Folk migration is a historical and social phenomenon worthy of study. However, most of folk migration in Chinese history does not have detailed written records and accurate data. Folk migration has left many traditional villages, whose settlement forms contain information on the “genes” of their folk groups and their evolution. Thus, traditional villages can be used as side evidence to reveal historical migrations. However, there is still a lack of means to study traditional settlement morphology on a large geographic scale, which makes it difficult to obtain overall universal patterns. Currently, artificial intelligence techniques for satellite remote sensing images show strong potential in earth observation. Applying these techniques is a promising way to restore the wholeness of traditional settlement patterns. In this study, we propose a remote sensing-based AI method to explore the evolution of traditional settlement morphology on a large geographic scale. In detail, a self-supervised deep learning model, called convolutional autoencoder, is applied to automatically extracting the folk prototype information of settlement forms from building footprint data and remote sensing images. The extracted folk prototype information is represented as feature vector format, which then is input to KMeans clustering algorithm to categorize the folk prototypes. In addition, cosine similarity is used to calculate the similarity space of the selected villages, in order to discover the topological relationship between the folk prototypes. Hanjiang River Basin area is taken as the study area and the traditional villages of Hakka and Teochew folk groups are the research objects of this study. The result of the convolutional autoencoder is combined with historical records of folk migration, in order to restore the process of the evolution of the settlement morphology. The results show that the prototypes of Hakka and Teochew villages exist in the basin of the Hanjiang River, and show a pattern of morphological gradual change along the migration path. From the data-driven perspective and historical records’ perspective, this article demonstrates that the Hakka and Teochew settlement prototypes derived from the migration-driven cultural fusion process have created a transitional settlement form “Hundred Birds Toward the Phoenix”, which combines the characteristics of Hakka and Teochew prototypes, and thus complements the existing knowledge of traditional settlement morphology. In detail, from the data-driven perspective, Type 2 is recognized as the transitional type between the Type 1 and Type 3 in the feature similarity space. Also, the villages of Type 2 are mainly located between the villages of Type 1 and Type 3 in the geographic space. From the historical records’ perspective, the villages of Type 1 and Type 3 are recognized as Hakka and Teochew villages. And the villages of Type 2 are identified as the settlement form of “Hundred Birds Toward the Phoenix”. In summary, the settlement form of “Hundred Birds Toward the Phoenix” is recognized as the transitional form between the Hakka and Teochew settlement prototypes. Using traditional villages as a medium, this article provides a feasible tool for exploring the morphological evolution of folk settlements on a large scale without manual survey and labelling. Currently, the method proposed in this article automates feature extraction, but the input remote sensing images of traditional villages still need to be manually selected by the researcher. In order to further improve the automation of traditional settlement pattern recognition, in the future, this study will combine other artificial intelligence tools to achieve automatic selection of traditional village remote sensing images and combine them with the method in this article to form a set of automated processes, which will further break through the limitations of traditional surveys, and bring more data for the study of historical population migration.

This study systematically analyzes the spatiotemporal pattern evolution characteristics of wildfires in the Da Hinggan Mountains by comprehensively applying spatial analysis and Ensemble Empirical Mode Decomposition (EEMD) methods, based on the MCD64A1 burned area dataset. Additionally, by constructing a logistic regression model, this research deeply reveals the key driving factors influencing wildfire occurrence in this region. The research results show that during the period of 2001—2021, the overall burned area of wildfires in the Da Hinggan Mountains showed a significant decreasing trend. In terms of seasonal distribution characteristics, wildfire occurrences were mainly concentrated in spring and autumn, with the burned area in spring accounting for 73% of the annual total. In terms of spatial distribution characteristics, spring wildfires in the Da Hinggan Mountains exhibit a significant clustering pattern, primarily concentrated in the northeastern and central regions of the study area. The spatial distribution of summer, autumn, and winter wildfires is relatively centralized, with the central part of the study area as the core region. Further analysis shows that wildfires in spring and autumn have a high degree of spatial clustering. Monitoring data over the past 20 years indicate that the burned area has shown an upward trend in 37% of the study area, while a downward trend has been observed in 63% of the area, demonstrating remarkable spatial heterogeneity. The influencing factors were ranked by their impact on wildfire occurrence probability as follows: settlement density > monthly total precipitation > water system density > monthly average saturation vapor pressure deficit > vegetation type > slope aspect > monthly average wind speed > slope gradient > road network density > altitude. These findings highlight that anthropogenic factors, humidity-related variables, and vegetation types are critical drivers of wildfire occurrence in the Da Hinggan Mountains, providing a scientific basis for regional wildfire prevention and ecological management.

Current research on urban networks still has some shortcomings, such as insufficient analysis of industrial chains, neglecting the distinction between local and non-local embedding, and insufficient attention to industrial connections in the digital economy. In view of this, this article attempts to take the Chinese digital game industry as an analytical case. Based on the basic paradigm of urban network research, it starts from the perspective of industrial chain division of labor, focusing on exploring the urban network structure and cooperation models formed by the digital game industry through industrial chain division of labor. It further analyzes the industrial chain position and the evolution of local-global embeddedness of the Chinese digital game industry, thereby promoting the dialogue between urban network research and industrial chain theory and solidifying the analysis of the industrial connection connotation in urban network research. This study selected cooperation data at both the national and global scales from 2013 to 2023, involving 283 cities worldwide. Social network analysis was employed to measure the urban cooperation network structure of China’s digital games, and the internalization link index was used to analyze the differences in local and cross-city connections of game enterprises at different spatial scales. The findings are as follows: 1) The national-level city collaborative network of China’s digital gaming industry has a polycentric structure, reflecting the decentralized characteristics of the digital economy; at the global scale, there exist 3 collaborative models: “Overseas R&D and Publishing-Domestic Operation” (Model 1), “Domestic R&D-Overseas Publishing and Operation” (Model 2), and “Domestic Publishing and Operation-Overseas R&D” (Model 3). Model 1 has the largest network of cities, and Models 2 and 3 are expanding rapidly. 2) Overall, Chinese cities are still in the middle and lower segments of the global digital gaming industry chain, with significant dependence on cities in the United States, Japan, and Europe in the R&D and publishing sectors. However, with the rapid development of Models 2 and 3, cities represented by Beijing, Shanghai, Chengdu, and Xiamen are continuously upgrading their positions in the global industry chain. 3) Over the past decade, the local-global connectivity of China’s core gaming cities has undergone significant spatial restructuring, mirroring the global industrial chain reconfiguration. Nationally, most cities have transitioned from localized to cross-city domestic collaborations, aligning with the delocalization trend in digital creative industries. Globally, Chinese cities demonstrate reduced external dependency, shifting from strong international-weak domestic ties to domestic-dominated collaboration patterns, with Shanghai emerging as the sole metropolis sustaining robust dual local-global linkages. These evolving spatial embeddedness patterns reflect the industry’s self-optimization and value chain upgrading trajectory. This study contributes to the dialogue and incorporation between industrial chain and urban network research. Future research needs to analyze in more detail the driving mechanisms, globalization strategies and local embedding patterns of Chinese cities’ participation in the division of labor in the global digital game industry chain through more in-depth enterprise surveys and industry chain analyses.

Rural tourism serves as a vital instrument for China's rural revitalization, playing a significant role in diversifying rural economies, promoting urban-rural integration, and achieving sustainable development. Based on the ‘institutional-cultural' coupling perspective and utilizing the CiteSpace bibliometric tool, this study systematically reviewed the literatures on rural tourism development in China from the CNKI and Web of Science (WoS) core databases (2000—2024). It comparatively analyzed the differences and commonalities in research stages, hot topic, and methodologies between domestic (Chinese) and international (English) contexts. The findings reveal that: 1) Research in both Chinese and English contexts shows rising popularity, yet with distinct stage characteristics. Domestic studies are policy-driven, forming a practice-oriented framework around rural revitalization, cultural-tourism integration, and common prosperity; international studies focus more on micro-level issues like sustainability, community empowerment, and rural gentrification, reflecting critical perspectives. 2) The dynamic interplay between institutions and culture constitutes the core logic of rural tourism development. Domestic research emphasizes the enabling role of policy rigidity in cultural capitalization, while international research examines the contest between cultural authenticity and institutional power under globalization. 3) There are significant methodological divergences: Domestic studies prioritize policy validation and quantitative analysis centered on industrial development, whereas international studies predominantly employ qualitative deconstruction, centering on communities and individuals. Key implications suggest that future research should deepen exploration in three critical dimensions: theoretical integration and framework innovation, methodological innovation, and thematic refinement and expansion. Particularly, there is a need for cross-disciplinary approaches that bridge the gap between policy implementation and community-based sustainable development models. Additionally, longitudinal studies tracking the socio-economic impacts of rural tourism could provide valuable insights for both academic and practical purposes.

Scientific awareness of the multiple effects of cultural-tourism integration and effective stimulation of the inherent potential of cultural-tourism integration in constructing a strong economic pattern of resilience are important ways to cope with the risk of uncertainty in the external environment and to achieve high quality economic development. The coupled coordination model and entropy method are used to measure the cultural-tourism integration level and economic resilience in China’s inter-provincial from 2006 to 2022, and combined with a panel model to empirically analyse the impact of cultural-tourism integration on economic resilience. Furthermore, this study investigates the specific mechanisms through which cultural-tourism integration influences economic resilience by constructing an innovative theoretical framework of “demand driven traction-policy regulated coordination-technology enabled empowerment”. The results of the study show that: 1) The level of China’s cultural-tourism integration rises significantly, and the eastern provinces are the “pioneering area” of cultural-tourism integration, with the overall differentiation characteristics of high in the east and low in the west. The economic resilience continues to increase, achieving a good transition from mainly low level to mainly medium level, with a spatial pattern of decreasing distribution of “eastern- central-western”. 2) Cultural-tourism integration has a significant positive impact on economic resilience, and the positive effect of cultural-tourism integration on economic resilience is positively moderated by the upgrading of cultural-tourism consumption demand, the strength of financial support for cultural tourism and the level of information technology,and the strength of the effect is shown as the upgrading of cultural-tourism consumption demand > financial support for cultural-tourism > the level of information technology. 3) Cultural-tourism integration has regional heterogeneity in its impact on economic resilience, and the positive effect of cultural-tourism integration on economic resilience is more prominent in the western and northern regions than in the eastern and southern regions, while the effect in the central region is not significant. The study provides a policy reference for promoting high-quality integration of culture-tourism integration in the region and thus contributing to the building of economic resilience.

There are numerous traditional villages in China. Most of them are concentrated in mountainous and hilly areas, which are the organic integration of agricultural and ecological culture. During the formation and development of village, cherishing water resources has always been the core idea of adapting and changing the environment. Based on the traditional ecological thoughts, spatial planning concepts, and landscape adaptability theories, the water adaptive research framework of spatial morphological characteristics and landscape mode in the mountainous tradintional villages can be constructed by the methods such as spatial analysis, morphological quantification and landscape visualization. Taking Baojing County in Xiangxi as an example, the differences of water adaptability in villages can be sorted out, including spatial distribution, morphological pattern and landscape pattern. The result shows that: 1) Traditional villages are usually located in relatively low altitude river valleys, exhibiting obvious characteristics such as living by water, facing the sun, and building on gentle slopes. They are not only convenient for water use, but also effective in flood control and drainage, water and soil conservation, which reflects the wisdom of site selection for harmonious coexistence between humans and water. 2) Under the influence of complex water environmental factors, traditional villages can be classified into 3 typical spatial forms: belt shaped, cluster shaped, and finger shaped. With the development of the economy and society, the village space structure exhibits distinct differentiation characteristics. For example, the villages of belt shaped are usually crossed by a small river, and the development relies on cultivated land economy. The villages of cluster shaped are usually built in the river bay area, and the development relies on forestry economy. The villages of finger shaped are usually far from the river but adjacent to ponds, and the development relies on terrace economy. 3) Based on typical topography, 3 water adaptive landscape models can be summarized to deal with drought and flood, such as “forest-village-farmland-river” in flat area of mountains, “forest-orchard-village-river” on the hillside, “forest-village-farmland-pond” on the mountain plateau.

Ice and snow tourism is an important engine to stimulate the vitality of the ice and snow economy and promote the high-quality economic and social development of ice and snow cities. Previous studies have mostly focused on single factor impact analysis or empirical strategies to explore the tourism development of ice and snow cities, with few exploring the logic and path of ice and snow city tourism development from a systematic perspective of multiple factors. Therefore, based on the TOE (technology-organization-environment) framework, this paper utilizes fuzzy set qualitative comparative analysis (fsQCA) to comprehensively analyze the key influencing factors of the high-quality development of tourism and its grouping paths in 15 key ice and snow cities across the country: Harbin, Zhangjiakou, Shenyang, Jilin, Changchun, Urumqi, Hulunbuir, Mudanjiang, Heihe, Chifeng, Chengde, Yichun, Qiqihar, Fushun, and Beijing. The results show that: 1) The high-quality development of ice and snow city tourism is the result of the linkage of multiple factors. A single factor is not the key factor restricting the high-quality development of ice and snow city tourism, but the complex combination of multiple factors is an effective path for the high-quality development of ice and snow city tourism. To promote the high-quality development of tourism in ice and snow cities, it is necessary to pay attention to the linkage and adaptation of multiple factors at the same time. 2) There are 4 types of driving paths for the high-quality development of ice and snow city tourism, which can be divided into technology-driven, technology-organization dual driven, technology-environment dual driven and technology-organization-environment balanced driving according to the core conditions of each combination path. The combination of different antecedents forms a variety of driving types, indicating that there are diversified combination paths and driving types for the high-quality development of ice and snow city tourism. 3) The technical factors composed of technical infrastructure, smart culture and tourism platform, and ice and snow equipment manufacturing technology are the core factors for the high-quality development of ice and snow city tourism, indicating that technical factors play a relatively common role in promoting the high-quality development of tourism. This study can enrich the theoretical research on the high-quality development of ice and snow economy and tourism to a certain extent, and also provide academic support and decision-making reference for the high-quality development of ice and snow city tourism.

Severe air pollution disrupts traffic, poses significant health risks, and adversely affects economic development. Therefore, systematically analyzing the causes of severe pollution in typical urban areas and accurately predicting the occurrence of severe pollution events is of considerable scientific and practical importance. The northeastern region of China, a major hub for heavy industry and agriculture, is characterized by its northernmost latitude and longest heating period, making its emission sources and meteorological conditions highly representative. This study focuses on Baicheng City, a plain-type city in Jilin Province, and utilizes multi-source data from 2015 to 2022, including air quality, meteorological, satellite, and remote sensing data. Through a systematic analysis of the underlying causes of severe pollution events, we identified the most effective machine learning algorithm for predicting PM_2.5 concentrations during such events.The results indicate that, prior to 2017, Baicheng City experienced a high frequency of severe pollution events, primarily occurring in late autumn, early winter, and deep winter. However, after 2017, the number of severe pollution days significantly declined. Severe pollution events were classified into four primary types: local emission-driven, transmission-dominated, meteorologically-induced, and composite pollution, with composite pollution being the most prevalent. A machine learning-based prediction algorithm was developed using air quality, meteorological, and remote sensing data during severe pollution episodes to forecast PM_2.5 concentrations in Baicheng. Among the tested models, the XGBoost algorithm demonstrated the best performance, with an R² of 0.92 and a root mean square error (RMSE) of 24.6 µg/m³, significantly outperforming other algorithms such as Random Forest (R²=0.87) and Support Vector Machine (R²=0.67). This study provides a straightforward, accessible, and highly accurate process and algorithm for predicting severe pollution events in plain-type cities of northeastern China, offering valuable insights for the effective management of atmospheric environmental conditions.

The study of functional division within urban agglomerations holds paramount significance in optimizing the urban system and promoting the coordinated development of large, medium, and small cities. While existing research predominantly examines the pattern of functional division from the perspective of urban functional disparities, there is a notable dearth of studies focusing on the relational aspect of inter-city functional linkages. By integrating functional monocentric-polycentric theory, supply chain relationships, and urban network analysis, this paper establishes a functional division model and delineates partitioning methods alongside corresponding thresholds, and conducts empirical analysis of data from the supply chain of Chinese listed companies across 19 urban agglomerations in China, offering a comprehensive understanding of functional division within urban agglomerations. The main findings are as follows: 1) The functional division linkage patterns from the supply chain perspective can be divided into centrifugal monocentric pattern, centripetal monocentric pattern, and balanced polycentric pattern. Furthermore, these division linkage patterns effectively discern the structure of urban agglomerations in China, with each linkage pattern linked to the hierarchical structure of functional linkages and the stage of development within urban agglomerations. 2) The centrifugal monocentric pattern is dominated by urban agglomerations with sparse functional linkages and the stage of optimization and upgrading, such as Lanzhou-Xining, Ningxia along the Yellow River, and Huhhot-Baotou-Ordos-Yulin. The centripetal monocentric pattern is dominated by urban agglomerations with less-connected functional linkages and the stage of development and growth, such as the Pearl River Delta, Chengdu-Chongqing, and the Central Plain. The balanced polycentric pattern is dominated by urban agglomerations with well-connected functional linkages and the stage of optimization and upgrading, such as the Yangtze River Delta, the Shandong Peninsula, and the middle reaches of the Yangtze River. 3) Diverse urban agglomerations adhere to distinct evolutionary pathways in terms of functional division patterns, while most pattern evolution occur between centrifugal monocentric pattern and centripetal monocentric pattern, evolutions between monocentric pattern and polycentric pattern are relatively rare, indicating that the current functional division relationship in China’s urban agglomerations is primarily characterized by a core-city-led monocentric pattern, with the formation of polycentric pattern facing a certain threshold. By scrutinizing the functional division linkage pattern of China’s urban agglomerations through the lens of supply chain dynamics, this paper contributes to expanding the scope of regional division studies beyond attribute data and lays a solid foundation for subsequent research grounded in related data analysis.

The rapid development of digital technologies has made digital narrative an emerging and important means for the protection and utilization of cultural heritage. This new form of interpretation not only provides fresh opportunities for the preservation and presentation of heritage resources, but also exerts significant influence on the development of heritage tourism and on tourists’ heritage responsibility behavior. Building on the perspective of digital affordance and the cognitive-affective personality system theory, this study constructs a mechanism model to examine how the affordance of cultural heritage digital narrative affects tourists’ heritage responsibility behavior. To empirically test the model, the research adopts structural equation modeling and combines it with qualitative data obtained from semi-structured interviews, thereby investigating how different dimensions of cultural heritage digital narrative affordance exert influence through cognitive and emotional pathways. The findings indicate 3 main results. First, under the setting of cultural heritage digital narrative, the digital affordance can be conceptualized as comprising physical and cognitive dimensions, both of which show significant positive impacts on tourists’ heritage responsibility behavior. Second, destination psychological ownership together with destination image serve as mediating variables, jointly transmitting the effects of physical and cognitive affordance on tourists’ heritage responsibility behavior. Third, destination familiarity plays a positive moderating role in this relationship: when tourists possess a higher level of familiarity with the destination, the positive influence of digital affordance is strengthened, while lower familiarity weakens the relationship. In conclusion, this study supplements existing theoretical research by identifying the digital antecedents of tourists’ heritage responsibility behavior and by clarifying how digital affordance functions through emotional and cognitive mechanism. At the same time, it provides practical reference for cultural heritage managers, demonstrating how digital technologies can be effectively applied in heritage protection and in the development of heritage tourism.

The challenge of land desertification presents a critical obstacle to regional ecological security. Establishing a scientifically grounded ecological security framework is imperative to maintain the ecological equilibrium of sandy regions and ensure sustainable economic and social development. This study focuses on the Horqin Sand Land as the research area. It analyzes the significance of ecosystem services using land use and precipitation data spanning 4 periods: 1990, 2000, 2010, and 2020. Through ecological demand assessment, ecological source areas are identified. Ecological corridors are delineated using Circuit Theory, employing the Linkage Mapper toolbox and Circuitscape software. The study reveals that: 1) From 1990 to 2010, both the number and area of ecological source land increased, attributed to the expansion of forest and grassland, whereas from 2010 to 2020, there was a decline, indicating recent degradation despite intervention efforts. 2) The ecological security pattern of the study area between 1990 and 2020 underwent construction, optimization, management, and degradation. 3) The number of ecological corridors increased from 32 to 120 between 1990 and 2010, then decreased by 30 from 2010 to 2020, suggesting a significant influence of changes in ecological source land area on flow intensity of ecological processes. 4) Pinch points are primarily situated within forested and grassland areas, while improvement areas are concentrated in unused land and urban peripheries. Building upon these findings, the study proposes an optimization strategy for the ecological security pattern titled “one belt, two axes, and three centers”, providing a practical and reliable theoretical basis and technical approach for future ecological environmental protection and utilization of the Horqin Sand Land.

Lake surface water temperature (LSWT) is a crucial physical parameter in lake ecosystems and an important indicator of climate change. Studying the spatiotemporal variation patterns of LSWT in China over long-term time series and exploring the driving factors behind these changes hold significant scientific importance for understanding the impacts of future climate change on lake ecosystems. Furthermore, such research provides valuable insights for formulating practical and effective lake ecological management measures. This study systematically analyzed2260 Chinese lakes with a surface area exceeding 1 km² to characterize the spatiotemporal dynamics of LSWT from 1985 to 2022. Multivariate regression modeling was employed to quantify the relative contributions of climatic drivers (e.g., air temperature, precipitation, etc.) and lake-specific attributes (e.g. area, depth) to the observed variations in LSWT. 1) From 1985 to 2022, the annual mean LSWT in China exhibited a significant upward trend at a rate of (0.126 ± 0.016)℃/10a, with the most pronounced increase observed in the eastern plain lake region [(0.214 ± 0.037)℃/10a] and the slowest in the Qinghai-Tibetan Plateau Lake region [(0.05 ± 0.015)℃/10a]. 2) The annual mean LSWT and its changing trend have significant spatial differences, showing a spatial distribution trend of gradually increasing from northwest to southeast. 3) The LSWT in the eastern plain lake region shows a warming trend in all four seasons, with the fastest warming in spring and the slowest in winter. In contrast, the Qinghai-Tibetan Plateau Lake region showed an insignificant decrease in spring due to the long duration of the lake freezing period but the fastest warming in autumn. The other three lake regions all experienced the fastest warming in autumn. 4) The primary factors influencing LSWT variations in China are latitude, mean air temperature, altitude, and lake area. Among them, latitude, altitude and lake area have a reverse effect on LSWT, while mean air temperature has a positive effect. The five major lake regions are all affected to varying degrees by the above factors as well as wind speed and lake water depth. From 1985 to 2022, the annual LSWT in China and the five major lake regions showed varying degrees of warming trends. On a seasonal scale, except for the insignificant cooling in the Qinghai-Tibetan Plateau Lake Region during spring, LSWT showed warming trends in all other regions and seasons. The main factors affecting the changes in LSWT in China are latitude, average temperature, altitude, and lake area. Among them, latitude, altitude, and lake area have an inverse effect on LSWT, while average temperature has a positive effect. In summary, climatic factors and lake’s own factors are interrelated and interact with each other, jointly influencing the variations in LSWT.

The ‘International Charter on Geography Education’ explicitly states that helping students understand human-environment relationships is crucial for sustainable development education. Among these, the human-environment coordination concept, as a fundamental value in geography, is a key objective in the development of core competencies at the higher education level. However, current research on the assessment of human-environment coordination concept mainly focuses on basic geography education and predominantly utilizes explicit measurement methods such as paper-and-pencil tests or questionnaires. This study first uses literature analysis and the Delphi method to collect and design vocabulary for the experiment, followed by the use of the Implicit Association Test (IAT) and survey methods to measure university students’ implicit and explicit attitudes towards human-environment coordination concept. Finally, interviews are conducted to triangulate the evaluation results. The participants were third- and fourth-year undergraduate students from both geography and non-geography majors at a comprehensive university in China. The results show: 1) Both the IAT effect values and explicit measurement scores of students are relatively high, reflecting a generally positive recognition of human-environment coordination concept; 2) Geography students exhibit significantly higher IAT effects than non-geography students, indicating a notable difference in implicit connection strength; 3) Geography students’ response times in both compatible and incompatible tasks are significantly longer than those of non-geography students; 4) A dissociation effect is observed between implicit and explicit recognition of human-environment coordination concept, validating the effectiveness and value of the IAT as a tool for measuring human-environment coordination. These findings not only verify the positive role of geography in cultivating college students’ human-environment coordination concept, but provide dual-perspective measurement evidence of explicit attitude and implicit attitude for geography education reform and offer practical insights for enhancing university students’ core competencies.

As the world’s longest border road spanning 10 051 km from Xinjiang to Guangxi, China’s G219 Border Road epitomizes a linear tourism destination integrating ecological, cultural, and mobility functions. This study constructs a tourism corridor experience system framework comprising 4 core dimensions: 1) landscape resources, 2) catering and cuisine, 3) hotel accommodation, and 4) self-driving services. Deploying a mixed-method approach combining spatial resource visualization, network spatial structure analysis, and field investigations, we dissect the resource distribution patterns and tourism flow dynamics along the G219 corridor. Our findings reveal three critical insights: Firstly, the G219 manifests as a mobility-dominated linear system characterized by spatial viscosity, flow aggregation, and path radiation. Its structural uniqueness arises from the “node-zone” interaction, where key attractions concentrate tourism flows while roadside service clusters extend spatial stickiness through multi-day stays. Secondly, the network nodes and tourism flow along the route are the core elements that characterize the tourism corridor and its impact. Therefore, there are significant differences in tourism resources, geographical space, and self driving service systems among 4 provinces/regions along the G219 Border Road for self-driving tour. Thirdly, the image of tourism corridors is the driving force that attracts the flow, process, and velocity of self-driving tourists during their travels. We argue that optimizing the G219 requires differentiated spatial governance: 1) Node enhancement, upgrading gateway facilities to anchor tourist flows; 2) Zone integration: weaving greenways, ethnic villages, and cultural assets into thematic itineraries; 3) Cross-regional coordination: standardizing self-driving services while preserving segment uniqueness. This study contributes to linear tourism theory by validating the “corridor-system” framework in border road and offers actionable models for trans-provincial tourism planning. Future research should quantify flow-service mismatches and assess socioecological impacts of corridor-driven development.

Geo-economy is an important factor affecting the global political and economic landscape, and the industrial chain is an important foundation for the formation of geo-economic relationships. China and India are respectively at the core of the East Asian and South Asian industrial chains. Against the backdrop of the continuous development of industrial chain specialization, it is imperative to clarify the competition and cooperation dynamics between China and India in key industry sectors. This study uses the export similarity index, revealing comparative advantage index, trade complementarity index, and intra industry trade index to analyze the competition and cooperation between the main industrial chain links of China and India. The research results reveal three key insights: 1) The economic partnership between China and India is mainly concentrated in the chemical and equipment manufacturing industrial chains, exhibiting distinct collaborative dynamics. In the chemical industry, upstream cooperation is underdeveloped, while midstream cooperation is the closest. In terms of equipment manufacturing, India still heavily relies on China’s industrial chain contribution; 2) The chemical cooperation between China and India presents distinct industrial chain characteristics formed by complementary advantages. Although the upstream departments have shown strong complementarity, competition in the midstream departments is becoming increasingly fierce. The current cooperation model has not fully utilized the potential of both parties in the industrial chain. This highlights the inherent complementarity in bilateral cooperation and the coexistence of unrealized cooperation dividends; 3) The industrial chain of equipment manufacturing in China and India is mainly competitive, showing structural competition concentrated in upstream industries. Both sides have a relatively small market share in each other’s export market, highlighting the misalignment of industrial policies and the lack of coordination in industrial chain positioning in this competition. By analyzing the current situation of industrial chain cooperation in detail, this study provides actionable insights for deepening China and India cooperation. These measures can transform the current paradigm of “asymmetric competition” into a mutually reinforcing geo-economic partnership.

This study investigates the spatiotemporal evolution of vegetation coverage in the Liupan Mountain area from 1990 to 2024, based on the Google Earth Engine (GEE) global geospatial cloud platform. Utilizing GEE’s efficient data acquisition and processing capabilities, the research employs methods such as the Pixel Binary Model, trend analysis, and the Hurst index to explore the spatiotemporal changes in vegetation coverage and predict future trends. Furthermore, the study conducts an in-depth analysis of the primary geographic factors influencing vegetation coverage, using the Geographic Detector. The results show that: 1) The spatial distribution of vegetation coverage follows a high-medium-low-high pattern from southeast to northwest, with significant differences in vegetation coverage across different land use types; 2) Over the 35-year period, vegetation coverage generally showed a fluctuating upward trend, with an average value of 0.3577 and a growth rate of 0.0012/a. Low-coverage areas decreased by 15 270.54 km², while high-coverage areas increased by 3 969.71 km²; 3) Trend analysis of vegetation coverage reveals 40.71% of the area without significant increase, and 27.93% without significant decrease. Combining the Hurst index analysis, it is predicted that in the next 3-5 years, 60.99% of the area will experience a decreasing trend in vegetation coverage, while 38.86% will show an increasing trend; 4) Precipitation is the primary geographic factor influencing vegetation coverage, with an explanatory power of 0.634. The interaction between factors has a higher explanatory power than individual factors, with the interaction between precipitation and temperature having the strongest explanatory power of 0.752. These findings provide insights into the spatio-temporal changes of vegetation coverage in the Liupan Mountain area and its driving factors, offering valuable reference for ecological protection in the region.

Accurate assessment the impact of regional transport on atmospheric concentrations of coarse particulate matter (PM_2.5-10) is essential for effective joint prevention and control measures. In this study, the Shandong Peninsula serves as the case study site. Utilizing hourly concentration monitoring data from 2015 to 2022, we employed backward trajectory assessments, and concentration-weighted trajectory (CWT) model to quantitatively evaluate trends and spatial variations in the impacts of regional transport on atmospheric PM_2.5-10 pollution. The results indicate that: 1) The annual mean atmospheric PM_2.5-10 concentration in the Shandong Peninsula significantly decreased from (58.06±15.36) μg/m³ in 2015 to (33.27±6.96) μg/m³ in 2022 (Z<0, P<0.001). However, the proportion of PM_2.5-10 concentration relative to PM₁₀ concentration increased from 43.17% to 46.27%, with the period of heavy pollution primarily occurring from March to April. 2) Spatially, the overall trend of atmospheric PM_2.5-10 concentration in the Shandong Peninsula exhibited a decline from the western inland areas to the eastern coast, with Dezhou, Jinan, Liaocheng identified as highly polluted areas. 3) The airflow trajectories affecting the Shandong Peninsula in spring, autumn and winter are primarily influenced by long-range transport from the northwest, while summer is predominantly characterized by close-range transport. The proportion of air masses transported over long distances from the northwest is gradually increasing, while the proportion of air masses transported from the southeastern ocean is gradually decreasing. 4) CWT analysis reveals that the concentration of atmospheric PM_2.5-10 transported from regions such as China’s Inner Mongolia, Hebei and Henan decreased from 28.47 μg/m³ to 9.00 μg/m³. Notably, the reductions are more pronounced for transport from Kaifeng, Hengshui, and Puyang, while regions such as Changsha, Xiangtan and Tianmen require enhanced control measures. This study quantitatively assesses the contribution of regional transport to atmospheric PM_2.5-10 pollution in the Shandong Peninsula and provided a valuable reference for future joint prevention and control strategies strategies atmospheric PM_2.5-10 pollution.

This article investigates the inherent tensions between technology and institutional frameworks in smart city development across the Global South, advocating for a departure from Western-centric paradigms to construct a theoretically robust, locally adaptive, and critically reflective analytical framework. The study identifies two defining characteristics of Southern smart city practices: “technological catch-up”, driven by the imperative to bridge digital divides, enhance urban competitiveness, and respond to rapid demographic change, and “post-colonial critique”, which exposes how historical legacies of colonialism continue to shape contemporary technological adoption and governance structures in subtle yet enduring ways. Theoretical innovation is framed around integrating concepts such as spatial justice—addressing unequal distribution of digital resources across urban spaces—and digital sovereignty—safeguarding Southern nations’ control over data, technological autonomy, and regulatory authority—to redefine the dialectical relationship between technology and society. The research highlights the symbiotic interplay between informal economies (e.g., self-organized community networks, grassroots digital initiatives) and digital technologies, which challenges technological determinism and reveals a resilient development logic rooted in local adaptability and everyday practices. These insights deconstruct the linear narrative of technological progress, proving that Southern countries can balance efficiency with equity while pursuing differentiated urban modernities. By prioritizing context-specific innovations and inclusive governance, the Global South is reshaping the global knowledge production landscape, shifting from a passive “technology testing ground” to an active contributor of alternative urban theories and normative agendas. The study concludes by emphasizing the transformative potential of such frameworks to democratize technology, redirect smart cities toward social equity, and empower Southern nations to reclaim agency in global technological discourse. Through China’s practice of scale-sensitive smart cities and tripartite governance models, the research exemplifies how Southern-led innovations can challenge neoliberal techno-utopianism, alter dominant policy imaginaries, and foster pluralistic urban futures that reflect diverse socio-spatial aspirations.

In the era of information, smart cities represent the future of urban development. The integration of intelligence can enhance the economic, environmental and social performance of cities, which is crucial for the sustainable development of urban areas. Since the reform and opening-up, China has achieved remarkable progress in urbanization and economic growth. However, these advancements have also brought about risks stemming from the inefficient use of land. Intelligent cities contribute to the promotion of intensive urban land use and enhancement of urban land use efficiency. Since the launch of the Smart City Pilot Project in 2012, the transformation of cities towards intelligence has had a significant impact on the economic productivity, social services and environmental pollution. Existing research on urban land use efficiency primarily focuses on its measurement and influencing factors. However, there is a lack of studies that integrate the intelligence transformation of cities with urban land use efficiency within a single research framework. This study addresses a research gap by investigating the impact and mechanism of intelligence on urban land use efficiency. This study has developed a theoretical framework through literature review to examine the influence of intelligence on urban land use efficiency. Then, the study utilized the entropy value method and super-SBM model to assess the level of urban intelligence and urban land use efficiency of 281 Chinese cities from 2010 to 2020. The panel regression model and mediated effect model are used to investigate the mechanism through which intelligence influences urban land use efficiency. The findings indicated that urban intelligence and land use efficiency in Chinese cities have shown overall improvement, albeit with regional disparity. Intelligence has a positive impact on urban land use efficiency. Technological innovation, upgrading of industrial structure, and improvement in labor skill structure all play a mediating role in this process. The impact of intelligence on urban land use efficiency exhibits spatially heterogeneity. Intelligence significantly enhances urban land use efficiency in the eastern and central regions, but not in the western region. The mediating effects of technological innovation, industrial structure and labor force skill structure upgrading are significant in the eastern and central regions, while labor force skill structure does not play a mediating role in the western region. Finally, specific policy recommendations tailored to different regions are proposed in order to improve urban land use efficiency, drawing from the research findings.

In order to quantify ecosystem carbon storage and effectively identify the potential area for improving the quality and efficiency of ecosystem carbon sink service in the Nanliu River Basin (NRB), the InVEST model and GIS spatial analysis techniques were applied to quantitatively assessing the spatiotemporal evolution characteristics and distribution pattern of ecosystem carbon storage in the NRB in 1990—2020. Moreover, this study analyzed the impact of land use types on ecosystem carbon storage changes and the spatial pattern of their hot and cold spots cluster area, and revealed carbon storage change rate in the town scale. Based on these, the potential enhancement area priority protection of ecosystem carbon sink was identified and determined, and the corresponding management measures and suggestion were discussed and proposed. The results indicated that: 1) The land use types were mainly cultivated land and forest land in the NRB, and the transformation from forest and cropland to construction land mainly occurs. From 1990 to 2020, ecosystem carbon storage showed a decreasing trend with a cumulative loss of 0.77×10⁶ t. 2) In space, ecosystem carbon storage of the NRB showed obvious spatial differentiation characteristic by a “low-high-low” pattern along the northeast-southwest direction. The high areas of ecosystem carbon storage were mainly concentrated in the middle reaches, while the low and medium areas were distributed in the lower reaches and upper regions of the NRB, respectively. 3) The spatial distribution of ecosystem carbon storage was closely related to the type and distribution of land use. The high value and hot spots of carbon storage were mountain forest lands, where there was less construction land and more concentrated ecological land, with high vegetation cover. The cold spots areas were mostly concentrated in urban and farming areas where the intensity of land development and ecological land fragmentation were higher. 4) Based on the combination of vegetation landscape heterogeneity, ecological function zoning and carbon storage change, this study took the improvement of carbon sink function as the target orientation and classified 77 administrative town units into 4 carbon sink potential subareas in the NRB, namely key potential area, important potential area, general potential area and ecological construction area. Meantime, differential protection and management measures were proposed for each subarea. The research results showed that we should continue to maintain the important position of the ecological protection barrier in the nature reserve and mountainous forest areas, slow down the intensity of construction land development plain area and downstream delta, and form a balanced and coordinated development pattern in the NRB. The results would provide scientific references for the strategic planning and the management of carbon neutral in Guangxi.