Tourism Geography is a cutting-edge interdisciplinary field closely related to the national economy and people’s livelihood. It spans both the natural and human domains and is of great significance for the formulation and implementation of national strategies. On the new journey of building a modern socialist country in all respects, Tourism Geography in China assumes increasingly important historical missions and core tasks in serving national strategies such as coordinated regional development, building China into a tourism powerhouse, and rural revitalization. This paper conducts a systematic discussion of the historical missions, practical responsibilities, and innovation paths of Tourism Geography in China. The research findings are as follows: 1) Grounded in the overall strategy of the great rejuvenation of the Chinese nation, Tourism Geography focuses on major tasks such as rural revitalization, coordinated regional development, and harmony between humanity and nature, continuously promoting the high-quality development of culture and tourism and achieving new breakthroughs in disciplinary system building; 2) Tourism Geography, grounded in a spatial perspective and centered on tourism-related human-environment relations, continuously expands its disciplinary boundaries in serving the building of China into a country with a strong culture and a tourism powerhouse; 3) The core essence of major national strategies lies in meeting the people’s aspirations for a better life, and their foundation lies in the sustainable development of industries. Tourism Geography directly addresses this core proposition and provides support for national strategies from multiple dimensions, including the economy, society, culture, and ecology; 4) Based on the strategic overall situation of Chinese modernization, Tourism Geography should further highlight problem awareness and demand-driven orientation, produce a number of research results with explanatory power, predictive power, and operability, and promote the discipline to become a key force serving major national strategies. This article helps to further enhance the theoretical consciousness of Tourism Geography in China regarding its historical missions, practical responsibilities, and innovation paths, and provides academic support for building China into a tourism powerhouse.
Green is the foundation of agriculture, and ecology is the bedrock of agriculture. The “Black Land Protection Law” requires the comprehensive adoption of measures such as engineering, agronomy, agricultural machinery and biology to protect the black soil and its surrounding ecological environment, and to improve the quality of black soil. The northeastern black soil region of China, acting as the “ballast” for national food security and an important ecological safety barrier in the north, is facing severe issues such as declining soil fertility and soil erosion caused by the mismatch between agricultural production and water-soil resources, as well as serious ecological landscape destruction and structural imbalances. By focusing on arable land, the integrated model of “mountains, waters, forests, farmland, lakes, and grass” will become crucial for the comprehensive, ongoing, multi-type, and multi-scale system integration to enhance soil quality and ensure the green and sustainable development of agriculture. The article summarizes the mechanisms by which resources from different regions contribute to the protection and utilization of black soil in this context. This paper puts forward the opportunity and direction of optimal allocation of resources and ecological landscape construction in black soil region from 3 aspects: theoretical gap, management demand and technical bottleneck. At the same time, it is pointed out that how to build ecological barrier islands, ecological corridors and ecological networks of cultivated land, to promote the efficient utilization of soil, fertilizer, water, light and heat resources, and to improve the ecological warning and safety of river basin are important challenges for optimal allocation of resources and ecological landscape construction in the future. The results provide theoretical foundations and technical references for the protection and sustainable use of black soils in northeast China.
Peatlands, which serve as critical carbon reservoirs, play an essential role in the global carbon cycle and the climate system; accordingly, accurately simulating their developmental dynamics is of fundamental importance for clarifying not only their functional role within the global carbon cycle, but also the feedback mechanisms through which they are linked to climate change. Having originated in the late 1970s as tools specifically developed to quantify peat production and decomposition processes, peatland development models have, through continuous refinement, adjustment, and expansion, gradually evolved into a diverse modelling framework in which conceptual models and process-based models coexist and interact as complementary approaches. Drawing upon both the historical trajectory of peatland modelling research and its current state of development in domestic and international scholarship, this review systematically synthesizes the concepts and classifications of peatland development models, while further examining the structural characteristics, internal linkages, and core differences that distinguish the major model types, and simultaneously discussing representative modelling cases that are mature, widely applied, and influential within the field. The synthesis indicates that these models have advanced the interpretation of peat-profile records, the understanding of peatland formation mechanisms, and the assessment of future carbon-sink dynamics, although their further development remains constrained by process simplification, limited spatiotemporal applicability, and insufficient representation of abrupt disturbances. By identifying these key bottlenecks and outlining future priorities for improvement, this review aims to provide a theoretical basis for the systematic construction and refinement of peatland development models.
It is still necessary to achieve comprehensive rural revitalization on the basis of consolidating the achievements of poverty alleviation. Based on the panel data of 272 prefecture-level cities in China from 2014 to 2023, the paper explores the mechanism and differentiated effect of digital factors on the growth of new rural collective economy. The results show that the investment of digital factors can effectively promote the growth of the new rural collective economy, and the level of rural governance plays a moderating role in it. The results of heterogeneity show that the impact of digital factors on the growth of new rural collective economy is mainly concentrated in the residents, and the empowerment effect of digital platform factor is the most obvious. In addition to individual heterogeneity, the impact of digital factors on the growth of new rural collective economy also has heterogeneity in development stage and regional heterogeneity. The marginal impact of input per unit of digital factors in the initial stage is 0.141, and with the continuous improvement of factor input level, this marginal impact gradually decreases to 0.058 and 0.015. The regional heterogeneity is mainly manifested in the spatial agglomeration characteristics of “high-low” dependence between the growth index of China’s new rural collective economy and the input level of digital factors. The enabling role of digital factors in the growth of new rural collective economy is mainly reflected in the eastern region, and this unbalanced development will also bring spatial spillover effects. Therefore, this study concludes that the growth of the new rural collective economy can take advantage of the advantages of digital development to realize the transformation of rural governance to “intellectual reason”, but it is also necessary to formulate rural personalized development strategies according to the differentiated impact effects of different digital factors and the current situation of regional development.
Responding to the global digital development trend and the practical challenges of rural revitalization, promoting the synergistic interactive between the digital economy and rural revitalization constitutes a crucial pathway for achieving Chinese-style modernization. Taking 110 cities in the Yangtze River Economic Belt as the study area, this research constructs indicator systems for the digital economy and rural revitalization, integrates the spatial Markov chain and panel vector autoregressive model to systematically analyze their spatio-temporal evolutionary patterns and interaction mechanisms. It is found that: 1) Both the digital economy and rural revitalization indices exhibit sustained growth. The spatial distribution of the digital economy index transitioned from ‘low in the middle, high in the east and west’ to ‘high in the east, low in the west’, while the rural revitalization index has consistently maintained a long-term pattern of ‘high in the east, low in the west’. 2) The spatial evolution of both the digital economy and rural revitalization demonstrates neighborhood dependence. High-level areas exhibit a higher probability of upward transition, whereas low-level areas show a higher probability of maintaining stability, and the distribution of stable cities is relatively clustered. 3) Both the digital economy and rural revitalization exhibit strong self-reinforcing characteristics, with a significant mutual promotion mechanism existing between them. The digital economy promotes the reorganization of the economic structure, spatial relationship and social function of the rural territorial system through technological spillover effect, spatial reconstruction effect and social governance effect. Conversely, the heterogeneous demands of rural revitalization reshape the development trajectory of the digital economy through technological upgrading and model innovation. This study offers a novel theoretical perspective for analyzing the digital pathways of integrated urban-rural development and provides practical guidance for differentiated promotion of digital village construction within the Yangtze River Economic Belt.
As the “bridge” and “medium” of urban-rural factor flow, rural innovation and entrepreneurship breaks through the boundaries of rural physical space and gradually becomes an emerging driving force to promote the transformation of agricultural modernization in China in the new period. Rural innovation and entrepreneurship has led to the integration of primary, secondary and tertiary industries, but its productive projects are still centered on modern agriculture. The mechanism of rural innovation and entrepreneurship's impact on agricultural modernization has not been fully explored in the research field. This study uses county panel data of Zhejiang Province from 2014 to 2021 to identify the spatial and temporal evolution characteristics of rural innovation and entrepreneurship, and constructs a mechanism framework and empirical model based on the background of urban-rural integration and the dialectical relationship between innovation and entrepreneurship, to explore the effects and the mechanisms of rural innovation and entrepreneurship on the Agricultural Modernization. The results show that: 1) Rural innovation and entrepreneurship in Zhejiang Province is characterized by obvious spatio-temporal heterogeneity. In terms of temporal characteristics, the level of rural innovation and entrepreneurship is on an upward trend. The level of rural innovation also shows an upward trend, but the level of rural entrepreneurship is more stable. Regarding spatial characteristics, there are concentrated and continuous high-value areas of rural innovation and entrepreneurship in northeast Zhejiang. Southwest Zhejiang is developing rapidly in rural entrepreneurship. 2) Rural innovation and entrepreneurship effectively drive the development of agricultural modernization, and the narrowing of the urban-rural income gap strengthens the driving effect of rural innovation and entrepreneurship on agricultural modernization. 3) Rural innovation and rural entrepreneurship are closely related, but essential differences exist. Rural innovation can promote the development of agricultural productive services to accelerate the process of agricultural modernization. Rural entrepreneurship can promote the growth of regional consumption level on the demand side to drive the development of agricultural modernization. 4) On the geospatial scale, rural innovation and entrepreneurship in northeast Zhejiang have a more significant effect on agricultural modernization than that in southwest Zhejiang. On the administrative scale, the driving effect of rural innovation and entrepreneurship on agricultural modernization at the county level is more significant than that of county-level cities. In the future, the rural innovation and entrepreneurship system should be improved according to local conditions, giving full play to the role of rural innovation and entrepreneurship as a medium in integrating urban and rural factors, and guiding the coordinated development of rural innovation and entrepreneurship.
As urban development transitions into the stock-based development phase, exploring the coordinated matching relationship between community development intensity and spatial livability is of profound significance for evidence-based decision-making in urban renewal planning and the rational allocation of spatial resources. Taking 2 123 communities in Hefei City as empirical research objects, this study integrates Spatial Autocorrelation Analysis and the Multi-scale Geographically Weighted Regression (MGWR) model. It systematically investigates the spatial heterogeneity and underlying influence mechanisms bridging development intensity and livability, thereby proposing targeted optimization strategies. Regarding spatial patterns, community building density exhibits a clear “high in the center, low in the periphery” gradient, whereas community land scale displays an inverse “low in the center, high in the periphery” distribution. The spatial distribution of the Floor Area Ratio (FAR) lacks significant clustering characteristics. Meanwhile, community livability presents a prominent polycentric configuration characterized by evident spatial differentiation. In terms of influence mechanisms, substantial regional disparity exists in the spatial coupling of development intensity and livability. Notably, the Spatial Autocorrelation Analysis reveals global spatial correlations: FAR exhibits a weak negative correlation with livability, building density shows a distinct positive correlation, and land scale demonstrates a significant negative correlation. Conversely, the MGWR results capture localized impacts: FAR exerts a uniformly weak positive influence on livability; building density demonstrates spatially non-stationary effects, generating significant positive or negative impacts depending on the specific regional context; and land scale generally yields a significant positive impact overall. Consequently, the mechanisms by which different development intensity indicators affect livability vary significantly. Building density acts as the rigid fundamental constraint determining baseline livability, land scale dictates the future potential for spatial quality enhancement, and FAR exhibits high planning elasticity within urban development strategies. Furthermore, the overarching spatial divergence is co-driven by multiple intertwined macroscopic factors, primarily including urban spatial expansion trajectories, the intensity of human socioeconomic activities, and the macro-orientation of planning policies. Based on these findings, the study formulates differentiated urban renewal interventions precisely tailored to two typical mismatched community typologies: “high development intensity-low livability” and “low development intensity-low livability”. Ultimately, this research provides robust theoretical methodologies and pragmatic insights for urban community renewal, spatial layout optimization, and the comprehensive enhancement of urban livability in the stock development era.
Urban shrinkage has generated a range of socioeconomic challenges, including declining factor allocation efficiency and impediments to industrial upgrading, with structural land-use mismatch emerging as a critical factor exacerbating these dynamics. Drawing on panel data from 278 Chinese cities, this study develops an econometric framework to investigate the effects of land-use structural mismatch on urban shrinkage. The results reveal that: 1) The spatial distribution of urban shrinkage remains broadly stable over time, although notable local variations persist. Meanwhile, land-use structural mismatch exhibits a dual pattern of spatial agglomeration and dispersion, and the two phenomena demonstrate significant spatiotemporal coupling. 2) Land-use structural mismatch significantly intensifies urban shrinkage, with substantial spatial heterogeneity across regions. The adverse effects are particularly pronounced in Northeast China, where difficulties in the transformation of traditional industries have amplified urban decline. In contrast, eastern regions are able to partially mitigate these negative impacts through innovation compensation mechanisms driven by the agglomeration of high-end industries. 3) Compared with industrial land, mismatches in commercial and residential land exert a stronger effect on urban shrinkage. Industrial land-use mismatches can, to some extent, be alleviated through industrial upgrading and improvements in production efficiency. In contrast, mismatches in commercial and residential land use more directly disrupt the dynamic nexus among population, land, and consumption, thereby undermining consumer vitality and weakening the foundational capacity for innovation. Moreover, shrinking cities are considerably more vulnerable to the consequences of land-use structural mismatch than growth-oriented cities. 4) Land-use structural mismatch primarily aggravates urban shrinkage by suppressing innovation capacity, distorting industrial structure, and weakening industrial agglomeration effects. Overall, this study underscores the pivotal role of structural land allocation in constraining urban economic vitality, enriches the theoretical framework of urban shrinkage, and offers important policy implications for optimizing land-resource allocation and promoting sustainable urban transformation.
In recent years, intensified climate change and increasing human activities have significantly amplified the frequency and impacts of natural hazards, posing persistent threats to urban areas. Extreme rainfall, urban flooding, river floods, earthquakes, and related secondary disasters have occurred repeatedly in many regions, particularly affecting large cities characterized by high population density and complex spatial structures. As critical nodes of population concentration, economic activity, and infrastructure networks, cities exhibit heightened exposure and vulnerability to disaster risks. Traditional emergency shelter planning approaches, which often rely on expert experience or assume relatively static population distributions, are insufficient to capture temporal variations in urban residents’ activity patterns between daytime and nighttime, thereby limiting their ability to represent the spatiotemporal dynamics among population activities, accessibility, and environmental constraints during disaster events. Emergency shelters constitute a vital component of urban disaster prevention and mitigation systems, and their spatial configuration directly affects evacuation efficiency and public safety during emergencies. Improving the spatial suitability and service capacity of emergency shelters is therefore essential for enhancing urban resilience and optimizing urban safety patterns. This issue has also emerged as a key research frontier in Human Geography and Urban Geography, particularly from the perspectives of spatial equity and risk governance. Against this backdrop, this study proposes a comprehensive analytical framework for urban emergency shelter site selection, using the main urban area of Nanjing, China, as a case study. Multiple heterogeneous datasets are integrated, including mobile user portrait data, urban road network data, building distribution data, digital elevation model (DEM) data, and points of interest (POI) data. These data enable a multidimensional characterization of shelter site suitability in terms of population activity patterns, spatial environmental conditions, and infrastructure configurations. The spatial suitability of potential shelter locations is evaluated under both daytime and nighttime scenarios, reflecting temporal variations in population distribution and mobility. By overlaying the spatial distribution and service capacity of existing emergency shelters, areas with pronounced supply-demand imbalances are identified. Candidate sites are then selected by jointly considering spatial suitability and practical construction demand. Finally, POI data and high-resolution remote sensing imagery are used to assess construction feasibility and confirm optimal shelter locations. By systematically incorporating land-use conditions, existing shelter capacity, and temporal variations in population demand, this study provides a practical and operational framework for emergency shelter planning. The findings contribute to a deeper understanding of the spatial mechanisms underlying shelter allocation and offer valuable policy implications for urban emergency management and resilient spatial planning.
Based on the system coupling theory, this study comprehensively employs methods such as the coupling coordination degree model, spatial autocorrelation analysis, Dagum Gini coefficient, and spatial Durbin model to conduct an empirical study on the coordination relationship between the allocation of elderly care resources and new urbanization in 31 provinces (municipalities and regions) of China from 2013 to 2022. The study found: 1) In terms of time sequence, the coupling coordination degree has undergone a continuous evolution from moderate imbalance to medium coordination, but overall it remains in the medium-low coordination stage. 2) In terms of space, the degree of coupling and coordination has formed a single-core radiation pattern centered on Shanghai and Jiangsu in the Yangtze River Delta region, presenting a regional gradient feature of “eastern leadership-central improvement-northeastern improvement-western catching-up”. 3) The spatial correlation analysis reveals that the coupling coordination degree among different provinces exhibits significant spatial dependence and heterogeneity, resulting in four clustering patterns: high-high, low-low, high-low, and low-high. 4) The results of the spatial difference decomposition show that the overall difference in coupling coordination degree has decreased, and the regional differences are the main source of the overall difference. 5) The results at the national level indicate that the fiscal support shows a pattern of “local promotion and neighborhood inhibition”. Economic level and human resources have a significant dual promoting effect. The social environment drives the surrounding areas through spatial spillover. Technological innovation only has a local promoting effect. The organizational efficiency failed to pass the significance test; The regional regression results show that there are significant regional heterogeneity in the impact mechanisms of each driving factor. Based on this, on the basis of grasping the overall coordinated evolution pattern, differentiated regional policies should be formulated to promote the high-quality coordinated development of the allocation of elderly care resources and the new urbanization.
With the new technological revolution and industrial transformation, the digital economy has become a key economic form, driving research on urban networks’ shift from physical to digital space. Existing studies mainly focus on the spatial distribution and determinants of digital economic networks, with limited attention to the impact of digitalization policies on the evolution of spatial networks. The “National Information Consumption Pilot” program, as a major digital economy initiative, fosters online-offline integration and the fusion of digital technologies with the real economy, boosting regional development. However, whether this policy influences the development of intercity investment networks remains empirically unverified. Based on enterprise investment data and city characteristics data, this study examines 265 cities above the prefecture level from 2008 to 2021. Using the quasi-natural experiment of the “National Information Consumption Pilot Project”, it employs the progressive DID method and social network analysis to explore the evolutionary characteristics of intercity investment networks and the impact of digital consumption on these networks. The results show that: 1) The intercity investment network is transitioning from a single-core to a multi-core network topology, and the spatial structure of “small agglomeration and large dispersion” is becoming more and more obvious. After the implementation of the information consumption policy, the degree centrality of the intercity investment network of pilot cities and non-pilot cities shows a clear differentiation trend, and the weighted outdegree centrality of the pilot cities is higher than the weighted indegree centrality of non-pilot cities. 2) Digital consumption policies, represented by the “National Information Consumption” pilot, significantly strengthen intercity investment networks, and this conclusion remains robust after a series of robustness checks. 3) Heterogeneity analysis shows that the effect of policy implementation is more pronounced between cities across provinces,in regions with a high level of digital infrastructure development and within urban agglomerations. Compared to the Beijing-Tianjin-Hebei and Chengdu-Chongqing Urban Agglomerations, the promotion effect of digital consumption on the intercity investment networks in the Yangtze River Delta and Pearl River Delta Urban Agglomerations is more pronounced. The conclusions of this article provide important policy implications for accelerating the development of digital consumption, promoting interregional investment connections, and thereby accelerating the formation of a unified national market.
Urban wetlands provide multifaceted ecosystem services, including water conservation, microclimate regulation, and mitigation of the urban heat island effect, thereby contributing to a comfortable living environment and valuable recreational space for residents. A thorough understanding of residents’ preferences and willingness to pay for proximity to urban wetlands is essential for formulating effective wetland conservation, restoration, and management strategies. Consequently, quantifying the capitalization effect of urban wetlands on adjacent residential property values has become a critical area of academic inquiry. Taking Hangzhou’s Xixi National Wetland Park (abbreviated as Xixi Wetland) as a case study, this research employs a hedonic price model to investigate the premium effect of the wetland on neighboring residential properties and its spatial-temporal heterogeneity. The analysis utilizes a comprehensive dataset of second-hand housing transactions in Hangzhou spanning the period from 2015 to 2023. The empirical findings reveal several key insights. 1) Positive premium and spatial heterogeneity. Xixi Wetland exerts a significant positive premium on adjacent residences. Notably, this impact exhibits pronounced spatial heterogeneity in both horizontal and vertical dimensions. Horizontally, the premium first increases then diminishes with increasing distance from the wetland boundary, while vertically, high floor properties with unobstructed wetland views command higher prices than low floor properties. 2) Temporal heterogeneity and market cycles. The capitalization effect of Xixi Wetland is not static but varies significantly with the dynamics of the real estate market cycle. The premium effect is most pronounced during market boom periods, whereas it attenuates during recessionary and stable phases, indicating that environmental amenities are valued more highly as luxury goods during economic upswings. 3) Nonlinear premium across housing price tiers. Utilizing quantile regression analysis, the study finds that the premium effect of Xixi Wetland and the nearby West Lake increases monotonically with rising housing prices. Conversely, the impact of neighborhood parks and community greening rates exhibits a U-shaped pattern, decreasing initially before rebounding at the highest price quantiles. 4) Interaction effects with other urban amenities. The analysis reveals significant interaction effects between Xixi Wetland and other landscape features. A substitution or compensatory effect is observed: the marginal premium of Xixi Wetland is substantially stronger for residences located farther from West Lake, farther from smaller urban parks, or situated in communities with lower baseline greening rates. This study contributes to the literature by providing a nuanced understanding of how large-scale urban wetlands interact with market conditions and existing amenities to shape property values. The findings underscore the importance of incorporating heterogeneous and dynamic amenity values into urban planning and cost-benefit analyses for wetland preservation.
This study employs methods such as the CRITIC-entropy weight method, rank-size rule, and XGBoost-SHAP model to analyze the spatio-temporal evolution characteristics and influencing factors of the supply scale of urban cinema resources in China from 2012 to 2023. The results show that: 1) The supply scale of urban cinema resources in China has shown a notable expansion trend, although the growth rate has gradually slowed down, with relatively stable patterns of “regional disparities and hierarchical differentiation”. 2) The supply level of urban cinema resources has shifted from being dominated by low-value zones to medium-value zones, with the quantitative structure evolving from a “pyramid-type” to an “olive-type”. The overall size distribution of urban cinema resources generally conforms to the rank-size rule, with cities in the middle and lower ranks demonstrating strong development momentum, yet displaying a noticeable “drooping tail” phenomenon. 3) There are significant spatial disparities in the supply scale of urban cinema resources, with both agglomeration and diffusion effects coexisting. An agglomeration pattern has emerged, centered around four major urban agglomerations and supported by regional central cities such as municipalities and provincial capitals, exhibiting a trend of “dual diffusion”. Additionally, the resilience and recovery capacity of urban cinema resources show significant spatial differences before and after the COVID-19 pandemic, with the distribution of their combined types demonstrating clustered characteristics. 4) Market size serves as the dominant factor influencing the supply scale of urban cinema resources, while population scale and technological innovation play crucial supporting roles. The effects of various influencing factors exhibit significant non-linear relationships and complex interactive effects.
As an important spatial carrier for regulating social health, urban green space plays a crucial role in promoting resident’s physical and mental well-being and enhancing social cohesion. However, the academic community has not yet reached a consensus on which characteristics of green space are most closely related to resident’s individual social health levels. Based on the survey data on green spaces and health collected in Guangzhou in 2021, this study first classifies resident’s individual social health using the latent profile analysis (LPA) method. Subsequently, it estimates the association between the characteristics of nearby green spaces in the residential areas and resident’s individual social health through unordered multinomial Logistic regression. The results reveal that socio-demographic attributes such as age, work status, per capita monthly household income, and whether the household with a car can explain the differences in individual social health among residents. Relevant planning and policy formulation should consider the diverse needs of groups with different socio-demographic characteristics for urban green spaces to fully leverage their social health benefits. In addition to resident’s socio-demographic attributes, nearby green spaces in the residential areas have a significant impact on their individual social health. Specifically, there is a significant positive correlation between resident’s subjective perception of green space accessibility and their individual social health. Quality characteristics of green spaces, including cleanliness, the number of recreational service facilities, safety, and aesthetics, are key factors influencing resident’s individual social health. Therefore, constructing a complete and continuous urban green space system and improving the walking environment and walkability in neighborhoods and surrounding areas to enhance resident’s subjective perception of green space accessibility are effective ways to promote individual social health. Meanwhile, enhancing green space cleanliness, aesthetics, and safety can be achieved through regular cleaning and maintenance, strengthening green space management, and organizing volunteer activities. On the basis of balancing community needs and resource investment, appropriately increasing and diversifying recreational facilities in green spaces can further encourage residents to engage in physical activities and strengthen social interactions. The conclusions can provide a scientific basis for improving the spatial quality of urban green spaces, enhancing the interactive connection between residents and neighborhood green spaces, and fully leveraging the health effects of green spaces.
Clarifying the impact mechanism of spatial structure on urban carbon emissions is an urgent requirement for implementing the national “Dual Carbon” goals. Selecting Lanzhou, a typical valley city in western China, as a case study, and based on carbon emission data and land use data from 2000 to 2022, this research investigates the impact mechanism of spatial structure on carbon emissions by employing panel data regression models and the optimal parameters-based geographical detector, following an analysis of the spatiotemporal evolution characteristics of carbon emissions and spatial structure. The results indicate that: 1) The distribution of carbon emissions shows a pattern of high levels in the central urban area and low levels in the peripheral areas, with high levels in the east and west and lower levels in the middle part of the central urban area, exhibiting significant differences around 2010. 2) The continuous expansion of construction land has led to increased aggregation and connectivity, alongside decreased fragmentation and complexity of urban construction space, while improving the uniformity but reducing the aggregation of the overall urban space. 3) Increased aggregation, uniformity, and complexity of the overall space, as well as increased scale and connectivity of construction space, promote carbon emissions. In contrast, higher connectivity of the overall space and stronger aggregation along with more complex shapes of construction space inhibit carbon emissions to varying degrees, which is closely related to the city’s unique geographical environment and its polycentric, clustered spatial structure. 4) Compared to the overall spatial structure, construction space structure factors exert a stronger driving force on carbon emissions, with the construction land scale factor being the most significant. Regarding the interaction of dual factors, those of the overall space all show enhancement effects, while weakening types emerge among those of the construction space. Constrained by its unique geographical environment, urban expansion process, and clustered layout, the impact mechanism of spatial structure on urban carbon emissions in Lanzhou differs from that in other cities. Therefore, the low-carbon organization of its urban space should be promoted scientifically in accordance with its own characteristics.
The inbound tourism development of micro-economies exhibits greater vulnerability and volatility to crises compared to medium and large-sized economies. As one of the world’s representative micro-economies, the Macao Special Administrative Region (abbreviated as Macao) of China has faced relatively frequent impacts from economic and pandemic crises over the past 3 decades. Based on inbound tourism relationship data of Macao, China from 1995 to 2022, this study constructs an index model from a “type-scale” perspective and employs qualitative comparative analysis to examine the pattern differences in how two distinct types of crises influence Macao’s inbound tourism pathways. The research results validated the proposed hypotheses. The study found that the formation of high inbound tourism relationships is significantly influenced by soft power and visa policies, whereas non-high inbound relationships rely more on hard power and cultural institutional factors. Regarding different types of crisis, pandemic crises are more likely to drive non-high inbound tourism relationships than economic crises, and their impact is more widespread. Furthermore, concerning crisis of different scales, the impact of economic crisis manifests in the presence or absence of economic factors, while the scope of pandemic crises varies depending on whether they occur at the regional or global scale. Finally to enhance the resilience and sustainable development capacity of inbound tourism in Macao, a micro-economy, and to effectively respond to the impacts of crisis events, the study recommends that Macao coordinate the development of soft and hard power on a daily basis and strengthen hard power support during crises to stabilize the market. At the same time, differentiated crisis response plans should be formulated; for pandemic crises, resilience collaboration with neighboring regions should be strengthened, while for economic crisis, the focus should be on consumption incentives and market expansion measures.
Taking Guizhou Province—a Karst region with ecologically fragile tourism areas—as the study area, this research employs multi-source data including 2011—2022 Amap traffic big data and land cover ecological monitoring data. It constructs a progressive analytical framework of “element correlation-dynamic coordination-risk warning” progressive analytical framework. By quantifying interaction intensity, assessing coupling coordination status, and identifying differentiated coupling types, the study diagnoses decoupling risks between these elements and proposes synergistic optimisation pathways for transport and tourism ecosystem health tailored to Karst regional characteristics. The research indicates: 1) The expansion intensity of land transport exhibits varying degrees of path dependency and spatial lock-in characteristics with tourism ecological health. However, tourism ecological health exerts stronger and more persistent constraints on land transport expansion, while land transport expansion demonstrates a positive feedback effect on the health of tourism ecosystems along its routes. 2) The coordination degree between land transport and tourism ecosystem health progressively increases, with the spatial pattern of coupling coordination evolving from initial relative equilibrium to a terminal ‘core-periphery’differentiation pattern centred on Guiyang City, diminishing towards provincial peripheries. 3) The overall trend in the coupling type between road transport expansion and tourism ecological health is favourable. Coupling sub-types generally transitioned from the “disharmonious decline”category to the “coordinated development”category, with the primary basic coupling type being the“coordinated development with lagging tourism ecological health” (IV-1) category. 4) The decoupling relationship between the intensity of road transport expansion and tourism ecological health is predominantly weak decoupling. Road transport expansion has not yet posed a significant threat to tourism ecological health levels.
This study focuses on the recreational service demand preferences and valuation of typical wetlands in Hebei Province, including Baiyangdian Lake, Hengshui Lake, the Shandian River, and the Golden Coast. Wetlands in this region play a vital role in ecological protection, biodiversity conservation, water regulation, and cultural services, yet they are increasingly affected by human activities such as urban expansion, agricultural reclamation, and tourism pressure. Given the pressing need to optimize ecological value utilization while ensuring environmental sustainability, understanding the recreational service demand and economic valuation of these wetlands is critical for policy-making and resource management. Based on comprehensive questionnaire surveys conducted from 2022 to 2024 during peak tourism seasons, this study collected detailed information on visitor demographics, travel behavior, travel costs, and visitor satisfaction. A total of 3 516 valid questionnaires were obtained across the 4 wetlands, with response rates exceeding 96% in all sites. The survey captured multiple aspects of visitor behavior, including travel distance, transportation mode, multi-destination travel, overnight stays, and wetland visit priority. Visitors were categorized into high, medium and low affinity types according to their attachment and dependence on wetland destinations. To quantify recreational value, a modified travel cost model was used, accounting for multi-destination cost allocation and visitor affinity. Travel costs were allocated based on time spent at each site, and individual demand functions were adjusted for affinity. A double-logarithmic demand function estimated visitation sensitivity to travel costs, and consumer surplus was calculated under different cost truncations. The results demonstrate that travel cost significantly affects visitation rates, with visitors in the Shandian River and Hengshui Lake being most sensitive, while Baiyangdian Lake and Golden Coast show relatively lower sensitivity due to higher ecological and cultural attractiveness. Per capita consumer surplus varied across wetlands, reflecting differences in ecological function, cultural content and visitor aggregation patterns. Baiyangdian exhibited high per capita value conversion efficiency due to the integration of ecological and cultural attributes, whereas Hengshui Lake achieved high total and per-unit-area value outputs under short-distance travel conditions. The Shandian River displayed pronounced value density under near-natural, low-intensity use scenarios. In contrast, the Golden Coast’s recreational service value was constrained by limited visitor recognition of its core ecological attributes, resulting in relatively low value conversion efficiency.
Rainstorm is a significant driving force for soil erosion, and extreme rainstorm may cause severe soil and water loss. Taking the extreme rainstorm brought by Typhoon Doksuri in early August 2023 as an example, the Zhuqi River watershed in Shulan City, Jilin Province, located in the northeastern low mountain and hilly area, was selected as the research object. The changes of gullies before and after the extreme rainstorm, the accumulation of flood and debris flows during the extreme rainstorm, and the reactivation of abandoned river channels due to river capture were analyzed by combining remote sensing images with field investigations. The occurrence mechanisms of different types of erosion were also discussed. The results showed that the density, length and area of gully in farmland in the typical small watersheds were significantly greater than those in forest. During extreme rainstorms, the soil erosion in the watersheds manifested as floods and debris flows, the formation and expansion of gullies, landslides and collapses. An extreme rainstorm led to a sharp increase in the number of gullies, rising from 25 to 78. The newly formed gullies were mainly distributed on sloping farmland, and human activities had a significant impact on the formation of these gullies. During extreme rainstorms, the expansion of farmland gullies was mainly characterized by widening of the gully banks. It extended in two directions: downward at the gully tail and upstream at the gully head. In forested areas, the expansion of gullies had a faster length growth rate than the area of the gully banks, mainly moving downward at the gully tail. The extreme rainstorm caused severe gravity erosion and gully erosion in small watersheds in the low mountain and hilly areas, and also triggered the reflow of the eroded river channels, presenting certain safety hazards. The study found that in low mountain and hilly areas with low cultivation rates and a high proportion of forest land, extreme rainstorms could still cause serious soil erosion. Although the current soil erosion degree in the study area is in a mild state, soil erosion is in a state of rapid development. Prohibiting the conversion of steep slopes to farmland, strengthening the ecological restoration of retired farmland and the construction of water conservation measures for sloping farmland are effective means to alleviate soil erosion and mountain flood disasters in the low mountain and hilly areas of northeast China.
To address the uncertainty of runoff evolution in the Second Songhua River Basin under future climate change, this study develops a CNN-Transformer neural network to perform high-precision bias correction on NEX-GDDP-CMIP6 climate projection data in the period 2026—2045. The corrected meteorological variables are subsequently used as climate forcing inputs for the Soil and Water Assessment Tool (SWAT) to simulate runoff responses under three Shared Socioeconomic Pathway scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). This integrated framework aims to improve the reliability of climate-driven hydrological simulations in north temperate river basins. Results indicate that the CNN-Transformer hybrid architecture significantly outperforms single-structure networks in meteorological bias correction, increasing the coefficient of determination (R²) for precipitation to 0.74. Consequently, the SWAT model driven by the corrected data demonstrates strong performance during both calibration and validation periods, with the Nash-Sutcliffe efficiency coefficient (NSE) exceeding 0.66. Future runoff projections reveal an overall increasing trend under all scenarios, with the magnitude of increase following the order SSP5-8.5>SSP1-2.6>SSP2-4.5. Under the high-emission SSP5-8.5 scenario, runoff increases by 31.4%, accompanied by pronounced interannual variability and elevated risks of extreme flood and drought events. These results suggest that intensified climate forcing may further amplify hydrological instability within the basin. The findings demonstrate that the CNN-Transformer model effectively captures both local features and long-term temporal dependencies in meteorological bias correction, while the SWAT model provides a physically based representation of hydrological processes. The one-way coupling framework therefore integrates the predictive capability of deep learning with the process interpretability of physical hydrological models. This approach offers a reliable pathway for improving runoff prediction under climate change and provides scientific support for sustainable water resources management in north temperate river basins.
