individual factors alone. The research findings have facilitated the optimization and sustainable development of regional ES, providing a foundation for ecological conservation and restoration in Hulunbuir. Full article

Motor vehicle crashes are a persistent cause of unintentional deaths in the United States. Scholarship on how manmade interventions and natural phenomena interact to effectuate such calamitous outcomes is longstanding. One manmade intervention of interest in the literature is daylight saving time (DST). Unfortunately, results on how the natural phenomena attributable to DST interact with driver behavior are inconsistent. To advance knowledge on DST-safety interactions, this study adopts a multilevel model approach to fatal motor vehicle crash outcomes in the contiguous United States. Results from a national analysis contextualize results from zonal analyses to unmask within- and between-time zone differences in DST-safety interactions. In the national analysis, motor vehicle crash fatalities decrease somewhat during DST (−0.10%). In the zonal analyses, motor vehicle crash fatalities decrease more so in the Central and Eastern time zones (−2.00% and −2.00%, respectively), but increase somewhat in the Pacific and Mountain time zones (+0.30%) during DST. The spatiotemporal context of the national analysis highlights specific policy implications from the zonal analyses to decrease the lethality of motor vehicle crashes. Specifically, interdictions to target alcohol and/or drug involvement in the northern latitudes of the Pacific and Mountain time zones during DST, the Central time zone at dawn or dusk before or after DST, and the northern latitudes in the Eastern time zone before or after DST are important. Generally, national DST-safety benefits mask zonal DST-safety costs in the Pacific and Mountain time zones. Full article

Autonomous navigation in dynamic, multi-vessel maritime environments presents a formidable challenge, demanding strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). Conventional approaches often struggle with the dual imperatives of global path optimality and local reactive safety, and they frequently rely on simplistic state representations that fail to capture complex spatio-temporal interactions among vessels. We introduce a novel hybrid reinforcement learning framework, D* Lite + Transformer-Enhanced Soft Actor-Critic (TE-SAC), to overcome these limitations. This hierarchical framework synergizes the strengths of global and local planning. An enhanced D* Lite algorithm generates efficient, long-horizon reference paths at the global level. At the local level, the TE-SAC agent performs COLREGs-compliant tactical maneuvering. The core innovation resides in TE-SAC’s synergistic state encoder, which uniquely combines a Graph Neural Network (GNN) to model the instantaneous spatial topology of vessel encounters with a Transformer encoder to capture long-range temporal dependencies and infer vessel intent. Comprehensive simulations demonstrate the framework’s superior performance, validating the strengths of both planning layers. At the local level, our TE-SAC agent exhibits remarkable tactical intelligence, achieving an exceptional 98.7% COLREGs compliance rate and reducing energy consumption by 15–20% through smoother, more decisive maneuvers. This high-quality local control, guided by the efficient global paths from the enhanced D* Lite algorithm, culminates in a 10–32 percentage point improvement in overall task success rates compared to state-of-the-art baselines. This work presents a robust, verifiable, and efficient framework. By demonstrating superior performance and compliance with rules in high-fidelity simulations, it lays a crucial foundation for advancing the practical application of intelligent autonomous navigation systems. Full article

Urban tourism has evolved into a complex adaptive system, where unregulated expansion disrupts the ecological balance and intensifies resource stress. Understanding the dynamic evolution and resilience mechanisms of the tourism ecological health network (TEHN) is essential for supporting sustainable urban tourism as a coupled human–natural system. Using Shanghai as a case study, we applied the “vigor–organization–resilience–services” (VORS) framework to evaluate ecosystem health, which served as a constraint for constructing the TEHN, using the minimum cumulative resistance (MCR) model for the period from 2001 to 2023. A resilience framework integrating structural and functional dimensions was further developed to assess spatiotemporal evolution and guide targeted enhancement strategies. The results indicated that as ecosystem health degraded, particularly in peripheral areas, the urban TEHN in Shanghai shifted from a dispersed to a centralized structure, with limited connectivity in the periphery. The resilience of the TEHN continued to grow, with structural resilience remaining at a high level, while functional resilience still required enhancement. Specifically, the low integration and limited choice between the tourism network and the transportation system hindered tourists from selecting routes with higher ecosystem health indices. Enhancing functional resilience, while sustaining structural resilience, is essential for transforming the TEHN into a multi-centered, multi-level system that promotes efficient connectivity, ecological sustainability, and long-term adaptability. The results contribute to a systems-level understanding of tourism–ecology interactions and support the development of adaptive strategies for balancing network efficiency and environmental integrity. Full article

As the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and plant physiological activities. Existing studies have made significant advancements in simulating LAI, yet accurate LAI simulation remains challenging. To address this challenge and gain deeper insights into the environmental controls of LAI, this study aims to accurately simulate LAI in the Loess Plateau using deep learning models and to elucidate the spatiotemporal influence of soil moisture and temperature on LAI dynamics. For this purpose, we used three deep learning models, namely Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Interpretable Multivariable (IMV)-LSTM, to simulate LAI in the Loess Plateau, only using soil moisture and temperature as inputs. Results indicated that our approach outperformed traditional models and effectively captured LAI variations across different vegetation types. The attention analysis revealed that soil moisture mainly influenced LAI in the arid northwest and temperature was the predominant effect in the humid southeast. Seasonally, soil moisture was crucial in spring and summer, notably in grasslands and croplands, whereas temperature dominated in autumn and winter. Notably, forests had the longest temperature-sensitive periods. As LAI increased, soil moisture became more influential, and at peak LAI, both factors exerted varying controls on different vegetation types. These findings demonstrated the strength of deep learning for simulating vegetation–climate interactions and provided insights into hydrothermal regulation mechanisms in semiarid regions. Full article

Situated in the southern hilly-mountain belt of China’s “Three Zones and Four Belts Strategy”, Gannan region is a critical ecological shelter belt for the Ganjiang River. Decades of intensive mineral extraction and irrational agricultural development have rendered it into an ecologically fragile area. Consequently, multiple restoration initiatives have been implemented in the region over recent decades. However, it remains unclear how relationships among ecosystem services have evolved under these interventions and how future ecosystem management should be optimized based on these changes. Thus, in this study, we simulated and assessed the spatiotemporal dynamics of five key ESs in Gannan region from 1990 to 2020. Through integrated correlation, clustering, and redundancy analyses, we quantified ES interactions, tracked the evolution of ecosystem service bundles (ESBs), and identified their socio-ecological drivers. Despite a 31% decline in water yield, ecological restoration initiatives drove substantial improvements in key regulating services: carbon storage increased by 6.9 × 10¹² gC while soil conservation rose by 4.8 × 10⁸ t. Concurrently, regional habitat quality surged by 45% in mean scores, and food production increased by 2.1 × 10⁵ t. Critically, synergistic relationships between habitat quality, soil retention, and carbon storage were progressively strengthened, whereas trade-offs between food production and habitat quality intensified. Further analysis revealed that four distinct ESBs—the Agricultural Production Bundle (APB), Urban Development Bundle (UDB), Eco-Agriculture Transition Bundle (ETB), and Ecological Protection Bundle (EPB)—were shaped by slope, forest cover ratio, population density, and GDP. Notably, 38% of the ETB transformed into the EPB, with frequent spatial interactions observed between the APB and UDB. These findings underscore that future ecological restoration and conservation efforts should implement coordinated, multi-service management mechanisms. Full article

Understanding the carbon emissions of multimodal travel—comprising walking, metro, bus, cycling, and ride-hailing—is essential for promoting sustainable urban mobility. However, most existing studies focus on single-mode travel, while underlying spatiotemporal and behavioral determinants remain insufficiently explored due to the lack of fine-grained data and interpretable analytical frameworks. This study proposes a novel integration of high-frequency, real-world mobility trajectory data with interpretable machine learning to systematically identify the key drivers of carbon emissions at the individual trip level. Firstly, multimodal travel chains are reconstructed using continuous GPS trajectory data collected in Beijing. Secondly, a model based on Calculate Emissions from Road Transport (COPERT) is developed to quantify trip-level CO₂ emissions. Thirdly, four interpretable machine learning models based on gradient boosting—XGBoost, GBDT, LightGBM, and CatBoost—are trained using transportation and built environment features to model the relationship between CO₂ emissions and a set of explanatory variables; finally, Shapley Additive exPlanations (SHAP) and partial dependence plots (PDPs) are used to interpret the model outputs, revealing key determinants and their non-linear interaction effects. The results show that transportation-related features account for 75.1% of the explained variance in emissions, with bus usage being the most influential single factor (contributing 22.6%). Built environment features explain the remaining 24.9%. The PDP analysis reveals that substantial emission reductions occur only when the shares of bus, metro, and cycling surpass threshold levels of approximately 40%, 40%, and 30%, respectively. Additionally, travel carbon emissions are minimized when trip origins and destinations are located within a 10 to 11 km radius of the central business district (CBD). This study advances the field by establishing a scalable, interpretable, and behaviorally grounded framework to assess carbon emissions from multimodal travel, providing actionable insights for low-carbon transport planning and policy design. Full article

In this paper, we propose a diffusion-type predator–prey interaction model with harvest and disease in prey, and conduct stability analysis and pattern formation analysis on the model. For the temporal model, the asymptotic stability of each equilibrium is analyzed using the linear stability method, and the conditions for Hopf bifurcation to occur near the positive equilibrium are investigated. The simulation results indicate that an increase in infection force might disrupt the stability of the model, while an increase in harvesting intensity would make the model stable. For the spatiotemporal model, a priori estimate for the positive steady state is obtained for the non-existence of the non-constant positive solution using maximum principle and Harnack inequality. The Leray–Schauder degree theory is used to study the sufficient conditions for the existence of non-constant positive steady states of the model, and pattern formation are achieved through numerical simulations. This indicates that the movement of prey and predators plays an important role in pattern formation, and different diffusions of these species may play essentially different effects. Full article

Video temporal grounding (VTG) aims to localize a semantically relevant temporal segment within an untrimmed video based on a natural language query. The task continues to face challenges arising from cross-modal semantic misalignment, which is largely attributed to redundant visual content in sensor-acquired video streams, linguistic ambiguity, and discrepancies in modality-specific representations. Most existing approaches rely on intra-modal feature modeling, processing video and text independently throughout the representation learning stage. However, this isolation undermines semantic alignment by neglecting the potential of cross-modal interactions. In practice, a natural language query typically corresponds to spatiotemporal content in video signals collected through camera-based sensing systems, encompassing a particular sequence of frames and its associated salient subregions. We propose a text-guided visual representation optimization framework tailored to enhance semantic interpretation over video signals captured by visual sensors. This framework leverages textual information to focus on spatiotemporal video content, thereby narrowing the cross-modal gap. Built upon the unified cross-modal embedding space provided by CLIP, our model leverages video data from sensing devices to structure representations and introduces two dedicated modules to semantically refine visual representations across spatial and temporal dimensions. First, we design a Spatial Visual Representation Optimization (SVRO) module to learn spatial information within intra-frames. It selects salient patches related to the text, capturing more fine-grained visual details. Second, we introduce a Temporal Visual Representation Optimization (TVRO) module to learn temporal relations from inter-frames. Temporal triplet loss is employed in TVRO to enhance attention on text-relevant frames and capture clip semantics. Additionally, a self-supervised contrastive loss is introduced at the clip–text level to improve inter-clip discrimination by maximizing semantic variance during training. Experiments on Charades-STA, ActivityNet Captions, and TACoS, widely used benchmark datasets, demonstrate that our method outperforms state-of-the-art methods across multiple metrics. Full article

The Inner Mongolia Plateau (IMP), situated in the arid and semi-arid ecological transition zone of northern China, is particularly vulnerable to both climate change and human activities. Understanding the spatiotemporal vegetation dynamics and their driving forces is essential for regional ecological management. This study employs Sen’s slope estimation, BFAST analysis, residual trend method and Geodetector to analyze the spatial patterns of Normalized Difference Vegetation Index (NDVI) variability and distinguish between climatic and anthropogenic influences. Key findings include the following: (1) From 1982 to 2022, vegetation cover across the IMP exhibited a significant greening trend. Zonal analysis showed that this spatial heterogeneity was strongly regulated by regional hydrothermal conditions, with varied responses across land cover types and pronounced recovery observed in high-altitude areas. (2) In the western arid regions, vegetation trends were unstable, often marked by interruptions and reversals, contrasting with the sustained greening observed in the eastern zones. (3) Vegetation growth was primarily temperature-driven in the eastern forested areas, precipitation-driven in the central grasslands, and severely limited in the western deserts due to warming-induced drought. (4) Human activities exerted dual effects: significant positive residual trends were observed in the Hetao Plain and southern Horqin Sandy Land, while widespread negative residuals emerged across the southern deserts and central grasslands. (5) Vegetation change was driven by climate and human factors, with recovery mainly due to climate improvement and degradation linked to their combined impact. These findings highlight the interactive mechanisms of climate change and human disturbance in regulating terrestrial vegetation dynamics, offering insights for sustainable development and ecosystem education in climate-sensitive systems. Full article

This study investigates the spatio-temporal dynamics of Climate Potential Productivity (CPP) in Central Africa during 1901–2019 using the Thornthwaite Memorial model coupled with Mann–Kendall tests based on high spatial and temporal resolution data. The results demonstrate the climate–vegetation interactions under global warming: (1) Central Africa exhibited a statistically significant warming trend (r² = 0.33, p < 0.01) coupled with non-significant rainfall reduction, suggesting an emerging warm–dry climate regime that parallels meteorological trends observed in North Africa. (2) Central Africa exhibited an overall increasing trend in CPP, with temporal fluctuations closely aligned with precipitation variability. Specifically, the CPP in Central Africa has undergone three distinct phases: an increasing phase (1901–1960), a decreasing phase (1960–1980), and a slow recovery phase (1980–2019). The multiple intersection points between the UF and UB curves indicate that Central Africa’s CPP has been significantly affected by climate change under global warming. (3) The correlation of CPP–Temperature was mainly positive, mainly distributed in the Lower Guinea Plateau and the northern part of the Congo Basin (r² = 0.26, p < 0.1). The relationship of CPP–Precipitation showed predominantly a very strong positive correlation (r² = 0.91, p < 0.01). Full article

The growing mismatch between ecosystem service (ES) supply and demand underscores the importance of thoroughly understanding their spatiotemporal patterns and key drivers to promote ecological civilization and sustainable development at the regional level in China. This study investigates six key ES indicators across mainland China—habitat quality (HQ), carbon sequestration (CS), water yield (WY), sediment delivery ratio (SDR), food production (FP), and nutrient delivery ratio (NDR)—by integrating a suite of analytical approaches. These include a spatiotemporal analysis of trade-offs and synergies in supply, demand, and their ratios; self-organizing maps (SOM) for bundle identification; and interpretable machine learning models. While prior research studies have typically examined ES at a single spatial scale, focusing on supply-side bundles or associated drivers, they have often overlooked demand dynamics and cross-scale interactions. In contrast, this study integrates SOM and SHAP-based machine learning into a dual-scale framework (grid and city levels), enabling more precise identification of scale-dependent drivers and a deeper understanding of the complex interrelationships between ES supply, demand, and their spatial mismatches. The results reveal pronounced spatiotemporal heterogeneity in ES supply and demand at both grid and city scales. Overall, the supply services display a spatial pattern of higher values in the east and south, and lower values in the west and north. High-value areas for multiple demand services are concentrated in the densely populated eastern regions. The grid scale better captures spatial clustering, enhancing the detection of trade-offs and synergies. For instance, the correlation between HQ and NDR supply increased from 0.62 (grid scale) to 0.92 (city scale), while the correlation between HQ and SDR demand decreased from −0.03 to −0.58, indicating that upscaling may highlight broader synergistic or conflicting trends missed at finer resolutions. In the spatiotemporal interaction network of supply–demand ratios, CS, WY, FP, and NDR persistently show low values (below −0.5) in western and northern regions, indicating ongoing mismatches and uneven development. Driver analysis demonstrates scale-dependent effects: at the grid scale, HQ and FP are predominantly influenced by socioeconomic factors, SDR and WY by ecological variables, and CS and NDR by climatic conditions. At the city level, socioeconomic drivers dominate most services. Based on these findings, nine distinct supply–demand bundles were identified at both scales. The largest bundle at the grid scale (B3) occupies 29.1% of the study area, while the largest city-scale bundle (B8) covers 26.5%. This study deepens the understanding of trade-offs, synergies, and driving mechanisms of ecosystem services across multiple spatial scales; reveals scale-sensitive patterns of spatial mismatch; and provides scientific support for tiered ecological compensation, integrated regional planning, and sustainable development strategies. Full article

PM_2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 µm) pollution is one of the most common problems triggered by the acceleration of urbanization. The coordinated development of cities and the environment has been a topic of significant interest in recent years. Based on the spatiotemporal relationship between the evolution of urbanization levels and PM_2.5 concentrations, and starting from multiple factors characterizing urbanization, this study constructs a coupling coordination degree model between PM_2.5 and urbanization levels to explore the interaction and degree of coordination between urbanization and PM_2.5 in Guangdong Province from 2000 to 2021. The research reveals that the conflict between the urbanization process and PM_2.5 pollution in various cities of Guangdong Province is gradually easing. The year 2011 was a turning point as the PM_2.5 pollution levels in cities that were in an uncoordinated phase began to improve. The coupling coordination degree between urbanization and PM_2.5 pollution in Guangdong Province exhibits significant spatial heterogeneity. The coupling coordination degree in most coastal cities is higher than that in inland cities. Cities in economically underdeveloped regions also face relatively lower pressure from pollution emissions. These regions are characterized by lagging urbanization, and their coupling coordination degree is slowly increasing as urbanization progresses. In economically developed regions, the coupling coordination degree between urbanization levels and PM_2.5 pollution has reached a basic level of coordination, although the specific types vary. Full article

In 2022, the Russia–Ukraine conflict has severely impacted the EU’s energy supply chain, and the EU’s natural gas import pattern has begun to reconstruct, and exploring the spatiotemporal differentiation of EU natural gas trade and its driving factors is the basis for improving the resilience of its supply chain and ensuring the stable supply of energy resources. This paper summarizes the law of the change of its import volume by using the complex network method, constructs a multi-dimensional index system such as demand, economy, and security, and uses the geographic detector model to mine the driving factors affecting the spatiotemporal evolution of natural gas imports in EU countries and propose different sustainable development paths. The results show that from 2000 to 2023, Europe’s natural gas imports generally show an upward trend, and the import structure has undergone great changes, from pipeline gas dominance to LNG diversification. After the conflict between Russia and Ukraine, the number of import source countries has increased, the market network has become looser, France has become the core hub of the EU natural gas market, the importance of Russia has declined rapidly, and the status of countries in the United States, North Africa, and the Middle East has increased rapidly; natural gas consumption is the leading factor in the spatiotemporal differentiation of EU natural gas imports, and the influence of import distance and geopolitical risk is gradually expanding, and the proportion of energy consumption is significantly higher than that of other factors in the interaction with other factors. Combined with the driving factors, three different evolutionary directions of natural gas imports in EU countries are identified, and energy security paths such as improving supply chain control capabilities, ensuring export stability, and using location advantages to become hub nodes are proposed for different development trends. Full article

Accurate and timely forecasting of cross-regional tourist flows is essential for sustainable destination management, yet existing models struggle with sparse data, complex spatiotemporal interactions, and limited interpretability. This paper presents SE-TFF, a multi-scale tourism-flow forecasting framework that couples a Squeeze-and-Excitation (SE) network with reinforcement-driven optimization to adaptively re-weight environmental, economic, and social features. A benchmark dataset of 17.8 million records from 64 countries and 743 cities (2016–2024) is compiled from the Open Travel Data repository in github (OPTD) for training and validation. SE-TFF introduces (i) a multi-channel SE module for fine-grained feature selection under heterogeneous conditions, (ii) a Top-K attention filter to preserve salient context in highly sparse matrices, and (iii) a Double-DQN layer that dynamically balances prediction objectives. Experimental results show SE-TFF attains 56.5% MAE and 65.6% RMSE reductions over the best baseline (ARIMAX) at 20% sparsity, with 0.92 × 10³ average MAE across multi-task outputs. SHAP analysis ranks climate anomalies, tourism revenue, and employment as dominant predictors. These gains demonstrate SE-TFF’s ability to deliver real-time, interpretable forecasts for data-limited destinations. Future work will incorporate real-time social media signals and larger multimodal datasets to enhance generalizability. Full article