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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

At present, resilience research is a hot area of sustainability science research, and livelihood resilience analysis is an effective analytical tool to solve the impact of regional or individual household livelihoods. Based on the systematic review of the research literature on livelihood resilience at home and abroad, this study combs the concept connotation, analysis framework and evaluation method of livelihood resilience. The study found that: 1) Livelihood resilience is a key support for people and their initiative to resist disturbances and achieve recovery and sustainable development when dealing with the impact of livelihood systems. Its conceptual connotation can be divided based on 4 perspectives: process, capability, strategy and transformation. 2) The theme of livelihood resilience research presents a ‘progressive’ development, from external climate change to the awareness of natural disaster response, and then to human disturbance factors and emphasizing human subjective initiative; the scale shows a global and multi-scale trend, and the construction of the index system of research content tends to be more subjective and objective. However, there are still limitations such as conceptual generalization, ignoring the transformation of livelihood strategies and long-term changes in livelihoods. 3) According to the dominant interference factors and the evolution of research topics, the analytical framework and evaluation models in the field of livelihood resilience research can be divided into 3 types: ‘climate change type’ ‘natural disaster type’ and ‘human disturbance type’. Climate change type livelihood resilience connects climate adaptation and livelihood security, and serves long-term climate adaptation policies; quantitative assessment of emergency response and short-term recovery capability of natural disaster-type focus on sudden disasters; the human disturbance type focuses on the interaction between human initiative and social structure, and the subdivision dimensions such as resource utilization and knowledge learning are more suitable for the social and economic scene. In the future, we should gradually build a systematic category-based livelihood resilience analysis framework, strengthen the comparative analysis of research objects and multi-case application research, pay attention to the multi-scale trade-off and spatio-temporal dynamics of livelihood resilience assessment, and explore the long-term dynamic characteristics of livelihood and livelihood strategies.

Exploring the causal relationships among various elements in surface processes and quantifying their causal effects are crucial for understanding the laws of surface changes. In recent years, the rapid development of remote sensing technology and causal inference research has provided opportunities and possibilities for revealing the interaction patterns in multi-scale surface processes based on remote sensing observation data. However, research on causal relationships, causal effects, and causal inference in the field of geoscience is still in its infancy, and the study of the causal mechanisms and laws of multi-scale surface processes still faces many challenges. This article first reviewed the research progress of the theoretical framework and methods of causal inference. Then, we explored the possibility of temporal causal inference methods in attributing surface vegetation changes through experiments. Finally, we summarized the opportunities and challenges faced by causal inference in the study of surface processes. The research results showed that: 1) Causal relationship discovery and causal effect assessment are urgent needs and frontier hotspots in the current research and development of surface processes. However, they still face many challenges, such as overly strong causal assumptions, difficulties in identifying false causal relationships, and a lack of verification datasets. 2) Experimental results based on the spatiotemporal change process of surface vegetation in Yunnan Province from 2001 to 2020 showed that the temporal causal inference methods had a significant effect in discovering the causal relationship and assessing the causal effect of surface vegetation cover changes. Experimental results showed significant causal relationships and lag effects between vegetation and climate factors. Among these factors, temperature and surface temperature are the primary influences on surface vegetation changes in Yunnan Province. At the same time, vegetation has obvious feedback effects on climate factors. 3) In the context of big geographical data and large geoscience models, the construction of datasets to support the verification of causal inference of surface processes, causal inference of spatiotemporal coupling and the interpretable machine learning approaches will be the future development direction.

Taking the State-Level Xiangjiang New Area, a typical case of deepening reform in China, as the research area, using rental housing data from household surveys and POI network data, combined with the geographically weighted regression model (GWR), this article constructs a rental intensity index for exploring the spatial distribution characteristics of rental housing in study area, the factors affecting spatial structure and it’s spatial heterogeneity are analyzed. It is found that: 1) The rental intensity generally decays from the core area to the peripheral area, and at the same time has obvious directionality to the landscape, transportation nodes, university cities and industrial parks; 2) There is obvious spatial heterogeneity in the factors affecting rental intensity. University facilities, landscape parks, housing quality, park distance, and rental factors have a low impact within the second ring, and the further away from the central city district the greater the intensity. The opposite is true for subway and high school facilities, where the intensity of impact is higher in the core area and decreases with distance; 3) The rental intensity and factor intensity show non-consistency in terms of location conditions, reflecting different trade-offs among heterogeneous groups of tenants in terms of location maturity, living quality and employment commuting. In the context of “sale and rental developing simultaneously”, in order to promote the development of the rental market, it is recommended to dynamically adjust the supply on demand, focusing on increasing the effective housing supply in high-density demand areas such as the core area, supporting the development of diversified market entities and large-scale professional long-term rental institutions, refining the rental security policy, implement differentiated and graded subsidies for migrant workers, graduates, multi-child families and other groups, guiding tenants to flow to areas with sufficient supply, easing the pressure on the core area, accelerating digital governance, building an information platform covering housing verification, contract filing, and credit evaluation to improve supervision and service efficiency. The findings of this article can guide the construction of rental housing and the spatial layout of related public service facilities, and provide reference for promoting the balanced development of sale and rent in new urban areas.

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).

The Qijia Culture, which flourished from the late Neolithic to the early Bronze Age in the Gansu-Qinghai Region, represents one of the earliest archaeological cultures in China influenced by trans-Eurasian cultural exchanges. However, research into the formation and development of the Qijia Culture has been limited due to the lack of systematic synthesis and critical evaluation of published radiocarbon dating data. This paper compiles and organizes radiocarbon data from 137 samples of 37 archaeological sites associated with the Qijia Culture in northwestern China in the literature. To enhance chronological precision, we selected 71 reliable dates short-lived botanical remains (charred crop seeds) and animal bones, which are considered reliable indicators of contemporaneous human activity. These dates were calibrated using the IntCal20 calibration curve and analyzed within a Bayesian statistical framework implemented in OxCal v.4.4 to construct a chronological model. The modeling results indicate that the Qijia Culture emerged between 4200—3400 cal a B.P., which is later than previously estimated. Furthermore, the analysis confirmed a 200-300 year of ‘old-wood effect' in charcoal-based radiocarbon dates. Spatial-temporal patterns suggest that the earliest phase of the Qijia Culture originated in the upper reaches of the Jinghe and Weihe River basin in eastern Gansu, followed by a westward expansion into the western Loess Plateau, the northeastern margins of the Tibetan Plateau, and the eastern section of the Hexi Corridor. The cultural focus contracted to the Hehuang Valley and Taohe River Basin on the northeastern edge of the Tibetan Plateau after 3800 cal a B.P., The internal differentiation and transformation of the Qijia Culture is hypothesized to have influenced the later contraction of the Qijia Culture into the Hehuang Valley and the Taohe River Basin, where the Qijia Culture persisted into its later stages. The construction of a robust Bayesian chronological framework based on high-quality radiocarbon evidence not only refines our understanding of the emergence, expansion, and decline of the Qijia Culture, but also contributes to broader discussions of early cultural transmission and interaction across prehistoric Eurasia.

Destination image constitutes a vital component of a region's overall competitive strength. Building on the model of narrative comprehension and engagement and persuasion theory, this study examines the impact of internal and external realism in intangible cultural heritage bearers' media narratives on destination image, as well as the mediating role of media character identification. External realism refers to the extent to which the story aligns with the real world (regardless of whether the story is fictional). Internal realism refers to the coherence within the story itself in terms of logic, character motivations, and the continuity of events. Media character identification includes three dimensions: the audience's emotional resonance with the media character, perspective-taking, and motivation internalisation. In addition, destination brand awareness is introduced as a moderating variable in the research model. Partial least squares structural equation modelling (PLS-SEM) is used to validate the net effects of antecedent variables on destination image. Fuzzy-set qualitative comparative analysis (fsQCA) is employed to explore the configurational pathways leading to positive destination image evaluations. The results show that: 1) Internal realism has a significantly positive direct effect on destination image, whereas the direct effect of external realism on destination image is not significant; 2) Both internal and external realism positively influence destination image through the mediating effect of motivation internalisation, while the mediating effects of perspective-taking and emotional resonance are not significant; 3) Destination brand awareness moderates the relationship between internal realism and destination image; 4) Destination image is the outcome of multiple interacting factors, with four types of condition configurations having high explanatory power for the formation of a favourable destination image evaluation. This study proposes a “Narrative-Character-Destination Image” framework, offering implications for destination image construction.