The tourism sector constitutes a critical platform for incubating new, quality, productive forces, leveraging its potential to accelerate structural economic upgrades and technological advancement. This study constructs a theoretical framework for new quality productive forces in tourism (TNQP) and establishes an evaluation index system. Using the entropy-weighted technique for order of preference by similarity to ideal solution method, we measure China’s TNQP and investigate its spatial-temporal differentiation, evolutionary patterns, and obstacle factors using geospatial measurement techniques and statistical methods. Key findings reveal that (1) The theoretical mechanism of TNQP relies on the synergistic interaction of three dimensions: New quality laborers, new quality means of labor, and new quality objects of labor. (2) China’s TNQP exhibits a wave-like periodic trajectory in temporal evolution, with a gradient decline from eastern to central and western regions. Dimensionally, new quality objects of labor outweigh new quality means of labor and laborers. Moreover, inter-provincial disparities have gradually narrowed, demonstrating a significant convergence trend. (3) TNQP development remains stable, showing a spatial pattern of “low-level clustering and medium-level contraction”. Inter-regional disparities primarily drive the overall Gini coefficient, while intra-regional disparities follow the order: Eastern>western>central. Furthermore, spatial evolution displays diffusion along the northeast-southwest axis, with the gravity center persistently shifting southwestward. (4) New quality objects of labor emerge as the key constraining dimension, with key obstacle factors including international tourist arrivals, general industrial solid waste, tourism research and development personnel, per capita broadband interfaces, and tourism foreign exchange earnings. This study’s findings provide a scientific basis and practical guidance for promoting the high-quality development of China’s tourism economy.
To investigate the impact of new quality productive forces on the high-quality development of tourism, clarify their mutual influence, and improve the high-quality development of tourism. This study used nine provinces and autonomous regions in the Yellow River Basin as the research subject to establish a measurement index system for new quality productive forces and the high-quality development of tourism. Entropy values, fixed-effects models, and mediation effect models were used to measure new quality productive forces and the high-quality development of tourism in the nine provinces and autonomous regions from 2013 to 2022, analyzing their mutual influence. The results reveal that (1) New quality productive forces significantly and positively affect the high-quality development of tourism. (2) Cultural-tourism integration and technological progress mediate the relationship between new quality productive forces and the high-quality development of tourism. (3) New quality productive forces exert a more pronounced promotional effect on the high-quality development of tourism in regions with lower economic development levels. (4) Each component of new-type productive forces (new technologies, laborers, objects of labor, and means of labor) promotes the high-quality development of tourism, with laborers and means of labor playing more prominent roles at present. Therefore, localities should completely recognize the guiding values of new quality productive forces, formulate differentiated developmental strategies based on regional realities, and advance the high-quality development of tourism to help achieve the goals of Chinese modernization.
Ice and snow tourism has emerged as a crucial driving force for regional economic and cultural revitalization. This study employs accessibility measurement, hotspot analysis, and geographic detectors to evaluate the accessibility of ice and snow tourism destinations in the Yellow River Basin. We also examine their spatial distribution characteristics and influencing factors. The main findings show that: (1) Ice and snow tourism destinations in the Yellow River Basin exhibit an uneven spatial distribution characterized by a “dense east and sparse west” pattern. Moreover, clustering tendencies are apparent, wherein the downstream region contains the highest number of destinations, whereas the upstream region has the fewest. At the provincial scale, Shandong, Shanxi, and Henan have relatively more destinations, and at the municipal scale, Jinan, Zhengzhou, and Qingdao lead in the number of destinations. (2) Accessibility to ice and snow tourism destinations in the Yellow River Basin reveals a spatial gradient, described as “convenient in the downstream, moderate in the midstream, and restricted in the upstream areas”. The experiential tourism destinations dominate numerically; however, their overall accessibility remains relatively weak. (3) Hotspots and subhotspots of accessibility to ice and snow tourism destinations in the Yellow River Basin are concentrated in cities within Shandong and northern Henan. In contrast, cold spots and subcold spots mainly occur in cities located within the upstream and midstream regions. (4) A combination of social, economic, and ecological factors jointly influences accessibility to ice and snow tourism destinations in the Yellow River Basin. Specifically, road mileage, urbanization rate, and annual average snowfall comprise the primary determinants. These findings provide a scientific reference for optimizing resource allocation and promoting coordinated regional development in the Yellow River Basin’s ice and snow tourism sector.
A thorough analysis of the spatial correlation network structure and influencing factors of tourism ecological security in the Yellow River Basin is essential for balancing economic benefits with ecological constraints and promoting regional sustainable development. Using inter-provincial panel data from the Yellow River Basin spanning 2009 to 2022, this study employed the entropy method, a modified gravity model, social network analysis, and quadratic assignment procedure (QAP) regression to examine the spatial correlation network of tourism ecological security and identify key influencing factors. The results reveal the following: (1) Tourism ecological security across the basin’s nine provinces exhibits significant cross-administrative characteristics, with inter-regional dependence steadily increasing. (2) The network displays pronounced spatial heterogeneity; the absence of dominant core nodes results in insufficient agglomeration effects and limited linkage development. (3) Differences in provincial plate ownership determine distinct roles and positions within the spatial correlation network. (4) Regional GDP, per capita tourism consumption, tertiary industry added value, wastewater discharge, and solid waste utilization rate emerge as the primary influencing factors. From a social network analysis perspective, this study elucidates the overall and local characteristics of the tourism ecological security spatial network in the Yellow River Basin and identifies critical drivers. These findings offer a novel theoretical framework to support the sustainable and healthy development of the region.
China is actively advancing its “dual carbon” goals, with the carbon emission efficiency of the comprehensive tourism industry attracting significant attention. This study employs the super-slacks-based-measure model to accurately measure the carbon-emission efficiency of the tourism industry across five northwestern provinces (autonomous regions) from 2011 to 2020. The Tobit regression model is employed to conduct an in-depth analysis of its influencing factors. The research findings indicate that (1) The overall carbon emission efficiency of the tourism industry demonstrates a “rising-declining-stabilizing” trend, with notable regional disparities. Inter-provincial gaps have gradually narrowed; however, a spatial pattern of “higher efficiency in the southeast and lower in the northwest” persists. (2) Regarding internal dynamics, the polarization phenomenon has weakened, with the technological progress index showing a stronger correlation with tourism carbon emission efficiency. (3) In terms of influencing factors, economic development level, industrial structure optimization, and technological innovation capacity exhibit significant positive effects. In contrast, the tourism reception scale demonstrates a significant negative impact. Technological innovation capacity proves most effective in reducing tourism carbon emissions. The promoting effect of openness to the global economy requires further enhancement, while transportation infrastructure construction currently shows no significant impact. Based on these findings, this study proposes optimization strategies to reduce tourism-related carbon emissions across the five northwestern provinces (autonomous regions), thereby facilitating the industry’s green transformation and achieving coordinated economic, social, and environmental development.
This research integrates the Flus-Markov model and the equivalent factor method to analyze dynamic changes in the land use patterns in the Tarim River Basin from 2012 to 2032, evaluate the spatiotemporal characteristics of ecosystem service value (ESV), and predict its future trends, based on Landsat remote sensing images and socioeconomic data. The results show that: (1) From 2012 to 2032, the area of unused land and water first decreased and then increased, while the cultivated land area expanded and grassland degraded significantly continuously, resulting in a decrease of 2093.72×108 yuan in the total ESV. The contributions of grassland and water decreased by 2984.17×108 yuan and 38.44×108 yuan, respectively. (2) The spatial distribution of ESV shows a significant gradient, decreasing from high values in the northwest to low values in the southeast. Hydrological regulation, climate regulation, and gas regulation are key service functions. (3) Sensitivity analysis shows that changes in water bodies and grasslands have the greatest impact on ESV. A low value of the elasticity of coefficient correction indicates that the estimation results are robust. Model validation reveals a Kappa coefficient of 0.95, indicating reliable prediction accuracy. (4) Land use and normalized difference vegetation index are the main factors influencing the ESV of the Tarim River Basin. This study reveals the correlation between the degradation of ecological service functions and increased land use in the Tarim River Basin. ESV could be improved by optimizing land use structure, strengthening grassland and water conservation, and providing a scientific basis for ecological security and sustainable development in arid areas.
Artificial forests of Haloxylon ammodendron are the largest windbreak and sand-fixing forests widely used in the northeast edge of the Ulan Buh Desert, China. An accurate assessment of the health status of this forest is crucial for ensuring its functioning, ecological protection, and implementing precise restoration methods. A quantitative health evaluation system based on the framework of “zoning classification grading” involving eco-surveys and a combination of weighted technique for order preference by similarity to ideal solution (TOPSIS) models, was applied. The results revealed that constructing a health evaluation system based on forest and community structure, environmental conditions, and health risks as the criterion layer can ensure accurate estimations. The contribution of factors such as soil moisture content, length of newly formed branches, mortality rate, shoot withering rate, and pests and diseases to such a system is significant. These artificial forests are generally in a moderately degraded state and may progress toward severe degradation. This phenomenon is a result of a combination of factors, including abnormal soil hydration conditions, low soil organic matter content, frequent occurrence of pest infestations and diseases, and inadequate maintenance and management. These results can provide a scientific basis for the ecological protection and restoration of artificial H. ammodendron forests in the region, as well as references for health assessments and the graded restoration of deteriorated forests as a part of the “Three North” project.
Xinjiang requires timely and accurate monitoring of soil salinization dynamics to support effective management and sustainable land use. This study examines the Weigan-Kuqa River Delta Oasis (Weiku Oasis) in Xinjiang. Based on Sentinel-1 synthetic aperture radar imagery and Sentinel-2 optical imagery from July 2022, characteristic parameters that were significantly correlated with measured soil salinity values were extracted and optimized. Six feature space models were constructed, including three polarization combination models from Sentinel-1 ([V2-H]-[H], [V2-H]-[(V2+H2)/V], and [V2-H]-[V-H]) and three spectral index models from Sentinel-2 (CRSI-COSRI, CRSI-NDWI, and CRSI-GARI). Using the optimal model, soil salinity in the study area was inversely estimated, revealing its spatial distribution patterns and enabling precise monitoring of typical salinized areas within the Weigan-Kuqa River Delta Oasis. The results indicate that (1) The Sentinel-2-based CRSI-COSRI model achieved the best inversion performance, with a correlation coefficient (r) of 0.639 and a coefficient of determination (R2) of 0.670, significantly outperforming all other models. (2) The simulated spatial distribution of soil salinization indicated that the overall degree of soil salinization in the study area is relatively high, with an increasing trend from west to east. This study verifies the effectiveness of feature space models in the remote sensing-based inversion of soil salinization in arid regions, providing reliable data support and methodological references for regional salinized soil monitoring and management.
Grassland vegetation biomass is a key indicator of grassland ecosystem productivity and carbon storage. Its spatial distribution pattern and driving mechanisms are of great scientific significance for understanding the maintenance of regional grassland ecosystem structure and function and their responses to climate change. Taking the Qinghai Lake Basin as the study area, this research integrates field sampling data (collected in July-August 2023) and remote sensing data to analyze the spatial distribution characteristics of grassland vegetation biomass (including aboveground and belowground components) and to explore its driving pathways using statistical analysis, correlation analysis, and structural equation modeling. The results reveal (1) Significant differences in biomass among different vegetation types, with meadow types demonstrating higher values than steppe types. Aboveground biomass is highest in mountain shrub meadows (311.54 g·m-2) and lowest in mountain solifluction meadows (64.67 g·m-2), whereas belowground biomass is highest in dwarf kobresia meadows (3534.05 g·m-2) and lowest in rhodiola desert (339.12 g·m-2). (2) High aboveground biomass values are primarily concentrated in the middle and lower reaches of the Shaliu River Basin and the southern region surrounding the lake, whereas the high-value areas for belowground and total biomass are primarily located in the middle reaches of the Buha River, the Quanji River, the Qiadangqu River Basin and middle reaches of the Shaliu River. Lower altitude areas provide more suitable temperatures and fertile soil, thereby promoting the growth of aboveground parts. Conversely, due to colder conditions and poorer soils, higher altitude regions drive plants to enhance root system development and improve resource acquisition capacity. (3) Structural equation modeling revealed that ecosystem carbon use efficiency (total effect: -0.44) and soil bulk density (total effect: -0.59) were direct factors affecting both aboveground and belowground biomass of grassland vegetation. In conclusion, vegetation type, and regional environment collectively affected grassland vegetation in the Qinghai Lake Basin, identifying ecosystem carbon use efficiency and soil bulk density as the primary determinants. This study provides critical data and scientific support for understanding vegetation biomass spatial patterns and for guiding grassland conservation and restoration in the Qinghai Lake Basin.
The Qinling-Daba Mountains are located at the convergence of the climatic transition zone between northern and southern China and the ecotone between warm-temperate and subtropical regions. As a climate-sensitive area, investigating the coupling relationships between vegetation and climate in this area is critical for understanding the evolutionary mechanisms of ecosystems under climate change. This study employs a multiple linear regression model to predict kernel normalized difference vegetation index (kNDVI) values under three Shared Socioeconomic Pathways scenarios from 2024 to 2100, based on MODIS data and climatic factor datasets from 2001 to 2023. The Theil Sen Median estimator and Mann-Kendall test were used to analyze spatiotemporal trends of vegetation changes, and path analysis was applied to dissect the driving mechanisms of key climatic factors. The results reveal that (1) Temperature is the dominant factor driving vegetation changes, spatially covering 67.27% of the study area, with its positive effects concentrated in the Qinling-Daba Mountain region, whereas the impacts of evapotranspiration and precipitation exhibit significant spatial heterogeneity. (2) The vegetation kNDVI increased by 0.1 from 2001 to 2023, demonstrating a “rapid initial growth followed by a gradual slowdown” trend, with degradation areas concentrated in low-altitude urbanized zones and high-altitude regions constrained by water-heat limitations. (3) Future scenario simulations reveal that vegetation dynamics stabilize under SSP119, whereas SSP585 demonstrates divergent trends, with the direct inhibitory effects of evapotranspiration coexisting with indirect facilitative effects driven by increased temperatures. (4) The replenishment efficiency of precipitation for vegetation diminishes with increasing climate extremes, whereas the direct climatic forcing of temperature significantly intensifies under elevated emission scenarios. (5) Regional vegetation responses indicate significant spatial heterogeneity, requiring differentiated ecological restoration strategies. These strategies should prioritize high-altitude vulnerable zones, low-altitude areas disturbed by human activities, and evapotranspiration-sensitive regions in the central-eastern sectors. This study reveals the nonlinear response of vegetation to climate change in the Qinling-Daba Mountains, thereby confirming the ecological stability advantages of the low-carbon pathway (SSP119) and providing spatially optimized strategies for vegetation conservation and carbon sequestration enhancement under regional carbon neutrality goals.
Using the CN05.1 precipitation dataset and the NCEP/NCAR reanalysis data, this study examines the spatiotemporal characteristics of summer precipitation on the Tibetan Plateau from 1981 to 2022 and explores the influence of westerly-monsoon synergy on this precipitation. The results show that: (1) Average summer precipitation over the past four decades is 216.6 mm, with a spatial pattern that decreases from the southeast to the northwest and an increasing trend of 4.85 mm per decade. The first empirical orthogonal function (EOF) mode indicates a spatially coherent change across the plateau, with a notable increase in wetness observed in the late 2010s, while the second EOF mode reveals pronounced interdecadal variability. (2) The westerly-monsoon synergy index provides a more robust indicator of summer precipitation over the plateau than individual westerly and monsoon indices, with a correlation coefficient of 0.5, effectively capturing interannual variability. (3) The westerly-monsoon synergy exerts a significant influence on summer precipitation over the Tibetan Plateau. When the synergistic effect strengthens, the upper-level divergent westerly circulation interacts with low-level warm and moist air transported by the westerlies, enhancing vertical ascent and promoting precipitation. Conversely, when the synergy is weak, anomalous easterly circulation dominates, accompanied by low-level dry and cold northerly flows, under which subsidence and moisture divergence suppress precipitation formation.
With the acceleration of global urbanization, the severe PM2.5 pollution in arid zone cities, owing to their unique geographical and climatic conditions, exhibits strong non-stationarity and complex spatiotemporal characteristics, making it difficult for traditional prediction models to effectively capture its dynamic patterns. To address this challenge, this study proposes a hybrid prediction framework of an “adaptive noise complete ensemble empirical mode decomposition-Ivy optimization algorithm-Kolmogorov Arnold network-bidirectional long short-term memory neural network” (CEEMDAN-IVY-KAN-BiLSTM), aiming to enhance the prediction accuracy of PM2.5 concentrations. This framework jointly extracts multi-scale features through noise reduction decomposition as well as parameter optimization and integrates the strong nonlinear fitting and bidirectional time series modeling capabilities of the KAN-BiLSTM model, effectively improving the prediction performance. The results reveal that the PM2.5 concentration in Urumqi City from 2021 to 2024 shows significant seasonal fluctuations, with an average of 41.97 μg·m-3 in winter due to coal heating and the influence of the inversion layer and drops to 14.04 μg·m-3 in summer due to enhanced atmospheric convection. Moreover, it shows an overall decreasing trend annually. Moreover, the importance ranking of the data indicates that PM2.5 is significantly positively correlated with air quality index, PM10, CO, and NO2, and negatively correlated with temperature and dew point temperature, suggesting that coal emissions, vehicle exhaust, and meteorological diffusion conditions are the main influencing factors. Moreover, the model effectively separates the high-frequency fluctuations (such as sandstorm events) and low-frequency trends (seasonal changes) in the PM2.5 sequence, reducing the impact of data non-stationarity. Finally, the experiments were based on daily air quality data in Urumqi City from 2021 to 2024, results of which demonstrate that this model achieves the coefficient of determination, mean absolute error, and root mean square error values of 0.991, 1.391, and 1.881, respectively, significantly outperforming conventional machine learning and common deep learning models. This verifies the applicability of the “decomposition-optimization-integration” deep learning framework in the prediction of arid zone cities.
Changes in the snowpack in the Three Rivers Source Region have important implications for regional and global climate, the hydrological cycle, and ecosystems. However, systematic, long-term monitoring of snow depth dynamics and climate attribution based on remotely sensed data across regions and elevation gradients remains limited. This study analyzed the spatial and temporal patterns of snow depth change in the Three Rivers Source Region from 1980 to 2020 using remote sensing data stratified by subregions and elevation bands, and quantified the relative contributions of temperature and precipitation. The results show that (1) Snow depth in the Three Rivers Source Region exhibited pronounced spatial heterogeneity over the past 41 years, with average snow depth in high-elevation mountain ranges generally exceeding 3 cm and maximum snow depth generally exceeding 6 cm. Average and maximum snow depths decreased significantly at rates of 0.15 cm·(10a)-1 and 0.49 cm·(10a)-1, respectively. A decreasing trend was observed in average snow depth across 68.44% of the region and in maximum snow depth across 63.83% of the region, with significantly decreasing areas accounting for 15.64% and 7.47%, respectively. (2) Pronounced regional and altitudinal differences in snow depth and its changes were observed, with the highest mean and maximum snow depths (2.41 cm and 9.86 cm, respectively) and the fastest decreasing rates [0.37 cm·(10a)-1 and 0.81 cm·(10a)-1, respectively] occurring in the Lancang River source area. Snow depth increased with altitude, with vertical gradients of 0.49 cm·km-1 for mean snow depth and 1.29 cm·km-1 for maximum snow depth. Mean snow depth declined across all elevation bands except the 3.5-4.5 km and >6.0 km bands, whereas maximum snow depth declined across all elevation bands except the 3.5-4.5 km band, with the fastest decrease occurring in the 5.0-5.5 km band. (3) The pronounced “warming and humidifying” climate trend over the past 41 years is the primary driver of snow depth decline in the Three Rivers Source Region, with temperature identified as the dominant controlling factor. The influence of climate change exhibits clear regional and altitudinal differences, with snow depth reductions particularly evident in low-altitude (<3.5 km) and high-altitude (>4.5 km) areas. These findings provide a scientific basis for optimizing snow water resource allocation, ecosystem protection and restoration, and predicting regional climate change trends in the Three Rivers Source Region.
Quantitative assessment of the long-term variations in cropland water use efficiency (WUEc) is crucial for optimizing water resource utilization and achieving high yields as well aseffective water-saving in irrigated agriculture in arid regions. This research integrates gross primary productivity of crops (GPPc), grop evapotranspiration (ETc), WUEc, and meteorological as well as vegetation data in the Aksu River Basin from 2002 to 2022, a typical arid region, and systematically identifies the spatiotemporal patterns of WUEc and the synergistic effects of multiple driving factorsby applying Sen’s slope, the Mann-Kendall trend test, seasonal and trend decomposition using loess, partial correlation analysis, and path analysis. The results indicate the following: (1) Temporal characteristics: GPPc and ETc in the basin increased significantly at rates of 0.6 g C·m-2·a-1 and 0.3 mm·a-1, respectively, while WUEc declined at a rate of 0.02 g C·mm-1·m-2·a-1. Intraannual dynamics showed a unimodal pattern for GPPc and ETc (peaking in August), and a bimodal pattern for WUEc (with peaks in April and October). (2) Spatial patterns: Regions with declining WUEc accounted for 60.3% of the area under consideration, while those with increasing GPPc and ETc covered 97.1% and 94.8%, respectively, highlighting a widespread phenomenon of “increased production without efficiency gains” in the basin. (3) Driving factor analysis: WUEc was significantly negatively correlated with temperature (T), vapor pressure deficit, and leaf area index (LAI), with the negatively correlated areas corresponding to 77%-89%, and positively correlated with precipitation (Pre), corresponding to 87% of the total area. (4) Path analysis: T and Pre primarily influenced WUEc by regulating GPPc, whereas LAI affected WUEc via ETc. Normalized difference vegetation index and enhanced vegetation index impacted WUEc through the combined regulation of both ETc and GPPc. T and LAI were identified as dominant drivers, suggesting a dual-stress mechanism acting on agroecosystems in arid regions. This study elucidates the multi-scale evolution patterns of WUEc in arid regions and its nonlinear driving mechanisms, providing a scientific basis for optimizing agricultural water resource management under climate change.
Promoting the coordinated development of different elderly care models is an effective way to optimize the allocation of elderly care service resources in our country. Based on panel data from 2018 to 2022, this study empirically analyzes spatiotemporal coupling coordination and influencing factors of home community elderly care and institutional elderly care in China using a coupling coordination model, a geographic detector, and other methods. The results indicate that (1) The coupling coordination degree and relative development degree between home community elderly care and institutional elderly care are generally on the rise. The coupling coordination degree has shown a continuous improvement trend over time. The relative development degree of home community elderly care is gradually becoming clear with the trend of “synchronous development>advanced development>lagging development”. (2) The number of provinces entering the coordination stage (III and IV) continues to increase and shows an evolutionary pattern of gradually spreading from the central and eastern regions to the western and northeastern regions in space. The phenomenon of stage transition in relative development is more pronounced, and the western region has significantly more provinces in transition than the central and eastern regions. (3) The coupling coordination degree has a positive spatial correlation and a fluctuating upward trend. The correlation strength shows a spatial pattern of “western>eastern>central>northeast”, with mostly HH-type and LL-type clustering. (4) Organizational strength, elderly care demand, and technological level are the main influencing factors of the coupling coordination degree. This study’s findings can provide a theoretical basis and policy recommendations for addressing structural contradictions in China’s elderly care service supply and for innovating the development of the elderly care policy system.
Evaluating rural development from a resilience perspective is significant for breaking the urban-rural dual structure and promoting comprehensive rural revitalization. This article takes 86 counties in Gansu Province as the research object and constructs a rural resilience evaluation index system based on three dimensions: “resilience, adaptability, and reconstruction capability”. Using spatial autocorrelation analysis and geographic detector models, it reveals the spatiotemporal evolution of rural resilience and its driving mechanisms in counties of Gansu Province from 2010 to 2022. The results indicated that (1) The resilience level of rural areas in Gansu Province has significantly improved, with obvious spatial differentiation characteristics. The resilience of rural areas in central and eastern Gansu is relatively high, while some counties in Hexi, Gannan, and Linxia have lower resilience. The number of high-resilience counties continues to increase, while the number of low-resilience counties continues to decrease. (2) There is a spatial positive correlation in rural resilience, and the correlation is gradually weakening. The degree of spatial agglomeration is declining, with high-agglomeration areas shrinking toward the central Gansu region and low-agglomeration areas concentrated in counties such as Gannan and Linxia. (3) The level of rural social services, economic development, and agricultural production has a significant impact on rural resilience. The explanatory power of rural industrial structure for rural resilience is steadily increasing, and rural resilience is driven by economic development, resource optimization, industrial transformation, and service guarantee mechanisms. This study’s results provide scientific reference for the implementation of rural resilience development in Gansu Province.
While the synergistic development of rural public services and rural tourism is critical for advancing rural revitalization, academic research focusing on coupling coordinated development between these two domains remains notably scarce. This study analyzes coupling coordination between rural public services and rural tourism and its impact on rural revitalization, This study uses methods including the coupling coordination degree model, the gravity center and standard deviation ellipse model, and spatial econometric models to quantitatively measure the spatiotemporal characteristics of the coupling coordination degree between the two in western China from 2010 to 2023, as well as their impacts and spatial spillover effects on rural revitalization. The findings reveal that the coupling coordination between rural public services and rural tourism in western China has increased year by year, with the overall level shifting from extreme disorder to intermediate coordination, and presenting a spatial differentiation pattern of high coupling coordination in the south and northwest, and low in the northeast. The gravity center of the coupling coordination degree exhibited a southwestward migration trend, while the standard deviation ellipse consistently maintained a northwest-southeast orientation, demonstrating a trend of first clustering and then dispersing. Furthermore, the degree of coupling coordination has a significant positive impact on rural revitalization and exhibits spatial spillover effects. The research findings can provide a theoretical basis and policy recommendations for the coordinated development of the two aspects and the region’s overall rural revitalization.
In the overall national security concept in the new era, historical blocks are facing dual challenges of disturbances and resilience building. Taking the historical block of Kashi Old City as the research object, this studybuilds a pressure-state-response model comprising six elements: Extreme disaster pressure, climate environment pressure, alley composition capacity, alley component density, infrastructure response, and public service response. It selects 44 key factors closely related to resilience to natural disturbances and uses the entropy weight method and the analytic hierarchy process to establish a multidimensional resilience evaluation system for historical blocks. The evaluation results show that (1) The comprehensive resilience index of the historical block of Kashi Old City is 1.84 (Level II), and the overall coping ability is weak. The resilience of the state layer (38.83%) is significantly higher than that of the response layer (27.45%), indicating its strong self-regulating ability but weak postdisaster management. (2) The key restrictive factors are the flood inundation radius, historical street and alley greening rate, and average summer solar radiation intensity, highlighting the synergistic risks between high-density built environments and climate sensitivity. (3) The wisdom of traditional construction is not linked to modern disaster prevention needs, leading to a contradiction between protection and safety. The results reveal a quantitative framework for integrated multi-hazard defense and living conservation of historic districts in arid zones, thereby promoting the synergistic development of cultural heritage conservation and ecological security.
