In the era of financialization and global financial integration, the Global Financial Network (GFN) serves as a crucial analytical framework for comprehending the landscape and the dynamic evolution of global financial geography. While existing research predominantly focuses on international financial centers centered around New York and London, there remains a notable gap concerning local economies successfully integrated into GFN and the hierarchy and regionality of GFN. This study examines the overseas Initial Public Offerings (IPOs) of 79 Quanzhou enterprises to illustrate the key geographic units, network structures, and formation of GFN through intra-enterprise services and collaboration linkages. This study uses Gephi to analyze the network characteristics of global financial networks quantitatively. This study forms the layout of financial centers participating in overseas IPOs of Quanzhou enterprises through the Fruchterman Reingold algorithm. Moreover, this study measured the centrality distribution characteristics of the network using indices such as intermediary centrality and eigenvector centrality. This study selected 79 companies listed on overseas IPOs in Quanzhou from 1993 to the end of 2023 as samples. We confirmed that the FABS enterprise sample includes 50 securities firms, 21 accounting firms, and 56 law firms. From a spatial scale perspective, these FABS corporate offices are distributed in 26 cities worldwide. The cooperative network of FABS enterprises is a network matrix of 371×371, including 205 inter-city collaborations and 166 intra-city collaborations. In addition, from 2020 to 2022, we interviewed 50 interviewees to understand the process of Quanzhou’s integration into GFN. The interviewees of this study include executives of listed companies in Quanzhou, founders of relevant upstream suppliers, leaders of industry associations, and heads of relevant government departments. 1) Quanzhou enterprises, primarily rooted in traditional manufacturing, chose to list neighboring regional financial centers for overseas IPOs, thus contributing to the emergence of a regional GFN. Geographic conditions and industrial profiles of local economics are pivotal in understanding the formation of regional GFN. 2) Within the regional GFN established via Quanzhou’s overseas IPOs, Hong Kong emerges as the central node. Hong Kong serves not only as a key IPO destination but also as a crucial intermediary facilitating Quanzhou’s integration into the global financial landscape. Due to the perspectives of financing entities and financing regions, this study reveals how the location conditions and industrial characteristics of financing regions affect the formation of regional GFN spatial structure. This enriches the perspective of GFN research and, more importantly, deepens the understanding of the existing spatial structure of GFN centered around London and New York. Additionally, the spatial relationships within regional GFN among regional FABS enterprises, regional financial centers, and financing regions, reveal the inherent connections and behavioral patterns of cross-border financial actors. This case echoes the “with” relationship in existing GFN research at the regional scale. This study proposes the hierarchy and regionality of GFN, emphasizing the importance of regional GFNs in global financial flows and responding to the attention of economic geography to the regional transformation of global networks.

Knowledge spillover is a crucial concept for explaining innovation. As a form of knowledge, technology possesses a spillover effect. This study posits that technology enterprises provide technical critical support for modern financial industry development, while financial institutions procure these technical services through market. This process facilitates the establishment of cross-industry urban innovation linkages between technology and financial firms through technological spillover. Distinct from previous research that constructed urban innovation networks based on homogeneous activities or internal connections within the same industry, this study employs Python web scraping technology to collect transaction information and constructs a cross-sector urban innovation network. This transaction data details purchase of technologies—such as Blockchain, Artificial Intelligence, Big Data, Cloud Computing, and the Internet of Things (IoT)—by commercial banks from technology enterprises across cities in China (excluding the data of Hong Kong, Macao and Taiwan). Leveraging these transaction data for technologies applied in emerging FinTech business services, an inter-city FinTech network is constructed from a technology spillover perspective. Network analysis reveals: 1) A FinTech network has preliminarily taken shape in China. Although the network density is not high, it exhibits a core-periphery structure and possesses small-world properties; 2) The geographical concentration of core nodes in the network is pronounced, with transactional linkages closely associated with eastern cities. The core node cities demonstrate outstanding technological spillover capabilities and higher administrative ranks, with Guiyang emerging as a core node in the western region; 3) Distinct network patterns emerge across 3 major urban agglomerations, with differential technology absorption tendencies among 3 types of commercial banks; 4) Urban innovation capability, financial environment, economic development, administrative hierarchy, and transportation accessibility differentially impact technology absorption versus spillover effects. Technology demand-driven spillovers establish the foundation for cross-sector urban innovation networks. The FinTech network will not only provides new paradigms for deconstructing urban innovation systems but also poses new propositions for assessing the competitiveness of cities housing major technology enterprises under integrated finance-technology ecosystems.

The scale characteristics of the city, as well as its functional status in the associated network, jointly shape a new pattern of urban system development. Based on the inter-regional investment data of listed companies from 2005 to 2022, a directed weighted city network is constructed to characterize the network structure and evolution characteristics of the urban system. The measurement method of alternative centrality for directed weighted city networks is improved. According to the dual dimensions of the city size centrality and directed weighted alternative centrality, cities are classified into different levels. The panel vector autoregression model is used to explore the dynamic interaction effects of the economic scale, population size, and network status of cities. The results are obtained as follows: 1) The pattern of city network connectivity is gradually evolving from being driven by the dual core of Beijing and Shanghai to a multi core linkage radiation. The distribution of network connections has gradually achieved full coverage of the “Hu Huanyong Line” in the southeast area with dense and extensive network connections, and shows obvious spatial differences. The high-intensity investment connections are gradually occupying a dominant position. And the city network shows a distinct feature of “hierarchy and network”. 2) The “core-periphery” hierarchical structure of the spatial distribution of directed weighted alternative centrality is constantly manifested. The directed weighted alternative centrality of most cities continues to increase, evolving from being distributed only in the fourth level to the sixth level to covering all 6 levels. The number of capital agglomeration cities has increased significantly, and the asymmetric characteristics of nodes in the city network are significantly enhanced. 3) Under the dual dimensions of “size-network”, cities are classified into 5 grades: national core cities, national sub-central cities, regional core cities, regional sub-central cities and local general cities. The typical manifestations are the “scale-network” status matching type cities represented by Beijing, Xi’an, Suqian, and there are also the “scale-network” status non-matching type cities represented by Chongqing, Hefei, Luoyang, Zhoushan. 4) There is a significant sustained positive interaction effect between directed weighted alternative centrality and economic scale. The change of population size has a relatively long-term one-way effect on directed weighted alternative centrality, while the change of directed weighted alternative centrality has only a short-term positive effect on population size. The conclusions are helpful to deepen the understanding of the structural characteristics of urban system and provide reference for optimizing urban development strategy and promoting regional coordinated development.

Intercity relationships can be categorized into multiple types, with collaborative relationships and flow relationships being 2 of the main types. Collaborative relationships stem from cities with similar functions achieving economies of scale or with different functions achieving functional complementarity, while flow relationships arise from the potential differences between source and destination cities. This study, based on an analysis of the differences in the formation of collaborative and flow relationships, examines the structural and influencing factors of collaborative and flow networks from the perspective of venture capital relationships. The study finds that the differing mechanisms of these two types of network relationships lead to differences in the influence of distance and city size combinations. However, both types of networks exhibit a certain degree of similarity in reflecting the regional economic landscape, such as displaying similar hierarchical structures. Moreover, while two-way aggregated flow networks are relatively similar to collaborative networks, one-way flow networks can more accurately reveal the characteristics of flow relationships. By analyzing 20 311 venture capital events across 251 Chinese cities, we highlight that core cities like Beijing, Shanghai, and Shenzhen dominate both networks, yet their roles diverge: Shanghai acts as a capital supplier, while Shenzhen exhibits stronger capital outflow. The gravity model reveals that city size significantly enhances collaboration and capital flow, whereas geographic distance inhibits only the latter. Notably, directional flow networks uncover asymmetric patterns, with 144 cities solely contributing capital outflows. These findings challenge the conventional aggregation of bidirectional flows in urban network studies, demonstrating that unidirectional analysis better captures power dynamics in resource allocation. The study underscores the necessity of integrating relational typologies—such as collaboration versus flow—into urban network frameworks to refine theoretical interpretations of intercity interactions. Practically, policymakers should tailor strategies for capital-attracting versus capital-exporting cities to optimize regional economic synergies. This study distinguishes the pattern differences between collaborative and flow relations, highlighting the importance of differentiating relationship types in urban network research.

Urban agglomerations serve as crucial bridges facilitating the flow of cross-regional innovation factors and promoting technological spillovers among cities. Most existing literature treats urban agglomerations as the analytical boundary, overlooking the differentiated impact of urban agglomeration strategies on inter-city technology transfer. To address this, based on Chinese patent transfer data from 2001 to 2020, this study analyzes the overall characteristics and spatiotemporal dynamics of patent transfers within cities within 19 urban agglomerations, between urban agglomerations and their surrounding cities, and among cities in different urban agglomerations across four time periods. Utilizing a difference-in-differences model, the study examines the impact of urban agglomeration strategies on patent transfers at different scales, focusing on the coordination effect, agglomeration effect, and linkage effect of urban agglomerations. The study found that: 1) The scale and density of the patent transfer network in 19 urban agglomerations have been continuously increasing, with deepening connections and intensities. The network efficiency and accessibility have been continuously optimized, and the spatial distribution of the patent transfer network overall presents a pattern of ‘high in the east and low in the west’. 2) The spatial structure of the patent transfer network in urban agglomerations exhibits characteristics of polycentricity and regionalization, with the ‘diamond structure’ centered around Beijing, Shanghai, Guangzhou, and Chengdu-Chongqing gradually emerging. This structure forms a "hub-and-spoke" knowledge flow channel with other regional central cities. 3) The proportion of patent transfers across agglomerations has gradually increased, indicating that the spatial scope of knowledge innovation is expanding and the spatial heterogeneity of knowledge flows is decreasing. 4) The results of the difference-in-differences model verify that the urban agglomeration strategy can promote patent transfers within cities within the same urban agglomeration, between urban agglomerations and their surrounding cities, and between cities in different urban agglomerations. Overall, the implementation of the urban agglomeration strategy has not only played a significant role in regional development but also had a profound impact on inter-city technology transfer in China. This study analyzes the dynamic changes of patent transfer networks within and outside urban agglomerations from multiple dimensions and delves into the differentiated impact of urban agglomeration strategy implementation on China’s intercity technology transfer network. It provides new perspectives and ideas for understanding the evolution of intercity innovation networks and the future innovation development of urban agglomerations. Through this study, it is hoped that the spatial organization mechanism of technology transfer networks can be further deepened, providing scientific support for better promoting innovative integration practices in urban agglomerations and promoting technological innovation and high-quality economic development in urban agglomerations.

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.

This study constructs a network based on investment data from listed companies among cities. It uses social network analysis and structural resilience indices to measure and evaluate the investment network structure and resilience in the urban agglomeration in the middle reaches of the Yangtze River. The results show that: 1) The urban agglomeration in the middle reaches of the Yangtze River is forming a triangular investment skeleton with Wuhan-Changsha-Nanchang as the vertex. The overall investment network density remains low but continues to grow. Cities play different roles in the network, and core cities are strengthening their control. 2) The investment network shows clear heterogeneity and hierarchy, making it vulnerable to shocks. However, it is gradually becoming more balanced and flat. 3) The network has strong matching resilience. Connections between nodes tend to be heterogeneous and diverse. Enterprise investment flows enhance the “robustness” of these connection paths through overlap. 4) The network has high and improving transmission efficiency. Overall, it demonstrates strong connectivity resilience. 5) The network shows some clustering. Investments mainly flow one-way from core cities to peripheral cities. This helps peripheral cities develop while reducing risks for core cities, improving the network’s structural resilience.

Accurately understanding the connotation and meaning of ecological interests in national territory space, explore the mechanism of the allocation of ecological benefits in national space, is the foundation for resolving spatial conflicts and optimizing the pattern of national territory space development and protection. Research has shown that: 1) The naturally formed spatial environment and the profit-seeking human mechanism, the market mechanism that plays a decisive role in resource allocation, and the policy mechanism for correcting market failure jointly affect the mechanism of ecological interest allocation in territory space. 2) The function of ecological benefit allocation in national territory space is to implement the structure of national land spatial planning, ensure the spatial redistribution of ecological benefits, and correct the profit seeking nature of market mechanisms. 3) The standardized spatial structure, rational allocation of public resources, and effective allocation of spatial rights are the predetermined benchmarks for ecological benefit allocation. At present, the spontaneous allocation of ecological interests in national territory space is characterized by prominent competition in spatial use, numerous misallocations of spatial resources, unequal spatial rights, and hidden concerns about the imbalance of secondary allocation boundaries. It is necessary to define the bottom line boundary of the ecological carrying capacity of the “three lines and one order” and the upper limit boundary of constraining government power for the allocation of ecological interests in national territory space, optimize the collaborative mechanism of ecological interests allocation in national territory space, reasonably allocate ecological interests at different spatial scales, and achieve the universal sharing of ecological interests.

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) produced by microorganisms such as bacteria are important tools for reconstructing past climatic and environmental changes. However, previous works on brGDGTs mainly focus on various proxies based on their distributions, whereas environmental controls on their concentration remains unclear. This hampers our proper understanding on the climatic significance of brGDGT concentration in geological records as well as the seasonality of brGDGT proxies. This study investigated brGDGT concentration in surface soils in different regions of China and analyzed its correlations with environmental factors. The aims were to: 1) Explore environmental controls on soil brGDGT concentration, 2) Verify whether soil brGDGT concentration can be used as a paleoclimatic proxy, and 3) Clarify the seasonal bias of brGDGT-based paleotemperature proxies in soils. Totaling 273 natural soils were collected during 5 fieldtrips from 5 regions including: the Chinese Loess Plateau and its surroundings, North China to Northeast China, South China, the Shandong Peninsula, and the Northeast Tibetan Plateau. The correlations between brGDGT concentration and aridity index (AI), soil pH, and mean annual air temperature (MAAT) were analyzed. For the whole surface soil dataset, brGDGT concentration showed stronger correlations with AI and soil pH (r = 0.81 and −0.74, respectively) than that with MAAT (r=0.10). For the 5 different regions, brGDGT concentration all correlated positively with AI and the correlations were strong or moderate (0.94>r>0.42). On the other hand, the correlations with pH or MAAT were generally weaker and not stable. Specifically, brGDGT concentration correlated negatively with soil pH with r ranging from −0.17 to −0.75, except for a positive correlation (r=0.98) for soils collected from the Shandong Peninsula, while negatively with MAAT with r ranging from −0.12 to −0.94, except that no correlation (r=0.02) was observed for soils collected from the Chinese Loess Plateau and its surroundings. The strongest and most consistent correlations between brGDGT concentration and AI across regions and the whole dataset indicate that soil moisture is the controlling factor for brGDGT production in surface soils. Therefore, we propose that variations in brGDGT concentration in geological records, such as loess-paleosol sequences, can potentially be used to indicate past drying and wetting events. Moreover, our results imply that the seasonal bias in brGDGT-based paleo proxies, traditionally believed to reflect the seasonality of temperature, can also be influenced by seasonal changes in soil moisture (precipitation).

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.

This paper aims to evaluate the level and temporal and spatial pattern of the collaborative development of digital technology and cultural tourism integration in Chinese cities. The collaborative development of digital technology and cultural tourism integration is the advanced stage of the evolution of the relationship between digital technology and cultural tourism integration. It is clear that the collaborative stage and current level of the integration of digital technology and cultural tourism in Chinese cities are the prerequisite for promoting the high-quality development of digital technology and cultural tourism integration. Based on the analysis of the connotation of the collaborative development of digital technology and culture and tourism integration, this paper constructs a multi-dimensional evaluation index system of digital technology and culture and tourism integration development at the city scale, and depicts the process status and temporal spatial differentiation characteristics of the integrated development of digital technology and culture and tourism in 284 prefecture level cities in China from 2010 to 2020. The research shows that: 1) The overall level of collaborative development of digital technology and culture and tourism integration in China is in the middle and low range, showing a heterogeneous spatio-temporal pattern, with obvious gradient differentiation. The cities with good collaborative situation of digital technology and culture and tourism integration are mainly concentrated in the Pearl River Delta region, Jiangsu and Zhejiang regions and Shandong Peninsula, and the slow regions are mainly distributed in some cities in the northeast and western regions. 2) From high to low, there are four types of collaborative development: collaborative development, tending to synergy, synergy imbalance and synergy lag, and the transformation among the latter three types is more active. 3) The development trend of the collaborative integration of digital technology and culture and tourism is further presented from the dimensions of foundation, scale and structure. There are differences in the degree of collaborative development of digital technology and culture and tourism in each dimension. Finally, through the problem identification of different types of collaborative development areas, this paper discusses the path of collaborative development of differentiated digital technology and the integration of culture and tourism, which provides a reference for the city to formulate the collaborative development strategy of the two systems.

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.

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.

Based on panel data from 110 resource-based cities in China from 2010 to 2022, this study uses a threshold effect model to explore the dynamic impact of environmental regulation on resource misallocation, further analyzing the moderating role of government governance capacity. The results indicate that: 1) The intensity of environmental regulation in resource-based cities has shown an overall upward trend, with the most significant increases observed in eastern regions and mature resource-based cities. While the overall conditions of capital and labor misallocation have been continuously improving, the northeastern regions and declining resource-based cities show limited improvement and face substantial transformation pressures. 2) Environmental regulation helps to improve resource misallocation. At the regional level, the improvement effect is strongest in the eastern regions and weakest in the northeastern regions. At the city type level, environmental regulation significantly improves resource misallocation in mature resource-based cities but has relatively weaker effects on declining resource-based cities. 3) Mechanism tests reveal that marketization progress partially mediates the relationship between environmental regulation and resource misallocation. Before the marketization process reaches the first threshold value, environmental regulation has no significant effect on resource misallocation. After surpassing the first threshold, environmental regulation significantly improves resource misallocation, and the improvement effect becomes increasingly pronounced once the marketization process crosses the second threshold. 4) Further analysis shows that enhancing government governance capacity facilitates the positive impact of environmental regulation on resource misallocation. Moreover, environmental regulation not only improves local resource misallocation but also exerts spatial spillover effects that improve resource misallocation in neighboring regions.

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.