As a strategic emerging industry, the new energy vehicle (NEV) sector exhibits complex spatial interdependencies between production and innovation. However, existing re- search has paid limited attention to this issue. Using Changzhou, a key NEV manufacturing hub in China, as a case study, this paper combines co-location quotient analysis with field inter- views to examine the spatial distribution, co-location synergy, and evolutionary pathways of NEV production and innovation. The findings reveal significant heterogeneity in production-in- novation co-location synergy across different segments of the NEV industrial chain. Core com- ponent segments (e.g., batteries) exhibit a high degree of production-innovation co-location, whereas complete vehicle manufacturing and auxiliary component segments are characterized by production-dominated agglomeration, reflecting structural and functional differentiation within the industrial chain. Accordingly, three localized evolutionary patterns are identified in Changzhou: a low-value-added production cluster dominated by component manufacturing; a production-chain cluster led by complete vehicle manufacturing; and a production-innovation synergy cluster driven by battery segments. Notably, the central urban area and Liyang-Jintan exhibit contrasting levels of production-innovation synergy. The central urban area centers on complete vehicle manufacturing and fosters tacit coordination through inter-firm learning and process improvements. Liyang-Jintan leverages the local embeddedness of leading battery en- terprises and the development of innovation platforms to transition from externally attracted production to endogenous innovation. This has fostered an enterprise-led system integrating in- dustrial production, technological innovation and supply-chain collaboration. The study demon- strates that production-innovation synergy depends not only on geographic proximity but also on technological characteristics of dominant segments, enterprise organizational structures, and local institutional support. Finally, the paper proposes policy recommendations to promote co- ordinated development of local production and innovation system.

Enterprise relocation constitutes a pivotal mechanism for spatial restructuring of manufacturing industries. Systematic evidence on aggregate patterns and interactive drivers, however, remains scarce. Drawing on a newly proposed Migration Force Model, this study maps the spatiotemporal evolution of inter-regional relocations among Chinese manufacturing firms from 2012 to 2020 and empirically tests the underlying mechanisms. Using panel data covering 1 359 migrating enterprises, the model quantifies migration force through the smoothed net difference in per-capita relocation scale between origin and destination districts. The key findings are as follows. Temporally, 2012—2017 represents the peak relocation period, domin- ated by technology-intensive firms, whereas traditional sectors mainly completed cross-regional moves before 2018. Spatially, relocation exhibits an “ east-out, west-in” pattern —departure points were concentrated while destinations were dispersed. Bidirectional flows between adjacent and conditionally similar regions were active, stabilising net migration force peaks in central districts. This study examines how production factors couple with firm demand. Labour demand strengthens the negative migration force that stems from external labour supply, while technology demand reinforces the positive force driven by specialisation intensity. Raw material cost advantages generated positive forces only when firms exhibited responsive material demand orientations. Concerning synergy between firm capabilities and market condition, marketisation exerted positive migration force, whereas government intervention operated inversely. Industrial agglomeration and firm capability in- dependently induced negative force, yet high-capability firms could reverse agglomeration’s neg- ative effect. Market openness and corporate social network accessibility generated positive and negative forces respectively, with social networks amplifying the positive effect of openness. In terms of interaction between production factors and market condition, both housing prices and market size exert positive forces in isolation. Yet rising housing prices attenuate the attractive- ness of large markets and eventually reverse it. Manufacturing firms’ relocation motives dis- play heterogeneity across time, space and firm characteristics, collectively driving the spati- otemporal evolution of inter-regional migration. These findings advance an integrated analytic- al framework for understanding firm relocation decisions and offer empirical foundations for re- gional industrial policies aimed at balancing agglomeration economies with spatial equity.

Based on data on innovation entities and patent applications in the new energy vehicle (NEV) industry in the Pearl River Delta from 2008 to 2022, this study employs kernel density analysis, the standard deviation ellipse, and the GeoDetector model to examine the evol- ution of the innovation spatial pattern and its influencing factors. The main findings are as fol- lows: 1) Enterprises are the dominant innovation entities, while universities and research insti- tutes play supporting roles. Spatially, innovation activities are concentrated in Guangzhou and Shenzhen and gradually diffuse to surrounding areas, showing an evolutionary trend of “large- scale diffusion and small-scale agglomeration”. 2) Patent innovation exhibits significant spatial agglomeration and forms a “ multi-core and belt-like clustered” spatial pattern. Innovation levels differ across industrial chain segments. The midstream segment accounts for the largest share of innovation outputs. Patent distribution in different segments presents diverse character- istics, including single-core and dual-core agglomeration. Relying on industrial parks, innova- tion clusters are mainly concentrated along the eastern coast of the Pearl River Delta. 3) Innova- tion linkages are centered on Guangzhou and Shenzhen as the primary radiation hubs, while Foshan, Dongguan, and Zhuhai act as secondary hubs. Together, they form a spatial structure characterized as “ two cores, three centers, and multiple nodes” . 4) Economic development level, government support, transportation accessibility, scientific research investment, and cap- ital support all have positive effects on NEV industrial innovation, whereas industrialization level shows a negative correlation. Among these factors, capital support (0.899 1), scientific re- search investment (0.805 7), economic development level (0.680 0), and government support (0.573 8) are the dominant driving factors. The interaction effects are mainly characterized by dual-factor enhancement. The new energy vehicle industry is a key vehicle for developing new quality productive forces. This study reveals innovation spatial clustering, connectivity charac- teristics, and determinants in the Pearl River Delta new energy vehicle industry, providing ref- erence for optimizing innovation resource allocation, strengthening industrial chain collabora- tion, and promoting high-quality development.

Coordinated development of economic growth quality improvement and ecological environment protection is an important component of achieving regional sustainable develop- ment. The development of low-carbon industries has become an urgent need to achieve syn- ergy between economic quality improvement and ecological environment protection. Under- forestry-economy has the dual strategic significance of economic transformation and ecological environment protection. Improving the under-forestry-economy growth quality is conducive to the transformation of regional economy to green and low-carbon. This paper analyzes the coup- ling coordination mechanism of under-forestry-economy growth quality and carbon emission in the Changbai Mountain area, constructs the evaluation index system of under-forestry-eco- nomy growth quality and Carbon emission evaluation index system, and uses entropy weight method, coupling coordination degree model, spatial auto-regression model and dynamic Qual- itative comparative analysis (QCA) method to explore the spatial and temporal evolution char- acteristics and influencing factors of the coupling and coordination degree of under-forestry- economy growth quality and carbon emission in 9 counties (cities) in the Changbai Mountain area from 2012 to 2022. The conclusions are as follows: The coupling coordination degree of under-forestry-economy growth quality and carbon emission in the Changbai Mountain area from 2012 to 2022 has experienced high-speed coordination stage, transformation and adjust- ment stage and high-quality coordinated development stage, and is currently in the stage of high- quality coordinated development. The coupling coordination degree shows a trend of ‘rise-de- cline-recovery’ in time series, and presents a pattern of ‘local high-value discrete distribution’ in space, and the spatial correlation is not strong. Labor data is the main factor affecting the coup- ling and coordination degree of under-forestry-economy growth quality and carbon emissions in the Changbai Mountain area, and forest tending area is the core condition affecting the coup- ling and coordination degree. The intra-group coverage of each configuration has regional dif- ferences. According to the orientation of influencing factors, the research area is divided into two categories: laborer-labor data-oriented and labor data-oriented, and corresponding develop- ment suggestions are proposed for the research results.

This study examines network externalities in China’s new energy vehicle industry from the perspective of knowledge division of labor. Using supply chain transaction data from 2010 to 2024, it analyzes knowledge division linkages between focal firms and supplier clusters across intra-city and inter-city scales. Intra-city network externalities are measured through value agglomeration indices, while inter-city network externalities are assessed using interlock- ing network analysis. The results show that: 1) intra-city externalities diffuse from national and regional centers to specialized manufacturing hubs, following hierarchical and contagious pat- terns with time-lagged polarization-trickle-down effects. 2) The role of localization economies gradually weakens, whereas urbanization economies first decline and then rebound, with intra- city externalities increasingly serving external markets. 3) Inter-city externalities are structured by hub cities, characterized by leapfrog cross-regional diffusion and stronger economic homo- phily. 4) Borrowed size effects peak and then decline, while borrowed function effects continue to strengthen. 5) Core nodes in network communities remain stable, and virtual and spatial ag- glomeration promote cross-regional integration as linkage motivations shift toward cognitive proximity. This study contributes a dual-scale framework for understanding urban network ex- ternalities and provides insights for improving knowledge coordination across heterogeneous cities.

Under the background of innovation-driven development strategy, low altitude eco- nomy industry has gradually become the new focus of global technology competition and an important engine for cultivating new quality productivity. This study takes the prefecture-level cities in the Yangtze River Delta from 2010 to 2021 as the research units, integrates low- alti- tude economy industrial chain and economic and social attribute data, and combines ArcGIS spatial analysis and spatial econometric model to explore the spatial agglomeration situation of low-altitude economy industry and its innovation effect. The results show that: 1) The spatial agglomeration scale and scope of the low-altitude economy industry in the Yangtze River Delta have continued to expand, forming a multi-center spatial agglomeration pattern centered on cit- ies such as Shanghai, Hangzhou, Nanjing and Hefei, as well as a belt-shaped agglomeration structure in the Suzhou-Wuxi-Changzhou region; 2) Low-altitude economy industry has strong spatial unbalanced agglomeration characteristics, but the agglomeration intensity of core cities has weakened, indicating that the spatial layout of low altitude economy industry in the Yangtze River Delta has evolved in a diversified and balanced direction; 3) From the perspective of spa- tial spillover effect, every 1% increase in low-altitude economy industrial agglomeration will increase the innovation level of local cities by at least 0.024 4%; However, it is difficult to pro- mote regional technical cooperation and collaborative innovation in the early stage of low-alti- tude economy industry development; on the contrary, it has a strong negative innovation spillover effect on neighboring cities because of the siphon effect of resources and technical barriers. From the perspective of distance spillover effect, low-altitude economy industry has certain distance attenuation characteristics on urban innovation ability, and it has strong “ag- glomeration shadow” and time lag effect; 4) Low-altitude economy industry has a certain de- gree of technology embeddedness and industrial integration, which can not only significantly enhance the technological innovation of the core cities in the Yangtze River Delta, but also provide support for the innovation and development of weak industries, which provides an ef- fective way to narrow the regional technological development gap.

In the era of the knowledge economy, cities serve as core hubs for innovation activ- ities. The ability to effectively convert local knowledge creation into economic value and con- tinuously introduce new technologies to diversify and upgrade its technological portfolios has become pivotal for measuring urban innovation vitality, developmental potential and urban competitiveness. This study employs panel data from 287 Chinese prefecture-level cities (ex- cluding data of Hong Kong, Macao and Taiwan) between 2005 and 2016 to construct a mul- tivariate regression model, examining the mechanisms through which urban capacity to convert local technological knowledge into products influences the evolutionary paths of new techno- logy entry. The findings reveal that a city’s capacity to successfully transform local patent knowledge into marketable products acts as a critical driver for new technology entry. However, this positive impact is not uniformly distributed: the role of local technology conver- sion capacity in propelling cities toward leapfrogging developments into unrelated technologic- al domains, thereby breaking path dependencies, is significantly stronger than its effect on ex- tending into related technological fields. This driving effect exhibits heterogeneity; in indus- tries with strong equipment-specific processes and significant differences in the technical paradigms across sub-fields, the positive role of local conversion capacity in introducing new technological varieties may be weakened or even reversed. Furthermore, interactions exist between local technology conversion capacity and external collaborative networks or internal technological relatedness. The marginal contribution of local technological conversion capacity to fostering new technology entry diminishes due to cognitive inertia or lock-in effects. These findings provide empirical support for policymaking aimed at guiding cities toward higher-level and diversified technological pathways, as well as cultivating and strengthening endogenous urban capacities for technological transformation. Specifically, policies should prioritize estab- lishing concept validation centers, technology transfer institutions, and market-oriented trading platforms to bridge the gap between patent generation and product commercialization. Differen- tiated strategies are needed across industries, for sectors with high equipment specificity and di- vergent technical paradigms, emphasis should be placed on shared technology platforms and cross-domain knowledge exchange. While encouraging open innovation, cities must balance ex- ternal technology cooperation with local capacity building, and resource-rich cities should al- locate dedicated resources for exploring non-consensus technologies to prevent lock-in risks.

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) serves as a world- class urban agglomeration and a frontier for the national innovation-driven development strategy, and its innovation network plays a critical supporting role in the high-quality develop- ment of the regional economy. The present study, based on invention patent transfer data and socioeconomic statistical data from 2005 to 2020, and using the 61 districts of the GBA as re- search units, applys social network analysis, entropy-weighted TOPSIS, and geo-detector tech- niques to analyzing the spatiotemporal evolution characteristics of the GBA’s innovation net- work from 3 aspects—node characteristics, linkage characteristics, and agglomeration patterns. This study also explores the influencing factors of innovation network in the GBA evolution from four di- mensions: economic development, market maturation, science-education resources, and institution- al differences. The findings are as follows: 1) The heterogeneity of innovation network node capabilities is significant, with core districts such as Nanshan in Shenzhen and Yuexiu in Guangzhou playing key roles in innovation agglomeration and diffusion, while peripheral dis- tricts like Heshan in Jiangmen and Fengkai in Zhaoqing lag behind in development; 2) The in- tensity of innovation links shows an increasing trend, and the gravity center of the regional in- novation network has shifted from a single-center to a multi-center structure, especially in the Bay Area urban belt, which exhibits close innovation cooperation relationships; 3) The spatial agglomeration effect of innovation interaction capabilities is significant, with high-high ag- glomeration areas serving as the centers of the innovation network, and the spatial center of gravity shifting from Hong Kong-Shenzhen to Guangzhou-Foshan, while low-low agglomera- tion areas are mainly and persistently distributed in the peripheral zones; 4) The formation and development of the innovation network are driven by multiple factors including the economy, market, scientific and educational resources, and institutional settings, with technological in- novation capacity and economic output level having prominent impacts on regional innovation interaction capabilities, and also act as key factors in multi-factor synergistic effects. In addi- tion, the administrative center effect is also an important factor affecting regional innovation in- teraction capability, albeit with a relatively low correlation. This study provides a theoretical foundation and empirical support for the formulation and implementation of regional innova- tion policies, and has positive implications for promoting the sustainable development of the GBA’s innovation network and accelerating the building of an international science and techno- logy innovation center.

Based on inter-provincial migration data for high-skilled talents and ordinary labor in China from 2000 to 2020, this study employs the network Moran index and complex net- work analysis to explore the temporal and spatial evolution and network characteristics of dif- ferent types of labor migration. The findings reveal that: 1) Over the past two decades, econom- ically developed coastal provinces have consistently been the primary destinations for both types of labor. Despite the pronounced spatial imbalance in their distribution, this trend has gradually diminished since 2010. 2) Both types of labor migration networks exhibit significant spatial agglomeration effects. The migration network for high-skilled talent showed an initial increase in agglomeration, followed by a decline. In contrast, the ordinary labor migration net- work experienced fluctuating trends. Its agglomeration first decreased, then increased, and ulti- mately decreased again. 3) Both migration networks exhibit “ small-world” characteristics; however, the “small-world” nature of ordinary labor migration has become more pronounced over time, indicating tighter connections in migration flows. 4) Both types of labor migration are influenced by geographical proximity and path dependency, yet high-skilled talent migra- tion is less constrained by geographical distance, allowing for long-distance migration capabilit- ies that enable rapid responses to economic opportunities. This paper enriches the understand- ing of heterogeneous skilled labor migration networks within Chinese population geography. It also expands the temporal dimension of existing research. Furthermore, these findings offer ro- bust support for decision-making. They can help policymakers optimize regional talent policies and efficiently allocate labor resources.

This article first analyzes the spatial mobility characteristics of rural left-behind eld- erly from three dimensions: mobility distance, mobility frequency, and mobility modes. Then from the perspective of mobility, we explore how movement reshapes the agency of rural left- behind elderly in their later years. Research findings indicate that rural left-behind elderly have developed a mobility pattern characterized by short-distance daily travel and long-distance high- frequency travel. Their high-frequency mobilities primarily serve basic physiological needs and intergenerational support activities, while low-frequency mobilities is associated with specific kinship enclaves and festive events. Transportation choices for left-behind elderly exhibit di- verse characteristics, with no apparent exclusion from transportation services. Mobility has be- come a crucial factor in reconstructing the agency of rural left-behind elderly individuals. In terms of embodiment subjectivity, the technological embeddedness within mobility enhances the physical capabilities of left-behind elderly individuals, disrupts gendered spatial orders, and through embodied practices of mobilities, revolutionizes subject cognition while fostering new bodily strategies. The relational agency emerging from mobility enables left-behind elderly in- dividuals to reconnect their social networks and actively construct local meaning and emotional bonds. The breakthrough of inherent labels in mobility, the production of new roles, and the performance of new identities embody the agency of identity creation among left-behind eld- erly. This study addresses the current academic gap in research on grassroots mobility in rural areas, revealing the unique and multifaceted significance of mobility for the construction of agency among marginalized populations.

With the extensive penetration and integrated application of digital science and tech- nology in the process of social and economic development, it has rapidly become a core ele- ment for the sustained growth of regional economies and the driving force as well as the key factor in the competition for resources in the future. Based on the big data from business regis- tration in Chinese enterprises and the social and economic data of Chinese cities, this paper first uses the equity penetration method to obtain 165 038 mutual investment records of digital tech- nology enterprises in 327 cities in China from 2000 to 2021 (data excluding Hong Kong, Ma- cao ang Taiwan), and then constructs a theoretical analysis framework for the evolution of the digital technology connection network space based on the flow space theory, urban network theory, and enterprise investment behavior. Secondly, by combining complex network analysis methods such as weighted centrality, dominant flow, spatial structure index, and quadratic as-signment procedure (QAP), it deeply explores the spatial structure evolution characteristics and influencing factors of China’s digital technology connection network from the three levels of node-path-pattern. The findings are as follows: 1) From the evolution characteristics of nodes, during the research period, the digital technology investment influence and control power of cit- ies such as Beijing, Shanghai, Shenzhen and Guangzhou have been gradually increasing, and they have occupied a dominant position and played a dominant role in China’s digital techno- logy connection network, while the capital cities in the central and western regions and a few emerging cities have the main growth engine. On the whole, China’s digital technology connec- tion network shows a significant high-high and low-high clustering feature in space, and the di- gital technology investment of core cities has the ‘siphon effect’ on peripheral cities. 2) From the evolution characteristics of paths, during the research period, the evolution of China’s digit- al technology investment paths has moved from ‘point breakthrough’ to ‘local equilibrium’, and there is a ‘leader’ effect in regions such as the Beijing-Tianjin-Hebei region, the Yangtze River Delta, and the Pearl River Delta, and digital technology investment also shows obvious net- work effects and scale effects. 3) From the evolution characteristics of patterns, during the re- search period, China’s digital technology connection network has shown a polarized develop- ment trend, among which the local investment pattern of digital technology has gradually evolved from scattered point distribution to a trend of multiple points in local areas. Also it has a significant ‘Hu Huanyong Line’ spatial distribution feature, while the scale of digital techno- logy investment has undergone a process of first strengthening and then weakening, and is mainly dominated by economically developed cities or a few core cities globally, and shows a diffusion and local concentration trend. 4) From the influencing factors, the effects of each in- fluencing factor on the spatial evolution of China’s digital technology connection network have obvious phased characteristics. For example, economic development level remains an import- ant factor affecting the form of China’s digital technology connection network, while the ef- fects of factors such as technological innovation, informatization level, industrial structure, and financial development level vary over time.

Universities and specialized, refined, distinctive and innovative (SRDI) enterprises are key supply and demand actors in regional innovation systems. Their coordinated spatial distribution is crucial for optimizing the allocation of innovation resources and building efficient regional innovation networks. Focusing on Beijing-Tianjin-Hebei, the Yangtze River Delta and the Pearl River Delta urban agglomerations, this study develops a theoretical framework to analyze the spatial mismatch between universities and SRDI enterprises. Methods including the spatial mismatch index, the optimal parameter-based geographical detector, and partial least squares (PLS) regression are employed to examine their spatial distribution patterns, mismatch characteristics, and influencing factors. The results indicate the following key findings. 1) Both universities and SRDI enterprises in the three major urban agglomerations exhibit significant spatial agglomeration characteristics. These can be broadly categorized into three typical patterns: a ‘single-center agglomeration’ model, a ‘multi-core networked’ model, and a ‘deep specialization broad higher education’ model. The latter is exemplified by the Pearl River Delta region, where SRDI enterprises are highly concentrated in Shenzhen, while higher education resources are clustered in Guangzhou. Bivariate spatial analysis further shows that the spatial distributions of universities and SRDI enterprises demonstrate a relatively weak positive spatial autocorrelation overall. Among the three urban agglomerations, the Beijing-Tianjin-Hebei region displays the strongest spatial association between the two, followed by the Yangtze River Delta, while the Pearl River Delta shows comparatively weaker spatial linkage. 2) A widespread spatial mismatch exists between universities and SRDI enterprises, with notable variations in both the degree and types of mismatch within each urban agglomeration. These mismatches exhibit multi-level structural differences across cities. Specifically, the Pearl River Delta demonstrates a relatively high overall level of matching between universities and enterprises. In contrast, the Yangtze River Delta exhibits more pronounced spatial mismatch phenomena, characterized by a higher prevalence of ‘dual-low positive mismatch’ types, where both universities and enterprises are relatively underrepresented but maintain a positive spatial relationship. Meanwhile, ‘enterprise-dominated negative mismatch’ and ‘dual-high negative mismatch’ types—indicating imbalances where either enterprises or both entities are disproportionately concentrated—are less common. 3) The spatial mismatch between universities and SRDI enterprises is jointly driven by multiple dimensions of factors. Among these, road network density and elevation provide fundamental geographical and infrastructural conditions. Economic development levels and the scale of talent cultivation in higher education institutions create structural tensions that shape spatial alignment. In addition, public cultural resources and the quality of the business environment play important moderating roles, influencing how effectively universities and enterprises can colocate and interact within the urban innovation system. Overall, these findings contribute to a deeper understanding of the spatial organization and interaction mechanisms of innovation actors, offering valuable insights for policy-making aimed at enhancing regional innovation efficiency.

Establishing off-site R&D centers in innovation-rich regions is crucial for enterprises in peripheral areas to overcome the constraints of the “agglomeration shadow” and construct a regional collaborative innovation system. Based on the data of high-tech enterprises and their associated enterprises in Hebei Province, this study systematically examines the layout characteristics, industrial correlations, and internal and external driving factors of enterprises' off-site R&D centers. The findings are as follows: 1) In terms of the layout characteristics of departure locations, there are significant disparities in the number of off-site R&D centers among prefecture-level cities. Shijiazhuang leads significantly with a 24.91% share, forming the first echelon. Leveraging their locational advantages, Langfang, Baoding, and Tangshan have prioritized setting up off-site R&D centers in Beijing and Tianjin, thus forming the second echelon. In contrast, Xingtai, Hengshui, and Chengde are relatively underdeveloped with sluggish growth in the later period. 2) In terms of the layout characteristics of destination locations, offsite R&D centers have gradually expanded from the initial local agglomeration adjacent to central areas to distant innovation-rich regions, forming a multi-tiered spatial layout that covers megacities with high innovation levels, regionally innovative provincial capitals, and coastal cities with a high degree of trade liberalization. 3) The driving factors for the layout of enterprises' off-site R&D centers exhibit the characteristics of internal and external coordination: at the enterprise level, capital reserves, locational attributes, and innovation cooperation levels are the main internal driving factors for their layout; at the urban level, talent cultivation, population density, and digital infrastructure play a facilitating role in the layout, while the lag in the business environment exerts a significant crowding-out effect.

The intensification of climate change and human activities has exposed the world to highly complex ecological risks. Identifying ecological security patterns based on ecological security early warning constitutes the theoretical foundation and practical support for ecological risk management. Taking the Poyang Lake Basin as the study area, this paper conducts an ecological security early warning assessment using a CNN-LSTM model. By integrating the early warning results with the MSPA method and circuit theory, a basin-scale ecological security pattern was constructed, and targeted protection measures were proposed for the identified ecological restoration areas. The results indicate that: 1) The CNN-LSTM model demonstrates strong predictive capability and high accuracy in ecological security early warning assessment. The ecological security early warning in the study area exhibits a spatial pattern of high in the surrounding areas and low in the central region. 2) Ecological source area 26 405 km2, 29 ecological corridors, 149 ecological pinch points, 968 ecological barrier points, and 96 ecological break points were identified, leading to the construction of an ecological security pattern with a “five zones and three belts” configuration. Based on an “early warning-pattern” coupled framework, this study organically integrates ecological security early warning with ecological security patterns to achieve comprehensive spatiotemporal ecological risk management. This research provides theoretical and methodological support for watershed ecological protection, restoration, and the enhancement of ecosystem functions.

The wine industry, as a representative sector combining both cultural attributes and consumption-oriented characteristics, serves as a crucial vehicle for promoting high-quality regional economic development and meeting the growing needs for a better life. However, in recent years, China's wine industry has fallen into a dilemma of simultaneous declines in both output and consumption. Existing studies have predominantly focused on the supply side, while insufficient attention has been paid to the spatial mechanisms of demand. This paper argues that inadequate market demand constitutes the core constraint underlying the current downturn, primarily due to the absence of the “home market effect” and the “price index effect,” which together result in weak consumption performance and limited market expansion. To address this issue, this study builds upon the theoretical framework of New Economic Geography and integrates an agent-based modeling (ABM) approach to dynamically simulate the evolution of China's wine industry under multiple counterfactual scenarios. By incorporating both supply-side and demand-side interactions into a unified analytical framework, the paper aims to uncover the endogenous mechanisms shaping industrial spatial dynamics and market outcomes, as well as their policy implications under different development paths. The results yield 3 main findings. First, the relaxation of cost constraints can fundamentally reshape spatial equilibrium. A reduction in production costs enhances the competitiveness of domestic wine by amplifying both the price index effect and the home market effect, thereby expanding market size and improving the relative position of domestic producers. Second, spatial product differentiation strategies play a critical role in fostering competitive advantages. By increasing product heterogeneity, domestic wine producers can attract more consumers, which in turn leads to a rise in the number of firms and an expansion of market share. Third, the coordinated interaction between demand and supply across space is essential for breaking the current impasse. When domestic wine gains a cost advantage, the expansion of demand significantly strengthens the home market effect, effectively reversing the import-dominated market structure. Meanwhile, the price index effect facilitates a mutually beneficial outcome for both producers and consumers and promotes a more balanced spatial distribution of production and consumption. These findings suggest that the development of the wine industry in China should not rely solely on supply-side improvements, but must also place greater emphasis on demand-side cultivation. In particular, fostering a wineoriented lifestyle and enhancing consumer engagement are critical to unlocking market potential and sustaining long-term growth. By integrating New Economic Geography with computational experimental methods, this study constructs a comprehensive analytical framework that jointly considers supply and demand dynamics. It not only contributes to the theoretical understanding of spatial economic processes in cultural consumption industries, but also provides methodological insights and policy implications for promoting the high-quality development of regionally distinctive industries.

In the context of new quality productivity, social media drives the reconfiguration of production factors, thereby triggers rural industrial transformation. The process, path, and interaction mechanism of “human-social media-place” in this industrial transformation urgently requires in-depth exploration. This study takes 3 internet-celebrity villages with different development levels in the desakota areas of Guangdong province (Nazhou Village, Huitong Village, and Yakou Village) as examples. From the perspective of new materialism, the study aims to analyze the process and path through which new quality productivity, represented by social media, facilitates industrial transformation in these villages. The findings reveal that: 1) The industrial transformation of internet-celebrity villages is the result of co-creation by human and nonhuman actors. 2) Social media influences the industrial transformation of internet-celebrity villages through two paths. First, as a social element, it empowers and enables both people and places, making them visible, discussed, and constructed. Second, as a social shaping force, it constructs virtual spaces while promoting the formation of a virtuous cycle and the conversion of virtual-real traffic between virtual and real spaces. 3) Compared with traditional rural industrial transformation, the key aspect of social media's participation in the industrial transformation of internet-celebrity villages lies in promoting the deep integration of data with traditional production factors, facilitating the leap from traditional productivity to new quality productivity, and thus driving industrial transformation. This study responds to new materialism's emphasis on non-human actors, validates the view that power is multi-symbiotic, and analyzes the logic of new quality productivity driving the industrial transformation of internet-celebrity villages, which holds theoretical and practical significance for rural sustainable development.

In the digital era, the upgrading of urban industrial structure is not only an important symbol of high-quality development of regional economy, but also a key engine for the remodeling of national competitiveness and the construction of modern industrial system. Based on the cross-perspective of digital technology and niche theory, this paper analyzes two dimensions: “state” (static endowment of digital technology resources, including infrastructure, technology accumulation, talent reserve and institutional environment) and “ potential” (dynamic evolution ability of digital technology system, including enterprise growth, cross-domain collaboration, open innovation and network effect). To construct and systematically define the conceptual framework and measurement system of “digital technology niche”. On this basis, using the panel data of 283 prefecture-level cities in China from 2009 to 2022, the two-way fixed effect model is used to empirically investigate the impact effect, transmission mechanism and regulatory conditions of digital technology niche on the upgrading of urban industrial structure. The results show that: 1) The digital technology niche significantly promotes the optimization and upgrading of local industrial structure, and this conclusion is still valid under a series of robustness tests such as changing the explained variable, controlling macro conditions and instrumental variable method. 2) The analysis of structure effect shows that the promotion effect of digital technology niche on the optimization of industrial structure is significantly stronger than that on the rationalization of industrial structure, reflecting its core advantage in promoting the industry to climb to the high end of the value chain. 3) The mechanism test shows that the optimal allocation of innovation factors and the improvement of entrepreneurial activity are important intermediary paths for the digital technology niche to promote the upgrading of industrial structure, in which the mediating effect of innovation factor allocation accounts for 40.2% of the total effect, and the mediating effect of entrepreneurial activity accounts for 28.8%. 4) The analysis of moderating effect further reveals that the improvement of digital infrastructure and the agglomeration of digital talents positively regulate the promoting effect of digital technology niche on the upgrading of industrial structure, that is, the higher the level of urban digitalization is, the more significant the industrial upgrading dividend of digital technology niche is. 5) Heterogeneity analysis shows that core cities can benefit more from digital technology niche than peripheral cities, reflecting that there are significant city-level differences in the impact of digital technology niche on industrial structure upgrading.

Liaoning Province, as a paradigmatic region with the highest concentration of shrinking cities in China, enhancing the development of resilience-related networks is essential for improving urban risk management capabilities. This study utilized the TOPSIS entropy weight method, an adjusted gravity model, social network analysis, and disruption simulation to investigate the correlation strength and structural characteristics of urban resilient spatial net- works in Liaoning Province from 2012 to 2022. We simulated changes in network structure un- der various attack scenarios and subsequently identified key influencing factors using the QAP analysis method. The findings are summarized as follows: 1) Spatial correlation network of urb- an resilience in Liaoning Province exhibits a distinct “core-periphery” structure, with overall correlation intensity remaining relatively low. Over time, the network has transitioned from a “dual-core independent leadership” pattern to one of “dual-core coordinated development.” At the subsystem level, networks related to economic, engineering, and innovation resilience demonstrate steady improvement, whereas the social resilience network undergoes a significant decline, adversely affecting overall network stability. 2) The overall network structure has steadily strengthened, with core cities exerting significant influence and maintaining substantial control over resources. Meanwhile, peripheral regions have leveraged their geographical ad- vantages to generate notable inter-provincial spillover effects. 3) Under random attack scenari- os, the resilient interconnected network displays marked structural vulnerability, evidenced by rapid declines in key indicators such as network efficiency and clustering coefficient. In con- trast, the network demonstrates greater structural robustness under targeted attacks, with these indicators declining at a more gradual pace. 4) Economic development, population size, indus- trial structure, and infrastructure investment exhibit positive correlations with resilient con- nectivity, whereas geographical proximity and ecological environment quality show negative associations. Specifically, spatial adjacency intensifies competition for resources and environ- mental stress, while uneven distribution of ecological assets further constrains regional resili- ence enhancement.

Cultural empowerment has emerged as a critical strategic pathway to promote rural revitalization and modernization. This paper constructs a logical framework of cultural em-powerment for rural revitalization, and analyzes the multi-dimensional value coupling mechan-isms and cultural IP construction pathways. The results show that: 1) Cultural empowerment for rural revitalization follows the logic of “value identification and coupling-resource capitaliza-tion and IP reconstruction-spatial restructuring and industrial operation”. The essence is to achieve innovative transformation with characteristic IPs through exploration and integration of cultural resources and value coupling, thereby promoting spatial restructuring and industrial op-eration to empower rural revitalization. 2) The multi-dimensional value attributes of cultural empowerment interact and transform with each other, and each value dimension forms a two-way coupling with the goal of rural revitalization. By fully activating the economic, spiritual, governance, ecological, and life well-being values of rural cultural resources, it promotes the construction of rural civilization, industrial integration, governance innovation, ecological liv-ability, and prosperous life in a coordinated manner. 3) Cultural IP construction constitutes a key path for cultural empowerment of rural revitalization. Through resource exploration and IP design, IP spatial structure and scene construction, as well as IP brand promotion and industrial integration, the industrialization of cultural resources and the comprehensive rural revitaliza-tion can be achieved, forming the logic of “value coupling- value embedding-value transforma-tion”. In the future, cultural empowered for rural revitalization should focus on the research of basic theories, spatial structures, path models and guarantee mechanisms for cultural resource industrialization, cultural IP construction, regional public brand cultivation, county-town-vil-lage cultural IP system construction, and gradually establishs a research system of cultural em-powerment to support comprehensive rural revitalization and urban-rural integrated develop-ment.

As the “bridge” and “medium” of urban-rural factor flow, rural innovation and en-trepreneurship breaks through the boundaries of rural physical space and gradually becomes an emerging driving force to promote the transformation of agricultural modernization in China in the new period. Rural innovation and entrepreneurship has led to the integration of primary, secondary and tertiary industries, but its productive projects are still centered on modern agri-culture. The mechanism of rural innovation and entrepreneurship's impact on agricultural mod-ernization has not been fully explored in the research field. This study uses county panel data of Zhejiang Province from 2014 to 2021 to identify the spatial and temporal evolution characterist-ics of rural innovation and entrepreneurship, and constructs a mechanism framework and empir-ical model based on the background of urban-rural integration and the dialectical relationship between innovation and entrepreneurship, to explore the effects and the mechanisms of rural in-novation and entrepreneurship on the Agricultural Modernization. The results show that: 1) Rural innovation and entrepreneurship in Zhejiang Province is characterized by obvious spatio-temporal heterogeneity. In terms of temporal characteristics, the level of rural innovation and entrepreneurship is on an upward trend. The level of rural innovation also shows an upward trend, but the level of rural entrepreneurship is more stable. Regarding spatial characteristics, there are concentrated and continuous high-value areas of rural innovation and entrepreneur-ship in northeast Zhejiang. Southwest Zhejiang is developing rapidly in rural entrepreneurship. 2) Rural innovation and entrepreneurship effectively drive the development of agricultural mod-ernization, and the narrowing of the urban-rural income gap strengthens the driving effect of rural innovation and entrepreneurship on agricultural modernization. 3) Rural innovation and rural entrepreneurship are closely related, but essential differences exist. Rural innovation can promote the development of agricultural productive services to accelerate the process of agri-cultural modernization. Rural entrepreneurship can promote the growth of regional consump-tion level on the demand side to drive the development of agricultural modernization. 4) On the geospatial scale, rural innovation and entrepreneurship in northeast Zhejiang have a more signi-ficant effect on agricultural modernization than that in southwest Zhejiang. On the administrat-ive scale, the driving effect of rural innovation and entrepreneurship on agricultural moderniza-tion at the county level is more significant than that of county-level cities. In the future, the rur-al innovation and entrepreneurship system should be improved according to local conditions, giving full play to the role of rural innovation and entrepreneurship as a medium in integrating urban and rural factors, and guiding the coordinated development of rural innovation and entre-preneurship.

To explore the variations of aeration zone soil specific yield under the condition of deep buried groundwater, The southern edge of Gurbantunggut Desert was taken as the research area by field in-situ observation.The complete specific yield under the condition of zero surface flux, the average releasing specific yield under the condition of evapotranspiration and the average charging specific yield under the condition of lateral leakage recharge were determined, and the effects of groundwater depth, infiltration and evapotranspiration on specific yield were discussed. Results showed that: 1) It is feasible to determine the soil specific yield under the condition of deep buried groundwater by the zone of aeration section water content method. 2) Under the condition of zero surface flux, the complete specific yield μ increases with the increase of groundwater depth H. When the groundwater depth exceeds the maximum rising height of capillary water, the change of complete specific yield is small and can be approximately regarded as a constant. 3) The average groundwater depth of interdune land in the southern edge of Gurbantunggut Desert is 8.80 m. The complete specific yield under the condition of zero surface flux is 0.36, the average releasing specific yield under the condition of deep buried groundwater evaporation is 0.13, and the average charging specific yield under the condition of lateral leakage recharge is 0.17. The results of this study can provide a new idea for the determination of soil specific yield under the condition of deep buried groundwater.

Return migration has become a notable socio-economic trend in the new stage of China's urbanization, and the analysis of its driving mechanism has received extensive academic attention. As a micro behavior, the return of migrants is not only affected by personal and family factors, but also closely related to external environment. However, existing studies have focused on the role of individual factors, but not enough research has been conducted on the relationship between regional contexts and return migration. We introduce a gradient boosting decision tree model in the field of machine learning, based on the data from the 2017 China Migrants Dynamic Survey, with the return intention as the response variable and the regional contexts—Both in the place of origin and destination—As well as migrants' personal and household factors as the explanatory variables, focusing on the non-linear influence of the regional contexts on the return migration intentions and the threshold effect. The results show that: 1) The total contribution of the local contexts of the place of place of the origin and destination to the intentions of the migrants to return is 44.1%, which is an important factor influencing the return intentions, and the contributions of the two places is roughly equal. Among these, medical and health resources and air pollution are extremely important in both places. In addition, economic growth in the place of origin is also important for the return intention of migrants, while the climatic condition in the place of destination is more important; 2) There are both non-linear and linear relationships between local contextual factors and migrants' intention to return. Among them, medical and health resources, basic education resources, air pollution have obvious non-linear effects on the return intention, while economic growth and temperature conditions have mainly linear effects; 3) The influence of individual factors on return intention is mainly nonlinear effect. There is an irregular U-shaped relationship between age, migration duration and return intention, and the non-linear influence of household income is more complex. There is an obvious threshold effect between household housing expenditure and return intention, and a negative correlation between migrant's education level and return intention. This study incorporates the local contexts of the place of origin and destination into the analytical framework for the mechanism of return migration, identifies the relative importance of the local context and individual characteristics of the two places on the return intention of the migrants, and reveals the specificity and complexity of internal return migration in China, which contributes to deepening the research on migration in the new era and provides scientific reference for policy makers.