To enhance the scientific rigor and practical relevance of disaster resilience evaluation in mountainous rural communities, this study developed a multilevel assessment framework based on the Pressure-State-Response (PSR) model by integrating the entropy method and Analytic Hierarchy Process (AHP). The framework comprised three dimensions (pressure, state, and response), nine elements, and 32 indicators tailored to the unique environmental and socioeconomic contexts of mountainous regions. Focusing on four representative communities (Taoyuan, Caogu, Niulang, and Qunying) in the Anning River Basin of Liangshan Prefecture, Sichuan Province, China, a combination of field surveys, GIS spatial analysis, and multi-source datasets were used to empirically evaluate community resilience. The key findings revealed the following: (1) The comprehensive resilience scores ranked Taoyuan > Niulang > Qunying > Caogu. Taoyuan's top performance stemmed from its designation as a national disaster prevention demonstration community featuring robust infrastructure and frequent emergency drills, whereas Caogu's lowest resilience resulted from its high-altitude topography, aging population, and inadequate infrastructure. (2) State resilience contributed most significantly to overall resilience (51.43%), with the building quality (C9) being the pivotal driver. Pressure resilience was predominantly influenced by the proximity to active faults (C2) and population exposure to geological hazards (C6), whereas response resilience relied on disaster-monitoring equipment (C26) and early warning efficiency (C27). (3) A synergistic optimization strategy was proposed, emphasizing risk zoning and engineering controls (pressure layer), housing retrofitting and social capital cultivation (state layer), and intelligent early warning systems integrated with indigenous knowledge (response layer). The study validates the applicability of the PSR model in mountainous rural contexts, highlighting a "state resilience dominance with response capacity gaps" pattern. Notably, communities with higher state resilience demonstrate stronger recovery capabilities despite elevated hazard pressures, underscoring the importance of robust infrastructure and social cohesion. Conversely, insufficient investment in monitoring technologies and external rescue coordination hinders response effectiveness in remote villages such as Caogu. The framework provides methodological support for tailored disaster-prevention planning, particularly in ethnic regions where traditional ecological knowledge complements modern governance. However, limitations include a focus on earthquakes and geological hazards, excluding concurrent multi-hazard scenarios (e.g., wildfires and pandemics), and a static assessment that overlooks temporal resilience dynamics. Future research should incorporate longitudinal monitoring and cross-scale interactions to refine the generalizability of the model. This study advances the theoretical integration of socioecological systems into resilience assessments and offers actionable insights for sustainable rural development in hazard-prone mountainous areas.

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.

Against the background of rapid urbanization, China's districts and counties are characterized by unbalanced, inadequate, and asynchronous development, accompanied by different degrees of population loss. It is of theoretical and practical significance to explore the spatial distribution, evolution, and influencing factors of population shrinkage in county and district units in order to adapt to population shrinkage and formulate locally adapted development plans. Taking Guangdong Province as an example, this study analyzed the spatial distribution characteristics of population shrinkage during 2000–2010 and 2010–2020 based on resident population data at the district and county scales from 2000 to 2020. The study also constructed a socioeconomic-natural-demographic indicator system, explored the factors influencing its formation and evolution from the perspective of non-linear influence with the help of a multi-classification logit regression model and a random forest model, and put forward relevant suggestions. This study has the following results: (1) In the spatial dimension, the population shrinkage areas in Guangdong Province are primarily distributed in the periphery of the Pearl River Delta, with a spatial core-periphery imbalance, as well as differences between counties (including counties and county-level cities) and municipal districts. Among these, counties and county-level cities are the main areas of population shrinkage, characterized by a wide range of shrinkage, a more profound degree, and a more extended period; (2) In the temporal dimension, in the two stages of 2000–2010 and 2010–2020, Guangdong Province has seen an increase in the intensity of population shrinkage, with a trend towards slower, more sustained, wider, and more widespread population shrinkage and a deepening of the shrinkage in the areas adjacent to the nine cities in the Pearl River Delta. The degree of population shrinkage deepened in the eastern part of the northern mountainous region of Guangdong, mainly Meizhou. In contrast, the northern mountainous region of Guangdong, mainly Shaoguan, has gradually recovered from shrinkage; and (3) In the context of globalization, regionalization, and aging, the formation of population shrinkage areas in Guangdong Province is affected by the interaction of multiple factors in the four dimensions of demographic structure, production, life, and nature, with complex mechanisms and different impacts on different types of population shrinkage. Persistent population shrinkage is mainly affected by the demographic structure, especially the increasing aging problem, which leads to a long-term stable population decline. At the same time, economic and social factors also impact the continuous population shrinkage. Additionally, the policy regulation of ecological reserves, which has a direct impact on population distribution and mobility, cannot be ignored. However, in addition to the endogenous factors of the population, intermittent shrinkage is also affected by social and economic aspects such as industrial adjustment and fiscal expenditure, which may lead to fluctuations in economic activities in the short term and thus affect the population distribution.

With the rapid growth of the digital economy, integrating the cultural and tourism industries has become a key driver of regional economic development and industrial upgrading. As a vital component of the cultural and digital sectors, the gaming industry facilitates integration through digital innovation and creative design. While existing research on cultural-tourism integration is extensive, little attention has been paid to how virtual cultural symbols transform and drive this process in the digital era. Recent advancements in gaming have blurred the boundaries between virtual and real experiences through virtual scene construction, cultural symbol reproduction, immersive interactions, and social media dissemination, accelerating the transformation of cultural resources into tourism assets. Understanding how the gaming industry promotes cultural-tourism integration enhances existing research frameworks, deepens insights into the dissemination and reproduction of cultural symbols in the digital economy, and offers new regional cultural tourism development strategies. Using Black Myth: Wukong as a case study, this research applies the field conversion theory to examine the flow and transformation of cultural symbols between virtual and real-world tourism contexts. It explores two key questions: (1) How does the gaming industry reconstruct traditional cultural symbols through digitalization and integrate them into real-world tourism using field conversion mechanisms? and (2) How does the participation and feedback of different groups influence the effectiveness of this integration, shaping the gaming industry's role in regional cultural tourism development? The findings indicate that digital technologies not only overcome spatial constraints on cultural resources but also enhance interactivity and dissemination, promoting the transformation of symbolic capital into cultural, social, and economic capital. However, engagement levels varied across groups. Players deeply immersed in virtual cultural symbols strengthened the connection between gaming and real-world tourism through social media, offline activities, and digital communities. In contrast, non-players rely on traditional tourism information sources and respond passively and indirectly to game-driven cultural symbols. This study identifies capital accumulation, habit migration, and stakeholder collaboration as the core mechanisms facilitating cultural-tourism integration. While gaming fosters cultural identity, tourism consumption, and economic diversification, it also presents challenges, such as infrastructure strain and tourism industry homogenization due to sudden visitor influxes. This research expands the scope of the theory's application by integrating the field conversion theory into the study of gaming and cultural-tourism integration. It examines how cultural symbols gain value through cross-field transformations. Furthermore, it highlights how digital games that leverage virtual reality, short videos, and social media facilitate cultural symbols' cross-regional flow and reproduction. Moving beyond static cultural transmission models, this study reveals the dynamic evolution of virtual culture and offers fresh perspectives on the development of the cultural industry in the digital economy.

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.

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.

With the rapid development of tourism, conflicts between the protection and utilization of tourist destinations have become increasingly prominent. The effective promotion of sustainable development of tourist destinations has become an important issue in academic circles. The essence of the conflict between the protection and utilization of tourist destinations is the disharmony and imbalance between the protection and utilization of tourist destinations. The root cause of this conflict is the restriction of the policy system and the difference in the interest demands of multiple subjects in the protection and utilization of tourist space resources, which leads to various disputes, contradictions, or opposites. Based on a review of relevant research at home and abroad, this study systematically reviews and summarizes the conceptual connotation, basic theory, identification and classification, feature analysis, occurrence mechanism, and adaptive governance of the conflict between tourism destination protection and utilization. The results show the following: 1) Based on a multidisciplinary perspective, scholars have enriched and refined the conceptual connotation and basic theory of the conflict between tourism destination protection and utilization, have gradually paid attention to the specific demand conflict between different stakeholders in tourism destination protection and utilization, and have attempted to reveal its intrinsic nature and development trends; 2) In terms of identification and classification, owing to the significant differences in research areas and perspectives, the types of conflict between the protection and utilization of tourist destinations also show a diversified trend; 3) Researchers mainly analyze features from the perspectives of subject, time, and space, and the conflicts between tourism destination protection and utilization are characterized by diverse interest subjects, complex spaces, and stages; 4) In terms of the occurrence mechanism, the research mainly explored the driving factors from the macro perspectives of policy system, environment, economy, and social culture, and micro perspectives of subjects' cognition, attitude, and behavior. The macro policy system and micro-subject perceptions were the focus of this study; 5) In terms of adaptive governance, research countermeasures mainly promote the organic combination of macrospatial governance and microsubject regulation to achieve the effect of adaptive governance. Macro-spatial governance provides an overall framework and directional guidance for the development of tourism destinations, while micro-subject regulation ensures that all stakeholders can act reasonably within this framework and jointly promote the sustainable development of tourism destinations. And finally, this research proposes that future research should include supplementing and improving the theoretical system of tourism destination protection and utilization conflict in the context of social change, expanding and deepening the research content of tourism destination protection and utilization conflict in the context of sustainable development, strengthening the research method innovation of tourism destination protection and utilization conflict with the support of geospatial information technology, and promoting the integration of tourism destination protection and utilization conflict in the perspective of multidisciplinary integration, combined analysis and application of results.

As an important economic form for the innovation and development of human society, since the 21st century, the low-altitude economy has become one of the key ways to promote China's modern industrial development, with its unique charm and broad application prospects. Research on the low-altitude economy of manned vehicles has a long history. However, this research is still growing; and there are many ambiguities within the human-cultural research of low-altitude economies. Therefore, it is necessary to review the existing results so as to promote the development of a theoretical and methodological system for low-altitude economic research. Based on data from the Web of Science (WOS), Scoups, and CNKI databases with the low-altitude economy as the research theme, the CiteSpace software was used to comprehensively sort out the publication status, research content, and research lineage of domestic and foreign low-altitude economy research. This study analyzes and summarizes the characteristics of the publication, subject matter, and characteristics and trends of the various phases. The results show that: (1) Although the leading figures and research teams of domestic and foreign low-altitude economy research have initially appeared, there are fewer cross-institutional academic contacts and a broad academic consensus is yet to be formed. (2) There are also differences in the development history and research characteristics of domestic and foreign low-altitude economy research, but in the early stage of the research, the focus is mainly on general aviation. It then moves to a new era of drone dominance that is centered on the low-altitude economy after 2010. (3) As is, foreign studies have focused on, for example, the exploration of UAV models and their engineering technology, UAV traffic management systems, UAV application scenarios. On the contrary, Chinese studies focus on the reflection and summary of the reform of low-altitude airspace, construction of UAV systems and their industrial development, etc. (4) The directional shift of the low-altitude economy from the natural space to the human-cultural space is an important feature in this growth period but the current low-altitude economy research on human culture is still in the trial phase. Therefore, it is recommended that scholars establish a scientific system suitable for the development of low-altitude economies in China, expand research perspectives and interdisciplinary cooperation, and strengthen extensive communication and exchanges among scholars, which helps construct a diversified inter-institutional, interdisciplinary, and inter-geographical cooperation network. In addition, it will deeply excavate the human-earth relations and spatial organization concepts in the study of low-altitude economies and integrate natural-social-economic-humanities perspectives on intelligent, synergistic, and sustainable development of UAVs, as well as systematically expose the UAV industry chain, UAV-related supporting facilities, market consumption, and public attitudes. This study aims to accelerate the high-quality development of China's low-altitude economy and comprehensively promote its modern development.

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.

Intangible Cultural Heritage (ICH) labels represent elements of ICH, highlighting the features and attributes of ICH products or destinations and reflecting optimized ICH tourism resource allocation. However, destinations often misunderstand ICH label implications and mechanisms, leading to issues such as over-commercialization due to a lack of regulation. This study adopts a tourist micro-perspective to deepen the understanding of ICH label connotations and origins, and to explore their impact on destination perception and potential value in cultural and tourism integration and new productive force development. Given the infancy of ICH label research and unclear conceptual understanding, this study sought to explore the relationship between ICH labels and tourist destination perception through in-depth interviews and grounded theory. Based on 25 in-depth interviews, the study clarified the unique essence of ICH labels as geographical indications recognized by local governments based on local culture, integrating intrinsic and extrinsic values, and being both reliable and distinctive. This study also elucidates how ICH labels affect destination perception: cultural empowerment is fundamental to ICH label formation, label value and attributes are key expressions of local cultural empowerment, diverse stakeholders promote sustainable ICH label development and regulate market activities, and online and brand marketing effectively influence tourist perceptions of destinations. The research contributes in three ways. First, it analyzes the relationship between geographical indications and ICH labels, clarifying their connotations and origins, strengthening the link between ICH and local culture, and broadening heritage research perspectives. It deepens the analysis of the cultural factors behind spatial phenomena and enhances the conceptual refinement and framework of heritage tourism theory, emphasizing the role of tourism in dynamic heritage protection. Second, it explores the role of ICH labels as innovative labor material factors, systematically explaining their impact on destination perception. This study found that ICH labels influence perceptions of ICH resources, tourism infrastructure, services, and experiences, reflecting how ICH and inheritor dynamics affect local development and discusses the utility of ICH labels. Third, the essence of ICH is shaped by local and translocal geographical and cultural practices involving diverse actors. This study reveals that the government, ICH inheritors, tourists, and businesses play significant roles in the ICH label mechanism, responding to the ICH and social justice initiative, an important topic at the intersection of ICH and geography, providing a theoretical basis for fairer ICH label development. The detailed insights presented here are intended to guide policymakers, tourism professionals, and cultural heritage managers in their efforts to harness the potential of ICH labels to benefit local communities and the tourism industry.

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.

Coastal cities are highly vulnerable to compound flooding in which multiple flood drivers interact via complex nonlinear mechanisms under climate change. Although numerous studies have focused on individual flood drivers, integrated analyses of the spatiotemporal variations and compound effects remain limited. This study applied a high-resolution MRI-AGCM3-2-S climate model and the TempestExtremes tracking algorithm to construct a 6-hourly Tropical Cyclone (TC) track dataset affecting Haikou from 1960 to 2099. Storm tides during the TCs were simulated using the D-Flow FM model, whereas upstream river discharges were modeled with CaMa-Flood, incorporating climate-model-derived runoff data. Using rainfall data from the climate model, we applied the peak-over-threshold method and extreme value analysis to systematically assess changes in storm tides, rainfall, and upstream discharge under climate change. These analyses guided the construction of compound flood scenarios for simulating extreme events. Using a compound flood simulation model, we assessed the hazards under 10-year and 50-year Return Periods (RPs) for historical (1960–2014) and future (2015–2099) periods. Results indicate that significant differences exist in the compound flood characteristics between historical and future periods. In the 90^th percentile scenario, all three flood drivers exhibited higher future thresholds, suggesting an increased risk of compound extreme flood events. The probability of concurrent heavy rainfall and high discharge events increased by 40.9%, whereas the probability of simultaneous high storm surge and high discharge events increased by 58.3%. Despite the potential reduction in extreme event intensity, the frequency of compounding events has increased significantly. Extreme value analysis revealed that extreme storm surges and upstream discharge events became more severe and extreme rainfall events showed a decreasing trend. For high RPs (e.g., 50-year events), the projected storm tides and upstream discharges significantly exceeded historical levels. Specifically, projected increases in storm surge levels (+0.24 m under 50-year RP) and upstream discharge (+1,271.13 m³/s) are offset by a 16.5% decline in 100-year accumulated rainfall for Haikou when compared to historical period. Third, compound flood simulations showed that under the 10-year RP scenario, the total inundation area slightly increased, but the flood volume and maximum depth decreased, indicating the stabilization of the flood hazard. However, under the 50-year RP scenario, both the inundation area and flood volume increased substantially, with the area experiencing flood depths greater than 3 m expanding by 56.5%. The most severe flooding occurred along the northern coastal areas and banks of the Nandu River, where the inundation extent and flood severity increased markedly. These findings provide valuable insights for flood risk assessments and adaptive planning in coastal cities facing intensifying climate-induced hazards.

Typhoons are among the most destructive natural disasters affecting China's coastal regions, often resulting in substantial economic loss and casualties. The annual average Direct Economic Loss (DEL) caused by typhoon disasters in China exceeds 60 billion yuan, accounting for 10%-30% of the DEL caused by all disasters each year. Consequently, the accurate assessment and prediction of typhoon-induced DEL are essential for improving disaster mitigation strategies and optimizing resource allocation. Rapid development of artificial intelligence and the growth of multi-source spatiotemporal big data have introduced data-driven methods for assessing disaster losses. These methods have the advantage of using large samples to improve adaptability and consider more risk factors. In this study, DELs of 30 typhoon events in Fujian Province at the county level and a total of 911 samples were collected from 2009 to 2021 to establish an assessment model. Owing to the large range of the DEL in different districts and counties during the same typhoon, the logarithm of the DEL was used as the model output. This study included three steps for constructing the model. First, 24 influencing factors of typhoons, including disaster-inducing factors, disaster-forming environmental factors, and disaster-bearing body exposure factors, were calculated using the Pearson correlation coefficient and variance inflation coefficient to analyze the multicollinearity effect, and 20 key factors were selected to assess the DEL. Second, a LightGBM-based model is developed using the selected indicator factors as model inputs. Of the 911 samples, 734 were used to train the model, and 177 were used for validation. Finally, Super Typhoon Meranti was used as a case study to evaluate the applicability of the model in the dynamic DEL assessment of a typhoon. This study evaluated predictive performance of the model using five indicators: the Pearson correlation coefficient (R), coefficient of determination (R ²), mean squared error, mean absolute error, and median absolute error. The importance of LightGBM factors shows that the maximum daily wind speed, river network density, maximum daily precipitation, cumulative precipitation, and GDP per unit area are the primary determinants of typhoon-induced economic losses in Fujian Province. In the training set, R between the predicted results of the model and the actual loss was 0.836, and R ² was 0.66, indicating good fitting ability. In real-world applications, the proposed model effectively captured the spatial distribution of losses from Typhoon Meranti, demonstrating its potential for disaster loss prediction. This study provides valuable insights into typhoon risk assessment and emergency management in Fujian Province and other coastal areas. We sorted the relevant research literature and found that economic loss assessment is more difficult than population, housing, and other loss assessments because economic loss is a comprehensive statistical indicator in China. Therefore, we drew on the method of processing DEL as logarithms from the literature. By comparing with other studies, the results of this study can improve model performance in terms of data quality inspection and sample size.

With the acceleration of climate change and urbanization in recent years, extreme rainstorms and urban flooding have increasingly threatened urban safety. Their impact on cultural, commercial, and tourism industries is widespread and significant, often leading to traffic paralysis, closure of tourist attractions, business shutdowns, and passenger stranding. In severe cases, this can endanger personal safety and result in significant economic losses. Shanghai, a representative coastal tourist city in China, is highly prone to rainstorm-induced flooding events from June to October each year due to the Meiyu front, extreme rainstorms, and typhoons. Conducting flood inundation simulations in Shanghai during the flood season is essential to identify high-impact urban flood areas and evaluate flood effects on densely populated cultural, commercial, and tourism hubs. This study used daily rainfall data from Shanghai between 1990 and 2020 to construct nine rainstorm scenarios based on three flood season periods (Meiyu, midsummer, and autumn) and three rainfall thresholds (maximum, 99th, and 95th percentiles). Using the SCS-CN and Mike21 hydrodynamic models for urban rainstorm flood simulations, a fuzzy comprehensive evaluation index system was developed based on a combination of Analytic Hierarchy Process(AHP) and Entropy Weighting Method (EWM) to assess the impact of flooding on Shanghai's cultural and tourism cluster areas. Results indicate the following: (1) Shanghai experiences the highest impact from rainstorm-induced flooding in the midsummer period. In the 95th percentile scenario, suburban areas experience minor flooding, whereas in the maximum value scenario, central urban areas experience a significant increase in flooding impact. (2) Control rules effectively improved the rationality and adaptability of the flood impact evaluation system. Resident and transient populations are key factors in evaluating flood impact. The flood impacts in Shanghai's cultural and tourism clusters showed significant spatial and temporal gradient characteristics, with medium-to-high- and high-impact areas primarily concentrated in the central urban cultural and tourism clusters. (3) Midsummer had the largest medium-to-high and high-impact zones, reaching 3.1 km² (8.79% of the total area), followed by the Meiyu period, whereas the autumn period has the smallest impact. (4) During midsummer, the largest proportion of high-impact areas was found in street- and road-type clusters, followed by waterfront leisure and comprehensive cultural tourism clusters, with areas accounting for 27.52%, 8.30%, and 6.44%, respectively. Cultural and tourism clusters should strengthen early warning, regulation, and preventive measures based on seasonal variations, especially during midsummer, when effective countermeasures must be implemented to reduce flooding impacts on visitor experience and regional safety. This study provides valuable insights for urban flood forecasting, early warning, and emergency response, as well as recommendations for sustainable development of the urban cultural, commercial, and tourism industries.

As the pace of life accelerates and the demand for tourism quality increases, slow tourism, which emphasizes experiences, relaxation, and sustainability, has emerged. However, slow-tourism behaviors and perceptions differ widely across different urban contexts. We applied the basic framework of landscape perception theory to popular Citywalk routes in Chengdu, Wuhan, and Shanghai, which were obtained from the Xiaohongshu platform. By integrating spatial, multimodal data, and content analyses, as well as other methods, we explored the spatial behavioral patterns, perceptual differences, and the associated mechanisms of tourists during Citywalk activities in different urban contexts. The findings indicate that Citywalk activities mainly occurred within the second rings of cities, representing small-scale urban exploration that emphasizes experiential feelings over conventional mobile tourism. Tourists preferred culturally and artistically vibrant urban destinations. Citywalks are generally free, thereby embodying a subcultural phenomenon that contrasts with the stressful rhythm of life emitomized by "involution" and "996" work culture. Notable differences in cognitive imagery, emotional imagery, and cultural perception were present among the tourists in different cities, which shaped unique urban Citywalk tourism experiences. Based on different models and perceptual differences, Chengdu's Citywalk was defined as "a slow city tour centered around creative cultural districts that blends creative spaces and gourmet exploration," whereas those in Wuhan and Shanghai were defined as "a slow city tour centered around historical architecture, that blends cultural spaces and natural scenery" and "a slow city tour centered around urban landscapes that blends humanities, arts, and modern fashion," respectively. Differing geographical locations, planning concepts, development orientations, and historical backgrounds affected the Citywalk tourism experiences by influencing aspects such as the natural environment, spatial layout, developmental direction, and cultural characteristics of each city, which created different place perceptions. Geographical location affects the natural environment, tourism facilities, and cultural atmosphere of a city, whereas planning concepts influence urban spatial layouts, functional zoning, and the mode of tourism resource development, which affect the form and experiences in slow tourism. Development orientation determines the development direction of a city, thereby crafting unique attractions. Differing historical backgrounds create distinct urban cultural features, lifestyles, and tourism resources, which affect the direction of slow-tourism development. The findings of this study present the differences in Citywalk behaviors and perceptions in various urban contexts, filling a gap in comparative studies of cities within slow-tourism scenes. The findings also provide a new theoretical perspective for understanding the interactions between tourism behavior and urban spaces and offers reference experiences for other cities to develop slow tourism, enhance urban cultural tourism competitiveness, and promote sustainable urban tourism development.

In recent years, the number of marathons in China has expanded rapidly, but the impact of high-temperature weather has become more apparent. Current high-temperature early warning systems lack tailored thresholds and unified standards for sports scenarios. This study aimed to develop an advanced early warning system for high-temperature exposure during marathons to enhance event safety and sustainability. The urgency of addressing high-temperature risks in sporting events is underscored by an increase in the frequency and severity of extreme heat events. These events not only threaten participants' health, but also challenge the organizational resilience of sporting events. Traditional early warning systems, designed primarily for general public health protection, fall short of providing the specificity required for sports settings. This study addresses this gap by proposing a refined early warning framework that is sensitive to the unique demands of marathons. Methodologically, this research moves beyond the limitations of a single air temperature index by employing the Wet-Bulb Globe Temperature (WBGT) as the primary indicator for thermal environment assessment. WBGT is recognized as a comprehensive metric that integrates temperature, humidity, and radiant heat, making it more suitable for evaluating heat stress during outdoor activities. By analyzing the relationship between human thermal comfort and meteorological factors, the study maps the Thermal Humidity Index (THI) sensory grading criteria to the WBGT system, creating a dynamic "red-orange-yellow" three-level early-warning system. The threshold setting considered the metabolic heat accumulation of marathoners during prolonged activity and was validated using six decades of national-scale meteorological data. Based on this, this study introduced the Marathon Exposure Index (MEI), which quantifies risks from three dimensions: exposure intensity (early warning level weight), exposure quantity (event frequency), and exposure value (event-grade coefficient). Results indicate a significant "long-south-short-north" pattern in China's marathon high-temperature exposure period. Southern regions, such as the Yunnan-Guizhou Plateau and the southeastern coast, have experienced extended high-temperature exposure periods compared with the historical baseline (1961-1990), with frequent red-alert zones coinciding with high-density Class A event areas (such as the Yangtze River Delta and Pearl River Delta). Further temporal analysis revealed that with accelerating global warming, extreme high-temperature red-alert events in China are becoming more frequent and prolonged. The innovative value of the study's findings is reflected in three key aspects. The early warning mechanism design established a graded-threshold dynamic-response-linked paradigm. By linking WBGT thresholds with event response measures, it enables a management paradigm shift from "passive response" to "proactive prevention and control," aligning general meteorological early-warnings with event safety management. In assessment technology, it breaks the traditional single-factor analysis framework, integrating an "intensity-quantity-value" three-dimensional model for comprehensive risk evaluation, integrating a refined early warning system with region-specific management measures, this approach ensures the safe operation and sustainable development of events. In practical applications, the proposed dynamic circuit-breaking response mechanism (e.g., event cancellation upon a red alert) was validated through situational simulations to significantly reduce heat-related injury rates and provide more forward-looking warning and response measures. Additionally, the research findings are broadly applicable to other outdoor sports and provide robust theoretical and practical tools for ensuring the safety of public sports activities amid climate change. The implications of this study extend beyond immediate applications to marathon event management. This study contributes to a broader discourse on climate change adaptation strategies in the sports and public health sectors. By offering a flexible and scalable framework, this study will enable stakeholders to tailor heat risk management strategies to diverse regional and event-specific contexts. Future research could explore the integration of real-time weather forecasting and participant physiological data to further enhance the precision and responsiveness of high-temperature early warning systems in sporting events.

With the trend of the "Internet of Everything" breaking the time and space boundaries of information dissemination, virtual and real interactive activity spaces are gradually replacing physical activity spaces and becoming the dominant form of carrying out human activities. For the tourist town, the "user-generated content" and community sharing platform represented by TikTok not only improves the convenience of tourists in planning trips, booking products, obtaining real-time information, and sharing experiences, but also enhances tourists' perception and interest in the living environment and cultural atmosphere of the ancient town. Under the background of the integration of virtual and real in the digital age, it is very important to explore the spatial characteristics and internal relations of the online and offline heat of the ancient town for understanding the phenomenon of large-scale tourist gathering. From the perspective of environmental behavior and attention economy theory, this study used multi-source heterogeneous data and spatial econometric analysis methods to take Dayan Ancient Town in Lijiang as an example to explore the following: 1) What are the spatial performance characteristics of online and offline popularity in tourist towns? 2) What is the potential relationship between online and offline popularity in tourist towns? The results were as follows: 1) The spatial correlation between online and offline popularity heat in Dayan Ancient Town was high, and the overall distribution was extremely uneven. The top 10% of the space unit's online popularity contributed to more than 90% of the traffic and attendance, showing clear power-law attenuation characteristics. The top 10% of the space units of offline popularity contributed more than 33.6% of the total tourists, showing a tourist gathering mode with Sifang Street as the core and decreasing to the periphery. 2) The spatial and temporal differentiation of tourist volume in ancient towns was the result of the interaction between online and offline environments. In the online dimension, the concentration of tourists, number of digital content punch cards, and number of digital content views formed a positive promotional effect. In the offline dimension, tourists' mobile behavior was positively affected by shopping service facilities, attractions, leisure and entertainment facilities, functional density, building density, and sDNA (spatial Design Network Analysis) proximity (r=400 m), and negatively affected by infrastructure, educational service facilities, and sDNA accessibility (r=n). Tourists' calling behavior was positively affected by accommodation service facilities, infrastructure, and functional density. 3) The online and offline popularity of ancient tourist towns was transformed by the influence of digital media traffic on tourists' punching and mobile behavior. In this process, the environmental characteristics and cultural landscapes of traditional villages were packaged as tourism attractions. Local tourism resources were transformed into digital content through tourists' card-making behavior. High-quality digital content accumulated attention capital through traffic transmission, drove tourist movement and consumption demand gathering, and created a new "net red card" in the physical geographical space.

Global climate change has increased the frequency and severity of urban flooding, posing significant risks to critical infrastructure. The resulting disruption of essential services has profoundly impacted the daily lives of urban residents and, in extreme cases, endangered their safety. A systematic framework has been developed to address this, integrating flood process simulation, critical infrastructure modeling, and social vulnerability analysis. This framework elucidates the complex interdependencies among urban infrastructure systems, evaluates the impacts of flood-induced service disruptions on urban populations in the context of climate change, and assesses the resilience of various infrastructure services. The Shenzhen-Maozhou River Basin, prone to flooding, was selected as the study area. Based on the Delft3D model and using the "Mangkhut" typhoon event as a benchmark, rainfall and sea-level rise were selected as uncertainty factors to simulate and identify three future extreme flood scenarios. A network-based approach was employed to construct an infrastructure system network model that included seven types of infrastructure: substations, communication base stations, hospitals, fire stations, police stations, shelters, and water supply plants. The flood simulation results were used as inputs to the infrastructure system network model to obtain the simulation results, which were then analyzed. The results revealed the following. 1) Within the system's topological structure, the degree value of substation nodes is significantly higher than that of other facilities, making them a critical node for cascading failures triggered by floods. The power-outage areas simulated by the constructed model demonstrate a 62% concordance rate with the historical validation data, indicating a relatively high credibility level. However, due to the sensitivity and confidentiality of the data, the validation work is not yet sufficient. The types of infrastructure involved in the validation are limited, affecting the model's reliability and parameters. Therefore, in future research, it is necessary to collaborate with relevant stakeholders to obtain the relevant data, which will be used for model validation and parameter calibration, to enhance the model's reliability. 2) During the floods triggered by Typhoon Mangkhut, the cascading effect significantly increased the number of fire stations and police stations affected, which rose from 1 to 11 and from 1 to 7, respectively. Meanwhile, the disruption of communication and medical services had a more pronounced impact on the urban population, with the ratio of the population affected by service disruptions exceeding 5 (a ratio of "1" represents 140,672 people). 3) Under the backdrop of climate change, the disturbance of future extreme flood disasters on the infrastructure system network is significantly intensified. After taking into account the cascading effects, the overall number of affected infrastructure facilities is, on average, 38% higher than the baseline scenario of "Mangkhut" 4) Thanks to the relatively rational spatial layout and the flood resistance of the facilities, the power system, emergency response (covering police and fire services), and shelter services in the Maozhou River Basin have demonstrated a certain degree of stability. This study helps clarify the complex interdependencies among urban infrastructures, assess the impact of floods on the stability of service systems, and identify potential cascading effects on residents ' lives. It provides decision-making support for urban disaster prevention, mitigation planning, and emergency response strategies.

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.

In recent years, the integration of red culture into rural areas has led to a remarkable upsurge in red tourism in the revolutionary old areas. The residents of these tourist destinations play a crucial role as carriers and stakeholders in tourism development. However, the role of residents' red culture-inspired awe in tourism development and its underlying mechanisms have not yet been thoroughly explored. Against this backdrop, this study aimed to fill this research gap. This study is firmly grounded in the broaden-and-build theory of positive emotions. To achieve the research objectives, a quasi-experimental design and a field survey method were employed. In the quasi-experimental study, materials related to the red culture of Jinggangshan were carefully selected to induce awe. Participants were randomly assigned to experimental and control groups and a series of measurements were conducted, including assessments of red culture-inspired awe, red cultural identity, and support for tourism development. For the field survey, the Jinxiang Coastal Red Tourism Area in Lufeng City, Guangdong Province, was chosen as the research site. Questionnaires were designed and distributed to collect data on the relevant variables after conducting reliability and validity tests. Our study revealed several significant findings. First, awe inspired by red culture has a direct and positive impact on residents' support for tourism development. This indicates that in the context of red tourism, residents' awe towards local red culture can effectively stimulate their prosocial behaviors. Second, red cultural identity mediates the relationship between red culture-inspired awe and support for tourism development. It was found that when residents experienced a higher level of red culture-inspired awe, their identification with red culture became stronger, which in turn led to a greater inclination to support tourism development. Third, trust in the government also serves as a mediator. Red culture-inspired awe can enhance residents' trust in the government, and this trust significantly influences their attitude towards tourism development policies and their willingness to support tourism. Finally, there exists a serial mediating effect of red cultural identity and trust in the government in this process. This study made several important contributions. Theoretically, this broadens the application scope of the broaden-and-build theory of positive emotions in the field of red tourism, providing a new perspective for understanding resident attitudes towards tourism development. It also deepens our understanding of the role of emotions in promoting cultural identity and trust in the government. This study offers valuable suggestions for sustainable development of red tourism. For example, it emphasizes the importance of protecting and inheriting red cultural resources to enhance residents' feelings of awe, promote residents' in-depth identification with red culture through various means, and establish a transparent policy communication mechanism to strengthen residents' trust in the government. Future research should expand the sample range and explore the dynamic changes and long-term effects of red culture-inspired awe to provide more comprehensive theoretical support and practical guidance for the development of red tourism.

Rapid urbanization and geological disasters pose significant challenges to regional ecological security. Although Ecological Security Pattern (ESP) construction is important for ecosystem stability and sustainable development, traditional approaches rarely incorporate vertical geological factors, such as land subsidence. This study proposes a framework that integrates land subsidence into ESP construction through machine learning and multi-source data fusion methods. Using Zhuhai City as a case study, we analyzed 30 environmental variables, including historical land subsidence data, topography, soil distribution, land use, climatic factors, and human activity indicators. The methodology consisted of four main steps: (1) correlation and principal component analyses to identify key factors and reduce dimensionality; (2) development of a multilayer perceptron (MLP) deep learning model with three fully connected hidden layers using ReLU activation functions and dropout regularization to predict ecological pattern types; (3) comparison of four fusion methods (weighted average, nonlinear sigmoid transformation, information entropy, and principal component analysis) to integrate prediction results; and (4) spatial analysis of the relationship between land subsidence and ecological security patterns using chi-square tests and spatial overlay analysis. Results showed that the MLP model achieved an average prediction accuracy of 84.5% with an F1-score of 0.844, demonstrating the feasibility of deep learning approaches in ESP construction. The principal component analysis showed that the first four principal components cumulatively explained 71.4% of the total variance, with the first two components explaining 27.1% and 19.8%. The first principal component was dominated by climatic factors, whereas the second primarily reflected the topographic and geological vulnerability characteristics. Spatial analysis revealed significant spatial heterogeneity in the impact of land subsidence on the ESP, with moderate historical subsidence (8-41 mm/year) showing more notable effects (x²= 57.008, P<0.001). Land subsidence in the 8-16.5 mm/year range showed particularly significant differences in the corridor areas compared to the non-subsidence zones (P = 5.7e-05). Source and construction areas exhibited higher proportions of mild subsidence (7.14% and 9.84%, respectively), which should be prioritized for monitoring and management. Different fusion methods showed varying effectiveness. Principal component analysis and information entropy performed better in identifying construction and corridor areas, whereas nonlinear fusion showed advantages in source area identification. This study makes three key contributions: (1) it establishes a novel methodological framework for incorporating vertical geological factors into ESP construction, addressing a significant gap in traditional approaches; (2) it quantitatively reveals the spatial heterogeneity of land subsidence impacts on different functional ecological zones, providing evidence-based guidance for targeted management; and (3) it demonstrates the effectiveness of deep learning and multisource data fusion techniques in complex ecological-geological system modeling. These findings provide methodological support for developing an ecological security pattern centered on coastal wetlands and estuarine systems in Zhuhai City and suggest potential approaches for coordinating ecological protection, disaster prevention, and urban development under land subsidence conditions. Future research should focus on utilizing high-resolution spatiotemporal data, refining algorithms, and developing mechanisms to translate research findings into practical urban planning and ecological management policies.

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.

Historical precipitation data are crucial for assessing the risks associated with natural disasters such as droughts and floods. However, some extreme precipitation scenarios may not have been included in historical records, particularly in China where the observed precipitation time series is relatively short compared to the return periods of rare extremes. This limitation poses a considerable challenge in disaster risk assessment, because the absence of data on certain extreme events can lead to risk underestimation. Therefore, the generation of spatially correlated stochastic precipitation events based on historical data is a key issue in disaster risk assessment. Current methodologies tend to focus on generating stochastic precipitation events for either a single site or a small number of sites. However, methods designed to generate stochastic precipitation events on large-scale grids have not yet been fully developed. To address this gap, we aimed to explore a method for generating daily stochastic precipitation events set at a 0.1° grid scale nationwide based on empirical orthogonal function (EOF) analysis and probabilistic fitting of principal component coefficients. We applied the EOF analysis method to decompose daily precipitation data for China from 1961 to 2022 (62 years). For each day of the year, 62 spatial modes and their corresponding mode coefficients were generated. Multiple probability distribution functions were used to fit the probability distributions of the mode coefficients for each day, with the optimal fitting function selected for each day. Based on these probability distributions, thresholds were set using twice the maximum and minimum values of the historical mode coefficients as the upper and lower boundaries, respectively. Monte Carlo sampling of daily precipitation scenarios was conducted using the 62-year historical data (1961-2022). Finally, using 62-year historical data (1961-2022), we performed Monte Carlo sampling to generate daily precipitation scenarios. To compare the consistency and differences between historical and stochastic precipitation characteristics, 5000 years of simulated daily precipitation events were generated. A comparative analysis was conducted using five statistical metrics: maximum value, mean, standard deviation, typical return period precipitation, and spatial correlation. The analysis results show that: (1) The stochastic precipitation adequately preserved the intensity-probability characteristics of historical precipitation, with the average difference between the two at the grid scale being less than 0.9 mm, which is considered negligible. The differences in the precipitation intensities for return periods of 10, 20, and 50 years were all less than 15%, and the differences in their standard deviations were all less than 8%. (2) The stochastic precipitation effectively extended the upper bound of the annual maximum values, with the maximum value in the grid with the greatest difference being 36% higher than the historical precipitation. (3) The stochastic precipitation maintained a good spatial correlation, with the daily Moran's index and Pearson correlation coefficient for all grids across the country having minimum values greater than 0.96 and 0.95, respectively. The national daily precipitation stochastic event set, based on empirical orthogonal decomposition, provides a robust data foundation for subsequent quantitative disaster risk assessments.

Disaster prevention and mitigation policy texts serve as a guidance and basis for the government to respond to disasters. They contain rich information on disaster risk factors, records the degree of damage caused by disaster hazard factors to disaster-bearing bodies, and provide disaster prevention measures. Risk factors form the foundation of a disaster risk assessment index. This study considered storm surges as an example and deconstructs risk factors into three dimensions-hazard, vulnerability, and disaster resistance capacity–by integrating disaster prevention and mitigation policy texts. Text mining techniques were used to analyze the composition and evolution characteristics of risk factors in policy contexts, with a focus on emphasizing disaster prevention and reduction at different stages. This study constructed a policy text-driven theoretical framework for disaster risk assessment, overcoming the limitations of traditional indicator systems that rely on statistical data and expert experience, and revealed the key role of institutional factors in risk formation. The results are as follows: (1) Policy texts can be used to extract a large number of storm surge risk factors, with hazard factors linked to high-frequency terms such as "sea level rise" and "typhoons," vulnerability to "coastal areas", "coastal zones", "land use", etc., and disaster resilience to "engineering defense," "financial support," etc. (2) There are significant differences in the focus on risk factors in policy texts at different stages. Before 2010, the focus was on identifying and monitoring disaster risks. From 2010-2015, the focus was on further refining the vulnerabilities of disaster-bearing bodies. After 2015, there was greater emphasis on the role of technological development in disaster resistance. These changes reflect the gradual deepening of policymakers' understanding of storm surge disaster risks. (3) The elements extracted from the policy text, such as "astronomical tide," "land reclamation," and "disaster insurance," have compensated for the neglect of human intervention and institutional factors in traditional indicator systems. This study pioneered a new paradigm of policy text analysis in disaster risk assessment at the methodological level, breaking through the traditional reliance on structured data in storm surge disaster risk assessments. Policy evolution analysis revealed changes in risk concerns.

The transformation of livelihood tourism is a key factor for fishermen in optimizing their livelihood strategies and achieving sustainable development. This is an effective strategy for improving the marine ecological environment, protecting marine biodiversity, and supporting the development of destination tourism. Recent research has demonstrated that the transformation of livelihood tourism is one of the most promising strategies for rural tourism destinations. In addition to considering the individual livelihood capital status, livelihood tourism transformation must assess the risks and uncertainties that may exist. Based on sustainable livelihood and prospect theories, this study considered the unique characteristics of coastal fishermen and established a livelihood capital evaluation index system for them. An integrated model was constructed using livelihood capital, risk cognition, and livelihood tourism transformation intention. An empirical test was conducted using 310 valid questionnaires obtained from a survey of fishermen in Tanmen Town, Qionghai City, Hainan Province, China. The results were as follows. (1) The overall level of sustainable livelihood among coastal fishermen was relatively low, with significant differences in its dimensions. The level of natural capital was relatively high, with a livelihood capital value of 0.646. Human and material capital were at moderate levels, with livelihood capital values of 0.555 and 0.510, respectively. Financial and social capital levels were relatively low, with livelihood capital values of 0.280 and 0.192, respectively. The livelihood capital levels of coastal fishermen were generally lower than those of farmers in other regions. (2) Livelihood capital significantly influenced fishermen's willingness to engage in livelihood tourism transformation, with human, material, and social capital showing significant positive effects, consistent with previous research findings. Natural and financial capital had significant negative effects. Owing to the unique attributes of coastal fishermen, the impact of financial capital on their willingness to engage in livelihood tourism transformation differedO from previous research conclusions. (3) Risk cognition had an intermediate effect between human, social, and financial capital on livelihood tourism transformation intention. The mediating effects of natural and material capital on livelihood tourism transformation intention were not significant. This study revealed the relevant factors influencing livelihood tourism transformation intention. It innovatively discusses the mediating role of risk perception in the influence of livelihood capital on livelihood tourism transformation intention by combining it with prospect theory. It deepens the understanding of existing studies on the livelihood level and structure of coastal fishermen and enriches the application of prospect theory in tourism research. It provides theoretical support and a scientific basis for improving the livelihood level of fishermen and the driving force of livelihood tourism transformation, offering new insights for optimizing livelihood strategies and sustainable development of coastal fishermen.

The accelerated promotion of new urbanization has brought about population mobility, which has placed greater pressure on megacities in terms of inter-regional competition for high-quality resources in compulsory education and their carrying capacity. To achieve high-quality and balanced development of compulsory education, it is not only necessary to achieve a balanced allocation of resources in terms of quantity and structure, but also to pay attention to the spatial balanced distribution of quality. This study uses Wuhan, a central Chinese megacity, as a case. Integrating Point of Interest data of primary and secondary schools, educational resources, road network information, this study uses GIS methodologies, such as the nearest neighbor index, coefficient of variation, kernel density analysis, service area network analysis, and an improved 2SFCA method to evaluate the spatial distribution characteristics of compulsory education resources in Wuhan and the accessibility and resource distribution equilibrium across districts. In terms of spatial quantity equilibrium, although the distribution of primary and secondary schools in Wuhan's districts is relatively uniform, the resource density is biased towards the central urban areas and shows a single-center clustering, resulting in sparse resource distribution in the remote urban areas. In terms of spatial structural equilibrium, considering students' commuting modes, time cost and the matching of supply and demand, primary schools exhibit better accessibility and resource equilibrium than junior high schools. In terms of spatial quality equilibrium, the human, material, and financial resource allocation equilibrium of primary schools is generally better than that of junior high schools, and both primary and junior high schools in the remote urban areas are less balanced than those in the central urban areas. This imbalance in resource distribution is in conflict with the population expansion and uneven distribution in the urbanization process of Wuhan. Therefore, it is suggested that the future layout for compulsory education in Wuhan should be combined with regional functions, population size, and educational needs to strengthen the forward-looking allocation of educational resources and land use planning in the remote urban areas, optimize the educational layout of functional areas to adapt to population growth and enhance accessibility, and build a balanced system of teacher resource allocation to promote the high-quality and integrated development of compulsory education. This study builds a spatial analysis framework based on a three-dimensional perspective of 'quantity-structure-quality', which expands the research perspective of high-quality and equilibrium allocation of compulsory education resources, and the spatial accessibility analysis from the perspective of supply and demand provides methodological references for other megacities to optimize the layout and resource allocation of primary and secondary schools in different regions, and also enhances the understanding of the differentiated layout of compulsory education resources in different administrative districts of megacities.

Artificial intelligence (AI) has profoundly reshaped human society and significantly impacted academic research. In the current era of intelligence, geography requires the development of new theoretical frameworks. By constructing and elaborating the theoretical framework of the "Poetics of Life," this study explores new pathways for geographical expression within the context of human-AI integration. The relationship between AI and humans manifests in four modes: tool, partner, friend, and soul. In the process of human-AI integration, place, body, and emotion emerge as three key vectors that are currently irreplaceable by AI. Drawing on existentialist philosophy, geographical poetics, and humanistic geography, and employing a digital autoethnographic approach, this article conducts an in-depth analysis of 122 songs co-created by Ye Chao(The first author) and AI and published on the personal WeChat Channel "Ye Shenxun." It compares the characteristics of individual writing with those of human-AI collaborative creation and summarizes their public communication effects. What distinguishes Poetics of Life in the new era from geographical poetics lies in three fundamental shifts: the creative subject has transformed from a solitary author to human-AI co-creation, the form of expression has expanded from single-text delivery to multisensory stimulation, and media dissemination has evolved from one-way output to multidimensional interaction. The song samples exhibited diverse styles and themes, reflecting the interplay of emotion, place, and AI, thereby highlighting the importance of new forms of geographical writing and expression in the intelligent age. In terms of communicative effects, a top-ten analysis of the texts revealed that audiences with a background in geography paid more attention to the mutual construction of place and everyday life, whereas other audiences focused more on emotional resonance. Surreal works, such as Chronicle of Light and Dust, demonstrate a cross-disciplinary, future-oriented dimension. The Poetics of Life in the intelligent age not only extends and deepens the humanistic tradition of geography but also provides new theoretical insights for interdisciplinary fields such as digital art and media geography. The expression, performance, and public communication of the Poetics of Life constitute key directions for future research.

The advent and deep permeation of digital media technologies have precipitated a paradigmatic shift in the ontological and phenomenological understanding of place. No longer conceived as a stable, bounded physical container, place in the contemporary era is dynamically produced, mediated, and continually reconstituted through the intricate interplay of platform architectures, algorithmic operations, locative media, and networked social practices. This transformation has catalyzed the emergence of "digital sense of place" as a critical interdisciplinary concern. Scholars across the disciplines of human geography, environmental psychology, sociology, anthropology, and media studies have engaged with this phenomenon, yet their inquiries have largely progressed in parallel, resulting in a fragmented intellectual landscape characterized by conceptual dispersion, methodological insularity, and theoretical compartmentalization. A cohesive framework capable of elucidating the complex, recursive coupling between the digital and the placal remains conspicuously absent. To address this gap, this article conducts a systematic knowledge archaeology and synthesis of relevant literature spanning the years 1980 to 2025. Employing bibliometric analysis and critical discourse review, we trace the genealogical development of place scholarship within each of the core disciplines and map their convergent trajectories toward the digital. Our analysis identifies a fundamental theoretical evolution: a move from essentialist, static, and physically deterministic models of place (exemplified by Tuan's topophilia and Relph's place identity) toward relational, processual, and mediated conceptions. Human geography's "relational turn" and its subsequent engagement with "hybrid space" dismantled the physical-digital binary. Environmental psychology meticulously operationalized and measured place attachment, later extending its quantitative paradigms to validate the psychological reality of digital emotional bonds. Sociology and anthropology foregrounded the social construction of place, revealing how power dynamics, cultural practices, and embodied rituals undergird place-making—a perspective extended to digital communities and virtual belonging. Media studies evolved from treating media as mere representational tools to recognizing platforms and locative media as constitutive infrastructures that actively shape spatial perception and social interaction. The synthesis of these multidisciplinary insights exposes their collective yet unintegrated recognition of digital sense of place as a multifaceted, systemic phenomenon. Building on this foundation, this paper makes a central theoretical contribution by proposing "digital sense of place" as a systemically generative integrative analytical framework. This framework posits digital sense of place not as a possessed attribute but as an ongoing, emergent process generated within a dynamic system composed of five interconnected subsystems: (1) the technological-infrastructural subsystem (platforms, algorithms, interfaces); (2) the affective-psychological subsystem (digitally mediated attachment, identity, meaning); (3) the social-relational subsystem (networked communities and mediated interactions); (4) the cultural-semiotic subsystem (the remediation and circulation of place-based narratives and memories); and (5) the power-political economic subsystem (the governance, ownership, and algorithmic curation of digital space). These subsystems operate in continuous feedback loops, co-constituting the lived experience of place in a digital society. This systemic, generative perspective facilitates a critical analysis of core tensions inherent in digital place-making, such as between delocalization and re-localization, authentic affective experience and platform-engineered engagement, and discursive openness and algorithmic exclusion. Consequently, this integrated framework advances the field from multidisciplinary parallelism toward theoretically robust, holistic explanation. It provides a potent lens for examining pressing contemporary issues, including the affective politics of platform societies, the governance of smart cities, the preservation of digital heritage, and the ethical implications of algorithmically modulated spatial experience. The framework thus repositions digital sense of place as a central analytical node for understanding how locality is persistently forged, contested, and lived within the matrix of contemporary techno-social life.

Artificial intelligence (AI) has emerged as a key driver of high-quality regional development by reshaping innovation systems, industrial structures, and spatial economic dynamics. Consequently, the scientific measurement of the spatial distribution and evolutionary trajectories of AI technologies has become a critical issue in economic geography. Existing empirical studies typically measure AI activity using enterprise registration data or granted invention patents based on proxy variables, keyword searches, or the International Patent Classification system. However, these methods often suffer from limited semantic accuracy and incomplete coverage, making it difficult to fully capture the rapidly evolving and context-dependent nature of AI technologies. To address these limitations, this study developed a semantic-based identification framework based on large language models. Drawing on approximately 1.2 million granted invention patent abstracts from Guangdong Province between 2001 and 2021, we employed Bidirectional Encoder Representations from Transformers (BERT) large language model to identify AI-related technologies based on deep semantic understanding. This approach yielded a dataset of approximately 200,000 AI-related patents and provided a more comprehensive and accurate representation of regional AI innovation activities. Building on this dataset, we applied BERTopic for topic modeling to identify major technological themes and trace their temporal evolution. The empirical results reveal several key findings. (1) From a temporal perspective, the evolution of AI technologies in Guangdong Province followed a clear two-stage trajectory. During the initial stage from 2001 to 2014, AI patenting activities remained at a relatively low level, gradually increasing from 37 patents in 2001 to 3,514 in 2014. By contrast, the period from 2015 to 2021 represents a phase of rapid expansion, characterized by a sharp increase in AI patenting activities and a substantial acceleration in innovation intensity. This shift indicates the growing strategic importance of AI in regional innovation systems. (2) From a spatial perspective, AI technologies are highly unevenly distributed across Guangdong Province, exhibiting strong agglomeration in the Guangdong-Hong Kong-Macao Greater Bay Area. Shenzhen and Guangzhou together account for 75.1% of all AI patents in the province, forming a pronounced core region of AI innovation. Shenzhen contributed to more than half of the provinces' AI patents, demonstrating a strong primacy position. Beyond these two leading cities, Dongguan, Zhuhai, and Foshan constituted the secondary tier in terms of patent volume. Further analysis of co-invention patents revealed the network characteristics of AI technological collaboration. Within Guangdong Province, inter-city cooperation exhibited a clear dual-core structure centered on Guangzhou and Shenzhen, with dense collaborative linkages concentrated in the Greater Bay Area. While Shenzhen dominates AI patent production, Guangzhou demonstrates the highest level of intraprovincial collaboration, indicating a stronger coordinating and connective role within regional innovation networks. (3) In terms of technological content, topic modeling identified five major AI technology themes: data and image processing, robotics and automation devices, intelligent transportation and fault detection, smart homes and environmental control, and bio-simulation and image analysis. Among these themes, data and image processing constituted the most active and foundational domains throughout the study period, entering a phase of rapid growth around 2013 and peaking in 2019. Robotics, intelligent transportation, and smart home technologies have expanded markedly after 2015, reflecting the increasing diversification and application-oriented nature of AI innovation. By contrast, biosimulation and image analysis exhibited modest growth, suggesting a narrower range of applications. Moreover, cities within Guangdong displayed differentiated thematic advantages, reflecting the distinct trajectories of regional AI specialization. Shenzhen has maintained a leading position in image and data processing, as well as robotics; Guangzhou has developed distinctive strengths in intelligent transportation and urban service applications; Zhuhai integrated AI into its home appliance manufacturing base and marine technologies; Dongguan focused on AI applications in intelligent manufacturing and environmental governance; and Foshan emphasized the integration of smart home technologies with industrial automation.

In the era of Artificial Intelligence (AI), Large Language Models (LLMs) have become important informational mediators through which the public perceives urban spaces, and AI discourse has emerged as a powerful force in the construction of urban spaces. Using Guangzhou as a case study, we categorized urban consumption spaces into four types: shopping spaces, catering and entertainment spaces, tourism and leisure spaces, and residential and commercial housing. We constructed an evaluation question set for hallucinations in urban consumption spaces within a discourse-power framework and used hallucination tests to examine the commonalities and differences between Chinese and international AI models, namely, DeepSeek and ChatGPT, in the production of spatial discourse, thereby explaining how AI hallucination discourse constructs urban consumption spaces. The main findings of this study are as follows. (1) In the hallucination tests of urban consumption spaces, ChatGPT exhibited lower hallucination rates than DeepSeek at both the overall level and across individual categories. Residential and commercial housing emerged as high-incidence domains of hallucinations for both AI models, whereas the most pronounced divergence in hallucination rates between the two models occurred in tourism and leisure spaces. The primary sources of AI-generated content, in descending order, were news media, individual or commercial institutions, government agencies, and online encyclopedias. Both models tend to respond to mainstream spatial discourses, demonstrating a limited capacity for revealing the complex, diverse, and contradictory realities of the city. Specifically, ChatGPT favors generalized frameworks in its depiction of urban consumption spaces, whereas DeepSeek's spatial narratives display a planning-oriented logic aligned with urban development strategies. (2) By integrating and reproducing specific discourses originating from governments, news media, and commercial institutions, AI discourse operates as a novel power subject that constructs multiple "realities" and promotes the production of meanings attached to consumption centers, symbolization of architectural landscapes, and technologization of consumption spaces and also adjudicates spatial value, allowing its power to operate in a "rational" manner. (3) The AI hallucination discourse constructs space by producing subject positions tailored to users, such as "supporters of urban development," "experience-oriented consumers," "beneficiaries of technological progress," and "astute investors." As users identify with and accept these positions, they enact specific consumption-space practices grounded in particular forms of knowledge, generating new data that are subsequently mobilized to reproduce the same discursive system. In this process, a specific knowledge regime is sustained, and power continues to operate. From a discourse-power perspective, this study elucidates the pathways through which urban consumption spaces are constructed by AI in the era of artificial intelligence. Although, it advances our understanding of the modes and impacts of urban knowledge circulation amid the rise of generative AI, critical reflection on the discursive and power relations embedded in technological products contributes to ethical scrutiny of smart city practices.

With the pervasive penetration of artificial intelligence (AI) technologies, traditional paradigms of social space governance are undergoing a fundamental shift—from "digital governance" to "intelligent governance." In the governance space dimension, AI innovations such as AI-generated content (AIGC) and spatial intelligence have endowed digital twin spaces with unprecedented capabilities, transforming them from static reflections of physical reality into dynamic systems capable of proactive inference, simulation, and real-time optimization. This transition extends governance functions beyond mere representation to include predictive intervention and anticipatory regulation. On the dimension of governance subjects, algorithms have qualitatively mutated—from passive instruments of execution into "artificial agents" or "auxiliary governance actors" possessing autonomous learning, environmental adaptation, and predictive decision-making capacities. This mutation fosters an emergent symbiotic "human-machine collaboration," challenging established power structures and reconfiguring accountability boundaries. In this study, building upon this analysis of the data-to-intelligence governance transition, we examine—through case studies including Shanghai's "Quantum City" and Hangzhou's "City Brain"—the expansion logic and practical manifestations of multidimensional social space governance in the AI era. First, the governance space has expanded from a tripartite "physical-social-data" framework to a quadrilateral "physical-social-data-algorithm" structure. Spatial intelligence technologies and "real-world model" paradigms have positioned algorithms as the core of digital twin systems. Empowered by spatial intelligence, digital twin environments achieve heightened precision and synchronicity, enabling real-time and efficient interactions with physical spaces while demonstrating enhanced generative capacity and operational autonomy. These developments constitute the multidimensional spatial arena of public governance. Second, the governance subject has evolved from a "government-market-society" triadic relationship to a "government-market-society-intelligence" quadrilateral synergy. As AI agents gain greater autonomy, their subjectivity becomes increasingly manifested, elevating AI from a mere instrument to a co-constitutive governance actor that must operate in parallel with traditional subjects. This transformation necessitates fundamental theoretical and practical interpretations of the relationships among all governance stakeholders. This multidimensional expansion has engendered a series of novel challenges for public governance practices. First, AI and digital twin technologies have accelerated the convergence of the physical, social, and digital domains, yet this nascent "hybrid space" has precipitated profound normative conflicts in governance practices. Second, as AI transitions from a tool to an intelligent agent, algorithmic bias becomes more acute, and an "accountability vacuum" risk emerges within human-machine collaborative frameworks. Finally, persistent digital divides are metamorphosing into a new configuration—the "intelligence divide"—exacerbating social stratification. To address these emergent challenges, social space governance in the intelligence era requires innovative pathways. First, cross-spatial coordinative governance mechanisms must be constructed to enable the synergistic integration of virtual and physical domains, shifting from normative fragmentation to spatial order reconstruction. Second, a human-machine coordinative governance framework should be built upon technical foundations of "trustworthy AI" and institutional safeguards ensuring "ultimate human control." Third, governance must uphold a people-centered value orientation, ensuring that the benefits of intelligent governance are equally distributed across all citizens.

Based on seventh population census data, Point Of Interest (POI) data, road network data, elevation data, and night light index, we used the entropy method, nuclear density analysis method, spatial dislocation index, geographic detector, and other methods to study the phenomenon and driving factors of population aging and the spatial disequilibrium of pension service resources in each township (street) of Wenzhou in 2020. This research plays a positive role in realizing the fine allocation of resources for elderly care facilities at the township level in Wenzhou City, improving elderly care service facilities in urban and rural areas, and promoting the equalization of public service facilities at the township and village levels. The results can be summarized as follows: 1) As Wenzhou enters a deeply aging society, the spatial distribution of the elderly population, elderly population density, population aging coefficient, elderly population support ratio, and comprehensive aging index are basically the same, showing a gradually increasing spatial pattern from the municipal district to the peripheral streets of the municipal district to the remote towns; 2) Elderly care service resources in Wenzhou present an asymmetrical and unbalanced north-south spatial distribution pattern with the municipal districts as " diversified supply in municipal streets, basic guarantee in developed coastal towns, and shortage of supply in inland mountainous towns "; 3) The streets of the municipal district have rich elderly care service resources, including Grade 3 general hospitals and comprehensive nursing homes, to provide diversified elderly care services for the elderly in the municipal district; there are many old-age care facilities such as hospitals, clinics, and nursing homes in Ruian, Yueqing City, and central towns such as Hongqiao and Aojiang, which provide basic old-age security for the elderly in and around the area. In the northern part of Yongjia County, Wencheng County, Taishun County, and other remote towns and villages, the number of old-age medical resources and facility resources is scarce, and the accessibility of health centers and nursing homes to residents in marginal villages is low, which makes it inconvenient for the elderly to see a doctor in time and enjoy professional old-age services; 4) The size of the elderly population, nighttime Light Index, and road network density are the main driving factors causing the spatial imbalance of elderly service resources in the townships (streets) of Wenzhou City. Specific suggestions based on this study are as follows: Wenzhou should break the restraints of administrative divisions, speed up the process of co-construction and sharing of elderly care service resources between Lucheng District, Ouhai District, Longwan District and Yongjia County, southern townships of Yueqing City and eastern streets of Ruian City, achieve cross-regional linkage of elderly care facilities, improve the utilization rate of elderly care service resources, and prevent the situation of idle elderly care service resources; financial support for deeply aging towns in Wencheng County, Taishun County and Yongjia County should be increased, the construction of elderly care facilities in remote towns and health service consulting and cross-regional medical security capabilities of the elderly population in towns and cities should be increased, and the gap between the elderly service resources and the level of elderly care services in developed towns and cities should be narrowed; Yueqing City, Longgang City, Cangnan County, and Pingyang County should coordinate the existing resources, use the stock of land, combine urban renewal and the transformation of old urban communities, accelerate the construction of public infrastructure such as health service stations, nursing homes, leisure squares, and green parks within the 15-minute life circle of neighboring communities, and constantly improve the suitability of elderly care service resources for the elderly population.

Against the strategic backdrop of "Digital-China" and the "Dual-Carbon" goals, the synergistic advancement of digital economy and carbon emission reduction is crucial for achieving high-quality, sustainable development. As a leading region in China's economic and digital transformation, the Yangtze River Delta (YRD) urban agglomeration provides a critical-case study for examining the complex interplay between digital growth and decarbonization. In this study, we aimed to systematically analyze the spatiotemporal-coupling characteristics and underlying influence mechanisms between the digital economy and carbon emissions in the YRD region from 2011 to 2023. Moving beyond aggregate-analysis and linear-assumptions, this study seeks to reveal the spatial heterogeneity, nonlinear-relationships, and threshold-effects to provide a nuanced empirical basis for differentiated-regional policymaking. Methodologically, we integrated the Geographically Weighted Random Forest (GWRF) model with SHapley Additive exPlanations (SHAP). We constructed comprehensive evaluation systems for both the digital economy and carbon emissions, and calculates the coupling coordination degree (D) between these two systems for 41 cities. The core analytical approach uses the GWRF model, which embeds a spatial-weight matrix into the Random Forest algorithm to simulate the spatially-varying and nonlinear effects of multiple influencing factors on the degree of coordination. Subsequently, the SHAP framework was applied to interpret the GWRF " black-box model and quantify the global-importance, directional-contribution, and potential nonlinear or threshold-behavior of each explanatory variable. This study yielded several key findings. Regarding temporal evolution, the overall coupling coordination degree of the YRD urban agglomeration shows a clear upward trend, increasing from 0.411 in 2011 to 0.505 in 2023, marking a transition from an "imminent-imbalance" to a "barely-coordinated" stage. However, this progression is not monotonic; the significant dip observed in 2021 reflects dynamic tension and potential lagged-adaptation between technological-advancement cycles and stringent emission-reduction targets. In terms of spatial patterns, a distinct hierarchical "core-corridor-periphery" radial structure has formed. Shanghai, leveraging its advanced technological foundation and institutional advantages, remains at the forefront, achieving "high-quality coordination" by 2023. The provinces of Jiangsu and Zhejiang exhibit follow-up growth, entering the "barely-coordinated" stage. In contrast, Anhui province, despite exhibiting the fastest growth rate, remains at the threshold of "imminent-imbalance," highlighting persistent regional disparities within the agglomeration. At the city level, high-coordination cores were concentrated along the Shanghai-Nanjing-Hefei-Hangzhou development axis, with coordination levels gradually diffusing along major transport corridors and weakening in northern Anhui and southwestern Zhejiang. Concerning the model validation and identification of key drivers, the GWRF model demonstrated significantly superior explanatory power and predictive accuracy compared to the standard-Random Forest model, confirming its efficacy in capturing spatial-non-stationarity. The SHAP analysis identified variables from the digital economy subsystem, specifically, the number of mobile phone subscribers, employees in information transmission and software services, and postal business volume, as important positive drivers. Their intensity-of-influence exhibited a spatial-diffusion pattern, radiating outward from core metropolitan areas to key manufacturing nodes and emerging industrial zones. Conversely, variables from the carbon emissions subsystem, particularly carbon emissions intensity and per-capita carbon emissions, act as primary inhibitors of coupling coordination. In summary, this study elucidates a dual-path mechanism, wherein the agglomeration of digital elements drives synergistic improvements, whereas high-carbon economic structures exert inhibitory pressure. This study makes substantive contributions to both the theoretical and methodological fronts. Theoretically, it provides robust empirical evidence for the complex, nonlinear-interdependencies between digital and green transitions, challenging simplistic linear-assumptions and enriching the understanding of their coupling dynamics in a regional context. Methodologically, the integrated GWRF-SHAP framework was validated as a powerful tool for dissecting high-dimensional and spatially-heterogeneous problems in urban and regional studies, offering a replicable-analytical pathway. These findings provide actionable-insights for policymakers to advocate tailored-strategies that reinforce positive digital diffusion, especially in lagging areas, while implementing targeted measures to decouple economic growth from carbon emissions in high-pressure zones. Ultimately, this approach aims to foster a more balanced and synergistic development pathway for the YRD and similar regions.

Using images and interview data from the Nanzhi Street in the Songyang County, combined with K.-S. Lee's five-dimensional theory of food memory, this study explores the impact of media and commercialization on traditional food and local memory. This work analyzes how five factors—population hollowing, commercialization of preparation methods, standardization of sensory experiences, weakening of emotional connections, and uniformity—affect the relationship between food and local memory. It also discusses the mediating role of media as an intermediary factor. The findings indicate: (1) Loss of native residents: The departure of native residents has led to external operators maintaining emotional ties but failing to restore the community atmosphere. The demographic shift in the Nanzhi Street has transformed local memory from the emotional memory of native residents to the commercial memory of external operators. Media has simultaneously enhanced commercial vitality and accelerated the commodification and symbolization of local memory; (2) Differences in shop styles: There is a clear distinction between the styles of registered and non-registered shops. Registered shops preserve local characteristics but tend toward symbolic traditional appearances under policy support and media influence, while non-registered shops cater to influencer-driven culture, leaning towards commercialization. This dual influence maintains commercial vitality but also speeds up the commodification of local memory, reflecting the tension between preserving local culture and pursuing commercial development; (3) Changes in traditional food and sensory experiences: The preparation methods and sensory experiences of traditional food have changed to meet consumer demands, leading to differences in how tourists and locals perceive local memory. Media's simplified narratives and excessive commercialization reduce the cultural depth of local cuisine, reinforce stereotypes, and overlook the importance of craftsmanship and deep-rooted culture. These shifts not only affect consumer perceptions but also undermine the authenticity and completeness of local memory. Additionally, under the influence of commerce and media, traditional food has become increasingly standardized, with weakened artisanal techniques and local characteristics. Younger consumers are more exposed to adapted, standardized flavors, further simplifying the cultural essence of local cuisine and diminishing its role in cultural diversity and regional identity; (4) Impact of media on emotional connections: Media's influence on emotional connections is dual-faceted. For locals, private memories are made public, transforming traditional food from a familial emotional symbol into a symbol of local culture. For tourists, media transforms local memory into a commodified and emotionalized product, replacing personal connections with consumer-driven experiences. This shift reflects the commercialization of local memory and highlights the disconnect in emotional ties between locals and tourists, as private memories are gradually replaced by mass-consumption emotions. The work reveals the conflict between commercialization and local characteristics in the Nanzhi Street under media and policy guidance, emphasizing the importance of preserving local memory and emotional connections during urban transformation.

In recent years, advances in pre-training techniques and improvements in computing hardware have led to substantial breakthroughs in Artificial Intelligence (AI). Large-scale models, such as ChatGPT and DeepSeek, have demonstrated unprecedented capabilities in natural language processing and generation, accelerating the deployment of intelligent technologies across diverse domains. Nevertheless, current large models still face notable challenges in terms of physical common-sense understanding, causal reasoning, and the modeling of dynamic environments. In response to these deficiencies, the concept of "World Models" has recently emerged, with the goal of constructing cognitive engines that internally model, simulate, and predict physical environments. In this review article we describe the origins and research pathways of World Models, tracing their technical evolution from representation learning and dynamic modeling to embodied interaction. We summarize the core approaches to understanding environmental structure, simulating future states, and supporting decision-making and reasoning. From a geographical perspective, the generative, multimodal, and interactive capabilities emphasized by World Models are regarded as key requirements for characterizing complex spatial structures and dynamic processes. These capabilities are conceptually aligned with key research topics in geography: spatial organization, behavioral processes, and interactions with the environment. With the development of video generation, large-scale multimodal learning, and embodied intelligence, the field of AI is increasingly shifting from symbolic descriptions of the world to computable forms of spatial cognition, reflecting an intelligence paradigm fundamentally oriented toward space. The advancement of World Models not only provides new ways in which AI can understand the structure and processes of the real world, but also offers important opportunities for geography to explore spatiotemporal process modeling, mechanisms of spatial cognition, and the construction of integrated virtual-physical environments. With this overview we seek to establish a systematic framework for interdisciplinary research at the intersection of AI and geographical science and to provide references for future studies on spatial intelligence and AI.

The preservation of traditional architecture, which is a tangible carrier of regional culture and identity, requires precise and scalable methods for style classification. Here, we address the critical need for such methodologies by developing and applying an integrated computational framework that synergizes deep learning with spatial analytical techniques from cultural geography to document and analyze traditional architectural heritage. The primary research objectives were threefold: first, to construct a high-performance automated system for quantitatively classifying major Chinese traditional architectural styles(CTASs) from visual data; second, to leverage the system's outputs to analyze the multiscale spatial patterns of these styles; and third, to interpret these patterns to identify cultural boundaries and regional diversity, thereby providing a data-driven foundation for heritage conservation and planning. To achieve these aims, the research methodology began with the curation of a multimodal image database derived from 646 nationally designated traditional villages. An EfficientNet model, enhanced for multiscale feature fusion to capture both global and local details, was trained on this dataset to classify six typical styles: Jing, Jin, Chuan, Wan, Su, and Min. To ensure transparency and interpretability, Grad-CAM was used to visualize the key architectural components that informed the model's decisions. Subsequently, the geographically referenced classification results were subjected to a comprehensive spatial analysis suite, including spatial autocorrelation, standard deviational ellipse analysis, and calculation of Shannon diversity and Simpson evenness indices at the provincial level. The results and conclusions of the integrated analysis are detailed and multifaceted. The EfficientNet model achieved an overall classification accuracy of 90%, confirming the efficacy of the deep-learning approach. However, a misclassification rate of 10.7% was observed between the Jing and Chuan styles. Grad-CAM analysis provided critical insights into this phenomenon, revealing that the model's confusion stemmed from a shared, significant focus on similar wooden walls and colonnade features in both the Jing and Chuan styles, highlighting subtle inter-style visual overlaps. At the macroscale, the spatial distribution analysis confirmed that CTASs generally follow a cultural dissemination model that conforms to mountains, waters, and other natural barriers. For instance, the northern Jing and Jin styles cluster in the Yellow River Basin, the southern Su and Wan styles follow intricate water networks, and the Min and Chuan styles are dispersed across hilly and mountainous terrain. At the mesoscale, Local Indicators of Spatial Association (LISA) analysis identified statistically significant high-high clustering areas for each style. Synthesizing these with topographic and hydrological data allowed for the delineation of coherent cultural geography: five distinct cultural core areas (where a single style is intensely concentrated), three well-defined cultural transition belts (exhibiting high stylistic mixing), and two cultural fracture zones (areas of low style density and diversity). This regionalization provides a precise spatial template for designing cross-provincial heritage corridor protection systems. At the micro (provincial) scale, the diversity and evenness indices revealed three characteristic patterns: Multi-Style Fusion Zones (high diversity, high uniformity), Oligarchic Equilibrium Zones (low diversity, high uniformity), and Style-Pure Zones (low diversity, low uniformity). These patterns offer crucial insights for tailoring provincial-level conservation strategies and regional planning. This study makes significant strides on these two fronts. Methodologically, it pioneers a replicable and scalable framework that successfully bridges computer vision and cultural geography, offering a new and robust tool for automated architectural stylistic analysis. Substantively, it provides the first comprehensive, quantitative, and multiscale spatial dissection of CTASs based on large-scale empirical data. Collectively, these findings provide a rigorous scientific foundation for evidence-based policies in multiscale heritage conservation, cultural landscape management, and sustainable regional development.

City serves as a medium for communication and, in turn, reshapes the city. Since the 21^st century, social media has rapidly spread worldwide, providing users with a platform for self-presentation and channels for expression. This has greatly changed people's lives and exerted a substantial influence on the reconstruction and gentrification of urban social spaces. However, few studies have focused on the underlying mechanisms. To gain a deeper understanding of the role of social media in commercial gentrification, Nantai Alley, a renowned Internet-famous block in Nanjing, was selected as a case study, and Xiaohongshu (Red note), whose main active user group is young women, was chosen to represent social media. This study conducted an in-depth analysis of the occurrence process, formation mechanism, and comprehensive effects of commercial gentrification under social media intervention. The research found that social media is deeply involved in the commercial gentrification process and continuously promotes the gentrification process through media information dissemination. Social media involvement in commercial gentrification is mainly achieved through two types of entities: merchants and consumers. On the one hand, social media provides merchants with replicable Internet celebrity aesthetics and business models and serves as a platform for self-marketing, increasing the probability of occurrence and promoting a more simplified and rapidly evolving trend of gentrification. On the other hand, consumers, engage in trendy check-ins and act as "discourse investors," accelerating commercial gentrification. Social media's representation of urban space amplifies and reinforces commercial gentrification; the progression and outcomes of gentrification are magnified on social media, occupying its central discursive spaces, whereas the daily lives and consumption practices of local residents are marginalized and rendered invisible in these digital representations. Furthermore, the profit-driven behaviors of certain local residents have laid the groundwork for gentrification, and the government has further consolidated the achievements of gentrification through urban renewal plans. The comprehensive effects triggered by commercial gentrification present significant dual characteristics: it exerts positive effects, such as commercial revitalization and beautification of the built environment, while also generating negative impacts, such as commercial exclusion, displacement, and cultural distinction from the neighborhood. Therefore, in future urban renewal processes, it is imperative for the government to intervene in a timely manner to preserve the community's original public value orientation and sense of place. This study enriches research on gentrification in the digital age by incorporating the factor of social media, and provides references for the renewal and management of urban space in the context of stock development.