Ecological resilience refers to an urban agglomeration's ability to resist, adapt, and recover from external disturbances. Enhancing ecological resilience is crucial for promoting high-quality development in the modern era. However, few studies have evaluated the ecological resilience of urban agglomerations from the perspective of human-land interactions or explored its evolutionary mechanism. This study assesses the interannual variation of ecological resilience in the middle reaches of the Yangtze River urban agglomeration, focusing on resistance, adaptation, and recovery. The Least Squares Structural Equation Model is employed to analyze the impact degree and pathways through which human-land interactions affect ecological resilience. The results indicate that from 2000 to 2020, resistance, adaptability, and recoverability exhibited fluctuating trends over time and an unbalanced spatial distribution. Ecological resilience initially decreased but later improved, with significant spatial heterogeneity, forming a "higher in the southeast and lower in the northwest" pattern. Natural environment improvement (coefficient: 0.42) and regional policies (coefficient: 0.18) had significant positive direct effects on ecological resilience, while urbanization exerted a significant negative effect (coefficient: -0.26). Although green innovation had no significant direct effect, it exhibited significant indirect influence, with the most significant pathway being green innovation→regional policy→natural environment→ecological resilience (coefficient: 0.18). This study enhances our understanding of how the natural environment, green innovation, and regional policies shape ecological resilience, providing a scientific reference for urban agglomeration development that fosters harmony between humans and nature.

This paper provides a comprehensive reflection on the evolution of globalization research in the Pearl River Delta (PRD), considering the current international context and national strategies. It identifies several challenges in existing studies, such as the ambiguity of globalization patterns and the insufficient representativeness of key indicators. In response to these challenges, this paper draws upon the theory of strategic coupling to propose a new theoretical framework for analyzing globalization in latecomer regions. Based on the concepts of spatial stickiness and locational advantages, this paper further develops a two-dimensional quantitative indicator matrix. Using the PRD as a case study, this paper conducts empirical measurements and analysis, drawing three main conclusions. First, the theory of strategic coupling proves well-suited for analyzing the globalization of latecomer regions, exemplified by the PRD. It offers a more systematic, clearer, and more robust explanatory framework compared to traditional measurement methods. Second, the empirical analysis from the PRD reveals that the pattern of regional globalization does not follow a simple linear growth or cyclical model. Instead, it exhibits a circuitous, complex, and upward spiral, unfolding along an S-shaped evolutionary trajectory. Third, through comparisons of the east and west coasts of the study area, as well as segmented city analyses, this study finds that locational advantages significantly shape the evolutionary pattern of globalization. This influence is not only apparent during the region's initial take-off phase but also plays a more profound role in shaping its subsequent developmental trajectory. This study makes a distinctive contribution to both the theoretical understanding of globalization in latecomer regions and the practical field of regional economic development in China. Additionally, it introduces a novel measurement approach for studying regional globalization.

Domestic reinvestment by foreign enterprises (referred to as "domestic reinvestment") represents an emerging yet understudied dimension of foreign investment from a geographical perspective. Drawing on firm-level investment data from over 210,000 enterprises in Guangdong province (2000-2021), this paper constructs a domestic reinvestment network relationship matrix to systematically analyze its network structure, industrial spatial patterns, and underlying mechanisms. A comparative analysis is further conducted against foreign direct investment (FDI), investments by Hong Kong-Macao-Taiwan (HKMT) enterprises, state-owned enterprises (SOEs), and private domestic firms. Key findings reveal that: (1) Domestic reinvestment has transitioned from constituting 2.42% of total foreign investment in 2000 to surpassing FDI at 61.16% by 2021, emerging as Guangdong's predominant foreign-invested modality; (2) Spatially, exhibiting "local network embeddedness" and "hierarchical diffusion" characteristics, with capital concentration in Shenzhen-Guangzhou dual cores (82.3% of the total) and interprovincial flows favoring regional hubs; (3) Industrially, policy-driven expansion dominates power industries (76.4% state-guided projects), while manufacturing and service sectors demonstrate industry-chain dependent patterns-low-value-added industries cluster locally versus high-value-added sectors' hierarchical dispersal; (4) Compared to other types of enterprises, domestic reinvestment is influenced by distance and market size, ranking between direct investment and domestic enterprise investment, and relies more on talent and technology, external connections, policy support, and industrial linkages than direct investment. This study provides critical insights for optimizing China's foreign capital governance framework through differentiated industrial spatial policy instruments.

In response to climate change and global environmental crisis, more and more countries have started to pursue a low-carbon economy. With significantly reduced or no tailpipe emissions, new energy vehicles (NEVs) - vehicles that are powered by alternatives to fossil fuels, such as electricity and non-traditional fuels - are gaining popularity and becoming the future of the automotive industry. Using NEV supplier data of Tesla's Shanghai Gigafactory (Tesla Shanghai), the paper performed a network analysis based on firm headquarter-subsidiary connections to map out the factory's global production network (GPN), revealed the spatial configuration of the network, and explored the forces driving its formation and development. The research yielded several findings. First, the global production of auto parts for Tesla Shanghai resembles a typical GPN with a core-periphery structure. Tesla Shanghai has established strong high-frequency connections with economic centers (or manufacturing centers with strong R&D capability) located in East Asia, North America, and Europe. Second, within the Tesla Shanghai's GPN, four supply chain systems - smart electronic components, battery and electric drive systems, thermal management systems, and chassis and trim - display varying hierarchical structures. Third, within each of the four supply chain networks, most communities (or subnetworks) span across geographical and administrative boundaries, connecting cities around the world through modular production of auto parts. These communities demonstrate strong internal heterogeneity with an emerging core-periphery structure of network nodes. Using the four explanatory variables of the GPN 2.0 framework (i. e., cost-capability ratio, market imperatives, financial discipline, and risk environment), the paper further analyzed the main factors that shape the highly complex NEVs GPN of Tesla Shanghai from the perspectives of global environment, market demand, technological linkages, and local competitive advantages. The study contributes to the new economic geography literature by providing an interesting case study. The findings of the research provide theoretical insights and practical implications for the innovation and sustainable development of the NEV industry.

Intercity connections are crucial for understanding regional structures, with business activities serving as key elements in the formation of city networks. Although big data is increasingly applied in urban network studies, its inability to distinguish between various groups and travel purposes limits a deeper understanding of intercity business connections. This paper proposes a method that integrates big and small data to identify inbound business visitors. It uses survey-based small data to construct a model and applies it to large-scale mobile phone data to identify and analyze the activities of inbound business travelers in Shanghai. The analysis focuses on temporal characteristics and the spatial distribution of their originating cities, main business destinations, and accompanying activities, summarizing spatial structures and regional patterns. The study reveals that approximately 37% of Shanghai's inbound travelers are for business purposes and they mainly originate from the core Yangtze River Delta region and distant major cities. These visits are mostly short-term, lasting less than four days, and show regular weekly fluctuations. Spatially, inbound business travel and related activities exhibit significant polycentric characteristics, forming a four-tiered structure: dual hubs (Hongqiao and Lujiazui-Nanjing Road), a horizontal core area, contiguous districts, and corridors. The spatial distribution of inbound business destinations regarding different originating cities varies significantly by city size and distance, presumably associated with industrial characteristics. The proposed method of integrating big and small data offers broad applicability and holds substantial value for city network research and spatial planning.

Foreign aid is a tool of U.S. foreign policy and provides a window into U.S. national strategy. In the era marked by frequent local conflicts and global turmoil, delving the motives of U.S. aid can enhance our comprehension of U.S. national strategic objectives. Utilizing U.S. aid data from 2000 to 2019, this paper examines the spatial evolution patterns in U.S. aid and its driving paths, considering altruistic, egoistic, and utilitarian motives. This analysis is conducted using GIS spatial analysis and Fuzzy-Set Qualitative Comparative Analysis (fsQCA). The results show that: (1) Total U.S. aid exhibits a basic trend of initial growth followed by stabilization, with military security aid and economic development aid as the primary categories of U.S. aid. (2) There are significant spatial and temporal differences in the distribution of U.S. aid. The Middle East and North Africa region has been a long-standing aid priority but is trending downwards, with an emphasis on military security aid, economic development aid, and humanitarian assistance. Sub-Saharan African countries have emerged as new hubs for U.S. aid, with primary focuses on social sector aid, economic development aid, and humanitarian aid. Meanwhile, other regions have seen diminishing amounts of U.S. aid. (3) The key drivers of U.S. aid have expanded from the level of economic development and U.S. exports to geostrategic, political relations and institutional quality factors, with an overall trend of a gradual shift from being driven by economic interests to geopolitical interests. This may be related to the eastward shift of the U.S. strategic centre of gravity. In terms of specific types of aid, economic development aid, social sector aid and humanitarian aid are economic interest-driven, military security aid is geopolitical interest-driven, and democratic political aid is democratic value-driven. The different types of aid serve different foreign policy objectives and together constitute a tool for the U.S. to pursue its political and economic interests. This paper can provide some reference for China's foreign policy making.

Trademarks increasingly serve as indicators of product innovation and broader industry shifts, encompassing a wide range of innovation activities and soft innovations often overlooked by patents. However, research on the relationship between trademarks and innovation remains in its early stages, with a lack of exploration into China's soft innovation from the trademark perspective. This paper addresses this gap by analyzing trademark data from 1980 to 2021. Integrating big data mining, statistical methods, GIS spatial analysis, and econometric models, it explores the spatio-temporal evolution patterns and factors influencing soft innovation in Chinese cities. The results find that: First, the scale of trademark applications exhibits a complex evolution characterized by continuous growth and fluctuation, showing a dual-cycle and three-stage trend. Soft innovation output in cities follows a stepwise spatial distribution pattern, decreasing from the eastern coastal areas to central and western regions. This pattern reflects a regional agglomeration, with hierarchical changes showing both stability and variability. Second, the distribution of trademark categories exhibits significant heterogeneity, notably with a continuous rise in the proportion of service categories. Subcategories are concentrated in the consumer goods sector and maintain steady growth. Spatially, these categories show a mix of clustering and dispersion, predominantly aligning with industrial development trends and innovation-driven features, presenting a pattern congruent with the industrial structure. Finally, factors such as the development of the internet, establishment of new enterprises, and technological innovation have a significant positive impact on trademark applications. Population size, GDP, and urban commodity exports also contribute significantly to increased trademark activity. The influence of industrial composition on trademark application intensity exhibits certain heterogeneity. There is a clear correlation between knowledge-intensive service industries and trademark activity, whereas a negative relationship is observed with government service departments.

Industrial transfer and diffusion are traditional fields of economic geography and regional economics. They mainly include two main directions: transnational industrial transfer and interregional industrial transfer. Accurately grasping the characteristics and laws of the industrial diffusion process in the new era is of great significance for implementing regional coordination strategies and solving the main problems faced with regional coordinated development at this stage. Using industry investment data from prefecture-level cities and municipalities directly under the central government, this study compares and analyzes the diffusion process of 13 major industries in China. The zero-inflated negative binomial regression model is employed to clarify the different effects of diffusion intensity and diffusion possibility. The results indicate that: (1) The expansion of city size enhances inter-city industry diffusion intensity, with the diffusion intensity being more sensitive to changes in the size of the source city. (2) The 13 industries tend to spread to cities that are closer in distance and larger in scale. Spatial distance primarily affects the possibility of industry diffusion. Long spatial distances lead to a significant increase in the probability of zero investment scale. However, for most industries, hierarchical distance has a greater influence on diffusion intensity. Large hierarchical distances lead to a decrease in investment scale. (3) The 13 industries can be divided into four types based on the differentiated influence of spatial distance and hierarchical distance: double strong constraint type for spatial distance and hierarchical distance, strong spatial distance-medium hierarchical distance constraint type, strong spatial distance-weak hierarchical distance constraint type, and strong hierarchical distance-weak spatial distance constraint type. (4) The producer service industry in the central and western regions should establish a two-level central system at the regional and provincial levels, while the consumer service industry should establish a three-level central system at the regional, provincial, and key city levels.

Narrowing the gap of regional economic development and maintaining the balanced development path is the indispensable goals of China's regional coordinated development. In this study, the nighttime light image data and GDP statistical data are integrated to construct the Genuine Economic Index (GEI). Based on GEI, the Theil index, scale variance model, and the spatial filtering model are combined to analyze the spatial and temporal evolution of China's regional economic discrepancy from a multi-scale perspective. The results show that: (1) China's absolute regional economic differences expanded year by year, while the relative regional economic differences tended to shrink overall from 2000 to 2020. (2) Under different regional divisions, the scale variance of China's regional economy is manifested as municipal scale > county scale > provincial scale > regional scale, with the largest increase and highest contribution rate on the municipal scale. Narrowing inter-municipal gap is the key to decrease China's regional economic differences. (3) The contribution rate of spatial autocorrelation increased from 23.804% to 26.079% during 2000-2020. Regardless of spatial autocorrelation factors will lead to an overestimation of China's regional economic differences. (4) The impact of spatial autocorrelation on regional economic differences is scale sensitive, the smaller the spatial scale is, the stronger the sensitivity is. After spatial filtering, inter-county economic differences have been the largest part of China's regional economic differences. (5) Human capital, industrial structure, consumption scale and fiscal decentralization have a significant positive impact on China's Genuine Economic Index, however these factors show an obvious regional heterogeneity. This study could provide a multi-scale, refined and differentiated reference for the implementation of China's regional coordinated development strategy.

The matching of land-sea multi-elements is crucial for the evolution of coastal resilience and the achievement of sustainable development. Understanding the impact mechanism of this matching on coastal resilience is essential for the healthy development of coastal regions. Based on panel data from 2005 to 2021, this study employs a variable fuzzy recognition model and a rank matching degree to measure coastal resilience and the degree of land-sea multi-element matching in China. A fixed-effects model is used to explore the influence mechanism of land-sea multi-element matching on coastal resilience. Additionally, a system dynamics approach is applied to establish four simulation scenarios, so as to simulate the developmental trends of land-sea multi-element matching and coastal resilience. The results show that: (1) Coastal resilience exhibits a steady growth trend, with Guangdong and Shanghai exhibiting higher resilience levels, while Guangxi and Hainan having lower levels. (2) The rank matching degree of land-sea multi-elements fluctuates in a pattern of "match→mismatch→ match", with Guangdong and Shanghai having higher matching degrees, and Liaoning and Guangxi showing lower degrees. (3) Coordinated matching of land-sea multi-elements enhances coastal resilience, with economic development, openness, and urbanization also positively impacting coastal resilience. (4) Among the four scenarios, Scenario 4, which involves comprehensive regulation of land-sea multi-element matching, proves most effective in maximizing coastal resilience. The conclusions provide valuable scientific support for multi-scenario simulation, assessment, and decision-making in the development planning of China's coastal resilience.

Addressing inequality in residential electricity consumption is crucial for achieving the UN Sustainable Development Goal 7 (SDG 7). Guangdong province, one of the most developed areas in China, is a representative case for examining inequality and its changes between urban and rural areas. However, there is currently a lack of high-resolution energy consumption data to conduct urban and rural comparison analysis. To address this gap, this study combined nighttime light remote sensing data and European Union's Global Human Settlement Layer dataset to develop a novel residential electricity consumption dataset (500 meter grid) from 2000 to 2020. The results are as follows: (1) Inequality index indicates a sharp downward trend from 7.57 to 0.83, with the overall Theil index declining to 0.83 and 0.013. (2) Inequality index is the lowest in the Pearl River Delta among all sub-regions, while per capita residential electricity consumption is the highest in urban center and very low density rural clusters among different settlement types. (3) Lorenz curve and Gini coefficient indicate that urban centers show the greatest equality in terms of per capita residential electricity consumption across prefectures, while semi-dense urban clusters and rural clusters show the lowest. This study enriches research on urban and rural energy consumption analysis from a multi-spatial perspective,and overcomes the limitations of traditional statistical survey data featured by high cost and low accuracy. It provides a framework for energy and electricity consumption analysis in urban and rural areas in China and beyond. The findings are useful for a better understanding of urban-rural energy inequality in Guangdong.

As the "Third Pole" of the Earth and a crucial ecological security barrier for China, the sustainable development of the Qinghai-Tibet Plateau is of great significance. Based on the United Nations Sustainable Development Goals, this paper constructs a sustainable development indicator system across four dimensions: economic development, social inclusion, public infrastructure, and environmental protection. Using counties as the unit of analysis, it examines the sustainable development effects of industrialization, urbanization, and transfer payments in the Qinghai-Tibet Plateau for the years 2000, 2010, and 2020, revealing the impacts of the region's industrial development model and urban population aggregation on sustainability. The study found that: (1) The different dimensions of sustainable development in the Qinghai-Tibet Plateau vary significantly. The environmental index is the highest, while the economic index is the lowest, indicating that human activities exert less pressure on the natural environment. From 2000 to 2020, significant improvements were made in the economy, society, and public infrastructure, while the environmental dimension remained relatively stable, resulting in considerable overall progress in sustainable development. (2) There are clear regional disparities in sustainable development across the Plateau. The core areas of Xizang and Qinghai lag behind the periphery. High levels of sustainable development are observed in the Qaidam-Huangshui Valley, the eastern Hengduan Mountains, and the eastern section of Xizang's "Yarlung Zangbo River and its two tributaries" region, while areas such as the Sanjiangyuan region, the Northern Tibetan Plateau, and the upper reaches of the Yarlung Zangbo River are development "lowlands". (3) Industrialization, urbanization, and transfer payments have a significant positive impact on economic development, social inclusion, and public infrastructure, but exert a negative effect on the environment. Overall, they contribute positively to achieving the UN sustainable development goals. The prioritization of ecological protection in the Plateau inevitably restrains economic and social development. In this context, transfer payments play a crucial role in balancing the relationship between people and the environment. (4) An increase in the number of large-scale industrial enterprises at the county level and higher population concentration in the leading towns can effectively reduce the negative impact of industrialization and urbanization on resources and the environment. Therefore, abandoning small-scale and fragmented industrialization in favor of a more centralized urbanization approach will help balance socioeconomic development with resource and environmental protection, leading toward comprehensive sustainable development.

Large cites play a leading role in the industrial transformation and upgrading. Based on data on municipal districts for 284 cities at the prefecture level and above in China from 2006 to 2019, this paper investigates the relationship between urban industrial transformation and population size of cities and its evolution pattern, under the perspective of urban scaling laws. There are two main findings in this study. Firstly, the development of urban industries in China is influenced by both the overall development of industries and the change in urban population size, and the correspondence between scaling exponent and location quotient for industries is confirmed: specifically, large cities, which are ranked in the top 5% in terms of population size, are characterized by industries with a superlinear scaling factor of employment population, such as real estate, rental and business services, and scientific research and technical services; small cities, which are ranked in the bottom 75% of population size, are typified by industries with a sublinear scaling factor of employment population, like agriculture, forestry, animal husbandry, fishery, and mining; and cities ranked from 5% to 25% of population size are observed to have an industrial structure which is in a transitional phase between the first two types of cities. Secondly, the evolution pattern of urban industries is verified by Chinese evidence, with average recapitulation index of 0.49 and 0.37 respectively for industries and cities. It is thus evident that the industrial structure in cities shifts gradiently with their population size, that is, industrial upgrading in large cities comes first, and then it will exert a radiating effect on the industrial transformation in small cities.

This study employs the interlocking network model approach of the Globalization and World Cities Research Group (GaWC). Using data from 2702 modern financial institutions, including Chinese banks, foreign banks, and old-style native banks, mentioned in the 1935 National Banking Yearbook, we construct a modern Chinese urban network. The study analyzes the network characteristics, spatial structure, and distribution of cohesive subgroups, and ultimately explores the influencing factors using a Generalized Linear model. Our findings are as follows: (1) The modern Chinese urban network based on the financial industry is dominated by Chinese banks, followed by foreign banks and old-style native banks. (2) In terms of network linkage intensity, the urban network of Chinese banks forms a development axis structure with "(Shanghai-Nanjing)-(Tianjin-Beijing)" as the vertices. The urban network ties of foreign banks are centered in the coastal and northeastern regions. The network linkages of the old-style native banks show a discrete distribution pattern. (3) Concerning cohesive subgroups, subgroup 0 of the urban network of Chinese banks covers areas north of the Yangtze River and the coastal provinces, and a number of provincial subgroups survive in areas south of the Yangtze River. The city network of foreign banks is divided into two major subgroups: the northeastern region and the coastal provinces. The spatial extent of the subgroups of the urban network of the old-style native banks varies greatly, with much overlap between subgroups. (4) Compared with the modern inter-port trade network, the finance-based city network has a more balanced north-south distribution and a more integrated spatial configuration; compared to the modern financial industry-based urban network, the early-modern network shows a more concentrated distribution of vertices and greater alignment with political centers. (5) Regression analyses show that economic privileges, the prosperity of the national capital, and new transport conditions are major factors influencing the modern finance-based city network.

Innovation output is an important embodiment of urban innovation ability, increasingly driven by innovation cooperation. While the classic buzz-and-pipelines model distinguishes the theoretical influence of local versus external cooperation on innovation output, empirical research on their relationship remains weak. Leveraging data on invention applications and patent cooperation in China from 2010 to 2019, we depicted the spatial and temporal evolution of urban innovation output and cooperation. Using Geodetector, we identified the influence of intra- and inter-city innovation cooperation on urban innovation output and analyzed variations in their effects across different periods and city types. Our findings reveal substantial growth in both innovation output and cooperation across Chinese cities. There has been a shift from an endogenous mode depending on buzz (intra-city cooperation) to an extraverted mode based on pipelines (inter-city cooperation). The buzz-and-pipelines effect on urban innovation output displays periodic patterns, with the mechanisms of intra- and inter-city cooperation differing significantly among cities due to varying regional contexts, development stages, and status. We posit that nurturing buzz provides primordial impetus for innovation factor agglomeration and the output, while developing pipelines transfuses vigor for urban knowledge renewal and complexity. These effects shape the stable structure of urban internal and external linkages through a path-dependent evolution, facilitating mutual prosperity through synergy and resonance. Our study extends the buzz-and-pipelines theory to the urban scale, integrating intra- and inter-city innovation connection into a unified research framework. It reveals the evolutionary mechanism of their interaction, deepening our understanding of the spatial mechanisms on urban innovation output and guiding spatial practices in urban innovation cooperation.

The collaborative agglomeration of manufacturing and producer services is a new industrial form and has become an important growth pole to achieve high-quality development of regional economy. Based on the perspective of complex spatial network, this paper first constructs an improved collaborative agglomeration index of manufacturing and producer services, and then adopts the revised gravity model to calculate the spatial correlation matrix. Secondly, social network analysis method is used to describe the structural characteristics of the spatial correlation network of industrial collaborative agglomeration in 31 provinces (autonomous regions and municipalities directly under the Central Government, excluding Hong Kong, Macao and Taiwan) in China from 2003 to 2021. Finally, this paper explores the driving mechanism of correlation network under the spatial interaction theory and resource field theory by combining regional geographical conditions and socio-economic factors. The results show that: (1) During the study period, the spatial directivity of the collaborative agglomeration between manufacturing and producer services has not changed, the regional differences tend to converge, and the unbalanced distribution pattern has been improved. (2) The collaborative agglomeration of manufacturing and producer services gradually presents a complex network form of multi-thread interweaving, the overall network density is constantly increasing, and the spatial correlation has a large room for improvement. The network connectivity is good, the stability of the network structure is continuously enhanced, and the "core-edge" structure in the individual network is prominent. (3) The eastern region is mainly the "net benefit" and "broker" blocks, the northeast region and the western region are the "two-way spillover" and "net spillover" blocks. The spatial correlation network is dominated by the spillover effect and correlation effect between blocks. (4) Spatial adjacency, differences in economic development level, differences in labor input level and other factors jointly drive the formation of spatial correlation network. The conclusion of this study is helpful to optimize the spatial correlation network of manufacturing and producer services and promote the realization of high-quality economic development and regional coordinated development.

Industrial collaborative agglomeration is the product of industrial development to the advanced stage, and it is also the concrete manifestation of industrial collaborative development in space. This paper starts with the measurement method of industrial collaborative agglomeration, and constructs a comprehensive measurement method system aiming at the modifiable area unit problem (MAUP) and poor visualization effect existing in the current research methods. The paper uses the method to make an empirical study on the collaborative agglomeration of manufacturing and producer services in Beijing, and analyzes the dynamic evolution process and characteristics of collaborative agglomeration in time and space. The results show that: (1) In 2018, the range of agglomeration span of industry pairs was 34 km, and the average agglomeration intensity was 0.0858. Compared with 2008, the agglomeration scope was more dispersed and the agglomeration intensity was reduced, but knowledge-intensive industry pairs such as equipment manufacturing, science and technology, and information transmission services were more inclined to collaborative agglomeration. (2) In 2008, industry pairs with a high level of collaborative agglomeration were concentrated in the core areas of the city, but in 2018, they spread to the peripheral areas along the traffic arteries, forming a distribution pattern of "multi-point agglomeration", which roughly corresponds to the demonstration parks of the integration of the two industries planned by Beijng. (3) On the whole, the collaborative agglomeration of manufacturing and producer services showed a decline in the agglomeration intensity, but the grid distribution of medium and high collaborative agglomeration expanded, the inter-regional disequilibrium narrowed, and the spatial distribution was optimized. Industrial parks, transport accessibility and information technology development have played an important role in this.

In the context of U.S.-China trade war and COVID-19 pandemic and troubling global economy, the interaction between geopolitical risk (GPR) and global production network (GPN) has emerged as a new direction for GPN 2.0 research agenda. Exploring the reconfiguration process, strategic responses, and causal explanatory dynamics of lead firms is of practical and theoretical significance. This study uses interview data from 32 companies (Huawei and its suppliers) surveyed in Shanghai, Shenzhen, and Dongguan in 2023. Focusing on the spatial logic, regional redistribution, actor strategies, and causal interaction mechanisms, this study illuminates how Huawei navigates the causal dynamics and strategies of its production network, particularly the complex landscape of industrial transfer from Shenzhen to Dongguan. The results, based on the GPN 2.0 perspective, (1) reveal three actor-specific strategies employed by Huawei: inter-firm control based on the leader and follower effects, inter-firm partnership seeking alternative strategic partners. This strategy is further strengthened through extra-firm bargaining between Huawei and local governments in Shenzhen and Dongguan, where Huawei seeks to market entitlement of local real estate, expands its market reach for 5G technologies and fosters local industrial upgrading. (2) There are three dynamic factors influencing Huawei's restructuring: the first is the cost-capability ratio, primarily considering the reduction of land costs and the optimization of its R&D headquarters, southern production base, talent training, and employee facilities; the second is market-driven, with a focus on the To-Business (ToB) model to explore new domestic 5G markets, establishing the "three BGs + five major corps" model, and expanding local markets, particularly through smart city projects in Shenzhen and Dongguan; the third factor is the risk environment posed by the U.S.-China trade war, which has driven Huawei to support a diversified domestic supply chain. Overall, Huawei's production network restructuring is driven by these three considerations: reducing production costs, expanding a more feasible and stable domestic 5G and industrial internet market, and mitigating the geopolitical risks posed by U.S.-China tensions that could disrupt its current production network. (3) This, in turn, influences the interplay between Huawei's dynamics and strategies: The risky environment drives Huawei's inter-firm control, market imperatives and risks deeply affect Huawei's inter-firm partnership. The core of Huawei's extra-firm bargaining with local governments is optimising cost-capability ratios and sustaining 5G/real estate markets. Overall, this study extends GPN risk research to the major role and subjective initiative of lead firms beyond previously considered roles of the state, techno-nationalism, international relations and geopolitical interventions. This study demonstrates how Huawei internalises and interacts with specific GPR as a causal factor, shedding light on firm adaption and evolution in response to changing geopolitical challenges. This paper has three main research contributions as follows: firstly, it introduces actor strategies in the reconfiguration studies and extends to the issue of interaction between actor strategies and causal dynamics in the GPN 2.0; secondly, the internalisation of GPR as a causal consideration of corporate restructuring is a positive and effective attempt to bridge the new research agenda of GPN 2.0; and thirdly, the paper focuses on the reconfiguration of Huawei's DPN, and in particular explores the industrial transfer and reconfiguration of Huawei from Shenzhen to Songshanhu town in Dongguan.

Against the backdrop of China's rural revitalization strategy, this study clarified the concept of rural subjectivity and developed a framework from the perspective of the development status and reconstruction of the rural regional system. The study aimed to outline the impact of rural subjectivity on the rural regional system. Twenty villages in the Lijiang River and Yulong River basins in Yangshuo county, which have been reconstructed for tourism development, were taken as case studies. This study applied questionnaire data, an entropy weight TOPSIS method, and a coupled coordination model to assess the levels of rural subjectivity, the development of the rural regional system, and their coupled coordination. The fsQCA method was used to analyze the diverse influence pathways of rural subjectivity on the rural regional system. The findings showed that: (1) The theory of rural subjectivity held discursive significance in promoting the reinstatement of rural status; conceptual significance in fostering a dynamic and open rural ontology; and motivational significance in enhancing rural residents' subjective status and role. (2) The level of rural subjectivity in the two river basins of Yangshuo county was relatively balanced, but there were notable spatial variations in the levels of each dimension. (3) The rural regional system comprised five major subsystems: population, natural environment, human environment, economy, and resources. The rural regional system in the two river basins was at a low to medium level of development, exhibiting significant differences in the levels of each subsystem, both in terms of results and spatial distribution. (4) Individual elements of rural subjectivity did not constitute the necessary conditions to influence the coupling and coordination degree of the rural regional system. The pattern of influence for a high coupling coordination degree in the rural regional system can be summarized as "sufficient rural subjectivity" whereas a pattern of "insufficient heterogeneity of rural subjects" would result in a low coupling coordination degree.

Focusing on the investment security of the Belt and Road Initiative (BRI), this study analyzes the geopolitical environment of countries (regions) along the Belt and Road from two dimensions: the political-business environment and political inclination. Since the proposal of the BRI in 2013, the political-business environments of the participating countries (regions) have shown slight improvements, though there are significant regional disparities. Generally, these environments are better at the eastern and western ends of the BRI countries (regions), while the central regions lag behind. There is no significant spatial dependence in the political-business environments of these countries (regions), but a weak trend toward homogenization exists, with lower-scoring countries (regions) mainly located in the former Commonwealth of the Independent States, the Middle East, and the Indochina Peninsula. These regions show weak interconnections and exhibit characteristics of "being alike but not cohesive", suggesting that BRI efforts should focus on contiguous breakthroughs rather than scattering in isolated points. The evaluation of political inclination shows that the political division among the BRI countries (regions) is significant, with varying attitudes towards China. As the attitudes towards China warm up, the political-business environment tends to deteriorate. This negative correlation between the two factors indicates that countries (regions) actively participating in the BRI are relatively politically unstable and economically underdeveloped, seeking to leverage the initiative for their development. Therefore, in the construction of the Belt and Road, while ensuring investment security, we should actively promote Chinese values, striving for the support and participation of more countries (regions) with favorable political-business environments. Given the realities of underdeveloped political-business environments and political divisions along the Belt and Road, China urgently needs to explore a geopolitical theory that fits the geopolitical pattern of the BRI to ensure the progress and reclaim the political narrative power through reshaping the geopolitical strategic narrative. This paper's quantitative evaluation of political inclination provides a preliminary exploration into the political fragmentation among these countries (regions) and serves as a reference for advancing research on the geopolitical environment.

Eliminating inequality and achieving balanced development is one of the Sustainable Development Goals of the United Nations. Accurate measurement of development equilibrium is a primary scientific problem to be solved urgently. Nighttime lights data have been proven to be a good proxy for economic activity, and they have been used widely due to their advantages of convenient acquisition and wide spatial coverage. Therefore, in this study, we applied the Gini coefficients derived from nighttime lights data and pixelized population data to evaluate the development equilibria for China's 2900 counties from 2002-2020. We then examined the nexus of the development equilibrium and developmental vitality represented by the average nighttime lights intensity within each county. The results showed that the Gini coefficients based on nighttime lights data have generally been decreasing for 96% of the counties in China during 2002-2020. Moreover, in general, the Gini coefficients present a spatial pattern of being lower in the east and higher in the west. The results illustrate the synergetic development pathways of economic growth and equilibrium promotion of the economically developed counties, although the variation of their equilibria is significant. On the contrary, there is still distinct room for improvement of development equilibria for 60% of the economically underdeveloped counties in China. The results provide scientific support for achieving balanced development and promoting common prosperity in China.

The resources and environment carrying capacity (RECC) is a barometer of the development of urban territory space, so exploring the spatial characteristics of RECC is greatly significant for optimizing the territory spatial pattern. In this context, this paper uses new urban geographic information data, such as night-time lights and atmospheric remote sensing, points of interest, historical traffic situation and social media, on the basis of relevant procedures and guidelines. Furthermore, a theoretical framework based on the covert-resilient-overt analysis of the spatial characteristics of urban RECC is proposed, and a method combining spatial autocorrelation and fractal geometry is developed for spatial characteristics mining of RECC. Taking Wuhan as an example to carry out research, the results show that: (1) The distribution of RECC in Wuhan has significant spatial heterogeneity. Areas with higher covert carrying capacity are concentrated in the Huangpi District and Xinzhou District with superior ecological environment in the northern periphery of the city, areas with high resilient carrying capacity are concentrated in the central urban area with strong comprehensive risk response capabilities, and areas with high overt carrying capacity are located in the sub-center of the city with sound basic functions. (2) The RECC in Wuhan has positive spatial aggregation, and it shows significant radius-centripetal fractal and circle characteristics. The areas of high covert carrying capacity spread radially around, and the areas with high resilient carrying capacity are concentrated in the core of the city in a centripetal shape, the areas with high overt carrying capacity are between the areas with high values of covert and resilient carrying capacity. In conclusion, the spatial characteristics analysis and mining method system of urban RECC constructed in this paper make up for the shortcomings of the low dynamics of traditional data by introducing multi-source spatial temporal data, and improves the theoretical and technical paradigm of urban RECC research. The research provides a new way of thinking for the carrying capacity of urban resources and environment.

In the context of rapid urbanization, shrinking cities cannot be ignored. The classification of shrinking cities according to the patterns of urban population changes is the key to reveal the "hollow" and changing characteristics of the population. Researchers classified the shrinking cities in Western developed countries as "perforated", "doughnut" and "peripheral" types. However, there is a lack of evidence showing the types of shrinking cities in China. This paper takes the redefined physical cities in China (excluding Hong Kong, Macao and Taiwan) as the study area, and employs Baidu Huiyan's 200 m × 200 m grid cell permanent population data from November 2016 to November 2018 to identify different types of shrinking cities in the country. Our results show that 1506 physical cities have experienced a population reduction between 2016 and 2018, accounting for 49.8% of the total number of physical cities (3022). Furthermore, in order to accurately identify the types of shrinking cities, 126 densely populated and severely shrinking cities during the study period were selected as the research samples, mainly located to north of the Qinling Mountains and Huaihe River of China. Our analysis shows that the shrinking types of the sample cities can be classified into five categories: "perforated", "complete", "local", "doughnut", and "peripheral". The number of cities with perforated shrinkage is the largest, which numbered 47 (37.3% of the sample cities), most of which are found in Heilongjiang Province, Inner Mongolia Autonomous Region and Gansu Province. Complete shrinkage ranks the second, including 33 cities, which accounts for 26.2% of the samples, and mainly distributed in Gansu and Shaanxi provinces. The numbers of shrinking cities with local, doughnut and peripheral types are 25, 18 and 3, respectively. These cities are mainly distributed in Heilongjiang and Inner Mongolia. This is because the governments paid more attention to the development speed over quality during the first half of China's urbanization process, and adopted a growth-oriented urban planning strategy. Traditional industries declined due to resource depletion or economic transformation, and the transfer of industries under active government adjustment, all of which contribute to the emergence of perforated shrinkage. This study could provide scientific evidence for micro management of urban development, optimization of land use pattern, improvement of urban livability, and conduct of urban planning and design in the context of urban shrinkage.

Ecological priority and green development has become one of China's national strategies. Additionally, the scientific understanding of the green externality of tourism economy and its spatial spillover is still insufficient. Therefore, in terms of theoretical and practical significance, it is necessary to demonstrate whether China's tourism economy can promote green development and reveal its spatial spillover characteristics. On the basis of constructing the spatial spillover mechanism of green development effect of tourism economy, this paper selects green total factor productivity (GTFP) as the evaluation index of urban green development level based on bibliometric analysis; integrates multi-source data and uses EBM-GML model to calculate and decompose the GTFP of 284 cities at prefecture level in China from 2005 to 2016; uses the spatial analysis method to describe the spatio-temporal pattern and spatial clustering of GTFP at prefecture level. Based on the spatial Durbin model, this paper reveals the impact of tourism economy on green development and spatial spillover effect. The results show that: (1) the annual average of GTFP in eastern, central, western and northeastern China showed an overall upward trend. Eastern China has the largest improvement in GTFP (accumulated growth of 48.08%), followed by the western region (accumulated growth of 44.18%) and the northeastern region (accumulated growth of 36.05%), while the central region has the lowest improvement (accumulated growth of 26.56%), so that the "Central Collapse" feature is obvious. Moreover, there is a spatial dislocation between China's GTFP pattern and its economic map at the prefecture level. (2) The tourism economy could significantly promote the growth of local GTFP in tourist destinations by simultaneously promoting green efficiency change (GEC) and green technological change (GTC). (3) The spatial spillover mechanism of tourism economy on green development is reflected in the fact that tourism economy can significantly improve the GEC in neighboring cities, but it cannot significantly promote the GTC in neighboring cities. (4) In terms of policy, it is necessary to strengthen the linkage development of tourism economy within the region, and build an innovative spillover mechanism for tourism destinations. In addition, it is feasible to promote the coordinated development of tourism destinations and neighboring industries, and create a regional industrial ecosystem and complex with deep participation in the tourism industry.

Cellular automata (CA) has been widely recognized as an effective approach in the simulation of spatiotemporal dynamics of metropolitan areas, particularly for infilling and edge urban expansion processes. However, the traditional Logistics-CA has its severe drawbacks in simulating outlying expansion, since it evolves primarily according to the status of a set of neighboring cells, failing to identify other potential seeds that could also stimulate urban expansion in a significant way. This paper develops an advanced CA, called APCA, by using Affinity Propagation (AP) to comprehensively search for urban expansion seed points, as well as by realizing a synchronous simulation of diffusional and aggregational processes of urban cell. This paper uses the data of Wuhan, the largest provincial capital in central China, to validate the effectiveness of APCA. By simulating Wuhan's urban expansion dynamics between 1995 and 2025, the APCA (1) identifies that the total areas of outlying expansion amount to 8.67 km², accounting for 6.30% of added urban land of the city; and (2) successfully simulates a process of "first diffusion and then aggregation" in Wuhan, which is in harmony with the Phase Theory of urban expansion. Compared with the traditional Logistics-CA, the overall accuracy of APCA remains higher regardless of the number of seed points as long as they are within 1-8, while the accuracy of APCA reaches its highest (0.5217) when the seed point is set as 6. The APCA contributes to the two-dimensional CA framework by expanding surface-dimension simulation to point-dimension simulation, and thereby facilitates effective and accurate simulations of urban expansion patterns.

China's modernized transport strategies have been focused on building new infrastructures in areas with insufficient transport facilities, supplying the lack of new transport services, and investing in transport construction on a large scale since the 10th Five-Year Plan (2000-2005). However, these strategies are now facing new situations in population development, including population growth, migration, spatial relocation and agglomeration, and changes in lifestyle. It is imperative to investigate population-development oriented comprehensive modern transport system in order to implement people-oriented principles and the National Strategy of Strengthening Country in Transportation. This study presents some research results from the National Development and Reform Commission Key Research Project for the National 14th Five-Year Plan (2021-2025), "Population Development and Modern Transportation System Strategies in China". The study is based on theoretical mechanisms in the coupling and interaction between population and transport system. It conducts in-depth analyses on the new trends of population development and transport demand, and proposes a new "diamond" strategy for China's comprehensive transport system in order to cope with these new trends. According to this strategy, the new comprehensive modern transport system in China will have four "diamond pole" including Beijing-Tianjin-Hebei region, Yangtze River Delta, Guangdong-Hong Kong-Macao Greater Bay Area, and Chengdu-Chongqing region, four "diamond edges" including Beijing-Shanghai (Jing-Hu), Shanghai-Guangdong (Hu-Yue), Guangdong-Sichuan (Yue-Chuan), and Beijing-Sichuan (Jing-Chuan) transport corridors, two cross backbones including water transport along the Yangtze River and Beijing-Hong Kong-Macao transport corridors, and multiple transport hubs in cities with intensive intracity population movement flow. The study also proposes key missions to implement the new strategy in order to build population-development oriented comprehensive modern transport system in China, which includes supporting the national development and spatial plan, applying for the National Strategy of Strengthening Country in Transportation, and supplying high-quality transport services with a priority to the people.

Urban scaling laws depict the nonlinear relationship between urban indicators and population size within an urban system, but its applicability and significance in China's urban system is not clear. This study applied the data of 275 prefecture-level cities in China to explore the scaling relationship between different urban indicators and population sizes. We also attempt to present an empirical analysis of spatio-temporal characteristics of urban scaling exponent (β) from 2000-2017 to enrich the understanding of urban scaling laws. The result shows that as a typical fast urbanizing country, China's urban development conforms to the urban scaling laws but presents some different characteristics from developed countries. There is a significant increasing return to population size of economic output brought by China's urban population agglomeration. Nevertheless, the economies of scale for land use, infrastructure and public service are supposed to be strengthened. Some infrastructure and living facilities indicators have a super-linear or linear relationship with the population size, which are contrary to the expected sublinear regime. The scaling exponents of education, medical and health care and other urban indicators are far below the sub-linear thresholds of developed countries, which reflects the uniqueness in a fast urbanizing country that large cities are given priority to infrastructure construction and the unbalanced development of different-sized cities. The spatial distribution characteristic of scaling exponent reflects the differences in the degree of coordination of urban development, with the most significant differences in Northeast China. We also analyzed the evolution of scaling exponents of major urban indicators over time. Economic increasing returns to population size effect of large cities in China was the most significant in 2009 before falling slightly. Urban land expansion in large cities has continued to accelerate since 2000 and still remains at a high level after 2008. It is urgent to control the extensive urban land expansion and improve land use efficiency, especially in large cities. This study on urban scaling in China contributes to fully understanding the characteristics and evolution of urban systems in the fast urbanizing country. This paper is supposed to provide a support for achieving a balanced regional development and implementing new urbanization development strategies as well.

Resource-based cities refer to the cities where the exploiting and processing of natural resources, such as minerals and forests, dominate industrial development. This special type of city is obviously staged during its development. From a new perspective of urban spatial structure, this paper quantitatively splits the development stages of resource-based cities based on the data of night lights images and uses the methods, such as the threshold panel model and non-parametric estimation. To delve into the impacts of resource-based industries on the evolution of urban spatial structure, as well as the timing of transformation policy design, different effects of the spatial agglomeration of central urban areas on urban growth and transformation by development stages are also analyzed in this paper. The results suggest that: the resource-based cities are relatively internally fragmented with noticeable differences among resource types and individuals, such as oil and gas resource type, and ferrous metal resource type. This paper attempts to adopt the proportion of mining employees as the indicator. Some 1.9% and 31.0% can act as the tipping points to divide different development stages of resource-based cities. The resource-based cities are split into four stages of development given two threshold values. In various stages of development, the correlation between the concentration of social and economic factors in the central urban area and the urban economic growth is noticeably different. Furthermore, 'the mature stage' has acted as a critical turning phase in the transformation of resource-based cities.

Job-housing relationship in city has always been a concern of scholars. Excess commuting is a well-accepted concept for evaluating commuting efficiency and potential of optimizing it. By calculating difference between observed commute and theoretical minimum commute, excess commuting can measure to what extent the commute of a city is wasted. Studies on excess commuting are rather scarce in China, especially in perspective of different age groups. Using the mobile phone grid data in September 2017 in Shanghai, this paper analyzes the residential locations, job locations, and commuting distances of three age groups, namely, the young, the middle and the old. To calculate their excess commuting, a modified algorithm is devised, simulating individuals exchanging their residences based on the principle of Pareto Optimality, so that no one has to sacrifice their status quo. The new method also uses large-scale individual data instead of small-scale aggregate origin-destination data adopted in the traditional linear programming method. The job-housing relationships under the optimal commuting are estimated and compared between age groups. The results show that: (1) There is an evident inter-generational differentiation in the job-housing relationships. The young people are the disadvantaged group with the longest commute distances and the farthest residence locations from the city center; the elderly are the dominant group with the shortest commute distances and the closest residence locations from the city center; the middle-aged are in-between. (2) The excess commuting rate in Shanghai is 69%, indicating a large potential in optimizing the job-housing relationships. (3) The benefits of the optimization are immense: the average commuting distance declines, the inter-generational differentiation moderates, and the spatial structure of population age becomes more balanced. (4) The young group will benefit most from this process. It is suggested that the spatial planning and policy making of Shanghai should set a target for an ideal spatial structure of the urban population age, so as to increase the fluidity of the job and housing spaces, youthen the city center, and make the perimeter areas more livable for the middle-age and old people.

Urban planning plays an important guiding role in land use change. The planning at all levels supports and complements each other, and influences the evolution of land-use pattern from top to bottom. Vector cellular automata, which takes irregular geographical entities as its basic units, can express the objective and complicated urban land use structure in a more realistic way, and has become one of the focus in the land use change research. However, when we face urban planning with the characteristics of hierarchical synergy, spatial guidance and controlled conductivity, some key issues are highlighted, such as the establishment of CA hierarchical system, the construction of hierarchical synergy and the acquisition of transition rules. After constructing the multilevel vector CA model, we chose Jiangyin City as the study area and its land use data covering the years 2007, 2012 and 2017 as the basic data to simulate the land use changes from 2012 to 2017. On the basis of multilevel vector cellular automata model, through the comparative analysis between the simulation results and the current situation of land use in 2017, the individual parameters of the model were modified to further improve the feasibility and applicability of the model, and then the urban land use pattern in 2022 was predicted. The simulation results indicated that the development of construction land in Central District has become saturated, while the expansions of construction land in Chengnan District, Chengdongnan District and Chengdong District were obvious. From an overall perspective, there was a tendency to gradually form a three-level urban-rural spatial settlement system "central city area-urban agglomeration-village". Using FoM index, we concluded that the simulation results had great simulation precision and the model had high feasibility and applicability, because the FoM values of the whole city and each district were almost greater than or close to 0.21. This shows that the simulation results are more accurate and the model is more effective in the simulation of land use change based on urban planning.