Under the influence of climate change, drought poses a novel and urgent challenge to sustainable development in the humid regions of southern China. Therefore, it is essential to estimate future drought changes and population exposure comprehensively. Using CMIP6 climate models and population forecast data, we estimated drought variations and population exposure in the Xijiang River Basin of Guangxi from 2021 to 2100 under three scenarios of Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The findings are as follows: (1) By employing Taylor diagrams to evaluate the multi-model ensemble mean method (MME) of 18 CMIP6 climate models, we found that the method effectively simulated temperature and precipitation in the Xijiang River Basin, reducing the uncertainty associated with single-model simulations. Under all future scenarios, temperature and precipitation in the Xijiang River Basin are projected to increase, with effects of climate change becoming more pronounced in this region. (2) Using the Standardized Precipitation Evapotranspiration Index (SPEI), we observed a significant increasing trend in aridification in both historical and future periods. Significant differences and complex changes in the rate, occurrence time, frequency, intensity, and other drought characteristics were observed under various scenarios. Droughts are expected to be alleviated under low-emission scenarios but intensify comprehensively under high-emission scenarios. (3) The spatial variability of drought in the Xijiang River Basin will differ significantly under different scenarios. In low-emission scenarios, the intensity and extent of droughts nearly disappear in the long-term. Under medium-emission scenarios, the intensity and extent of droughts may increase. Drought events in this region are severe and worsen comprehensively, under high-emission scenarios, and the long-term impact will be extensive and serious. Drought events in this region are influenced by global climate change and are closely linked to the specific socioeconomic development path of the area. (4) Future, population exposure to drought will be highly correlated with different emission scenarios in the Xijiang River Basin. Under low-emission scenarios, the total population affected by droughts decreased. However, under medium-emission scenarios, the population exposed to each drought level will substantially increase in the medium- to long-term, and the spatial distribution will be more complex. In high-emission scenarios, although the exposure of populations may decrease in the short-term owing to extreme weather events, it will sharply increase in the medium- to long-term, especially with a sharp rise in exposure to severe droughts in the long-term. Climate change is the main factor affecting population exposure to drought; however, emission strategies are fundamental drivers, and population growth and structural changes cannot be ignored. Therefore, emission reduction measures play a key role in mitigating the risk of drought under the impact of global climate change. It is urgent to promote the transformation of low-carbon development models, strengthen regional coordination, and formulate adaptive strategies. This study provides scientific evidence for water resource management and drought response strategies in the Xijiang River Basin, and is of great significance for regional sustainable development.

The current risk assessment of single landslides and debris flow disasters ignores the increasing supply, accumulation, and superposition amplification effects of disasters from top to bottom, resulting in a serious underestimation of the risk of landslide-debris flow disaster chains. This study takes the "2010.9.21" mega-landslide debris flow disaster in the Magui River Basin in Gaozhou, western Guangdong as a case study. A landslide-debris flow disaster chain risk assessment index system, guided by the cumulative amplification effect, was established from the perspective of disaster chain initiation, transmission, and cumulative amplification. A comprehensive index model was used to scientifically evaluate the risk of the disaster chain, and actual investigation results were used for verification. The results are as follows: 1) The landslide-debris flow disaster chain in the Magui River Basin is characterized by multi-ditch collection, high impact force, and major terrain fluctuation. The landslide in the starting area is directly transformed into a debris flow during the instability process and flows into the debris flow branch ditch over a short distance. Several debris-flow branches received landslides along the path, converging into the main ditch. After potential energy accelerates through the circulation area, the flow rushes out of the ditch, leading to a large area of fan-shaped accumulations in the low- and slow-terrain areas, causing serious damage to residential houses and farmland. 2) A total of one small watershed unit carries an extremely high risk, accounting for 2.04% of the total number of small watersheds. The extremely high-risk area covers 3.64 km², accounting for 2.24% of the total area. It is mainly distributed in a small watershed east of Liutang Village. There were eight small watersheds in high-risk areas, accounting for 16.33% of the total small watershed number. The dangerous area covers an area of 20.50 km², accounting for 12.62% of the total area. Most watersheds are concentrated in Langlian Village, Shenshui Village, Makeng Village, and northern Longkeng Village in the Middle East region of Liutang Village. The number of small watersheds in the medium-risk area was 18, accounting for 36.73% of all the small watersheds, and the total area covered by dangerous area was 81.22 km², accounting for approximately 44.90% of the total study area. The medium-risk areas were widely distributed within the scope of the study, especially in the southern part of Longkeng Village, most of the small watersheds of Liutang Village, the southern part of Langlian Village, Magui Village, Chengdong Village, Gancheng Village, the central area of Daxi Village, Houyuan Village, and Shanxin Village. There were 22 small watersheds in the low-risk area, accounting for 48.98% of the total number of small watersheds. The low-risk area covers 57.07 km², accounting for 35.13% of the total study area. It is mainly distributed in the small watersheds of Shanxin Village, Houyuan Village South, Yadong Village South, and Zhoukeng Village in the northeast; Daxi Village in the west; Hemudong Village in the central region; and Longkeng Village in the south. 3) The evaluation results of this study were verified using actual investigation data, which showed high consistency with field survey results, thereby confirming the credibility of the method employed in this study. The index system and evaluation approach for the risk assessment of mass landslide-debris flow disaster chains proposed in this paper can serve as a reference for risk studies of landslide-debris flow disaster chains in South China and other similar areas.

In the context of global climate change, natural disasters pose increasingly serious threats to the Guangdong-Hong Kong-Macao Greater Bay Area. Therefore, in this study, we aimed to conduct integrated comprehensive zoning of natural disasters and to develop disaster prevention and mitigation countermeasures for the Greater Bay Area. To achieve this objective, we first carried out geomorphological division of the Greater Bay Area based on the land geomorphological classification system. Second, we performed comprehensive zoning of natural disasters according to the intensity of dominant natural disasters in various geomorphological units. Finally, we conducted risk zoning of natural disasters according to the main controlling factors of natural disasters in each zoning units. Based on the geomorphology of the Greater Bay Area and the risk of natural disasters, we proposed natural disaster prevention and mitigation countermeasures. The results show that: (1) The landform of the Greater Bay Area can be divided into four major first-class divisions: mountains, hills, platforms and plains. The landforms of the Greater Bay Area can be divided into 10 secondary subdivisions, including medium-altitude small undulating mountains, low-altitude small undulating mountains, low-altitude erosion and denudation hills, low-altitude erosion and denudation platforms, low-altitude alluvial platforms, low-altitude alluvial flood platforms, low-altitude alluvial plains, low-altitude marine plains, low-altitude marine alluvial plains, and low-altitude estuarine coasts. Among these subdivisions, low-altitude small undulating mountains represent the largest area (21,618.28 km²), while low-altitude erosion and denudation platforms represent the smallest area (849.77 km²). (2) The Greater Bay Area can be divided into three first-level major disaster subdivisions: mountain and hill disaster areas (52.77%), plain and platform disaster areas (40.43%), and estuary and coast disaster areas (6.80%). (3) The Greater Bay Area can be further divided into second-level disaster subdivisions, including the small undulating and low-altitude mountain disaster area, low-altitude alluvial plain land subsidence area, low-altitude plain and platform flood area, and 14 others. The largest second-level disaster subdivision area is the small undulating low-altitude mountain disaster area (20,892.18 km²), which is distributed in the east, north, and west of the Greater Bay Area, followed by the low-altitude plain and platform flood disaster area (13,320.98 km²), which is mainly distributed in Guangzhou, Jiangmen, Shenzhen, Huizhou, and Zhaoqing cities, among other areas. The karst collapse area of the low-altitude platform represents the smallest second-level disaster subdivision (163.62 km²) and is mainly distributed in some areas of Enping and Jiangmen cities. (4) The natural disaster risk in the Greater Bay Area can be divided into high-risk, medium-risk, and low-risk areas. The high-risk areas for mountain disasters are mainly in Deqing, Huaiji, and Guangning of Zhaoqing City; and Conghua District in the north of Guangzhou City, Longmen, Boluo, and other regions in Huizhou City. The high-risk areas for plain and platform disasters are mainly in Doumen District, Zhuhai City, Guanghua Basin, Huadu District, Nansha District, Guangzhou City, Foshan City, and other regions. The high-risk areas for estuary and coast disasters are mainly in Doumen District, Zhuhai City, and near the mouth of the Pearl River Delta. In this study, we proposed disaster prevention and mitigation countermeasures for natural disasters in the Greater Bay Area from four perspectives. Our results serve as a valuable reference for the Greater Bay Area urban agglomeration in regional development planning, comprehensive disaster reduction planning, and the improvement of disaster prevention and mitigation capabilities.

Amidst the intensifying global climate change, coastal cities face multiple marine disaster threats due to sea level rise and frequent extreme weather events. Storm surge-induced flood disasters and their secondary effects (e.g., urban waterlogging) pose systemic risks to the lives of the residents, properties, and coastal system infrastructure. Compared with traditional disaster prevention models, the synergistic mechanism between resilience theory and community risk management not only provides a theoretical framework for urban complex risk prevention, but also demonstrates dynamic adaptive advantages in pre-disaster prevention, disaster response, and post-disaster recovery. Accordingly, this study integrated the resilience community theory with the sponge city concept, selecting 25 storm surge-prone bay communities in the Xiamen Wuyuan Bay Area as samples to establish a community resilience evaluation framework encompassing exposure, vulnerability, adaptability, and spatial connectivity. By integrating 16 subjective and objective indicators, including the rescue facility coverage rate and residents' disaster preparedness literacy, we employed the AHP-CRITIC combined weighting method to determine indicator weights and quantify community resilience levels using TOPSIS analysis. The key findings are categorized as follows: (1) an overview of the marine disaster context, the theoretical evolution of resilient communities, and existing research gaps. The literature review indicated that marine disaster threats to coastal urban safety showed significant upward trends, where communities, as direct disaster-bearing entities, needed urgent refined resilience assessments considering their spatial heterogeneity and component vulnerability. International practice comparisons revealed three critical deficiencies in China's resilient community development: overreliance on infrastructure hardware while neglecting the landscape spatial resilience layout, insufficient innovation in social organizational resilience and collaborative mechanisms, and superficial resident participation lacking substantive interactive mechanisms. (2) Development of multidimensional resilience evaluation system Through meta-analysis and expert consultation, we established a dual-dimensional ("vulnerability-adaptability") evaluation system comprising 7 primary and 16 secondary indicators. The AHP-CRITIC combined weighting results indicated that hazard level (0.221), disaster prevention capacity (0.169), and emergency response capacity (0.168) constituted the highest-weighted primary indicators. Secondary indicators, including coastal length, shoreline protection intensity, and volunteer rescue station accessibility, demonstrated significant spatial exposure and emergency response weights, suggesting for their prioritization in coastal community retrofitting. (3) Implementation of a resilience assessment system for coastal community in Wuyuan Bay Field surveys and questionnaire data enabled quantitative resilience analysis of 25 communities. TOPSIS results revealed geographical location and residents' disaster preparedness as core drivers of resilience differentiation. Inner bay communities (e.g., D25) achieved maximum resilience (0.872) through wetland regulation, natural terrain barriers, and emergency facility clusters, whereas outer bay communities (e.g., D1) showed minimal resilience (0.312), owing to high-risk exposure and medical resource scarcity. Wetland ecosystems notably reduced drainage system loads through hydrological regulation and flood detention mechanisms. (4) Optimization strategies for coastal community resilience. This study systematically identified the core elements for developing community resilience during flood-related disasters through the establishment of a coastal community resilience assessment system and empirical research. Through a comparative analysis of typical domestic and international scenarios, we proposed an actionable resilience enhancement strategy system. For public space optimization, dual-purpose strategies for both normal and emergency conditions were emphasized for road networks and green systems, integrating traffic management with ecological protection. For ecological water system development, the water conservation mechanisms of coastal wetland ecological barriers were systematically elucidated, and a synergistic optimization pathway for wetland protection and community water systems based on nature-based solutions was proposed. Regarding emergency shelter spatial planning, an innovative comprehensive evaluation framework was established, incorporating location accessibility, per capita shelter area thresholds, disaster prevention facility standards, and emergency transportation systems. For social governance, resident participation mechanisms and smart management platforms were suggested to amplify community resilience through flexible interaction and resource integration.

In the context of global climate change and accelerated urbanization, coastal cities in China are facing increasing risks from compound disasters caused by the co-occurrence of extreme rainfall and high tide levels. These risks pose substantial threats to urban development and the safety of residents' lives and property. Therefore, it is essential to reasonably calculate the designed co-occurrence probability of rainfall and tide levels under different standards for the planning and design of flood control and drainage systems in coastal cities. In this study, we selected 105 drainage zones in Guangzhou, China with the aim of analyzing the spatial distribution characteristics and co-occurrence risk of extreme rainfall and high tide levels. Based on tide level and elevation data from Guangzhou, the 105 flood-prone zones were divided into 37 areas unaffected by high tide levels and 68 areas affected by high tide levels. Rainfall sequences and corresponding tide-level sequences for each zone were selected using the peak-over-threshold sampling method. On this basis, the designed combinations of rainfall and tide levels under different joint return periods were calculated using Copula functions and the co-frequency method, and their spatial distribution characteristics were analyzed. Our results show that, influenced by factors such as rainfall volume and elevation, the joint return periods of extreme rainfall and high tide levels for 50-year, 100-year, and 200-year events were approximately 30~35 years, 50~58 years, and 74~94 years, respectively. This indicates that the designed return periods for extreme rainfall and high tide levels individually were lower than their corresponding joint return periods, highlighting the obvious amplification effect of the co-occurrence of rainfall and tide levels. The designed storm intensity generally decreased from north to south, reflecting the spatial variability of rainfall patterns across the city. The probability of a 100-year daily rainfall event coinciding with a 100-year high tide level in Guangzhou showed an increasing trend from north to south, underscoring the heightened vulnerability of the southern regions to compound flooding. Additionally, areas in Guangzhou farther from the estuary were less affected by high tide levels than the central and southern regions, resulting in relatively lower risks of rainfall–tide level co-occurrence. This spatial heterogeneity emphasizes the need for region-specific flood control strategies. Our findings provide valuable insight into the spatial distribution and risk of compound flooding in Guangzhou, China. By quantifying the joint probabilities of extreme rainfall and tidal events, we offered a scientific basis for optimizing flood control and drainage infrastructure. The results of this study can guide policymakers and urban planners in developing targeted measures to mitigate the impacts of compound disasters, thereby enhancing the resilience of coastal cities to climate change and urbanization. This study not only contributes to the understanding of flood risks in Guangzhou but also provides a methodological framework that can be applied to other coastal cities facing similar challenges. The research outcomes serve as a critical reference for the planning and design of flood control and drainage systems in Guangzhou, offering practical solutions to reduce the risks posed by compound disasters and to safeguard urban development and public safety.

With rapid societal development, economic growth, industry, agriculture, and human activities, large amounts of sewage rich in nitrogen, phosphorus, and other nutrients are produced. This causes eutrophication in lakes and reservoirs, leading to frequent algal blooms that pose a serious threat to aquatic ecosystem stability and drinking water safety. Chlorophyll-a (Chl-a) concentration, a core indicator of algal biomass and the degree of eutrophication in water bodies, correlates positively with algal bloom outbreaks. However, Chl-a concentration variance under the influence of multiple factors displays highly nonlinear characteristics. Traditional prediction models are unsuitable for relationship analyses between environmental factors, generally producing low prediction accuracy with weak applicability. To address this challenge, we proposed a short-term chlorophyll a concentration prediction method based on multi-scene segmentation, and constructed a prediction model with enhanced adaptability by recognizing the characteristic laws of different environmental scenarios to improve the accuracy of Chl-a concentration prediction. By analyzing the laws influencing various factors, we proposed three partitioning strategies: 1) factor interaction and scene partitioning by analyzing connections between key environmental factors and adopting K-means method to partition these scenes, 2) diurnal difference scene partitioning based on diurnal cyclicity of algal physiological activities, dividing the data into two scenarios, and 3) trophic state scenarios, based on Trophic Level Index. A water body is divided into three trophic categories: anaerobic, mesotrophic, and eutrophic. Three machine learning models (Random Forest, Gradient Boosting Decison Tree, and eXtreme Gradient Boosting) using multi-scenario classification and a linear regression model were each adapted to perform short-term prediction of Chl-a concentration. The multi-scenario partitioning strategy proposed in this study substantially optimized model prediction performance. Factor interaction scenario partitioning yielded the best prediction results, with an overall RMSE(Root mean square error) prediction average of 0.0045, improving prediction accuracy by 4.26% compared to that of the unpartitioned scenarios. Overall improvement in prediction accuracy from diurnal and nocturnal scenario partitioning was limited. Its overall RMSE prediction average was 0.00474, improving prediction accuracy by 0.9%. In eutrophic scenario, the four prediction models (RF, GBDT, XGBoost, and linear regression) exhibited respective RMSEs of 0.0034, 0.0036, 0.0035, and 0.0039, with RF model giving the highest prediction accuracy. In summary, we propose an innovative short-term prediction model to improve the accuracy of low Chl-a concentration predictions in complex situations, providing a new paradigm for intelligent modeling and precise governance. The data obtained using a multi-scenario delineation system revealed the dynamic coupling mechanisms between the diurnal biological rhythms in a water body, nutrient grading, and the interaction of water quality factors. The idea of this study effectively solves the problem that the traditional prediction model can not meet the current prediction needs. This study can provide a systematic analysis for the prediction of cyanobacterial bloom, and can provide technical reference and theoretical support for the multi scenario prediction of Chl-a concentration.This study not only clarified the driving law of Chla concentration change under different scenarios, but also promoted the transformation of simplifying the analysis of complex problems of water environment, providing a new perspective for the mechanism research of complex water environment system.

Southeastern coastal China is sensitive to climate change and is characterized by an advanced economy and aging population. The region faces substantial exposure and vulnerability under climate change, making it a potential hotspot for Compound Heat-Drought Events (CHDEs). Therefore, in this study, we used multi-model integrated prediction data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate different climate change scenarios, along with the standardized precipitation evapotranspiration index (SPEI), sliding threshold method, and Copula joint probability distribution to define drought, heat, and compound events, respectively. Additionally, we aimed to analyze the temporal and spatial patterns of future CHDE hazards in southeastern coastal China under various climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) and during different periods (2021–2040, 2041–2060, and 2081–2100). To further understand the lag effect of heat events on drought, we applied a lagged logistic regression model to quantify the attributable fraction (AF) for delays ranging from 1 to 7 days. In particular, we used the CN05.1 high-resolution gridded daily observation dataset to compare and analyze CMIP6 model prediction data, verifying their applicability to the study area and the accuracy of the prediction results. Our results indicate that CHDE hazards (number of occurrence days, intensity, and duration) in southeastern coastal China are expected to increase in the future, with a faster increase under the SSP5-8.5 scenario than under the SSP1-2.6, SSP2-4.5, and SSP3-7.0 scenarios. The intensity is projected to increase faster than the number of occurrence days and duration. Under the SSP5-8.5 scenario, the CHDE intensity at the end of the 21st century is projected to reach 3.41 times that during the baseline period (1995–2014), while the corresponding occurrence days and duration are projected to be 1.74 and 1.61 times those of the baseline period, respectively. This indicates that the probability of high-intensity CHDEs is expected to increase significantly considerably in the future. As for the spatial pattern, the spatial heterogeneity of the hazards (occurrence day, intensity, and duration) was more pronounced under the SSP5-8.5 scenario than under the other scenarios, especially during 2081–2100. Under the SSP5-8.5 scenario, the combined hazard indexes exceed 0.6 in southeastern Fujian, eastern Zhejiang, Jiangsu, and Shanghai, with some areas having indexes as high as 0.9. Spatial variability was shaped by factors such as distance from the coastline, availability of water resources, land use patterns, and human activities. Notably, the spatial heterogeneity in the CHDE duration was significantly greater than that in the occurrence days and intensity. Under the SSP5-8.5 scenario, the CHDE duration was approximately 2.23 times higher in the high-value areas than in the low-value regions, whereas the differences in occurrence days and intensity were smaller, at 1.13 and 1.11 times, respectively. This highlights the urgent need for regional adaptation strategies that focus on the persistence of CHDEs. The lagged effect of heat on drought events in southeastern coastal China exhibits an east–west sea–land gradient, with differences between the northern and southern areas. Specifically, the lag effect gradually intensifies from the inland to coastal regions. This may be attributed to the fact that coastal regions are influenced by the combined impact of heat on both the land and ocean and are more likely to experience delayed droughts. In the north–south divergence, northern Jiangsu experienced a stronger influence of heat on subsequent droughts than the other areas. This is primarily because of its predominantly dryland environment, which is highly vulnerable and in which heat events are more likely to trigger drought events. Under the SSP5-8.5 scenario, the AF value exceeded 5% in the northeastern Jiangsu coastline, eastern Zhejiang coastline, and Shanghai, with lag times of up to 7 days in some areas. This indicates that heat events will have a prolonged effect on subsequent droughts. These results provide a scientific foundation for policymakers to formulate effective disaster prevention and mitigation strategies tailored to their regional needs. Furthermore, they support decision making to promote a climate-adapted society and contribute to sustainable development.

Based on a literature review and policy analysis, this study analyzes the evolution of the regional governance regime in China since its reform and opening-up, and examines how this process has influenced the development of contemporary metropolitan areas. Multiscale spatial development strategies and regional governance regimes have been formed because of different development goals and visions in regional development. As a crucial mediating scale in China's regional governance, the construction of metropolitan areas has exhibited notable variation in governance subjects, structures, and developmental effects during different development phases. From the perspective of spatial governance goals, factor allocation means, and their scalar characteristics, this study categorizes China's regional governance into four periods since the reform and opening-up in the late 1970s: i) regional economic growth driven by development zones (1978-1993), ii) regional resource reallocation through administrative adjustment (1994-2000), iii) regional coordinated development through the development of industrial parks and new national-level areas (2001-2015), and iv) multi-level governance of urban agglomerations and metropolitan areas (2016-present). Governance forms and functional characteristics varied across different periods, contributing to a shift in China's regional governance from a centralized planned economy to a multilevel regulatory governance model. As metropolitan areas have become a key strategy in the new type of urbanization process, their development at different stages has exhibited different governance structures and functional characteristics. This study systematically reviews the evolutionary characteristics and driving factors of the regional governance stages and compares the first three metropolitan areas approved by the central government: Nanjing, Chengdu, and Fuzhou. It was found that the differences in regional governance across these stages shaped the functional development and vision of metropolitan areas. The Nanjing metropolitan area is under a multilevel governance stage, representing developed regions, characterized by advanced cross-boundary cooperation and a well-established coordination system between the central city and surrounding cities and counties. In this model, multilevel governments and non-governmental organizations actively participate in cross-regional governance, with market integration playing a leading role in metropolitan development. This has resulted in multidimensional development, such as the cross-boundary development of infrastructure, industries, and ecological protection. The Chengdu metropolitan area remains in a hierarchical governance stage marked by uneven regional development between the central city and the surrounding cities and countries. Consequently, government-initiated projects, such as ecological protection and infrastructure development, have been implemented, but the lack of market actors has constrained the development of the industrial division of labor and cooperation. This region has yet to form a unified resource allocation platform, which limits the flow of resources and development factors across administrative boundaries. The Fuzhou metropolitan area is characterized by industrial collaboration initiatives through intercity cooperative zones, and its central city has a weak influence on the surrounding cities and counties. Support from the provincial government facilitated the establishment of cross-boundary cooperative parks; however, excessive reliance on administrative mechanisms hindered the emergence of market-driven mechanisms. The capacity for regional functional integration and cooperation was relatively weak compared to the Nanjing and Chengdu metropolitan areas. A comparative analysis of the three metropolitan areas reveals that more economically developed regions tend to focus on functional integration among different cities and counties. Meanwhile, regions with uneven economic development emphasize coordinated development and improve ad hoc cross-boundary development issues. The interaction of government and market actors leads to distinct forms of regional governance and impacts the realization of metropolitan area functions.

Technology transfer is an important way to promote technology sharing, optimize resource allocation, and improve the levels of innovation and overall efficiency. Existing research on regional technology transfer primarily focuses on eastern and developed regions of China, such as the Yangtze River Delta, Beijing-Tianjin-Hebei, Guangdong-Hong Kong-Macao, and the Pearl River Delta; therefore, inland regions are relatively underreported. The middle reaches of the Yangtze River are one of China's five national-level urban agglomerations. There is a gap between academic attention to its technology transfer system and the practice of regional development planning and construction. Against this backdrop, this study collected patent transfer information from 2010 to 2021 from the IncoPat patent service website and constructed intercity technology transfer networks in the middle reaches of the Yangtze River for four periods. Based on the application of network analysis methods and temporal exponential random graph models, the structural evolution and driving mechanism of intercity technology transfer in the middle reaches of the Yangtze River were quantitatively analyzed. The results showed that (1) Changsha and Wuhan were consistently at the core of regional technology transfer. After the release of the urban agglomeration development plan for the middle reaches of the Yangtze River, Nanchang emerged as a new growth pole. The leading roles of Wuhan and Changsha in this region were primarily supported by technology diffusion, whereas Nanchang's technology diffusion and absorption were relatively balanced. The three central cities organized three technology transfer communities through a hub network-shaped structure that was highly coupled with provincial boundaries, and the inter-provincial technology flows were weak. (2) The intercity technology transfer network in the middle reaches of the Yangtze River gradually evolved to be characterized by both a core-periphery hierarchy and small-world clusters. However, the polarization of the network weakened, whereas the clustering coefficient and transfer efficiency increased. Moreover, the technology transfer paths present a stable trend with slight changes, suggesting incremental innovation. (3) Intercity technology transfers are not only affected by innovation size, intercity spatial distance, and the provincial boundary effect, but also by the endogenous mechanism of network self-organization. The reciprocity, activity, popularity, and hierarchical transitivity of intercity technology transfers positively affect network development, and the stability effect is stronger than the innovation effect in the network evolution process. Based on these findings, policy suggestions for optimizing technology transfer paths are proposed from the perspectives of promoting inter-provincial technology flows, enhancing spatial spillover and sector integration, and taking advantage of the network self-organizing laws of preferential attachment and clustering development. This study contributes to the literature by applying network econometric models for panel data to the analysis of the influential mechanism of innovation networks and corresponding the statistical properties of networks at different scales to the variable specification of endogenous and exogenous driving factors. Empirical research has verified the applicability of this analytical framework and methods in innovation geography, which could also provide new findings from the endogenous micro-mechanisms of the network to better understand the processes of intercity technology transfer.

The population problem has always been an overall, long-term, and strategic issue facing China; the fertility rate can reflect the population problem, and the identification of the temporal and spatial evolution characteristics and driving factors of fertility rate is of great significance to the long-term balanced development of population and the coordination of human-land relationship. Based on the census data of 2000, 2010, and 2020, the Theil index, spatial autocorrelation analysis, and geographically weighted regression models were used to explore the spatiotemporal evolution characteristics and influencing factors of city fertility in China from 2000 to 2020. The results showed that: (1) Based on the perspective of time series characteristics, from 2000 to 2020, China's fertility rate presented a downward trend, the degree of distribution first increased and then decreased with the passage of time, the discrete trend between cities decreased, and the regional differences in fertility rate have narrowed. (2) Based on the perspective of spatial pattern, the fertility rate is high in the south and west and low in the north and east, whereas heterogeneity is obvious. Specifically, the west side of the Hu Line is higher than that of the east side, but the fertility rate fluctuation on the east side is higher than that on the west side. China's four major economic regions are in the order of Western > the Central> the Eastern > the Northeast, with fertility rates still declining in the Western and Northeast regions. Focusing on the five major urban agglomerations, the urban agglomeration in the middle reaches of the Yangtze River has the highest fertility rate, followed by the Chengdu-Chongqing urban agglomeration, the Beijing-Tianjin-Hebei urban agglomeration, and the Pearl River Delta urban agglomeration, with the Yangtze River Delta urban agglomeration having the lowest fertility rate. Overall, the H-H cluster agglomeration areas are mainly distributed in the southern and western regions of China, while the L-L cluster agglomeration areas are concentrated in the northern and eastern regions. (3) Based on the perspective of influencing factors, economic, policy, demographic, and social factors are always the key factors affecting fertility, with economic and policy factors generally having a greater impact on China's fertility rate. The spatial heterogeneity of economic factors represented by per capita GDP and urbanization rate is significant, the positive impact of policy factors in northern and eastern China is significant, the population quality and fertility rate in the west of the Hu Line are strongly negatively correlated, the population number and fertility rate in southeast China are positively correlated, the negative impact of marriage and childbearing on fertility on the east side of the Hu Line is greater than that on the west side, and the influence of the concept of raising children and preventing old age in some areas in the east is still greater.

Based on the analytical framework of geographical philosophy, this study investigated the production process at different stages, overall laws, ideological inspiration, and future issues of geographical knowledge production and practice in China. The main findings are as follows: First, philosophy is an important foundation supporting the production of geographical knowledge. The production of geographical knowledge should be seen as a process of content division and succession, in which new knowledge is gradually accumulated and there is movements towards wholeness. Second, in geographical knowledge production, geography reflects the actuality of knowledge through manifested features and reflects the reality of knowledge through unexpressed features. Geographical knowledge production exists dynamically in practice. Exploring the driving forces and processes of geographical knowledge production in practice can facilitate the comprehension of the existence and essence of geography. Only by coordinating stage differences in geography to achieve universal awareness can the effective function and ultimate pursuit of geographical philosophy be reflected. Third, in the future, China will inevitably enter a complex stage of development and practice, and "overlapping China" will require even more "overlapping geographical knowledge". Knowledge production and practice of geography require a comprehensive perspective that integrates wisdom from different temporal and spatial dimensions, establishment of clear philosophical ontology, epistemology, and methodology based on the changes and demands of the times, actively promoting the localization of geographical philosophy system construction to serve national strategic needs and high-quality socioeconomic development more effectively.

Hypothermia is a type of safety accident that is often neglected in field activities. Its occurrence is not only a medical problem but also a thermodynamic problem and involves a specific geographical environment. An effective way to improve public awareness of hypothermia risk is to analyze hypothermia accidents from the perspective of heat transfer and heat balance between the human body and the environment. However, few reports have been written on relevant research. Therefore, this study uses the heat balance theory to build a calculation model of the clothing thermal resistance required by the human body to maintain a normal body temperature. The two most serious hypothermia death events in Shilin, Yellow River, Baiyin, Gansu province, and Ailao Mountain, Yunnan province, in 2021 are used as cases for analysis. The theoretical clothing thermal resistance has been calculated according to the external ambient temperature and human activity conditions (including metabolic rate and consumption coefficient) at the time of the event. By comparing the actual clothing thermal resistance value of the human body with the model, the theoretical clothing thermal resistance value has been calculated to study the hypothermia risk of the human body in the incident environment. The results show that, in the death incident of the Shilin Marathon on the Yellow River in Gansu Province, the theoretical thermal resistance of clothing required by the human body to maintain a normal body temperature was between 0.72 and 4.45 clo under different temperature conditions (resting, walking, and long-distance running), while the actual thermal resistance of the clothing worn by the accident personnel was 0.32clo. The theoretical thermal resistance of the clothing is higher than that of the actual clothing, resulting in a high risk of temperature loss. Regarding the death event in the Ailao Mountain geological survey, the theoretical clothing thermal resistances required for the human body to maintain a normal body temperature under different temperatures while camping (sleeping), conducting field work, and mountaineering were 2.70-6.52 clo, 1.06-2.27 clo, and 0.55-1.75 clo, respectively. The actual thermal resistance of the clothing worn by the accident personnel was 1.86clo. During the accident, as long as the human body was in a climbing or working state, the difference between the theoretical and actual clothing thermal resistance was small, and the risk of hypothermia was low. However, while camping (sleeping), the theoretical clothing thermal resistance was higher than the actual clothing thermal resistance, and the lower the temperature, the greater the difference—especially at night when the temperature drops to its lowest point. At that point, the theoretical clothing thermal resistance could have been more than 3.5 times higher than the actual clothing thermal resistance, posing a serious risk of hypothermia. The results show the inevitability of hypothermia deaths in Shilin of the Yellow River in Gansu Province and Ailao Mountain in Yunnan Province. The insufficient prediction of hypothermia risk was the main cause of the hypothermia accidents. The calculation model constructed in this study can predict and evaluate the hypothermic risk of a certain outdoor activity in the future, provide a theoretical basis and research paradigm of thermodynamics and environmental science for improving public awareness about hypothermic risk, and is an effective means to prevent hypothermic accidents. Some measures and suggestions are provided for geographers engaged in long-term field investigation to avoid field hypothermia.

In the context of climate change, the escalating frequency of extreme weather phenomena has exacerbated the severity of compound floods in the southeastern coastal regions of China. Rising sea levels significantly contribute to the inundation of low-lying coastal urban areas. The quantitative assessment of compound flood risk offers scientific support for disaster prevention and reduction in coastal cities and for coastal management initiatives. Using Haikou City as a case study, the daily precipitation and maximum storm surge tide data from 66 typhoons that affected Haikou between 1960 and 2017 were utilized to construct compound flood combination scenarios. Based on the quantitative method of D-Flow FM (Delft3D-FLOW Flexible Mesh) numerical simulation, the potential risks of extreme rainfall and storm surge compound flood disasters under sea level rise scenarios were thoroughly investigated by integrating various scenarios. The findings revealed the following: 1) Storm surge was the primary factor contributing to compound flooding during typhoons, with the estuary of the Nandu River and the northern coast being the most affected. 2) In the scenario of maximum rainfall and storm surge combination, the inundation area of Haikou is about 148 km², which is approximately 15 times larger than the minimum rainfall and storm surge combination scenario. Moreover, in more than half of the inundated areas, the water depth exceeds 1 meter. 3) Under extreme rainfall and storm surge compound scenarios, the areas encompassing Haidian Island, Xinbu Island, and Jiangdong New Area were significantly affected by sea level rise. By 2100, the total flooding area is projected to reach about 203 km² under the RCP8.5 scenario. Sea level rise significantly amplifies urban flood risks, implying that coastal cities are poised to encounter heightened threats and manage future challenges. Through comprehensive comparisons of multiple rainfall and storm surge compound flooding scenarios under sea level rise, the temporal and spatial characteristics of the compound flooding risk were systematically evaluated. The results provide an important scientific basis for sustainable regional development, effective management, and prevention.

Typhoons are extreme weather phenomena that seriously affect the daily lives of residents and regular functioning of society. As one of the most typhoon-prone countries in the world, China is constantly affected by typhoons and their secondary disasters, which can cause significant casualties and economic losses. The extent of damage caused by typhoons is inversely proportional to the effectiveness of the emergency response. Therefore, accurate and comprehensive access to damage information is critical for rescue and recovery. Social media, which is characterized by low collection costs and rich content, is an important means of collecting disaster information. With the development of social media, it has become increasingly important to accurately and comprehensively identify social media texts related to typhoons. In this study, by combining typhoon attribute data and a multi-label classification method with Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) models, a typhoon damage identification method based on Weibo texts and deep learning is proposed to identify the damage caused by severe and super typhoons that made landfall in Guangdong Province from 2010 to 2019. First, texts related to typhoon damage were identified from the massive Weibo texts and further classified into five damage categories: transportation, public, electricity, forestry, and waterlogging. The typhoon damage characteristics were comparatively analyzed using spatial distribution, time curves, and quantity curves. The results showed that the accuracy of typhoon damage classification was high, with an F1 score of 0.907 for identifying typhoon damage-related texts and 0.814 for further classifying them into five damage categories. Typhoon attribute data and multi-label classification methods have improved the accuracy and comprehensiveness of typhoon damage identification. Compared to the use of Weibo texts only and the single-label classification method, typhoon attribute data provide information on the geographic context of the typhoon at the time of the texts' release, and the multi-label classification method allows the texts to belong to more than one damage category. This study shows that there are differences in the proportion of damage caused by different typhoons, which are related to the intensity and track of the typhoon, as well as the development level of the affected areas. In addition, before the typhoon makes landfall, precautions lead to transportation and public-related damage. After the typhoon makes landfall, the typhoon damage shows single and double-peak characteristics, and the different characteristics reflect the changing trends and features of typhoon damage. This study provides a scientific basis for typhoon damage identification and disaster relief in Guangdong Province.

China has numerous islands and reefs with complex terrain that are heavily impacted by tropical cyclone disasters. High-resolution tropical cyclone wind-field simulations are beneficial for representing the spatial variations in wind speeds. It is important to conduct high-resolution simulations on relatively small islands and reef areas. To explore the differences in tropical cyclone wind field simulations at various spatial resolutions in the island and reef areas of China, this study compared the modeled wind fields of historical tropical cyclones in China's island and reef areas, which have complex terrains, including plains, peaks, valleys, and cliffs, at three spatial resolutions of 1 km, 90 m, and 30 m. The wind fields were modeled using land cover and elevation data of the three spatial resolutions as inputs and validated against observed winds at eight stations. Comparisons were made regarding the differences in wind speeds of tropical cyclones with a 100-year return period at three spatial resolutions. The results showed that: (1) the 30 m resolution achieves the best accuracy, with a root mean square error of 4.28 m/s, lower than those of 90 m and 1 km by 0.08 m/s and 1.04 m/s, respectively. (2) Different spatial resolution simulations showed that wind speed errors were related to terrain types. For example, on Zhujiajian Island, located in Zhoushan City, the 30 m resolution captured the spatial heterogeneity of winds better than the other resolutions, especially for mountainous, valley, and cliff terrains. Comparisons between the simulated wind speeds at 90 m and 1 km resolutions versus those at 30 m resolution indicate that the differences in the simulation percentages are as follows: 10.06% and 12.90% for peak terrain, 19.91% and 10.44% for valley terrain, and 18.57% and 19.01% for cliff terrain, respectively. Additionally, the 30 m simulation was more sensitive to transitions between windward and leeward slope terrains. (3) For the 100-year return-period wind speeds, the 30 m resolution produced the highest values and largest spatial variations. On Zhujiajian Island, the maximum wind speeds at 1 km, 90 m, and 30 m resolutions were 71.13, 73.18, and 79.97 m/s, respectively, and standard deviations of 3.88, 3.72, and 7.18 m/s. This study demonstrates the importance of using high-resolution data to simulate tropical cyclone winds in complex terrain. However, this study had some limitations. First, the terrain correction factors need to be optimized further. The assessment method provided by the building codes tended to overestimate the impact of the terrain correction factors. In the future, more accurate terrain correction factors could be obtained using computational fluid dynamics and wind tunnel tests. Second, because of the limited types of land cover data used in the calculations, the subdivision of certain land types when calculating the surface roughness is not sufficiently detailed. Additionally, different years of land cover data were not incorporated, making it challenging to reflect the variations in surface roughness. Remote sensing can be used in the future to determine the high-resolution spatial distributions of surface roughness.

China is frequently affected by tropical cyclones, which can lead to severe economic losses. Rapid disaster loss assessment is crucial for effective emergency response. A variety of factors affect tropical cyclone disaster losses, which can be roughly categorized into hazard, exposure, and vulnerability. In the past, traditional statistical methods were used as the main tools for disaster loss assessment. To explore the potential of machine learning models, we explored five algorithms: the Light Gradient Boosting Machine (LightGBM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Back-Propagation Neural Network (BP). The maximum gust wind and rainfall of tropical cyclones were selected to represent hazards, fixed capital stock data were used for the valuation of exposure, and the GDP of each county was collected to reflect capacity or vulnerability. In addition, river network density data were used as a simple proxy to demonstrate the contribution of flood-induced tropical cyclone rainfall. The relationship between these input variables and disaster loss at the county level was developed based on the data of 81 tropical cyclone events from 2009 to 2020 in Fujian Province. The performance of these models was compared using accuracy, precision, recall, and F1 scores. The accuracies of the LightGBM, RF, XGBoost, SVM, and BP models were 0.7946, 0.7726, 0.7628, 0.2518, and 0.2681, respectively. The main findings are as follows: (1) The performance of the ensemble learning algorithms (RF, XGBoost, and LightGBM) was higher than that of the individual classifiers (BP and SVM). The LightGBM model exhibited the best performance, with accuracy, precision, recall, and F1 scores >79%. (2) Maximum hourly rainfall and maximum wind gust are two of the most important loss-inducing factors, and fixed capital stock is a better proxy for disaster exposure than GDP. (3) The modeled losses are consistent with the actual losses under different but typical tropical cyclone events, indicating that the models can be applied to future tropical cyclone events impacting Fujian Province. However, this study had some limitations. First, some natural hazards, such as floods, storm surges, and waves, were not fully considered, which introduced uncertainty into the model results. Second, the emergency response capacity and actual actions taken among counties may have varied dramatically and were neglected due to data unavailability. In the future, hazard and vulnerability variables should be obtained to extend the model inputs. In addition, whether the model parameters trained with data from Fujian Province can be applied to other provinces remains unaddressed. In the future, to develop an operational model for the whole of coastal China, county-level data of all typhoon-prone areas in China with long-term time series are needed.

Islands are sensitive zones of sea-land interaction and typical ecologically fragile areas that are highly vulnerable to natural disasters, especially marine aquaculture, which is sensitive and at high risk to typhoon disasters; additionally, they are home to aquaculture households with high economic vulnerability to typhoons and poor adaptive capacity. This study focused on Liuheng Town of Zhoushan and the Dongtou District of Wenzhou, which were severely affected by Super Typhoon Lekima, and Gouqi Town of Zhoushan, which was severely affected by Typhoon In-Fa and Super Typhoon Chanthu, as case areas. Based on data acquired from 344 questionnaire surveys of aquaculture households and interview data from various related bodies, this study used factor analysis of mixed data and hierarchical clustering on principal components to identify the types of vulnerability of island aquaculture households to typhoon disasters and reveal the characteristics of each vulnerability type, as well as to identify the discriminative indicators of household vulnerability types, for analyzing the impact of typhoon disasters and other stressors on the vulnerability of island aquaculture households to typhoons. The results showed that the aquaculture industry and aquaculture households in the island areas showed high economic vulnerability, with most shrimp, crab, and shellfish mixed farming, algae, and mussel farming households suffering serious losses from typhoons. Second, differences in exposure, sensitivity, and adaptive capacity led to three different types and characteristics of vulnerability in aquaculture households. The degree of household exposure varied across aquaculture species, with mussels having the highest, algae the next highest, and shrimp, crab, and shellfish the lowest. Island aquaculture households showed outstanding sensitivity, as reflected in their high dependence on aquaculture, significant household human capital problems, relatively limited support from social networks, and frequent exposure to typhoon disasters. The adaptive capacity of households varied across aquaculture species, with mussel households having superior adaptive capacity, and shrimp, crab, and shellfish households and algal aquaculture households having relatively poor adaptive capacity. Third, the common influencing factors of aquaculture households' vulnerability to typhoon disasters are labor shortages, frequent typhoon disasters, and inadequate infrastructure. The differences among the significant discriminant indicators, such as the degree of exposure, aquaculture species, average annual household income, age and education level of the household head, social support, number and type of adaptation strategies adopted, and cost–benefit situation, are key to the formation of different vulnerability types. Finally, multiple stressors from the climate, ecosystem, economy and markets, society, institutions, and policies mutually interact to exert cumulative effects that increase the vulnerability of fishery ecosystems and the socioeconomic vulnerability of households in island regions. This study provides important empirical evidence for governments, aquaculture households, and other relevant stakeholders in island regions to reduce their vulnerability and increase their adaptive capacity.

Islands are sensitive zones of sea-land interaction and typical ecologically fragile areas that are highly vulnerable to natural disasters, especially marine aquaculture, which is sensitive and at high risk to typhoon disasters; additionally, they are home to aquaculture households with high economic vulnerability to typhoons and poor adaptive capacity. This study focused on Liuheng Town of Zhoushan and the Dongtou District of Wenzhou, which were severely affected by Super Typhoon Lekima, and Gouqi Town of Zhoushan, which was severely affected by Typhoon In-Fa and Super Typhoon Chanthu, as case areas. Based on data acquired from 344 questionnaire surveys of aquaculture households and interview data from various related bodies, this study used factor analysis of mixed data and hierarchical clustering on principal components to identify the types of vulnerability of island aquaculture households to typhoon disasters and reveal the characteristics of each vulnerability type, as well as to identify the discriminative indicators of household vulnerability types, for analyzing the impact of typhoon disasters and other stressors on the vulnerability of island aquaculture households to typhoons. The results showed that the aquaculture industry and aquaculture households in the island areas showed high economic vulnerability, with most shrimp, crab, and shellfish mixed farming, algae, and mussel farming households suffering serious losses from typhoons. Second, differences in exposure, sensitivity, and adaptive capacity led to three different types and characteristics of vulnerability in aquaculture households. The degree of household exposure varied across aquaculture species, with mussels having the highest, algae the next highest, and shrimp, crab, and shellfish the lowest. Island aquaculture households showed outstanding sensitivity, as reflected in their high dependence on aquaculture, significant household human capital problems, relatively limited support from social networks, and frequent exposure to typhoon disasters. The adaptive capacity of households varied across aquaculture species, with mussel households having superior adaptive capacity, and shrimp, crab, and shellfish households and algal aquaculture households having relatively poor adaptive capacity. Third, the common influencing factors of aquaculture households' vulnerability to typhoon disasters are labor shortages, frequent typhoon disasters, and inadequate infrastructure. The differences among the significant discriminant indicators, such as the degree of exposure, aquaculture species, average annual household income, age and education level of the household head, social support, number and type of adaptation strategies adopted, and cost–benefit situation, are key to the formation of different vulnerability types. Finally, multiple stressors from the climate, ecosystem, economy and markets, society, institutions, and policies mutually interact to exert cumulative effects that increase the vulnerability of fishery ecosystems and the socioeconomic vulnerability of households in island regions. This study provides important empirical evidence for governments, aquaculture households, and other relevant stakeholders in island regions to reduce their vulnerability and increase their adaptive capacity.

The dynamic risk assessment of typhoon disasters is an important decision-making basis for disaster response in the event of a major typhoon. Its goal is to dynamically predict the expected loss and disaster risk level caused by a typhoon so as to provide a basis for disaster risk early warning and emergency response. The traditional risk assessment model mainly fits the vulnerability curves of the hazard-affected bodies using historical disaster losses, and then establishes a disaster risk assessment model by coupling the risk of disaster factors, exposure, and vulnerability. However, the vulnerability curves generated by this method have problems of regional applicability, particularly in small-scale regions with small sample sizes available for fitting, leading to insufficient generalizability of the model. In addition, such models are complex and require phased hazard and vulnerability of the hazard-affected bodies research. Moreover, when employing the 3-element coupling process, it is difficult to consider other risk factors in the disaster system, such as hazard-formative environment and disaster prevention and mitigation capability. With the development of information technology, the availability of disaster risk factor data has been significantly improved, affording conditions for the fusion and application of disaster risk multi-source data. In recent years, many data-driven machine-learning models have been used to establish disaster risk assessment models. These models have the advantage that they can use large sample to improve the adaptability of the model, whereby the modeling process can consider more risk factors, the concepts of hazard and vulnerability are diluted, and the steps of model building are simplified. The integrated learning algorithm can not only improve the prediction accuracy, but more importantly, can be used to effectively evaluate the contribution value of the index to the final evaluation result. At present, China has established a six-level disaster reporting system at the national, provincial, municipal, county, township, and village levels, forming a long-term, high-precision database of disaster event cases since 2009, providing rich disaster loss information for the data fusion of risk elements. This study was based on 108 typhoon cases affecting five provinces in southeast China during 2009-2022. Nearly 4 000 county-level typhoon disaster loss samples were used to establish a dynamic typhoon risk assessment sample database that integrates 30 types of multi-source risk factor indicators. Six typhoon disaster risk assessment models were developed using the random forest algorithm to evaluate the affected population, emergency relocation population, crop-affected areas, collapsed and severely damaged houses, direct economic losses, and comprehensive risk level. Through the verification of actual disaster situations and model results, the overall accuracy of the disaster risk assessment results was found to be greater than 80%, indicating that the model has good generalizability and can be used for actual disaster assessment work. The experimental comparison shows that increasing the training sample size by 1-2 orders of magnitude can improve the accuracy of the model assessment by 3%-14%, indicating that the accumulation of disaster risk big data is of great significance in the study of disaster risk assessment. This study is expected to constitute a scientific reference for the quantitative analysis of the multiple impact factors of typhoon disaster damage and explore technical ideas for the application of disaster big data in risk management.

China has numerous islands and reefs with complex terrain that are heavily impacted by tropical cyclone disasters. High-resolution tropical cyclone wind-field simulations are beneficial for representing the spatial variations in wind speeds. It is important to conduct high-resolution simulations on relatively small islands and reef areas. To explore the differences in tropical cyclone wind field simulations at various spatial resolutions in the island and reef areas of China, this study compared the modeled wind fields of historical tropical cyclones in China's island and reef areas, which have complex terrains, including plains, peaks, valleys, and cliffs, at three spatial resolutions of 1 km, 90 m, and 30 m. The wind fields were modeled using land cover and elevation data of the three spatial resolutions as inputs and validated against observed winds at eight stations. Comparisons were made regarding the differences in wind speeds of tropical cyclones with a 100-year return period at three spatial resolutions. The results showed that: (1) the 30 m resolution achieves the best accuracy, with a root mean square error of 4.28 m/s, lower than those of 90 m and 1 km by 0.08 m/s and 1.04 m/s, respectively. (2) Different spatial resolution simulations showed that wind speed errors were related to terrain types. For example, on Zhujiajian Island, located in Zhoushan City, the 30 m resolution captured the spatial heterogeneity of winds better than the other resolutions, especially for mountainous, valley, and cliff terrains. Comparisons between the simulated wind speeds at 90 m and 1 km resolutions versus those at 30 m resolution indicate that the differences in the simulation percentages are as follows: 10.06% and 12.90% for peak terrain, 19.91% and 10.44% for valley terrain, and 18.57% and 19.01% for cliff terrain, respectively. Additionally, the 30 m simulation was more sensitive to transitions between windward and leeward slope terrains. (3) For the 100-year return-period wind speeds, the 30 m resolution produced the highest values and largest spatial variations. On Zhujiajian Island, the maximum wind speeds at 1 km, 90 m, and 30 m resolutions were 71.13, 73.18, and 79.97 m/s, respectively, and standard deviations of 3.88, 3.72, and 7.18 m/s. This study demonstrates the importance of using high-resolution data to simulate tropical cyclone winds in complex terrain. However, this study had some limitations. First, the terrain correction factors need to be optimized further. The assessment method provided by the building codes tended to overestimate the impact of the terrain correction factors. In the future, more accurate terrain correction factors could be obtained using computational fluid dynamics and wind tunnel tests. Second, because of the limited types of land cover data used in the calculations, the subdivision of certain land types when calculating the surface roughness is not sufficiently detailed. Additionally, different years of land cover data were not incorporated, making it challenging to reflect the variations in surface roughness. Remote sensing can be used in the future to determine the high-resolution spatial distributions of surface roughness.

Myanmar is a key neighbor for China and an important link in advancing the "Belt and Road" initiative, contributing to both domestic and international economic flows. Despite the border closures and restrictions imposed during the COVID-19 pandemic, the New China-Myanmar Indian Ocean Corridor has seen substantial progress. However, this development has not garnered the attention it deserves, as both national and Yunnan provincial governments continue to prioritize the China-Myanmar Economic Corridor (CMEC). This oversight results from an incomplete understanding of the changes in Myanmar's geopolitical landscape since 2000. Using a framework for national geopolitical landscape analysis, this study examines Myanmar's basic national conditions, principal relationships, and inherent contradictions, revealing how Myanmar's geopolitical landscape has evolved due to the interplay of internal and external factors, cross-field interactions, and strategic games played by multiple geopolitical actors. Specifically, the study discusses the period from 2000 to 2010, characterized by external pressure and internal stability, and the years from 2011 to 2021, marked by external conflict and internal turmoil. The evolving geopolitical landscape in Myanmar has created favorable conditions for building the New China-Myanmar Indian Ocean Corridor. From a geopolitical perspective, this paper explores the reasons behind the necessity of this new corridor and suggests a re-evaluation of China's spatial planning for major infrastructure projects in Myanmar given the country's shifting geopolitical context. The corridor's feasibility—whether measured by distance, time, costs, spatial distribution of domestic ethnic armed conflicts, or Myanmar's post-pandemic economic trends—suggests it is highly workable. In the short term, the new corridor can complement the China-Myanmar Economic Corridor, progressing concurrently; in the long term, it could gradually replace it as the main route for China-Myanmar trade. This study not only enhances understanding of the New China-Myanmar Indian Ocean Corridor but also provides a scientific rationale for its vigorous promotion.

The rapid growth of social media has introduced new concepts and technical approaches for disaster management. This paper reviews the characteristics of social media data and its application potential in disaster management research, providing a new research perspective for the field of disaster management. Taking the impact of Typhoon Doksuri in Fujian Province in 2023 as a case study, this research employs Latent Dirichlet Allocation (LDA) topic modeling to analyze the practical application effectiveness of social media data at different stages of disaster management from three perspectives: the spatiotemporal distribution of posts, trend analysis of different types of entities, and evolution of topic content. These findings indicate that the synchronous relationship between the popularity of related topics on Weibo and the impact of a disaster event confirms the effective application of social media data in disaster management. By monitoring the dynamics of information dissemination on social media, we can determine the occurrence status and impact scope of disasters in real time. During disasters, different user types have different foci. Individual users tend to focus more on the restoration of living facilities and the supply of relief materials, whereas organizational users concentrate on disseminating information about disasters and emergency response measures. The information provided by different types of users can provide a more comprehensive and diversified perspective on disaster perceptions for disaster management. Analysis of the evolution of topic content can reflect the evolution of emergency response dynamics and public attention needs in different cities at different stages of disaster management, thereby developing more practical emergency response strategies. Through the mining and analysis of social media data, this study recognizes the entire process of disaster occurrence from the perspective of social media data, thereby enriching the relevant theoretical and empirical research. Future research could be conducted from perspectives such as utilizing other multisource data, integrating machine learning and deep learning technologies to enhance the accuracy of topic information extraction, and exploring the application of social media data to post-disaster emergency rescue and infrastructure support.

Catastrophe insurance is an important financial tool to mitigate the risk of catastrophes. After the 2008 Wenchuan Earthquake, China accelerated its exploration of a catastrophe insurance system. As one of the most natural disaster-prone provinces in China, Guangdong experiences frequent rainstorms and typhoons. Severe natural disasters have not only led to significant losses to economic development and people's lives, but have placed considerable financial pressure on governments at all levels. To promote the transformation of government functions and use of catastrophe insurance as a modern financial tool to cope with major natural disasters, Guangdong has conducted pilot work since 2016 to explore and experiment with different aspects of catastrophe index insurance. This includes the design and application of insurance systems and products. The pilot work achieved remarkable results and formed the Guangdong catastrophe index insurance paradigm. However, few studies have examined the development and application of catastrophe index insurance programs in Guangdong Province. This paper describes the research and design process, data, and key methods of typhoon catastrophe index insurance in Guangdong, in accordance with the specific catastrophe index insurance practices. Furthermore, the application of the current catastrophe index insurance program from 2016 to 2023 is reviewed. Additionally, the advantages, characteristics, and shortcomings of the program are systematically analyzed, and potential directions for improvement in the future are discussed. Several notable conclusions were drawn from this study. First, the typhoon catastrophe index insurance, which is based on the circular catastrophe box and uses typhoon intensity levels as a stratification criterion for the payout structure, offers a straightforward methodology, easy recalculations, readily accessible data, and transparent results. Second, this form of insurance facilitates rapid claim settlements, incurs low operational costs, and effectively mitigates moral hazard. Third, the existing typhoon catastrophe index insurance program may encounter high basis risk and underestimate the severity of typhoon hazards, particularly in the context of climate change and the situation wherein a single typhoon impacts multiple municipalities. Finally, improvements to the current typhoon catastrophe index insurance program in Guangdong could be achieved by more deeply and comprehensively analyzing the spatial and temporal patterns of typhoon events, incorporating additional parameters with clear physical meanings, and refining the probability distributions of typhoon disaster events. The insights outlined in this paper may potentially enhance understanding among scholars and practitioners of typhoon catastrophe index insurance programs and provide guidance for extending catastrophe insurance in other typhoon-prone areas.

A brief review of the development of ethnogeography over the past century and geography-oriented theories of minzu over the past 30 years shows that ethnogeography, which is a prominent subdiscipline of geography in the first half of the 20th century, is being eroded to an "important but not conspicuous" field in China. Geography-oriented theories of minzu highlight the significance of "geography" to the development, integration and identification of minzu, whereas the understanding of "geography" is complicated and diverse, and a direction for establishing ethnogeographical theory has not been proposed. Meanwhile, narrow ethnogeography as an independent research field or subdiscipline is devoid of a systematic theoretical system. Considering both modern and contemporary geographical perspectives as well as geography-oriented theories of minzu, three accessible approaches that can strengthen the integrity of ethnogeography and promote the integration of geographical disciplines are proposed. First, the theoretical system of broad ethnogeography could be integrated through a "state-region-place" framework. Second, the multidimensional interpretations of minzu could be bridged with disciplinary traditions of natural science, social science, and humanities to form a threefold "nature-society-humanities" interpretation system. Finally, geographical theories of minzu that emphasizes connection, mobility, and transformation could be developed from the perspective of geography as a discipline rather than a subject. The integrity of ethnogeography will be demonstrated through bridging the gaps among theories, interpretations, and knowledge, and the influence of geography will be extended. More importantly, minzu and nations are understood, not interpreted, geographically.

Owing to historical reasons, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) features a unique "one country, two systems" institutional framework. Facilitating the integration and connectivity of transportation among Hong Kong, Macao, and the Mainland is crucial for the high-quality development of the GBA. Previous studies about borders have primarily focused on national (supranational) or administrative boundaries within a country's territory. However, studies on the unique institutional differences in the GBA are insufficient. Additionally, most studies do not perform dynamic border effects measurements using big traffic flow data. This study utilizes toll-collection data from highways in the GBA for 2021 and 2023, as well as cross-border traffic data, to construct a traffic-flow network for the GBA. Complex network analysis and border-effect measurement methods are employed to investigate the spatial structure of the GBA traffic-flow network and its dynamic changes in border effects. The results indicate that, in terms of the overall spatial structure of traffic flow in the GBA, the network exhibits a unique "dual-core edge" structure, with the Guangzhou-Foshan, and Shenzhen-Dongguan-Huizhou regions serving as dual cores. In contrast, the overall coverage and connectivity strength of the passenger-flow network are higher than those of the freight-flow network. Regarding the dynamic changes in the spatial structure of traffic flow from Hong Kong and Macao, the coverage and density of the traffic-flow network in 2023 are significantly higher than those in 2021. Traffic flows from Hong Kong and Macao have begun to extend beyond the border toward the northern regions, thus accelerating the integration of transportation within the GBA and forming a spatial pattern of "cross-strait connectivity and all-area interconnection." However, because of their peripheral positions in the traffic network and the presence of border effects, the importance of Hong Kong and Macao in the GBA traffic-flow network remains relatively weak. Based on the dynamic measurement results of border effects, the obstruction coefficients between Hong Kong and the Mainland, as well as between Macao and the Mainland, are significantly higher than those between various counties within the Mainland. The obstruction coefficients for passenger vehicles are generally lower than those for freight vehicles. Following the outbreak of the pandemic, the obstruction coefficients of the GBA traffic-flow network have increased dynamically, thus indicating a reduction in obstructive border effects. This study expands the quantitative research framework of border effects in traffic-flow networks, thus promoting integrated transportation development in the GBA and facilitating its integration development goals.

Under the influence of information technology and high-speed transportation networks, which compress space and time, the region's population has achieved large-scale fluidity. Examining the temporal heterogeneity of intercity travel networks and its influencing mechanism can help optimize regional spatial organization and provide a scientific basis for regional integrated development. Based on Baidu migration data from January to April 2023, this study uses a PPML(Poisson Pseudo Maximum Likelihood) gravity model and interaction term testing to compare the scale, pattern, and influencing factors of intercity travel networks during weekdays, weekends, and holidays in the Yangtze River Delta region. The results indicate the following: 1) The intercity travel network in the Yangtze River Delta region exhibits temporal heterogeneity characteristics. During weekdays, intercity travel primarily consists of cross-city commuting and business trips, with the lowest daily average scale. This forms a V-shaped intercity travel structure covering Shanghai, southern Jiangsu, Northern Zhejiang, and Southern Anhui. The positive effects of destination city population size and economic status on intercity travel are enhanced. On weekends, intercity travel is dominated by business trips and leisure activities, and residents tend to take shorter trips, which means that intercity distances pose greater hindrances to intercity travel. During holidays, intercity travel is primarily for leisure and entertainment and for visiting friends and relatives, with the highest daily intensity. The promotional effect of destination city population size on intercity travel is weakened, and intercity travel is less hindered by intercity distances. Compared to the effects of geographical distance, economic status, and population size on the scale of intercity travel during weekdays, travel duration, or geographical distance, tends to pose a greater hindrance on weekends and a lesser hindrance during holidays. The promotional effect of economic status is intensified on weekends but diminishes during holidays. Meanwhile, the promotional effect of population size weakens both on weekends and during holidays.2) Push–pull factors encompass the level of urban development and the incentives that trigger individual travel. In terms of urban development level, indicators such as population size, economic status, and industrial structure reflect the comprehensive strength and development status of a city, influencing its ability to serve as both a starting and destination point for intercity travel. From the perspective of various individual travel incentives, residents pay more attention to various urban resources such as income levels, public service quality, and tourism resources to meet their personal needs for production and living. The primary types of population movements vary across different time periods, shifting between cross-city commuting, business travel, and leisure and entertainment. As a result, the dominant factors among push-pull elements also change, leading to significant variations in the effectiveness of each factor. Intermediate obstacles are the key factors limiting intercity travel. On the one hand, while the level of integration in the Yangtze River Delta region continues to improve, and transportation facilities are gradually improving, geographical distance remains a crucial intermediate obstacle. On the other hand, administrative and cultural differences between different provinces increase residents' adaptation costs, forming "invisible barriers" that hinder cross-province population interactions. The hindrance posed by intermediate obstacles to intercity travel also varies across different travel periods. The effects of push-pull factors exhibit temporal heterogeneity. The small-world characteristics of the intercity travel network during weekdays are more evident, and the central city has a more prominent structural core status. On weekends, the geographical proximity of the intercity travel network improves, with close "center-hinterland" connections and enhanced inter-provincial boundary effects. During holidays, the overall intensity of the intercity travel network increases, with the most significant increase in medium- and long-distance cross-provincial travel. The provincial boundary effect and spatial proximity effect decrease, weakening the structure of the intercity travel network.

Public transportation is a vital means to alleviate urban congestion. Despite substantial investments in public transit infrastructure in China, the development of urban public transportation has been unsatisfactory, with many city residents still favoring car travel. The extensive use of personal vehicles occupies limited road resources, thus exacerbating traffic congestion and environmental pollution. The built environment extensively influences residents' travel choices. Existing studies often describe the characteristics of the built environment from the perspective of the origin, destination, and public transit stops, lacking attention to the out-of-vehicle segments before and after using public transit; moreover, they mainly focus on the built environment faced by transit riders, without fully considering the alternative transit chains for car travelers. Accordingly, this study adopts a trip chain perspective. Combining resident travel surveys and streetscape data from the main urban area of Wuhan, and simulating travel paths using Baidu Maps, this study uses a random forest model to comprehensively analyze the impact of the built environment at the origin, destination, and out-of-vehicle segments on the choice between public transit and private vehicle. The results indicate the following: (1) The performance of the random forest model is superior to that of the traditional Logistic model, and it can reveal the nonlinear relationship between the built environment and travel behavior. At the same time, considering the out-of-vehicle environment also better understands the competitive environment between public transit and private vehicle, thereby improving the model's predictive ability, (2) the built environment is the main factor influencing the preference for public transit, and the out-of-vehicle environment's influence on travelers is no less than that of the built environment at the origin and destination. The preference for transit and built environment factors exhibit a nonlinear relationship, with some factors having different impacts at the origin and destination. Specifically, the population density, intersection density, and transit stop density at the origin and destination have very similar effects on the preference for transit, while the land use mix and job density differ. The proportion of roads and fences in the out-of-vehicle environment show a clear threshold effect, while the proportion of sidewalks and visible green index exhibit a saturation effect. (3) The mechanisms by which the built environment influences the choice of public transit and private vehicle can be summarized into three categories: elastic adjustment, limited support, and direct drive. These findings reveal the effective range of built environment factors in enhancing the attractiveness of transit, providing more rational and precise targeting for policy-making. This study addresses the issue of insufficient detail in the built environment in current research, incorporating the out-of-vehicle environment and alternative modes of travel into the analysis framework of transit preference, providing more intervenable built environment factors to enhance the attractiveness of transit, and offering insights for integrating nonlinear impact relationships into urban planning practice.

High-speed rail (HSR) stations can influence the expansion of the surrounding construction land. However, relevant studies face three main limitations: influence scope estimation lacking a theoretical foundation, less focus on whether the impacts of HSR stations on construction land expansion vary, and misjudgment of the drivers of HSR stations on construction land expansion. To address these research questions, this study first conducts a literature review to theoretically analyze the influence of HSR stations on the surrounding construction land expansion and then identifies the ideal curve for the influence distance of HSR on construction land expansion based on location theory and distance decay theory. Using the 24 stations of the Hainan Roundabout Railway (HRR) as an example, we revealed differences in the influence of various HRR stations on construction land expansion through GIS technology, buffer analysis, and nonlinear fitting to quantitatively analyze the expansion of construction land around HRR stations, identifying the impact range and direction of different HRR stations on the expansion of construction land. Building on the identification of heterogeneous impact results, the study further employed Geodetector to analyze the factors and reasons for the differentiated results of construction land expansion around different HRR stations from four dimensions: attributes of the socioeconomic environment, location conditions, HRR station attributes, and natural conditions. The results show that: (1) after the construction and operation of each HRR station, the surrounding construction land has expanded; the Hainan Eastern Ring HSR (the East Ring) has increased 1.70 km² around each station per year and the Hainan Western Ring HSR (the West Ring) has increased 1.25 km² around each station per year. (2) The changing trend of construction land expansion around 20 of 24 HRR stations conforms to the ideal curve, with the impact range of construction land expansion concentrated within 0.5 km-3.5 km, and the influence intensity of impact ranging from 0.06 km² to 6.64 km². (3) The impact directions of construction land expansion around 20 HRR stations are mainly in three types of directions: "HSR-main urban area," "HSR-town center," and "HSR-scenic spot." This is because the expansion of construction land around HRR stations is not only influenced by the spillover effects of the stations, but also by the traction effect of the main urban areas, town centers, or tourist areas where the HRR stations are located. The stations along the East Ring of Hainan mainly expanded towards the main urban areas, whereas the stations along the West Ring of Hainan mainly expanded towards town centers. (4) Differences in the scope of the influence of each HRR station on the surrounding construction land expansion were mainly related to several variables, ordered as follows: socioeconomic environment, location conditions, attributes of the HRR station, and natural conditions. The GDP density of the towns where the HRR stations were located had the highest impact intensity at 0.51, followed by population density at 0.49, whereas the average elevation had the lowest impact intensity at 0.12. This study analyzed the mechanism and ideal curve of construction land expansion around HSR stations, establishing a logical basis for studying the spillover effects of HSR stations. In addition, this study analyzes the various impacts of HSR stations on the expansion of surrounding construction land and the reasons for these differences, providing a scientific basis for the current operation and future location of HSR stations. This study also offers methodological insights into the impacts of other infrastructures on the expansion of construction land in surrounding areas.

Air cargo is an important component of transportation and plays a vital role in the efficient allocation of high-quality resources on global and regional scales. Air cargo contributes significantly to regional economic development by strengthening inter-regional cooperation and resource integration. However, air cargo geography has received relatively less attention from the research community. Existing studies have analyzed the spatial pattern of air cargo using a limited cross-sectional data from selected years, lacking an analysis of its influencing factors. Based on spatial statistics and panel data of air cargo, this study explores the evolution process and characteristics of China's air cargo pattern on a 20-year time scale and quantitatively reveals its key influencing factors. The research findings are as follows: 1) Air cargo in China has transitioned from the rapid development stage to the stable development stage in the past 20 years; 2) Air cargo volume in China is mainly concentrated in the eastern region, and in the past 20 years, China's air cargo center of gravity has been generally located at the junction of Anhui, Henan, and Hubei provinces, showing a spatial displacement trend from Henan to Anhui to Hubei; 3) The pattern of air cargo network in China remains relatively stable, forming a rhombic structure with Beijing, Shanghai, Guangzhou, and Shenzhen as the core; 4) Air cargo development in China is influenced by factors such as urban scale, industrial structure, and ground transportation development. Among them, urban economy, transportation, warehousing, postal and telecommunications industry, and technological investment have a significant positive impact on air cargo volume, whereas the wholesale and retail trade industries have a significant negative impact. For air logistics hubs, the influencing factors are consistent with those of the entire sample airport. However, for non-aviation logistics hubs, population size and research and technology services have a significant positive impact, whereas ground transportation accessibility has a significant negative impact. This study enriches the long-term time-series analysis and quantitative research content in the field of air cargo and has significance for the development of air transportation geography and the construction of a strong civil aviation industry in China.

The rapid development of information technology has triggered an explosion of data, marking the era of big data. A wide range of transportation big data has been used in urban space and travel behavior studies since the beginning of this century. Mobile phone signaling data in particular have many advantages: they have prevalent spatial and temporal coverage, high tracking stability, satisfactory resolution, and low cost. The description of urban phenomena and the analysis of their forming mechanisms using mobile phone signaling data are thoroughly studied by previous research. The next course of action is to tackle specific urban problems. This study summarizes the application progress of mobile phone signaling data in job-housing relationships and travel behavior studies, discusses the application prospects of mobile phone signaling data in transportation carbon emissions research based on past applications and the existing literature on low-carbon transportation, and proposes a research framework and several future directions for studies using mobile phone big data to examine job-housing relationships, travel behavior, and transportation carbon emissions. We first provide a brief introduction to the features of mobile phone signaling data in comparison with other commonly used data types, including their type, content, and spatial-temporal resolution. We then review the existing applications in job housing and travel research. Regarding the jobs-housing relationship, prior studies employ mobile phone signaling data to detect the spatial distribution of workplaces and residences of urban dwellers, analyze jobs-housing relationship features and urban spatial structure characteristics, and examine the factors influencing jobs-housing relationships. Regarding travel behavior, studies employ mobile phone signaling data to identify stays and trips, infer trip modes, detect trip routes, and explore the universal laws of human mobility. Next, we also discuss how mobile phone signaling data can be applied to transportation carbon emissions research. Indeed, mobile phone signaling data can be used in the calculation of transportation carbon emissions and analysis of the relationships between urban spatial structure, individual travel behavior, and transportation carbon emissions, and its wide coverage and large sample size can be exploited to fill research gaps and problems that have yet to be resolved using traditional traffic datasets. Finally, we present a research framework underlining the indirect and direct effects of the jobs–housing relationship and travel behavior on transportation carbon emissions. We also propose future directions in study contents and methodological innovations by recommending long time-series longitudinal studies, large-scale comparative studies, and new population and transportation phenomena. We further recommend fusing multi-source big and small data, incorporating machine learning algorithms into traditional statistical analyses, and constructing digital twin models. Examining the jobs–housing relationship, travel behavior, and transport carbon emissions using mobile phone signaling data is essential for clarifying the interactions between urban and regional structures, travel behavior characteristics, and transport carbon emissions. It has important implications for emissions reduction and sustainable development in the context of proposing carbon peaking and carbon neutrality goals.

Research on urban spatial networks based on "flow" data has become a new paradigm in the assessment of urban spatial connections and the delineation of metropolitan influence areas in urban geography and territorial spatial planning. Research on urban connections in Chongqing focuses primarily on districts and counties within the city's administrative region, utilizing passenger or cargo flow data to study the spatial structure of the network within Chongqing. However, few studies have been conducted on cross-provincial administrative regions between Chongqing and its neighboring areas, which does not align with the actual influence of Chongqing's metropolitan area. Based on highway traffic passenger flow data, this study employs social network and GIS spatial analysis methods to study the urban network spatial structure between Chongqing and its neighboring areas from the perspective of passenger flow connections. The results indicate the following: (1) Chongqing's central urban area serves as the absolute core of the urban network, with Changshou, Jiangjin, and Bishan as important nodal cities. Fuling, Bishan, and Changshou exhibit notable accessibility within the network, while Fuling, Qianjiang, Jiangjin, and Wanzhou play prominent intermediary roles. There are no prominent regional nodes outside Chongqing's administrative area, and the growth poles for the development of the Chengdu–Chongqing Economic Circle need further cultivation. (2) The passenger flow network between Chongqing and its neighboring areas exhibits a three-tiered axial connection, with the overall network displaying a distinct radial characteristic. The urban clusters in northeastern Chongqing form a distinct band-shaped axis along the Yangtze River with Chongqing. The urban clusters in southeast Chongqing and their neighboring areas exhibit radial axes, with relatively weak connections to the central urban area. Some areas in Guang'an and Dazhou have overcome provincial administrative boundaries, and the network hierarchy is distributed according to "4(level 1)+15(level 2)+18(level 3)." (3) The cohesive subgroups between Chongqing and its neighboring areas demonstrate a high degree of geographical proximity, forming a "core–periphery" structure. This reflects the significant influence that factors such as spatial distance and road extensions exercise on the road passenger transport network. Conducting research on the spatial structure of cross-administrative urban networks from the perspective of highway passenger flow holds significant theoretical and practical value for enriching research on the spatial structure of cross-administrative metropolitan areas and promoting the linkage between Chongqing's metropolitan area and its surrounding regions.

Implementing preferential policies for bus transfers is an important measure for promoting the development of public transportation. Although public transportation extends the travel time of passengers, the preferential policies reduce the travel costs to a certain extent. On the basis of IC card data of public transport, an income method model was constructed to evaluate the cost of passenger flow transfer time after the implementation of the preferential transfer policy in Xiamen and compare it with the reduced fees due to the policy regulations. To better assess the overall benefit of bus transfer travel, the transfer passenger flow was divided into four categories: transfer zero cost passenger flow, transfer additional cost passenger flow, transfer extra time cost passenger flow, and transfer extra time+cost passenger flow. The spatial distribution characteristics of various types of passenger flow are analyzed from five aspects: station, line, traffic area, density of travel starting and ending points, commuting and non-commuting of travel. With regard to stations, a large number of different types of passenger flowed into the area centered on Yueyang Community. As regards route, No. 24 mainly gathered a large number of different types of passenger flows. As regards transportation areas, numerous different types of passenger flows gathered in the transportation communities around the subway and the island's Bus Rapid Transit (BRT) lines. In terms of OD point density, each station of Rail Line 1 and the BRT stations had large numbers of passengers at the starting or ending points. In terms of commuting and non-commuting behaviors, the activity range of various passenger flows during commuting was smaller, the span was shorter, and the cross-island passenger flow was relatively small. In contrast, the passenger flow during non-commuting behavior showed a more evident cross-island trend, and the span was generally longer. This study devised a new passenger flow classification method to evaluate the effectiveness of a preferential policy for bus transfers. Further, it affords a reference for public transport operators to better comprehend the needs and behaviors of passengers and accordingly formulate more effective policies and measures.

Urban agglomerations are the main spatial carriers of national and regional urbanization development. The study of their spatial association networks is of great significance for optimizing the allocation of urban resources, promoting the process of regional integration, and facilitating the high-quality development of urban agglomerations. The Yangtze River Delta urban agglomeration (YRDUA) is one of the most economically active regions in China. Its spatial association network structure was the main feature of economic and social development in China's "14th Five-Year Plan." Based on the perspective of flow spaces in terms of both the city and township scales, multi-source data were used in this study and included online car-hailing origin-destination travel data, combined with complex network models and a quadratic assignment procedure, to analyze the structural characteristics and driving mechanism of the spatial association network of the YRDUA. Results show that: 1) spatial association network based on online car-hailing flow in the YRDUA has spatial dependence and hierarchical characteristics, and intensities of network association are mostly coupled with levels of economic development; 2) spatial association network in the YRDUA displays spatial spillover effects, leading to an overall pattern of high equilibrium in southern development and strong single-point development capacity in the north; 3) the structural features of spatial association network in the YRDUA differ between townships and urban scales, with some high-level townships in certain transportation networks failing to exert their driving role at the urban level; and 4) economic development status, population vitality, the level of urban construction, and administrative division ownership and geographical location differences between townships have significant impacts on the spatial association network structure in terms of the township scale. The differences in administrative divisions are most important. Online car-hailing travel data were used in this study to effectively supplement the links between township streets within and between cities. This data also revealed intercity links. Thus, the development characteristics of spatial units on different scales were reflected, and research and social management needs were satisfied on a fine scale. In addition, by introducing spatial big data and analyzing the influence mechanism from various aspects, such as socioeconomics, the driving factors of the spatial network of urban agglomerations were systematically identified at the small-scale level, which will help with more reasonable planning within the city and play a role in the development of urban agglomerations by enhancing the attractiveness of individual cities. This study expands the research perspectives on the cooperative development of urban agglomerations on different scales, providing theoretical references and practical support for the promotion of the coordinated development of urban agglomerations as a whole.

The Fourteenth Five-Year Plan of China has proposed the acceleration of the construction of a strong transport nation. The Outline for the Construction of a Powerful Transportation State emphasizes that it is necessary to focus on the general objective of the construction of a powerful transportation state and create a "123 traveling and transportation circle in the country" and reach the new standard of 1-hour commuting metropolitan area, 2-hour connecting urban agglomeration, and 3-hour coverage of the major cities in the country, which determines the importance of the accessibility of urban agglomeration in the strategy. The high-speed railway network in the twin-city economic circle of the Chengdu-Chongqing region enhances inter-city accessibility and has a spillover effect on socioeconomic development. Starting with HSR network accessibility, in this study, the evolution of the accessibility pattern after the opening of the HSR in 2015, 2020, and 2025 was analyzed using the shortest inter-city travel time, weighted average travel time, and daily accessibility index. Based on the gravity model used to measure the economic reinforcement effect generated by HSR network accessibility, the spatial Durbin model was used to explore the spillover effect of HSR network accessibility on the tertiary industry's economic development from 2015 to 2020. The study results demonstrate that the HSR network improves the level of urban accessibility, narrows the gap of accessibility level between cities, weakens the regional accessibility circle structure, and exhibits significant corridor effect. The HSR network generates a significant spatiotemporal convergence effect. The direction of spatiotemporal convergence of core cities is to expand uniformly to their surroundings, and the direction of expansion of edge cities is mainly to spread along newly opened HSR lines in the form of a belt. The improved accessibility of the HSR network will strengthen regional economic ties, narrow the gap between the attractiveness levels of non-core cities, and enhance the twin-core phenomenon. Increased accessibility is conducive to the economic development of the tertiary industry in neighboring cities, with an increase of 1 percentage point in the accessibility of the high-speed rail network in the neighboring region, leading to an increase of 0.3088 in the local tertiary industry. An increase in the tertiary economic level of neighboring cities will inhibit the development of the local tertiary economy, which may be because the Chengdu-Chongqing region is now more competitive than cooperatives in terms of urban relations; therefore, regional development is still in the siphoning stage. The possible contribution of this study is reflected in the use of accessibility as the core research and explanatory variable to explore the economic spillover effects of accessibility of high-speed rail networks in typical case regions. It aims to reveal the impact of high-speed rail network accessibility on the economy and industry, summarize the theory of spillover effect, and provide a theoretical reference for high-speed rail construction, regional planning, and economic layout optimization.

Chinese people have become important international buyers of second homes in many destination countries, particularly Malaysia, Thailand, and other Southeast Asian countries. In the past decade, the aging population and the quest for a better life have become pressing concerns in China and have triggered an increase in transnational second-home purchases in other countries. However, despite the significant and rapid growth of transnational second homes in China, little is known about the nuanced relationship between buying motives and life satisfaction. Current studies in the Western context offer limited theoretical and practical implications for Chinese transnational second homes because Chinese buyers exhibit different motives and have a distinct understanding of a good life. Based on the push-pull theory, this study examines Chinese transnational second-home buyers' motivation and life satisfaction and the relationship between these two constructs. Data were drawn from 340 Chinese transnational second-home buyers of R&F Princess Cove in Johor Bahru, Malaysia. Structural equation modeling (SEM), Importance-Performance Map Analysis (IPMA), and multi-cluster analysis (MGA) were used to process the data. Our empirical results show that, in comparison to Western second-home buyers, Chinese second-home buyers' tourism and residential experiences and overall life satisfaction are significantly affected by pull motivations, while push motivations exhibit less influence. Among all the dimensions of push motivations, the natural and tourism environment, cultural and life atmosphere, and service facilities are crucial motivations in order of priority. Economic factors (such as prices and cost of living) also influence but are not the most important factors. Both tourism and residential experience significantly affect Chinese second-home buyers' overall life satisfaction, with residential experience exhibiting a higher influence. Women and larger second-home groups value residential experiences more than other groups, while smaller buyers value travel experiences more. This study provides new evidence for future studies on Chinese transnational second homes and responds to the current academic discussions on second-home buyers' motives in transnational contexts. Finally, this study has practical implications for domestic second-home destination construction and marketing.

With the rapid development of urban regionalization and networking of high-speed transport, intercity travel has increasingly played a key role in China's economic and social development and socioeconomic functional connections. However, amidst global change and uncertainty, the event disturbance-oriented theory and empirical research on intercity travel is still insufficient to improve the ability of transportation systems to cope with disturbances. Since uncertainty is prevalent in transport operations, improving intercity travel behavior resilience (ITBR) and grasping the spatiotemporal pattern of demand-side intercity travel fluctuation to restrain risk is essential for resilient transport construction. Based on related theories and analysis methods of spatial interaction and intercity travel, this study refines the definition of ITBR. A measurement model of ITBR was constructed based on long-term intercity travel data and the general properties of disturbance events. Furthermore, COVID-19 disturbance was used as a case study to reveal the adaptive pattern of intercity travel and the spatiotemporal pattern of ITBR over three years. The results show that the evaluation of ITBR based on seasonal and holiday trends reveal spatiotemporal patterns of intercity travel fluctuations influenced by disturbance events. The impact of the COVID-19 pandemic on intercity travel is as follows: peak of the national pandemic > peak of the Omicron variant > peak of the multipoint fluctuation. The intensity of intercity travel decreased linearly with an increase in distance, and intercity travel during the three stages is lost by 0.86, 1.03, 1.15 percentage points, respectively, with an increase of 50 km. The average intercity travel distances of residents in these three stages were shortened by 52.55 km, 65.31 km, and 105.16 km, respectively. The value of ITBR decreased from the multipoint fluctuation period to the national pandemic period because of the Omicron outbreak. Overall, ITBR showed a gradual increasing trend during the study period. Meanwhile, ITBR in these three stages was characterized by obvious spatial differentiation and regional agglomeration. Compared to existing research, this study further expands existing research focusing on intra-city travel behavior resilience by exploring ITBR on the regional scale.

The construction and development of the metro have reshaped the activity spaces of residents and provided them with abundant opportunities to access various resources. However, existing literature often overlooks the impact of increased access to resources due to the metro system on housing prices. This study, based on residential transaction data from 2017 to 2019 obtained from Beike and metro line and station data from Chengdu, employed network analysis to define a 15-minute activity space for 56,999 residential samples in 2,609 neighborhoods around metro stations, serving as the basis for decomposing resource allocation. Subsequently, an XGBoost model was developed to explore the nonlinear impacts of accessibility and resource allocation at the neighborhood and cross-regional levels on housing prices. The results show that 1) the closeness centrality of the nearest metro station primarily affected housing prices. 2) Additionally, the relative importance of resource allocation on housing prices at the neighborhood and cross-regional levels was 34.60% and 19.55%, respectively, highlighting the significance of resource allocation at the cross-regional level. The development of the metro has reshaped residents' activity spaces, increasing access to various resources. The value of resources obtained through the metro has significantly affected residents' willingness to pay. Furthermore, resource allocation at the two levels reflected a different impact on housing prices. At the neighborhood level, the relative importance of factories and comprehensive hospital facilities was 8.02% and 7.47%, respectively. Meanwhile, the relative importance of parks and comprehensive hospital facilities were 4.85% and 3.86%, respectively, at the cross-regional level. 3) The relationship between accessibility, resource allocation characteristics, and housing prices is complex and nonlinear. Regarding accessibility, the travel time to the nearest metro station and housing prices exhibited a roughly U-shaped relationship, whereas the closeness centrality of the nearest metro station positively affected housing prices. In terms of resource allocation characteristics, different facilities had varying degrees of impact on housing prices at the neighborhood and cross-regional levels. Specifically, parks and primary and secondary schools at the neighborhood level showed an overall positive impact on housing prices, while factories, comprehensive hospitals, and shopping services at the neighborhood level generally suppressed housing prices. Additionally, business and financial service facilities at the neighborhood level showed an inverted U-shaped relationship with housing prices. At the cross-regional level, comprehensive hospital facilities and primary and secondary school facilities had opposite effects on housing prices compared to park facilities and shopping services. The impact of factories at the cross-regional level on housing prices was unstable. Business and financial service facilities at the cross-regional level demonstrated an inverted U-shaped relationship with housing prices, with a suppressive effect when the count of these facilities exceeded 423. The study findings provide valuable insights into sustainable metro development and rational resource allocation.

Livelihood capital is the core element of sustainable livelihood development for rural households. Based on the survey data of 615 growers in the main rubber-producing areas of Hainan and Yunnan province, this paper constructs an evaluation system of rubber growers' livelihood capital within the sustainable livelihood analysis framework and calculates key indicators, such as farmers' livelihood capital index, livelihood activity diversity index, and household income diversity index. The multiple regression model and the mediating effect model are used to explore the influence mechanism of livelihood capital on the sustainable livelihood of rural households. The following three conclusions are drawn: (1) The accumulation of livelihood capital of rural households is conducive to enriching the structure of household livelihood activities and income sources, especially the accumulation of financial capital, which is more likely to improve the diversity of household livelihood activities. Regional differences exist in the impact of livelihood capital structures on rural household livelihood outcomes. Natural capital endowment has a significant effect on the income diversification level in Hainan, but there may be a "curse" effect in Yunnan. (2) Geographical characteristics, production decisions, and farming household characteristics significantly impact sustainable livelihoods. The altitude of the village and the distance from the county seat pose certain challenges for farmers in diversifying their livelihoods. Although the law of rubber tapping production may weaken farmers' production and business activities, it can better stabilize farmers' incomes owing to its strong flexibility in labor arrangements, which may be beneficial for diversifying livelihoods; however, it does not necessarily lead to diversified incomes. While the characteristics of poor households can help rural households gain policy support, building capacity is the key to achieving a sustainable livelihood. (3) There are intermediary effects of livelihood capital on livelihood activities and household income, but the mechanisms of action are different. For the diversity of farmers' livelihoods, 52.70% of the catalytic effect of livelihood capital is achieved by improving the ability to integrate livelihood capital. Optimizing household employment strategies contributed to the promotion of livelihood capital on the income diversification index, with a mediating effect of 30.52%. The main contribution of this study is to clarify how livelihood capital can achieve the goal of diversifying household livelihood activities and income by coordinating livelihood capacity and employment strategies. Therefore, to enhance the comprehensive livelihood capacity of rural households, the coordination capacity of livelihood capital, livelihood capacity building should be enhanced, and employment information channels should be expanded.

As the final layer for precipitation interception in forests, the litter layer is crucial to the vertical structure of forest ecosystems, situated between the forest vegetation and soil layers. Exploring the litter accumulation and water retention characteristics in different tropical rainforest stands is essential for assessing their water conservation functions. This study focused on three forest stands (primary forest, secondary forest, and rubber plantation) within Qixianling Hot Springs National Forest Park in Hainan, China. The immersion method was used to measure litter accumulation and water retention characteristics, exploring variations among these forest stands. Results revealed (1) significant differences in litter accumulation among the stands, with the primary forest having the highest litter accumulation (2.791 t/hm²), followed by the secondary forest (2.077 t/hm²) and rubber plantation (1.660 t/hm²). Litter moisture content followed the order: primary forest (57.57%) > secondary forest (48.33%) > rubber plantation (46.44%). (2) Regarding water retention and water retention rate, both primary and secondary forests showed an increasing trend with immersion time, with the primary forest exhibiting higher water retention and retention rates than the secondary forest. In contrast, rubber plantations showed no clear trends in water retention or retention rates over time. The relation between litter water absorption rate and immersion time followed a well-fitted power function. The maximum water retention capacities were in the order of primary forest (8.041 t/hm²) > secondary forest (6.251 t/hm²) > rubber plantation (4.896 t/hm²). (3) In terms of water absorption and water loss rates, the rubber plantation had the highest water loss rate, followed by the secondary forest and the primary forest. The maximum water loss was observed in the primary forest (5.225 t/hm²) > secondary forest (4.626 t/hm²) > rubber plantation (4.079 t/hm²), and the maximum water loss rates followed the order: rubber plantation (246.319%) > secondary forest (222.649%) > primary forest (189.748%). The forest litter layer's water retention capacity and storage are closely related, with larger litter reserves correlating with greater water retention. Therefore, among the three forest types, the maximum and effective water interception capacities exhibited the following trend: primary forest > secondary forest > rubber plantation. The maximum and effective interception rates show the pattern: primary forests ≈ rubber plantations > secondary forests. The water conservation function of litter is reflected in its water retention capacity and rate of water loss. Given that primary forests exhibit a higher water retention capacity and lower water loss rates, litter accumulation in primary forests is more conducive to forest water conservation functions. Hence, future efforts should focus on protecting litter from primary forests. Conversely, the litter in rubber plantations tends to have a higher rate of water loss, resulting in poorer water conservation. Therefore, future considerations should include planting understory vegetation in rubber plantations to enhance litter water-conservation capabilities and prevent soil erosion. These findings provide crucial theoretical guidance for forest management and the enhancement of water conservation in tropical regions.

Small tourism enterprises are pivotal in the development and evolution of the spatial dynamics of scenic tourism and are the principal contributors to tourism services and experiences. They constitute a tangible base and medium for developing tourism in scenic areas. However, amidst the challenges of the tourism crisis and disaster management, there is a noticeable deficiency in the theoretical focus and case study analysis concerning small tourism enterprises. This study delves into the effects of the COVID-19 pandemic on the tourism industry and its associated sectors. It emphasizes small tourism enterprises in the Xijiang Qianhu Miao Village scenic region, drawing from case observations and field research alongside comprehensive interviews to gather authentic data. This study utilizes the proceduralized grounded theory to elucidate the inherent principles and mechanisms behind the organizational resilience of small tourism enterprises facing the pandemic's impacts. Additionally, it pinpoints the critical elements influencing the development of organizational resilience in these enterprises. The findings reveal the following: 1) The interplay between organizational capabilities and influential factors collectively elucidates the genesis and robustness of organizational resilience among small tourism enterprises in scenic areas, with resilience emerging and intensifying through the synergistic influence of internal and external organizational elements. 2) Organizational capabilities represent the intrinsic rationale and conceptual framework underlying the development of organizational resilience in small tourism enterprises. Such resilient organizations exhibit fundamental abilities such as environmental observation, impact forecasting, crisis response, and reflective learning. In essence, small tourism enterprises adapt by monitoring and recognizing shifts in the external milieu, predicting the duration and ramifications of the pandemic, and crafting proactive strategies in response to pandemic-induced challenges based on external changes and internal shortcomings. Throughout the pandemic, new insights were assimilated into the operational and developmental strategies of small tourism enterprises through ongoing reflection, summarization, and learning. Prompt adaptations in business approaches contributed significantly to cultivating and reinforcing organizational resilience. 3) Initial resources, social capital, and support initiatives foster organizational resilience among small tourism enterprises in scenic locales. These components are crucial for ensuring organizational survival, enhancing resilience potential, and sculpting organizational resilience. Specifically, foundational resources such as property ownership, operator expertise and skills, and surplus funds are vital for building organizational resilience during a pandemic. Concurrently, the relational networks between small tourism enterprises and their stakeholders, alongside social capital stemming from operators' power dynamics and identity within the tourism context, markedly strengthen organizational resilience. Furthermore, tourism relief policies and interventions that modify tourist influx and business sustainability within scenic areas play a pivotal role in enhancing the resilience of small tourism enterprises. This study broadens the theoretical understanding based on case studies of organizational resilience in small tourism enterprises amid the pandemic. It offers theoretical insights and practical guidance for the ongoing management and adaptive regulation of these enterprises during tourism-related crises and disaster management scenarios.