Developing new quality productive forces is an inherent requirement and an important focus for promoting high-quality development. This paper constructs an evaluation index system from three dimensions: technological innovation capability, industrial transformation and upgrading, and innovative allocation of production factors to measure the level of new quality productive forces in 30 provinces in China from 2011 to 2022. The Moran's I, kernel density estimation, Markov chain transition matrix, Dagum Gini coefficient, and convergence model are used to reveal the spatiotemporal evolution pattern, regional differences, and convergence characteristics of new quality productivity development. Furthermore, the obstacle degree model and geographic detector model are used to explore the influencing factors of new quality productivity development. The findings reveal that: 1) During the research period, the level of new quality productivity in China steadily increased over time, with a clear decreasing distribution pattern of "high in the east and low in the west". 2) The development level exhibits a positive spatial correlation, with high-level neighboring regions positively impacting their adjacent areas. 3) From the perspective of regional differences, there are large regional disparities in the development level of China's new quality productivity, with the contribution of inter-regional differences taking the first place, followed by intra-regional differences, and that there are significant absolute β-convergence and conditional β-convergence characteristics in the whole country as well as the three major regions of the eastern, central, and western regions. 4) From the perspective of influencing factors, scientific and technological innovation capacity is the core factor in cultivating and developing new quality productivity, openness, informatization, labor force level, and transportation infrastructure are the main driving factors that lead to the spatial differentiation characteristics of new quality productivity across the country as well as in the eastern, middle, and western regions.

Taking 272 prefecture-level and above cities in China as the research unit, we use two-period non-radial directional distance function and Luenberger index to measure urban industrial green total factor productivity(GTFP), so as to measure the level of urban industrial green development and to further explore the regional differences and influencing factors. We find that the industrial green efficiency at the municipal level in China generally shows a pattern of "high in the east, low in the west, high in the south and low in the north", with significant imbalance characteristics. The main source of overall differences in industrial GTFP at the municipal level in China is hypervariable density, followed by the intra-regional difference, with a lower but gradually increasing contribution from the inter-regional difference. Technological progress and green momentum promote industrial GTFP growth, while efficiency deterioration and development momentum hinder industrial GTFP growth in the central, western and northeastern regions. From the combined perspective of technology and input-output dimensions, technological progress stems from both developmental and green technology progress, and efficiency deterioration is mainly caused by developmental efficiency deterioration. Regional differences in industrial GTFP stem more from the developmental efficiency difference. Accordingly, the government should pay attention to the long-term spatial imbalance in industrial green development, implement differentiated industrial green development strategies according to local conditions, and narrow the development efficiency gap in order to synergistically enhance the level of industrial green development at the municipal level.

Establishing the imperative for green economic development, this study first quantifies green economic performance using a non-radial SBM model incorporating undesirable outputs. Concurrently,industrial capacity utilization is measured via a composite network DEA model, with decomposition into supply-side and demand-side components. Thereafter, employing spatial panel methodologies grounded in adjacency, inverse distance, and nested economic-geographic weight matrices, evaluated are the spatial spillover effects of green economic advancement upon industrial capacity utilization. Mechanism analysis is conducted through both supply-side and demand-side channels. Finally, regional heterogeneity is examined through separate analyses of eastern, central, and western China. The empirical results indicate: 1) Green economic development not only elevates local industrial capacity utilization via agglomeration effects, but also enhances utilization in neighboring regions through diffusion effects, thereby mitigating the constraining influence of the "Hu Huanyong Line" on economic geography. 2) The promotion effect of green economy development on industrial capacity utilization is mainly manifested as direct effects on the supply-side and spillover effects on the demand-side. 3) Despite significant regional economic disparities, green economic development contributes to fostering greater regional coordination.

Improving energy efficiency can alleviate the pressure on energy demand and facilitate the low-carbon transition of the economy, thereby achieving the objectives of energy conservation, emission reduction, and green development. Firstly, we employed the super-efficiency SBM model incorporating undesired CO₂ outputs and the Malmquist productivity index (MPI) to calculate the total factor energy efficiency of 30 provinces between 2006 and 2021. Secondly, this paper regarded the energy rights trading pilot policy as a quasi-natural experiment and constructed methods such as the DID approach to explore the impact and mechanism of the energy rights trading pilot policy on total factor energy efficiency. Our findings show that: 1) The energy rights trading pilot policy significantly enhances total factor energy efficiency, and this conclusion remains robust after a series of tests. 2) According to the mechanism study, the energy rights trading pilot policy boosts total factor energy efficiency by optimizing the energy consumption structure, upgrading the industrial structure, and fostering green technology innovation. 3) This paper constructs a synthetic control method to explore the policy effect of each pilot province. The research reveal that Fujian Province experienced the most significant impact from the energy rights trading pilot policy, followed by Sichuan Province, while Henan Province was least affected. Although Zhejiang Province experienced a short-term impact from the policy, it was not significant. This study provides theoretical and empirical support for advancing energy marketization reform to enhance energy efficiency and offers practical insights for innovating urban governance models and achieving sustainable development.

A resilient urban economic system under the complex economic situation has become an inevitable requirement for social and economic development, and the vigorous vitality of digital economy provides impetus for the enhancement of urban economic resilience. This study analyzes the spatio-temporal evolution process and characteristics of digital economy and urban economic resilience in China, tests the impact of digital economy on urban economic resilience, and further analyzes the time series evolution and structural of its impact. The results show that: 1) The level of urban economic resilience shows a multi-point scattered distribution, and there is a significant spatial differentiation between the north and the south. 2) The digital economy can significantly enhance the resilience of urban economy. 3) The structure analysist shows that digital innovation elements, digital industrialization and industrial digitalization have a great impact on urban economic resilience. The time series analysis shows that the enhancement effect of the improvement of the development level of digital economy on the resilience of urban economy is increasing year by year, among which the impact of digital industrialization, industrial digitalization and digital innovation factors on the resilience of urban economy also has an increasing time series analysis. According to the research conclusions, this paper puts forward relevant development suggestions.

The geographical environment constrains the roles of both government and market forces in promoting urban-rural integrated development, leading to spatial variations in their effectiveness. Based on county-level panel data from Hunan Province (2010-2020), this study constructs and measures indicators related to urban-rural integrated development and geographical environment. It explores the spatial differentiation characteristics of government and market roles in urban-rural integrated development from both temporal and spatial dimensions, employing econometric modeling methods. The results show that: 1) Both government and market forces significantly drive urban-rural integrated development in Hunan's counties, but overall, the government's role outweighs that of the market. There remains substantial room for improvement in the market's contribution to urban-rural integrated development in Hunan. 2) The geographical environment constrains urban-rural integrated development in Hunan's counties, exhibiting a threshold effect on the effectiveness of government and market forces, with pronounced spatial differentiation: in counties with favorable geographical conditions, the market plays a greater role than the government in promoting urban-rural integrated development, whereas in counties with poor geographical conditions, the government's role surpasses that of the market. Based on theoretical logic and quantitative analysis, the study proposes policy recommendations to effectively align the roles of government and market forces with the geographical conditions of different regions in decision-making for urban-rural integrated development.

Under the background of highly uncertain environments, understanding how county-level economies respond to shocks of different attributes is critical for fostering stable and high-quality regional development. This paper takes the county-level economic units of Shandong Province as the research subjects, and constructs a resistance index to reflect the county-level economic resilience under two distinct shocks: the 2008 financial crisis and the COVID-19 pandemic. With the help of multi-scale geographically weighted regression model, this paper explores the differences in the influence mechanisms of county-level economic resilience under different attribute shocks and the spatial heterogeneity of its influencing factors. The results show that: 1) The county-level economic resistance of Shandong Province shows heterogeneity characteristics under different attribute shocks. The overall economy and different industries' ability to resist financial crises in the county-level economy of Shandong Province is lower than the national average. However, the overall economy and the tertiary industry to resist the COVID-19 pandemic is higher than the national average. 2) Agency-based factors play a significant role in promoting regional economic resilience under different attribute shocks, but the scale of their effects varies greatly. Local fiscal support has spatial heterogeneity under the financial crisis, while it has a global role in the COVID-19 pandemic. Pandemic control and governance has significant spatial heterogeneity characteristics. 3) Structural factors have different effects on regional economic resilience under different attribute shocks. Industrial structure upgrading primarily exhibits positive spatial heterogeneity during the financial crisis, but it has a negative heterogeneity under the COVID-19 pandemic. 4) The influence scale of locational conditions varies significantly under different attribute shocks. This paper suggests that the attribute and spatial heterogeneity of different shocks should be considered in analyzing the characteristics and influence mechanism of regional economic resilience.

It is significant to accurately grasp the spatial correlation network structure of carbon emission efficiency and its influencing factors for the low-carbon development of urban agglomerations in the Yellow River Basin. Based on the three-stage super-efficiency SBM model, this study measures carbon emission efficiency of urban agglomerations in the Yellow River Basin from 2006 to 2022. And the spatial correlation network is constructed with the modified gravity model to explore the network structure characteristics and influencing factors of carbon emission efficiency. The results show that:1) During the observation period, the overall carbon emission efficiency in the Yellow River Basin shows a trend of "rising first, then declining, and rising again". There are differences in carbon emission efficiency among urban agglomerations, with the Central Plains urban agglomeration having the highest average carbon emission efficiency, and the Jinzhong urban agglomeration having the lowest. 2) Carbon emission efficiency in the Yellow River Basin has formed a complex spatial correlation network with strong network accessibility. However, the tightness of the network correlation relationship of the whole basin and urban agglomerations needs to be enhanced. 3) Over time, the influence of various driving factors on the spatial correlation network of carbon emission efficiency in the Yellow River Basin remains relatively stable in direction. Differences in geographical distance and population density have a significant negative impact on the formation of the spatial correlation network, while differences in economic development level and technological innovation exert a significant positive impact. Additionally, the factors influencing the formation of the spatial correlation network of carbon emission efficiency among urban agglomerations in the Yellow River Basin exhibit noticeable heterogeneity.

Based on elucidating the concept of urban green development efficiency (GDE) and clarifying the theoretical basis of digital economy driving urban GDE, this paper employs the Super-SBM model to assess the scale of urban GDE in the Yellow River Basin from 2011 to 2021, while also examining its spatial-temporal evolution characteristics, and this paper empirically analyzes both linear and nonlinear effects of the digital economy on urban GDE in the region by panel Tobit model and Threshold Regression model. The results show that: 1) The urban GDE in the Yellow River Basin is volatile and has great potential for improvement. The evolution of the sequence pattern across the three major regions is dynamic and hierarchical，and 60 cities present a typical pyramid-shaped hierarchical pattern. 2) The evolution of the spatial pattern reveals phased characteristics, from the random multi-center distribution to the agglomerated state with multiple-cores and multi-centers. High-concentration contiguous areas have emerged, while the improvement of urban green development efficiency is affected by the spatial network association effect and the spatial spillover effect in adjacent cities. 3) The digital economy has directly driven the green development in cities across the basin. These driving effects are heterogeneous in time, space and city types. The imbalance of economic structure, the lag in adjustment and the rapid increase in urbanization have further aggravated the restrictive effect of the environment in the midstream and downstream of the basin. 4) Government regulation and environmental regulation have a significant single threshold effect. The optimal intensity of the two should be at the balance point of maintaining market flexibility and promoting green development, so as to play a synergistic role with the digital economy. Therefore, this paper proposes countermeasures and suggestions to promote ecological protection and high-quality development in the Yellow River Basin by strengthening the construction of the digital economy, fully harnessing the green efficiency of the digital economy, and optimizing the spatial pattern of green efficiency.

Accessible elderly care resources serve as a pivotal indicator for assessing the effectiveness of elderly care service provision, holding immense significance for the development of a modern public service system. Focusing on the Yangtze River Delta Urban Agglomeration(YRDUA), this study employs multi-source data and analytical methods including kernel density analysis, consistency coefficient analysis, and geographical detector method to examine the spatial patterns, demographic-resource alignment, and driving mechanisms of accessible elderly care resources across different elderly life cycle groups. The results show that: 1) Accessible care resources exhibit a "Z-shaped" clustered spatial distribution under a "one-body, two-zones and multi-core" framework, with pronounced hierarchical disparities following a "core city-regional central city-marginal city" structure. The elderly care institutions are distributed "along the river and the bay", and the community elderly recreation places are "multi-point" clusters. 2) Elderly populations form a Shanghai-centric "wedge-shaped concentration zone" with a "one-body, two-wings" pattern along the Shanghai-Nanjing and Shanghai-Hangzhou corridors. Among specific subgroups, unhealthy seniors who are self-care capable and those who are self-care incapable are concentrated along the Shanghai-Nanjing axis, while healthy seniors show a trend of suburbanization. All three groups exhibit a "core-periphery" decline pattern, though at varying rates. 3) Spatial coordination between care resources and elderly demographics is weak, with resource-population alignment declining from central to peripheral regions. Resource-type analysis shows: cities with abundant elderly care institutions account for a relatively high proportion, cities with well-matched community healthcare places account for a relatively low proportion, and cities with lagging community elderly recreation places have a prominent proportion. Community elderly recreation places demonstrate best overall fit, followed by community healthcare places. Inter-provincial disparities are notable: Zhejiang resources generally surpass elderly population concentrations, while Anhui, Jiangsu, and Shanghai show resource deficits. The southern parts of the YRDUA forms a "surplus-matching" interlaced zone, with resource-deficient areas clustering in northern and western regions. 4) Spatial differentiation of accessible care resources stems from multi-factorial drivers. Human environment factors-particularly regional fiscal expenditure, elderly population size, and media attention-are primary determinants. Natural environment impacts are relatively limited but gain significance through interactions with human environment factors.

Urban scaling law reveals the scaling relationship between urban indicators and population size. This paper explores the scaling relationship between the spatial distribution of urban functional zones and population size at different scales with the help of POI data in Shanghai. This paper confirms that the scaling law also exists within the city and is affected by the Modifiable Areal Unit Problem (MAUP for short). The results show that: 1) The scaling law of the spatial distribution of urban functional zones in Shanghai under raster partitioning is more significant than that of administrative partitioning, and tends to be optimized as the spatial scale increases. 2) The spatial distribution of urban functional zones in Shanghai is with sub-linear scaling under administrative partitioning, while it is with linear scaling under raster partitioning. There are also differences in the scaling laws between different urban functional zones. Restaurant functioncal zone, commercial functioncal zone and residential functioncal zone are with super-linear scaling, workplace functioncal zone and education functioncal zone are with linear scaling, traffic functioncal zone is with sub-linear scaling. 3) The overall efficiency of urban functional zones in Shanghai is high, and most regions, including the main urban area, are generally better than expected, while there are differences in the efficiency of different urban functional zones.

Promoting the equalization of basic public services between urban and rural areas is a key pathway to enhancing household consumption, boosting domestic demand, and advancing high-quality economic development. Based on panel data from 272 prefecture-level and above cities in China from 2012 to 2022, this study constructs an index system for urban-rural basic public service equalization and empirically examines its impact on household consumption, underlying mechanisms, and spatial effects. The findings reveal that: 1) Urban-rural basic public service equalization and household consumption both exhibit significant spatial clustering, with the development pattern shifting from a dispersed "multi-point" distribution to a more clustered "grouped" configuration. Major urban agglomerations such as Beijing-Tianjin-Hebei, the Yangtze River Delta, Chengdu-Chongqing, and the Guangdong-Hong Kong-Macao Greater Bay Area have become the primary high-agglomeration regions. 2) Equalization of basic public services significantly promotes household consumption and generates strong positive spatial spillover effects. 3) Mechanism analysis indicates that public service equalization boosts consumption through income and expectation effects. 4) Heterogeneity analysis further indicates that the consumption-promoting effects of public service equalization are more pronounced in regions with high spatial clustering and in cities with lower levels of economic development. Based on these findings, this study suggests accelerating the equalization of urban-rural basic public services, enhancing cross-regional coordination in public service provision, fully leveraging spatial spillover effects, and continuously unlocking household consumption potential.

Breaking through the bottlenecks in the flow of production factors between cities has become a key path to promote the smooth circulation of the domestic economy. By analyzing the latest input-output data at the city level, this study explores the structural characteristics and influencing factors of China's urban production network based on social network analysis methods, model analysis methods, random walk network clustering algorithms, and exponential random graph models (ERGM). The following conclusion can be drawn: 1) Urban production networks have obvious regional heterogeneity characteristics, with a clear trend of multi center distribution. Eastern cities occupy a central position in the network, and developed coastal areas have established close industrial chain cooperation with inland areas. 2) The community characteristics of production network communities are prominent, with particularly tight network connections in regions such as the Pearl River Delta, Yangtze River Delta, and Bohai Rim. From a microscopic perspective, clustered modular structures have become the main mode of endogenous network formation. 3) The river density, road traffic, government support, digital technology innovation, industrial intelligence, and industrial upgrading in the attributes of network nodes have a positive effect on the formation of production networks, and the formation of production networks is significantly dependent on spatial adjacency, cultural distance, and innovation cooperation networks.

Evolutionary economic geography focuses on the development of new industries in a region, but it mainly examines knowledge relatedness between products to understand regional development path. This article takes Shunde robotics SME cluster of Guangdong as an example, using data and relevant information obtained from on-site surveys. It builds up an analytical framework drawing on global production network research, focusing on how changes in the external environment affect firms' internal organization and their external network relationships, which further facilitates the potential linkages between new and existing industries and drives the evolution of local industries. The findings show that increasing pressures of reducing cost and enhancing capabilities due to intensified market competition, as well as challenges such as financialization, market development, and risk management, have prompted local firms to adjust their strategies through internal coordination, inter-firm cooperation, and collaboration with universities, research institutes, governments. Relying on their resource and capability advantages in traditional industries, they build knowledge, technology, and production linkages between old and new industries. This article also indicates that the differentiated strategies of local firms have varying impacts on the economic and technological relatedness of industrial clusters, which provides some evidences for understanding the diversity of regional pathways for new industry development.

Private economic-dominated towns serve as a special type of towns which achieves industrialization and urbanization dominated by private enterprises "from the bottom up" in the process of rural industrialization. At present, there are few quantitative studies on the spatial distribution and its influencing factors of enterprises in private economic-dominated towns. This article takes Chendai, Jinjiang, Fujian Province as an example, the place where Sociologist Fei Xiaotong put forward the "Jinjiang Model" and where there is a world-class footwear and clothing production bases. Based on data of footwear and clothing enterprise, as well as built environment and socioeconomic data of Chendai in 2023, this article systematically analyzes the spatial patterns and influencing factors of footwear and clothing enterprises in Chendai by adopting a comprehensive approach including Kernel Density Estimation, Standard Deviation Ellipse, Spatial Autocorrelation and Geographically Weighted Regression. The results reveal that footwear and clothing enterprises in Chendai present the spatial characteristics of "multi-point agglomeration with some zonal distribution", while enterprises in different industrial chain links show significant spatial differentiation. Built environment mainly affects the manufacturing links, social economy and facility conditions mainly affect the upstream design-and-development link, the downstream packaging-sales link and other links, while business environment mainly affects the upstream design-and-development link and the midstream finished-product-manufacturing link. The influences of above factors show obvious spatial heterogeneity. This article will provide references for optimizing industrial layout and maintaining healthy and sustainable economic development of private economic-dominated towns.

Taking Guangdong Province as the research object, this study analyzes the spatiotemporal variation pattern of its newly added cross-border e-commerce enterprises. Then, an econometric model based on path dependence theory is constructed to illustrate the driving factors of cross-border e-commerce enterprise location selection and spatiotemporal pattern evolution, using methods such as nearest neighbor index, kernel density, standard deviation ellipse, and Theil index. The research results indicate:1) In terms of temporal development, the number of cross-border e-commerce enterprises in Guangdong Province has steadily increased, and new enterprises have shown periodic fluctuations due to the influence of economic environment and policy evolution. 2)The newly added cross-border e-commerce enterprises in Guangdong Province are characterized by both agglomeration and dispersion, with agglomeration as the main trend. The agglomeration direction tends to be horizontal, and the agglomeration centers are located in Shenzhen and Dongguan. The distribution differences between the four major regional sectors are greater than the differences within the sectors. 3)The new cross-border e-commerce enterprises in Guangdong Province have transformed from the gathering of double central axes in Guangzhou and Shenzhen to the gathering of multi central planes on the west bank of the Pearl River Estuary, and further spread to the eastern, western and northern regions of Guangdong by leaps and bounds. 4)The location selection of cross-border e-commerce enterprises presents spatial differences in the Pearl River Delta and non Pearl River Delta regions, with a focus on path dependence and path creation, respectively. The path dependence formed by location conditions and economic development level still dominates among the influencing factors, while policy factors show a path creation effect. The path creation effect of external shocks such as technological progress and openness are not significant.

The urban-rural fringe of metropolitan areas serves as a pioneer zone for advancing urban-rural integration and rural revitalization. Exploring rural revitalization in this fringe from the perspective of urban-rural integration carries significant practical importance. The study reveals the following: In terms of theoretical mechanism, the interaction among "elements, structure, and function" is the key to understanding urban-rural integration; regarding the formation mechanism, the urban-rural fringe emerges from the combined effect of the "push" from urban expansion and the pursuit of greater interests, alongside the "pull" from peripheral rural areas striving for development; in analyzing problems, the "rural diseases" in the urban-rural fringe of metropolitan areas manifest as "urban-rural comprehensive diseases", concentrated in aspects like "population, land, and industry", as well as the resultant rural functional disorders and spatial governance issues; in exploring paths, from the perspective of urban-rural integration, the approach to rural revitalization in the metropolitan fringe is proposed as follows: it requires the joint drive of endogenous and exogenous forces, namely, reconstructing rural production, living, ecological, social, and cultural functions through the integration of urban-rural elements and the optimization of rural structure.

The article calculates the contribution of China's 13 major grain producing areas to food security and the level of agricultural technological innovation from 2008 to 2022, the coupling coordination model and geographic detector model are used to analyse the coupling coordination relationship and influencing factors between the contribution of food security and agricultural technological innovation. The results showed that: 1) The contribution of China's major grain producing areas to food security shows a temporal change pattern of first increasing and then decreasing, with significant differences in provincial contributions. Among them, Heilongjiang had the highest contribution to food security, followed closely by Henan Province. Jiangxi and Liaoning, on the other hand, are relatively weaker in terms of their contribution to food security, and are at a lower level. 2) The agricultural technology innovation in China's major grain producing areas shows a trend of increasing first, then decreasing, and gradually stabilizing. Among them, Heilongjiang has the highest level of agricultural technology innovation, while Shandong has the lowest level. 3) The coupling coordination degree between the contribution of food security and agricultural technology innovation in China's major grain-producing regions is basically maintained between 0.28~0.65, showing a relatively obvious asynchronous feature. Among them,Heilongjiang has the highest coupling coordination degree, while Liaoning has the lowest coupling coordination degree. 4) The explanatory power of the coupling coordination between the contribution of different driving factors to food security and agricultural technological innovation varies. The key driving factors for the coupling coordination between the two are the grain sowing area, the amount of agricultural fertilizer applied, and local fiscal education expenditure.

Considering that rural e-commerce has become an important channel for linking smallholder farmers to large markets, and that many major production areas of geographical indications (GIs) are encouraging farmers to participate in rural e-commerce, it is necessary to investigate the online premium of GIs and its underlying mechanisms. Based on actual transaction data of rice products from JD.com, this paper utilizes the hedonic price model to estimate the online price premium of GIs more accurately and examines the indirect mechanism of GIs generating online price premiums from the perspectives of online reputation and traceability. The results of the study show that: 1) GIs are able to generate online price premiums, but online price premiums decrease as the price range increases. 2) GIs help accumulate a good online reputation and promote the use of product traceability codes, which in turn generates online price premiums. 3) The online price premium of GIs varies across regions, with GI rice from western areas or non-major grain-producing regions able to generate a higher online price premium. 4) Functional labels such as selenium-enriched, low-protein, and germ weaken the online price premium of GIs, while there is no evidence that organic certification substitutes for GIs.

Identifying multifunctional spatial patterns and conflicts is essential for integrated and efficient territorial space management. This study adopts a synergy-competition perspective to build a suitability assessment system for agricultural, urban, and ecological functions, uses the triangle model to classify dominant multifunctional types, and quantifies conflicts with current land use and spatial planning via a conflict diagnosis matrix. Taking Laizhou City, Shandong Province, as a case study, a hexagonal grid with a radius was used for empirical analysis. The results show as follows. 1) The average suitability indices for agriculture, urban, and ecology are 0.60, 0.51, and 0.54, with agricultural suitability is highest in the east and center, urban suitability clusters centrally, and ecological suitability dominant in coastal and hilly areas. 2) an agricultural-urban composite type is dominant, this type forms a northeast-southwest belt; ecological-dominant areas rank second, mainly along the coast and western forests. Overall multifunctional suitability varies nonlinearly with elevation, distance to coastlines, urban centers, and roads. 3) Existing spatial conflicts are mostly low to moderate and dispersed, but planned conflicts are higher; planning reduces conflicts in 18.69% of the area but increases them in 19.12% due to poor functional synergy or mismatch. The research suggests enhancing multifunctional integration and dynamic conflict monitoring to improve spatial governance.

Investigating the spatiotemporal evolution patterns of cultivated land multifunctionality and their influencing factors is essential for formulating sustainable land management policies, ensuring food security, and maintaining ecological security. The study employed the entropy method, geographical detector, and GIS technology to analyze the spatiotemporal evolution of cultivated land multifunctionality and its influencing factors in the Dongting Lake area of Hunan Province during 2012, 2017, and 2022. The analysis was conducted from three dimensions: economic production, social security, and ecological maintenance.The results show that: 1)From 2012 to 2022, the economic production function of cultivated land in the Dongting Lake area exhibited an overall strengthening trend, displaying a spatial pattern of "higher in the west and lower in the east";The social security function of cultivated land showed significant enhancement in the western Dongting Lake area, exhibiting an overall pattern characterized by "higher values in central and western regions";The ecological maintenance function of cultivated land exhibited an increasing spatial agglomeration trend, characterized by a "high center, low periphery" pattern. The comprehensive function of cultivated land demonstrated a sustained enhancement trend in the western and southwestern regions. 2)The influence mechanism of spatial differentiation of cultivated land multifunctionality from large to small is urbanization rate > population density > per capita cultivated land area > topographic undulation > Proportion of population aged 65 and above > non-agricultural output value ratio > effective arable land irrigation rate > per capita GDP > slope. The level of regional economic development is the dominant factor in the spatial differentiation of cultivated land multifunctionality. Regional population characteristics are essential, and regional natural conditions are fundamental factors. There are mutually reinforcing actions among the factors, which reflect the complex characteristics of the factors and mechanisms affecting the spatial-temporal evolution of cultivated land functions in the Dongting Lake area.

Forest wellness plays an important role in promoting public health. This paper takes ecological wellness suitability, landscape wellness suitability, dietary wellness suitability and cultural wellness suitability as the first-level criteria, and selects 18 indicators to construct the evaluation system of forest wellness suitability. Based on the above, it evaluates and zones the suitability of forest wellness in Hubei Province, and puts forward ideas and suggestions of product development in each region. It's found that: 1) The forest wellness suitability level is low in the plain areas of Hubei Province and high in the mountainous areas of Hubei Province. Overall, it presents the circular spatial differentiation pattern which is low in the middle of Hubei Province and high around the periphery of Hubei Province. 2) According to the suitability level and composition, the development of forest wellness industry in Hubei Province can be divided into four first-level regions (priority development zone, key development zone, local development zone and precise development zone) and eight second-level regions. 3) The development of forest wellness products in Hubei Province should be tailor to the person, the place, and the time, and implement a multi-level development strategy of diversification, specialization, synergy and empowerment.

Red tourism resources are the core carriers of red tourism development. Studying the spatial distribution of red tourism scenic spots (sites) is fundamental for scientifically formulating red tourism development plans and achieving high-quality tourism management. Based on spatial location data of 323 red tourism scenic spots (sites) across 122 counties (cities and districts) in Hunan Province, this paper employs Voronoi polygon variation coefficients and kernel density analysis to characterize their spatial distribution patterns. The geographic detector model is used to identify influencing factors, and fuzzy-set qualitative comparative analysis (fsQCA) is applied to explore the configurational effects of these factors. The results indicate that: 1) The spatial distribution of red tourism scenic spots in Hunan Province exhibits clustering characteristics, with significant variations among counties. The density distribution of red tourism scenic spots decreases from east to west, with the core density area concentrated in the Changsha-Zhuzhou-Xiangtan urban agglomeration, extending westward to the Wuling Mountain area and southeastward to the Luoxiao Mountain area, forming a "one core, dual axes" pattern. Outside the core area, multiple ring-shaped sub-density zones are sporadically distributed in southern, central, and northern Hunan. 2) The spatial distribution pattern of red tourism scenic spots is influenced by a combination of natural, resource, economic, and social factors, with the interaction effects of dual factors having greater explanatory power than single factors. 3) The fsQCA results show that none of the nine selected conditional variables alone constitutes a necessary condition for influencing the distribution of red tourism scenic spots in Hunan Province. Three configurational patterns are identified: "resource-driven", "resource-market linkage", and "resource-economy linkage".

This paper selects 88 counties in Guizhou Province as the research object and constructs a county tourism competitiveness evaluation index system covering four dimensions: development, resources, elements, and management. It measures the tourism competitiveness scores of each county using the entropy weight-TOPSIS method and systematically analyzes the spatiotemporal evolution characteristics of these scores. Based on the obstacle degree model, the main obstacle factors constraining the enhancement of county tourism competitiveness in Guizhou Province are further identified and analyzed. The results indicate that: 1) From 2014 to 2023, the overall competitiveness of county-level tourism in Guizhou Province has steadily improved, accompanied by a notable spatial agglomeration effect. However, the development gap between regions has widened, exhibiting a structural differentiation pattern characterized by "the strong becoming stronger and the weak becoming weaker". 2) The spatial distribution of tourism competitiveness across counties in Guizhou is markedly uneven. Counties with high competitiveness are mainly concentrated in economically developed and well-connected areas such as Guiyang, Zunyi, and Liupanshui, while remote mountainous and ethnic minority regions exhibit relatively weak competitiveness. 3) The core obstacles limiting the improvement of county tourism competitiveness include poor ecological environment quality, weak economic foundations, and inadequate transportation accessibility. 4) The main categories of barriers faced by counties include development-related, resource-related, factor-related, and management-related obstacles. Due to disparities in resource endowments and stages of development across counties, there is significant heterogeneity in both the types of obstacles encountered and their degrees of influence.

With the deepening of all-for-one tourism, building tourism corridors and optimizing tourism spatial layout have become inevitable choices for cultural-tourism integration and achieving sustainable development. Taking Weinan City of Shaanxi Province as research area, this study identifies tourism tourism core nodes based on the user visit volume and review volume of Ctrip, and then uses the minimum cumulative resistance (MCR) model and gravity model to delineate tourism corridors of Weinan City and evaluate the tourism network structure under multiple development scenarios. The results are as follows: 1) The foundational tourism spatial structure comprises 44 tourism core nodes interconnected by 118 tourism corridors (44 primary corridors, 15 secondary corridors, 59 tertiary corridors). The total length of the tourism corridors is 2423.02 km, with 46 tourism "pinch points" and 28 tourism obstacle points. 2) Scenario simulations demonstrate that the comprehensive development scenario of increasing tourism source areas has limited effectiveness in improving the tourism network efficiency, while the optimized development scenario of reducing tourism source areas proves more effective in enhancing the network system compared to the comprehensive development scenario. 3) Based on the comprehensive tourism resistance surface of Weinan City, this study categorizes tourism development zones into three types of areas: extremely important areas, important areas, and generally important areas. It proposes differentiated tourism development strategies, and provides a scientific basis for high-quality development of cultural-tourism integration in Weinan City and China.