Clarifying the impact mechanism of spatial qualities on regional price levels and improving the mechanism of spatial qualities leading to talent regional migration will, to some extent, enrich and develop existing theories in New Spatial Economics. This article constructs a analysis framework of New Spatial Economics to explain the formation mechanism of quality premium, that is, spatial qualities drives the total price index up through industrial product premium. Furthermore, it empirically explores whether the quality premium exists and whether it exhibits non-linear effects due to threshold effect. The study finds that: 1) Spatial qualities significantly improves regional price levels, confirming the existence of the quality premium phenomenon. 2) There is a threshold effect of quality premium based on economic development, talent scale, and urbanization, all of which show a trend of gradually increasing quality premium. 3) There are differences in the trend of quality premium in ecological environment, public services, cultural and entertainment amenities, and infrastructure.

Urban economic convergence is a significant criterion for judging the achievement of common prosperity. Based on the panel data of 285 cities in China from 2003 to 2022, this paper tests the convergence of urban economic growth in China using spatial error model (SEM), spatial Markov chain and kernel density estimation. It's found that: 1) There was an unconditional convergence trend in urban economic growth rate. Specifically, the convergence rates accelerated after controlling urban scale, financial situation, economic structure, economic openness, public service level and spatial error term. 2) The variation of urban economic level was gradually decreasing, but this trend mainly came from catching up with those cities outside the province, while the inter-city economic gaps of some provinces expanded during some periods. 3) The convergence of urban economy shows the global characteristic, and the three convergence clubs in eastern, central and western regions not only have convergence trends within themselves, but also have common convergence trends among clubs. 4) Urban economic convergence is dynamic. It divides urban economic development level into five categories in 2003 according to the quartile: lower level, low level, medium level, high level and higher level, which converges to steady states of 5%, 12%, 17%, 41% and 25% respectively after 100 times of Markov transition. The findings indicate that government financial behavior and market factor flow affect urban economic convergence, the complementarity of market mechanism and administrative mechanism helps to promote a higher level of dynamic and global common prosperity in the sense of economic geography.

Green innovation is an important pathway to achieve a win-win scenario between environmental protection and economic growth in the Yangtze River Economic Belt (YREB). Based on the green patent data of 108 cities at or above the prefecture level in the YREB from 2010 to 2022, this paper comprehensively employs the methods of spatial autocorrelation model, kernel density estimation and Markov chain to systematically examine the spatial heterogeneity evolution and convergence characteristics of green innovation in the YREB. The results show that: 1) The spatial distribution of green innovation presents a significant agglomeration characteristic in the YREB, with the downstream region forming an innovation hub, while the interregional correlation effects are prominent. 2) The dynamic evolution trend indicate that the overall level continues to improve and there is a path-dependent feature, and the innovation evolution of neighboring cities presents a synergistic effect. 3) Regional disparities display convergence trends, with the upstream region converging the fastest, while the convergence intensity gradually weakens from the upstream to the downstream. Therefore, this paper proposes that it should implement regional differentiation policies and strengthen cross-domain collaborative innovation to promote the balanced development of green innovation in the YREB.

This paper establishes a comprehensive evaluation index system for achieving common prosperity in Zhejiang Province from the aspects of income distribution gaps, disparities in regional urban-rural development, shared public services, material economy, spiritual and humanistic needs, ecological environment, and social harmony. This system investigates the development characteristics and influencing factors of common prosperity of Zhejiang Province at provincial and prefectural levels. The results show that: 1) From 1990 to 2020, the level of common prosperity in Zhejiang Province increased from 42.36% to 76.06%. From 2005 to 2020, the level of common prosperity showed a stable upward trend at prefectural level. 2) From 2005 to 2020, the overall level of common prosperity improved across prefecture-level cities from 2005 to 2020, regional disparities among these cities also widened. 3) Income distribution gaps gradually replaced the material economy as the primary obstacle at the criterion level for common prosperity in Zhejiang Province. The influence of the ecological environment on the development of common prosperity in Zhejiang Province has gradually increased, while the impact of common public services has decreased. The Gini coefficient emerged as the most extensive and profound obstacle factor at the criterion level for common prosperity among prefecture-level cities in Zhejiang Province. To promote the development of common prosperity in Zhejiang Province, it proposes the following strategies: improving the mechanism for public services to ensure high-quality common services, boosting residents' income to broaden the path from the well-off to the less privileged, advancing ecological civilization to firmly establish a path for green development, and encouraging regions to strive for excellence and disseminate exemplary experiences.

This study measures the level of enterprises' algorithmic collusion at the provincial level using Baidu Index, and analyzes its spatial distribution characteristics and influencing factors in 31 provincial-level regions of China from 2018 to 2023 on the basis of a two-way fixed effects model and a spatial Durbin model. The research findings are as follows: 1) The level of enterprises' algorithmic collusion is relatively high in the eastern coastal and central regions, where collusion phenomena are more pronounced and concentrated, showing a gradient decline from the coastal region to the western and northeastern regions. 2) The overall spatial correlation pattern of enterprises' algorithmic collusion level in China is relatively stable. Locally, it shows the polarized characteristics of "high-high" and "low-low" agglomerations. The "high-high" agglomeration areas are mainly distributed in the central and eastern regions, while the "low-low" agglomeration areas are concentrated in the western region. 3) Internet infrastructure and human capital have a significant positive impact on the level of enterprises' algorithmic collusion. Spatial analysis confirms the influences of Internet infrastructure and human capital retain significant positive impact on the level of enterprises' algorithmic collusion within regions, though inter-regional spillover effects are not significant. Therefore, it is necessary to strengthen supervision and prevention in the eastern coastal regions, promote regional coordinated development, enhance the construction of network infrastructure, and improve human capital level to reduce the occurrence of enterprises' algorithmic collusion behaviors.

Examining the population change pattern and its influencing factors is an important foundation for promoting the construction of new-type urbanization with the county as the carrier and promoting the integrated development of urban and rural areas according to local conditions. This paper constructs an analytical framework of "pattern-scale-function-mechanism" of the demographic change of counties in the process of Chinese-style urban-rural modernization, and explores the internal logic of the demographic change of counties and urban-rural development in the process of Chinese-style urban-rural modernization. It comprehensively uses the methods of rank-size, specialization index and gradient boosting decision tree (GBDT) to investigate the population change of counties, and explore the relevant influencing factors and their heterogeneity. It's found that: 1) The proportion of county population to urban population is declining, and the population loss of county is more serious in the north of China than that in the south of China, and is concentrated in the northeast region and the inter-provincial border areas. 2) The population size system of the counties exhibits the relatively flattened feature, the population change of counties in different types of main functional areas is not significant. The population growth is closely related to the distance from the central city in counties which are the urbanization areas and the main producing areas of agricultural products. 3) Economic development is the key factor affecting the population change of counties, its influence degree is constantly increasing, the impact of infrastructure and public services on population change in counties has not changed much and tends to be stable, the impact of policies and locations on population changes in counties is relatively limited, and the fluctuation degree of different factors on the population change of counties has obvious spatial heterogeneity.

This study explores the synergistic relationship between urban resilience and the tourism industry, aiming to clarify their intrinsic interactions and provide theoretical support for industry-city integration and regional coordinated development. Taking the Yangtze River Delta Urban Agglomeration as the research object, this paper analyzes the coevolution characteristics, interactive response and influencing factors of urban resilience and tourism industry with the help of Haken model, PVAR model and spatio-temporal weighted regression from the dual perspectives of synergy and interactive response. The results show that: 1) Both urban resilience and tourism industry development levels have improved during the study period in the Yangtze River Delta Urban Agglomeration, yet regional disparities remain pronounced, with a notable "catch-up effect" observed in small and medium-sized cities. 2) High synergy levels are concentrated in economically advanced cities like Shanghai, Hangzhou, Suzhou, the synergetic level shows a steady increase in the number of high-quality collaborative cities in the time dimension, and has the characteristics of "space locking" and "path dependence" in the spatial dimension. 3) Both urban resilience and tourism industry in the Yangtze River Delta Urban Agglomeration have positive progressive effects, with urban resilience showing a stronger positive response to the tourism industry. However, the tourism industry has a certain interactive stress effect on the urban resilience and its contribution rate is relatively low. 4) There are significant regional differences in the influencing factors of coevolution. Industrial structure and population urbanization show a positive driving effect, human capital and traffic conditions show an alternating spatial distribution pattern of promotion and inhibition, government intervention and scientific and educational guarantee have a certain inhibiting effect.

The impact of the COVID-19 pandemic on China's economic development has emerged as a prominent subject of academic concerns in recent years, However, limited research has been conducted to analyze the transformation in the survival patterns of the self-employed and private businesses and its determinants from a geospatial perspective. Based the data of self-employed and private businesses in 2019-2023 from the website of Tianyancha, this study employs descriptive statistics to analyze the pandemic's influence on the scale of Nanjing's self-employed and private businesses. Furthermore, it uses the methods of kernel density analysis and spatial autocorrelation to examine the evolution of the spatiotemporal patterns of self-employed and private businesses. This study also identifies the principal factors contributing to the spatial differentiation of the self-employed and private businesses through correlation analysis. It’s found that: 1) The COVID-19 pandemic decelerated the growth of the scale of Nanjing's self-employed and private businesses, leading to an overall decline in vitality, with the self-employed and private businesses of traditional industries experiencing more pronounced impacts. 2) The registration and cancellation of individual industrial and commercial households were predominantly concentrated in the city's tertiary business districts. The COVID-19 pandemic did not alter the "strong core and multi-level" spatial structure characteristic of the self-employed and private businesses. While the growth vitality of the self-employed and private businesses in central urban areas was relatively weak, its stability remained robust. Conversely, the self-employed and private businesses in suburban area exhibited growth potential, but were more vulnerable. 3) The self-employed and private businesses are so highly dependent on public transport accessibility, facilitating a quicker recovery in high-grade business districts after the COVID-19. In order to promote the recovery of self-employed and private businesses, urban renewal and rural revitalization should aim to provide a business-friendly environment for self-employed and privatebusinesses.

The optimization of spatial pattern in consumption competitiveness holds significant importance for unleashing consumption potential and promoting the integrated development of urban agglomeration consumption. This paper delves into consumption competitiveness from the aspects of actual competitiveness and potential competitiveness, evaluates the consumption competitiveness of 242 cities at prefecture level or above in China. It utilizes Moran's I and the gravity model to primarily dissects the spatial patterns of consumption competitiveness in five seminal urban agglomerations. Furthermore, it analyzes the driving factors of consumption competitiveness by the means of the Geodetector. The results show that: 1) Consumption competitiveness is on the rise, exhibiting a geographical pattern characterized by higher levels in the east and lower levels in the west, with a trend of expansion from coastal to inland areas and diffusion from highly competitive cities to their surrounding regions. 2) While the interconnectedness of consumption competitiveness among the five urban agglomerations is gradually intensifying, the correlations within each agglomeration exhibit distinct characteristics. 3) Consumption competitiveness demonstrates spatial clustering and positive spatial correlation. Specifically, urban agglomerations such as Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta exert a positive spillover effect, whereas provincial capitals in the central and western regions exhibit a siphon effect. 4) The levels of financial development, government support, and digital industry progression are primary factors influencing the spatial disparities in consumption competitiveness, and the interaction between different factors shows an enhanced relationship.

The collaborative promotion of digitalization and intelligentization is pivotal to high-quality economic and social development. This study analyzes the connotation of collaborative development between digitalization and intelligentization and constructs a multidimensional evaluation index system to assess their collaborative development at the prefecture level. It depicts the process state, spatial-temporal pattern and clustering characteristics of the collaborative development between digitalization and intelligentization in 284 cities of China in 2011-2020. The results show that: 1) Collaborative development level between digitalization and intelligentization was in the low range, cities with good collaborative development level are mainly concentrated in eastern coastal region, cities with low collaborative development level are mainly located in central and western regions. 2) Cities can be categorized into four types based on the collaborative development level between digitalization and intelligentization: collaborative development type, collaborative trend type, collaborative imbalance type, and collaborative lag type. Notably, the latter three types are more active. 3) The collaborative development trend follows a declining gradient from foundation to scale dimension, the efficiency development process between digitalization and intelligentization displays regional disparities. 4) There is a significant positive correlation between the collaborative development of digitalization and intelligentization in China, and the spatial distribution has a tendency of aggregation. Finally, this study identifies the problem of different types of collaborative development zones, proposes differentiated pathways to enhance the collaborative development between digitalization and intelligentization in Chinese cities.

In the context of the deep integration between the digital and real economies, enhancing the total factor productivity of digital economy (TFPDE) is crucial for cultivating new quality productive forces and achieving high-quality economic development. This paper uses the two-stage modified Epsilon-based measure and Luenberger productivity index to measure the TFPDE of 275 Chinese cities from 2012 to 2021, and investigates its spatiotemporal evolution and influencing factors. The findings are as follows: 1) China's TFPDE shows an overall upward trend, driven primarily by technological progress as a single driving force. 2) The overall Gini coefficient of TFPDE exhibits a fluctuating downward trend, and the excess variation density is the main source of differences in TFPDE among the four major regions. 3) Factors such as economic development level, industrial specialization and agglomeration, and regional integration have significant positive effects on TFPDE, and these effects exhibit spatial spillover effects. 4) There is a significant β-convergence characteristic in China's TFPDE. The local TFPDE and its rate of change have a positive spatial spillover effect on the rate of change of TFPDE in neighboring cities.

This study constructs a basic information database of China's fintech companies, uses ArcGIS spatial statistical methods to analyze the spatio-temporal evolution of fintech companies from 2011 to 2020, and utilizes Geodetector to investigate the primary factors influencing their layout. The findings reveal that: 1) The growth rate of fintech companies exhibits an "inverted U" trend over time, with overall spatial distribution disparities primarily stemming from regional variations, particularly evident in the eastern region. The distribution of these companies demonstrates a "point-axis-network" diffusion trend, centered around a "T-shaped" structure that combines the eastern coast and the Yangtze River. 2) The gravity center of fintech company distribution has shifted slightly towards the southwest of China, predominantly due to southward expansion, forming a "northeast-southwest" directional spatial pattern. 3) The agglomeration of fintech companies has intensified, resulting in a "two poles, one core, multiple regions" configuration, exhibiting a contiguous development trend. However, the agglomeration locations vary among different types of fintech companies. 4) The spatial distribution pattern of fintech companies is influenced by multiple factors. Across different developmental stages, economic strength, fiscal finance, innovation capability, and policy environment consistently serve as the core drivers of fintech company growth. Innovation incubation policies remain the strongest factor at every stage, while the second strongest factors differ across the three stages. Additionally, the influencing factors for different types of fintech companies exhibit heterogeneous characteristics. 5) The interaction among these dimensional factors has significantly intensified, albeit with notable variations. Fiscal finance, infrastructure, and policy environment all exert strong catalytic effects across various stages. In the comprehensive deepening stage, the innovation capability dimension not only exerts a significant impact but also substantially enhances the effectiveness of other factors.

Based on the data of Baidu search index of ChatGPT from December 2022 to June 2023 this article analyzes the evolution characteristics and influence mechanism of generative artificial intelligence (GenAI for short) attention degree by the ArcGIS, multivariable linear regression model, and spatial econometric model. The results show that: 1） GenAI attention degree showed the inverted-V evolution trend in China during the research period. 2) GenAI attention degree had the characteristics of regional agglomeration, which initially showed the distribution of a giant block shape with the Shandong Peninsula, the Yangtze River Delta and the Pearl River Delta as the cores, later evolved into the distribution of a small block shape. 3) Scientific and technological development, corporate development, advanced industrial structure, education level, degree of opening up, digital infrastructure and government support all had a significant positive effect on GenAI attention degree, and in addition to the digital infrastructure, the other influencing factors have the spatial spillover effect, which is the main reason for the regional aggregation of GenAI attention degree. Based on the research conclusions, this article proposes some suggestions that are increasing the technological investment and policy support for high-tech cities, establishing a high-level talent training system, improving relevant regulations and ethical standards to promote the development of GenAI industry and optimize the layout of high-tech industry.

Based on the framework of sustainable livelihood, this paper establishes an extended theoretical model of planned behavior, takes the farmers in the Maoershan Nature Reserve in Guangxi as the research object, uses the structural equation model to explore the influencing mechanism of farmers' ecological protection behavior, and examines the moderating effects of ecological compensation modes. The conclusions are as follows: 1) Farmers' livelihood capital is closely related to their ecological cognition, policy participation intention and ecological protection behavior. Farmers' livelihood capital has a positive impact on their ecological cognition. Behavior attitudes, subjective norms enhance farmers' willingness to participate in ecological compensation policies, and the enhancement of participation willingness further promotes ecological protection behaviors. 2) Farmers' ecological cognition and policy participation intention play a significant mediating role in the mechanism of ecological protection behavior. Livelihood capital can indirectly affect the participation intention of ecological compensation policies through the behavior attitudes and subjective norms of farmers, and the participation intention of farmers plays a significant intermediary effect in the relationships of "behavior attitudes-ecological protection behaviors", and "subjective norms-ecological protection behaviors". 3) The ecological compensation modes play an important regulating effect on the ecological protection behavior mechanism of farmers and have heterogeneity. The physical compensation mode positively moderates the relationship between livelihood capitals and farmers' behavior attitudes, and the technical compensation mode positively moderates the relationship between perceived behavior control and ecological protection behavior, the policy compensation mode positively moderates the relationship between livelihood capital and perceived behavior control, while the monetary compensation mode negatively moderates the relationship between livelihood capital and perceived behavior control.

Blue water resources primarily sustain human production and aquatic ecosystems, whereas green water resources primarily supports terrestrial ecosystems. Understanding the status and dynamics of blue and green water resources in Liuyang River basin is crucial for ensuring socio-economic development and ecological security. This study employs the SWAT model to simulate hydrological processes along Liuyang River basin from the perspective of blue and green water, and quantitatively analyzes the precipitation along Liuyang River basin, distribution characteristics of blue and green water resources, and their responses in flood and drought years in 1982-2018. It's found that: 1) The average annual green water volume (952.1 mm) exceeds blue water (759.6 mm) by 25%. 2) Both blue water and green water follow a similar seasonal distribution to precipitation, peaking in June (accounting for 19.54% and 12.19% of the annual total, respectively). 3) The spatial distribution of blue water exhibits higher level in the northeast of the research area and lower level in the central area of the research area, with a maximum difference of 217.34 mm. 4) Blue water demonstrates higher sensitivity to precipitation variability in flood/drought years, whereas green water shows no statistically significant difference between normal and drought years. Finally, this study proposes several adaptive management strategies, including: constructing rural ponds construction and green-water conservation forests, establishing nature reserves, expanding urban green spaces. These measures aim to enhance drought and flood resilience while safeguarding cultivated land protection red lines, providing a decision-making framework for basin-scale water security.

Ecological resilience plays a pivotal role in sustainable urban development. Enhancing ecological resilience is a critical pathway for advancing ecological civilization, improving the coordination of ecological resilience is an inherent requirement for achieving high-quality regional development. Based on a three-dimensional framework of resistance-adaptation-recovery, this study constructs an index system and analyzes the spatiotemporal dynamics and influencing mechanisms of the coupling coordination among internal subsystems of ecological resilience in the Yangtze River Delta from 2012 to 2021 by the means of dynamic coupling coordination degree model, geographical detector, and spatial Durbin model. The results show: 1) The three dimensions of ecological resilience in the Yangtze River Delta show a gradual upward trend. Each subsystem demonstrates a growth trend over time and displays a spatial distribution characterized by "high in the southeast of the Yangtze River Delta, low in the northwest of the Yangtze River Delta". 2) The coordination level of ecological resilience in the Yangtze River Delta has continuously improved. The evolution direction of resistance and recovery shifted from negative to positive, with their evolution speeds also increasing to varying degrees. In contrast, the evolution direction of adaptation shifted from positive to negative, and its evolution speed slowed down. The degree of deviation coordination in the region displays a spatial distribution characterized by "low in the southeast of the Yangtze River Delta and high in the northwest of the Yangtze River Delta". 3) Economic development, scientific and technological innovation, industry structure, openness to the global market, and urbanization level are the primary drivers influencing the coupling coordination degree of ecological resilience. Additionally, under the dual effects of external disturbances and internal forces, the coupling coordination degree of ecological resilience is comprehensively influenced by social regulation mechanism, economic power mechanism, and natural constraint mechanism.

Government audit, as a key component of the national supervision system, plays a vital role in achieving the carbon peaking and carbon neutrality goals. Based on the panel data of 277 prefecture-level cities in China from 2006 to 2019, this paper employs a spatial econometric model to investigate the spatial boundary and heterogeneity of the spatial spillover effect of government audit on carbon emissions. The research finds that: 1) Government audit not only reduces local carbon emissions but also promotes carbon reduction in adjacent cities. Moreover, this spillover effect shows the characteristics of geographical and economic boundaries, reaching its peak at a geographical distance of 400 km, and there is also an optimal economic distance spillover interval. 2) Heterogeneity analysis reveals that low-level government intervention and high-level energy consumption will make the geographical distance spillover boundary of government audit more significant. 3) Government audit can enhance public environmental awareness in local and neighboring cities, playing a role in coordinated carbon reduction. Based on the above, it proposes that the government can achieve the carbon peaking and carbon neutrality goals and promote the government audit development by the means of strengthening regional cooperation and coordination, reducing differences in urban characteristics, increasing public participation in supervision, and strengthening sci-tech innovation and R&D.

Based on the dual-dimensional (physical and virtual) evaluation index system, this study uses the methods of kernel density analysis, bivariate spatial autocorrelation and GeoDetector to analyze the spatial variation and influencing factors of the comprehensive development level of red scenic spots in old revolutionary area of Dabieshan Mountains. The results show that: 1) From the perspective of evaluation results, the top ten typical red scenic spots in the comprehensive score are mostly located in the center of the old revolutionary area of Dabieshan Mountains, and the development of Jinzhai and Xinxian is the most prominent. The Jinzhai County Revolutionary Museum is the highest virtual development level with a score of 0.76, and the forrmer site of Zhongyuan Military Region ranks highest in the physical development level with a score of 0.73. 2) The comprehensive development of red scenic spots displays the spatial pattern of "high-level aggregation in the middle of research area and peripheral low-level dispersion". However, overall development of red scenic spots remains low, with insufficient synergy between physical and virtual dimensions to form a virtuous circle. 3) The overall development of red scenic spots presents the agglomeration effect of "dual-core areas and multiple growth poles". The main core and the main growth pole are formed in Xinxian and its surrounding areas, and the secondary core and the secondary growth pole are formed near Jinzhai. 4) The significant HH points are mainly concentrated in Shihe District, Pingqiao District and Yu 'an District. The spatial dispersion of significant LL points and significant LH points is prominent, and there is no obvious agglomeration effect. The significant HL points are concentrated at the junction of Xinxian and Macheng, Yingshan and Yuexi. 5) The construction of scenic spots and regional development environment are the main driving factors, and the interaction between the proportion of red scenic spots and the total amount of post and telecommunication business has a significant impact on the comprehensive development of red scenic spots.

This study reveals the correlation characteristics and interactive mechanism between the rural tourism resource endowment and transportation patterns in karst mountainous regions, with Guizhou Province as the case study using the methods of spatial analysis and accessibility analysis. The results indicate that: 1) Rural tourism resources in Guizhou exhibit an overall cohesive and even distribution pattern, showing the characteristics of being concentrated in the south of Guizhou and scattered in the north of Guizhou, dense in the east of Guizhou and sparse in the west of Guizhou. The highway network demonstrates regional scale effects around the hinterland of central Guizhou and the ethnic group settlements in southeastern Guizhou. 2) The advantage of rural tourism resources presents a dual-peak and triple-valley pattern and concentrates in Qiandongnan Miao and Dong Autonomous Prefecture, the advantage of transportation presents a triple-peak and triple-valley pattern and mainly distributes in Guiyang City, Zunyi City, and Tongren City. The coupling coordination degree between rural tourism resources and transportation network in Qiandongnan Miao and Dong Autonomous Prefecture is the best. 3) The spatial imbalance between rural tourism resources and transportation network is influenced by the cultural history and social environment, showing a trend of shifting towards the southeast. The coordinated development of rural tourism economy in Guizhou is greatly influenced by regional social development, among which road network density and regional GDP are important factors affecting the collaborative process. In the future, it should deeply consider the integrated development strategy of transportation and tourism industries in karst mountainous regions, and empower the road-derived tourism vitality.

Based on the check-in spot data shared by users of Xiaohongshu platform and the POI data of Amap, this study explores the spatial distribution of urban Internet-famous recreational spaces (IRSs) and physical recreational spaces (PRSs) and their differences in main urban area of Changsha by the means of spatial analysis methods, and reveals its formation mechanisms using the grounded theory method according to online texts and interview transcripts. The results indicate that: 1) Overall, IRSs in Changsha's main urban area exhibit the distribution trend of a northeast-southwest orientation, while PRSs exhibit the distribution trend of a northwest-southeast orientation. The clustering intensity and scale distance of IRSs are both weaker than that of PRSs, showing a "polar-core with multiple points" distribution pattern with distinct core-periphery characteristics, while PRSs display a "single-core with multi-centered patchy" distribution. 2) Compared with PRSs, The high-cluster areas of IRSs in Changsha have notably shifted westward, primarily concentrating in high-vitality public recreational areas such as urban waterfronts, suburban mountainous regions, and commercial district. 3) The formation of IRSs' distribution characteristics is the result of the joint action of human-media-place and other factors. The characteristics and charms of urban PRSs provide a physical basis for the formation of IRSs' distribution characteristics. The popularity and influence of social media have strengthened the formation of IRSs' distribution characteristics, the change of the psychology and behavioural patterns of tourists is an important trigger for the formation of IRSs' characteristics. Based on the above, this research proposes specific measures for shaping IRSs from the perspectives of human, media, and place respectively.

Dominance degree of red resources which can reflect the regional red resource endowment and relative advantage is the material basis for the overall protection and utilization of red resources. Taking 1227 red resources belonging to the immovable cultural relics of Hunan Province as the research objects and the counties as the basic units, this article constructs a dominance degree measurement model of red resources from four aspects of richness degree, quality degree, agglomeration degree and accessibility degree, measures the dominance degree of red resources in Hunan Province comprehensively, and analyzes their spatial differentiation pattern. The results show that: 1) The red resources in Hunan Province show the agglomeration distribution pattern taking Sangzhi County as the high-density core area. 2) Richness degree and accessibility degree of red resources in Hunan Province are relatively high, but quality degree and agglomeration degree are relatively low. Furthermore, accessibility degree shows obvious "center-periphery" circular spatial distribution, while richness degree, quality degree and agglomeration degree of red resources mainly show scattered, clumpy, banded or sheet-like distribution. 3) The dominance degree of red resources in Hunan Province is at medium level, the quantitative structure of dominance degree of the county's red resources shows the fusiform characteristic. 4) The spatial pattern of the dominance degree of red resources in Hunan Province generally shows the pattern which is at high level in the eastern, central and southern Hunan, and at lower level in other areas.

Red tourism, as a distinctive hallmark and exemplary manifestation of the integrated development of culture and tourism under socialism with Chinese characteristics, has garnered significant attention. Government support and diversified effects are the core concerns of its scientific development. Based on the theory of destination competitiveness, this article investigates on the relationship between destination characteristics and the development of red tourism. It constructs an evaluation index system for red tourism destination competitiveness, encompassing four dimensions: core resources, supportive factors, destination management, and destination development. On this basis, this paper assesses the competitiveness levels and influencing factors of red tourism destination in Yan'an, Ji'an, Zunyi, Jiaxing and Linyi. The results show that: 1) From 2012 to 2021, the competitiveness of red tourism destinations in the five cities exhibited an upward trend, with Zunyi ranking highest, followed by Linyi, Yan'an, Jiaxing and Ji'an. 2) From the sub-dimensional evaluation results, Zunyi is the best in terms of the level of core resources, the level of supportive factors and the level of development of the destination. Jiaxing excels in the level of destination management. 3) Key factors influencing the enhancement of red tourism destination competitiveness include talent scale, industrial structure, and scientific-technological innovation. By applying the destination competitiveness theory, this article conducts empirical measurements, elucidates the role of destination attributes in the development of red tourism, and identifies the main factors influencing the development of the competitiveness of red tourism destinations. The research findings aim to provide valuable insights and references for the steady development of red tourism in the new era.

Based on the spatial interaction theories such as proximity diffusion and rank diffusion, this study takes spatial distance and scale differences to reflect the intercity relationships, and uses the methods of zero-inflated negative binomial regression, complementary cumulative distribution function and marginal effect analysis to systematically compare the transfer and diffusion characteristics of labor-intensive, capital-intensive and technology-intensive manufacturing industries in Beijing-Tianjin-Hebei region from 2000 to 2019, clarify disparities in spatial diffusion likelihood and intensity among these industries and analyze the differential effects of spatial distance and hierarchical distance. It's found that: 1)The high-intensity manufacturing investment links of Beijing-Tianjin-Hebei region are mainly concentrated among the cities of Beijing, Tianjin and Shijiazhuang. The growth of the population size of the investment source cities facilitates the transfer and diffusion of manufacturing industries in the Beijing-Tianjin-Hebei region, while the population size of the investment target cities exhibits a more complex influence on the diffusion of manufacturing industries. 2) Both spatial distance and hierarchical distance limit the spatial diffusion of three types of manufacturing industries in Beijing-Tianjin-Hebei region. Spatial distance always affects the diffusion possibility, and hierarchical distance always affects the diffusion intensity. 3) Unlike the broad influence of spatial distance, hierarchical distance exerts significant effects only within a specific threshold range. The threshold range for labor-intensive manufacturing is between 0.6 and 4.4, while for both capital-intensive and technology-intensive manufacturing, it is between 0.2 and 2.5.

Based on the panel data of the Beijing-Tianjin-Hebei urban agglomeration from 2006 to 2021, this paper analyzes the economic impact of the industrial coordinated development, and empirically tests the operational performance of the industrial coordinated development of the Beijing-Tianjin-Hebei urban agglomeration on the basis of using text analysis to identify the pattern of the industrial coordinated development. The results show that: 1) The industrial coordinated development has a significant positive impact on the total factor productivity, and it has a significant positive impact on the technical efficiency change index of decomposition item of the total factor productivity, but has no significant impact on the change index of technological progress and the change index of scale efficiency. 2) The industrial coordinated development has a significant positive impact on the total factor productivity of industry and service sector, but the impact on industrial total factor productivity is higher than that of service sector. 3) Further extended analysis shows that there are significant differences in the economic performance of different patterns of the industrial coordinated development, and the industrial park co-construction mode, the base economy mode of headquarters and the industrial chain integration mode have a significant positive impact on the improvement of total factor productivity, which plays an important role in the improvement of the industrial coordinated development performance of the Beijing-Tianjin-Hebei urban agglomeration.

Countries promote trade liberalization of environmental goods (EGs) based on environmental goals while implementing trade protection measures driven by economic objectives. These dual forces collectively shape the trade environment for China's EGs. Based on the China Customs data from 2009 to 2023, this paper examines the export patterns of China's EGs to destination countries, employs a panel data model to analyze the effects of trade liberalization and trade protection, and conducts heterogeneity analyses from the aspects of the provincial level, product, and destination country. The results indicate that: 1) From 2009-2023, the export scale of China's EGs initially increased but subsequently declined, with a noticeable downward trend emerging from 2021. Developed countries accounted for 63.7% of China's total EGs export, while developing countries represented 58.1% of incremental export growth. 2) Trade liberalization positively impacts the export of EGs, whereas trade protection exerts a inhibitory effect on the export of EGs. 3) Provinces with stronger innovation capabilities demonstrate higher export competitiveness and can effectively mitigate the negative impacts of trade protection. 4) Trade liberalization promotes the EGs export with lower export technological sophistication, whereas trade protection shows no differential impact across EGs. 5) Trade liberalization does not significantly increase exports to developed countries, while trade protection negatively affects exports to all types of destination countries. 6) Lower carbon emission intensity and larger market size in destination countries amplify the positive effects of trade liberalization. The findings suggest that continuously promoting technological innovation and advocating trade liberalization policies are essential strategies for China to sustain its global competitive advantage in EGs.