Search Results (6)

Urban Agglomerations (UAs), as the primary form of China’s new urbanization and an essential spatial unit for promoting coordinated regional development, play a crucial role in measuring the sustainable and healthy development of urban clusters through the assessment of spatial network connections among cities within the UAs. Taking the 16 prefecture-level cities of the Chengdu-Chongqing Urban Agglomeration (CCUA) as the research subject, this study constructs six types of element flow networks, including population flow, logistics, and information flow. Employing network visualization analysis, the Self-Organizing Maps (SOM) neural network machine learning models, and Quadratic Assignment Procedure (QAP) relational regression models, the research analyzes the spatial network characteristics of the CCUA from the perspective of multi-dimensional element flows and explores the influencing factors of the UA’s connectivity pattern. The results indicate that: The various element flows within the CCUA exhibit a bipolar spatial network characteristic with Chengdu and Chongqing as the poles. In the element network grouping features, a multi-centered group differentiation structure is presented, and the intensity of internal element flow varies. Based on the results of the SOM neural network machine learning model, the connectivity capabilities of cities within the CCUA are divided into five levels. Among them, Chengdu and Chongqing have the strongest comprehensive connectivity capabilities, showing a significant difference compared to other cities, and there is an imbalance in the connectivity capabilities between cities. In terms of the influencing factors of the urban connectivity pattern within the CCUA, the differences in permanent population size and urbanization rates have a significant negative impact on the information flow network, technology flow network, and capital flow network. The differences in the secondary industrial structure and public budget expenditures have a significant positive impact on the intensity of inter-city element flows, and the differences in per capita consumption expenditures have a significant negative impact, collectively influencing the formation of the spatial connectivity pattern of the CCUA. The findings of this study can provide a scientific basis for the construction and optimization of the spatial connectivity pattern of the CCUA. Full article

Urban agglomeration (UA) is a highly developed spatial form of urban complex, which is one of the important carriers of regional economic cooperation, international industrial division of labor, and flow of capital and information elements. In China, urban agglomerations (UAs) have become the spatial subject of the national new-type urbanization strategy since the early 21st century and have made irreplaceable contributions to China’s urbanization and economic development. The Guanzhong Plain urban agglomeration (GPUA) is an important economic growth pole in northwest China and a key node in China’s open-door pattern. Exploring the spatial and temporal characteristics and driving factors of its economic development will be an important revelation for the promotion of high-quality economic development of the GPUA. This paper characterizes the level of economic development of GPUA with a long series of nighttime light data between 2002 and 2022. The standard deviation ellipse, spatial autocorrelation analysis, the economic difference index, and grey correlation analysis are used to analyze the characteristics of spatio-temporal evolution, internal diversity, and driving factors of economic development of the GPUA. The results show that the economic development level of the GPUA continued to increase from 2002 to 2022. The spatial distribution of the GPUA economy is “northeast-southwest” axial distribution, and the center of gravity of economic development gradually moves westward. The differences in the level of economic development within the GPUA show a typical core–periphery structure, but the degree of difference tends to weaken over time. The internal expansion force and economic promotion force were the dominant factors for the economic development of the GPUA in the early years. However, with the passage of time, scientific and technological support and government support have gradually become the main influencing factors for the economic development of the GPUA nowadays. Full article

Regional science and technology cooperation networks are pivotal for fostering sustainable global innovation. The China–ASEAN science and technology cooperation network integrates regional innovation resources, thereby promoting the sustainable flow of innovation elements and complementing technological strengths among countries, which significantly enhances cooperation efficiency and outcomes. This study employs a Social Network Analysis (SNA) and the Temporal Exponential Random Graph Model (TERGM) to analyze co-authored publications between China and ASEAN countries from 2003 to 2022, constructing a cooperation network that integrates both endogenous network structures and exogenous driving factors. This study explores the distinct mechanisms through which these factors influence the formation of cooperative relationships and highlights the key features and determinants of the network. The findings reveal the following: first, the China–ASEAN science and technology cooperation network has evolved from an initial “star-shaped structure” with China and Singapore as central nodes to a more interconnected network exhibiting “small world” and “high clustering” characteristics. Second, endogenous network structures, including the number of edges, node centrality, and closed triadic structures, significantly shape the network’s evolution, with some structures inhibiting the formation of new partnerships, while an increase in shared collaborators promotes new connections. Third, the evolution of the network demonstrates both stability and variability. Fourth, human capital is a key driver of partnership formation, while higher per-capita GDP countries show less inclination to form new partnerships. Fifth, proximity factors have heterogeneous effects: linguistic proximity positively impacts the formation of partnerships, while institutional proximity negatively affects the establishment of new collaborations. Based on these findings, this paper suggests improving international cooperation mechanisms, optimizing resource allocation, and enhancing the development of cross-border scientific talent. These measures aim to enhance the connectivity within the China–ASEAN science and technology cooperation network, effectively improve the utilization efficiency of regional innovation resources and technological capabilities, and promote the sharing and long-term collaboration of innovation resources within the region, thereby advancing sustainable development at both regional and global levels. Full article

In the new stage of China’s economic transformation from high-speed growth to high-quality development, scientific and technological factors have become the new driving force for the high-quality development of China’s economy. Therefore, how the flow of scientific and technological factors affects the high-quality development of regional economy is worthy of in-depth discussion. Based on the panel data of 30 provinces (municipalities and districts) in China from 2006 to 2019, this paper calculates the high-quality economic development index by constructing a comprehensive index system, and empirically examines the direct effect, spatial effect and threshold effect of the flow of scientific and technological factors (including scientific and technological talent factors and scientific and technological capital factors) on high-quality regional economic development. The results show the following: (1) The flow of science and technology talents and science and technology capital is the main driving factor for the high-quality development of regional economy, and the flow of science and technology talents and science and technology capital can play a complementary role. (2) High-quality regional economic development in China presents a certain clustering phenomenon in geographical space. The flow of scientific and technological talent elements and scientific and technological capital elements can stimulate the improvement of high-quality economic development in surrounding areas through the spatial spillover effect of high-quality economic development. (3) Only when the flow of science and technology factors is matched with a higher degree of financial structure and industrial structure can the flow of science and technology factors more effectively promote the high-quality development of regional economy. (4) The higher the level of industrialization and economic development, the more obvious the impact of the flow of scientific and technological talent factors and scientific and technological capital factors on the high-quality economic development. Meanwhile, the lower the degree of marketization, the more obvious the impact of the flow of scientific and technological capital factors on the high-quality economic development. Full article

The rational flow of production factors is crucial for promoting benign interactions between urban and rural areas. To unveil the intrinsic mechanisms of factor flow pathways promoting mutual symbiosis between urban and rural areas, this study, based on symbiosis theory, takes total factor flow including land, technology, capital, and labor as inputs and urban–rural symbiosis level as output. Utilizing the Super-Efficiency Slack-Based Measure (SBM) model, this study calculates the urban–rural total factor flow efficiency of 27 provinces in China from 2011 to 2021 and explores specific improvement directions of urban–rural factor flow based on projection analysis. This study revealed the following findings: (1) The overall efficiency of urban–rural total factor flow in China shows a fluctuating upward trend but has not yet reached an effective state. There are significant regional disparities, with 8 provinces such as Guangdong and Fujian reaching Pareto optimality, while the remaining 19 provinces exhibit varying degrees of inefficiency. (2) Provinces with insufficient symbiotic production are mainly concentrated in the central and western regions and the northeast region, with 14 provinces including Inner Mongolia showing the inadequate transformation of urban–rural symbiosis. However, except for Hainan, the situation is gradually improving in other regions annually. (3) There is input redundancy in total factor, where land, labor, and capital redundancy are the main reasons for the inefficiency of urban–rural total factor flow in China. However, trends show that the redundancy of land, labor, and capital elements is improving annually, while technology redundancy is worsening. (4) Through a comprehensive analysis of input redundancy, output deficiency, symbiosis coefficient, and efficiency, this study categorizes the impact of factor flow on urban–rural symbiosis level into basic matching, redundancy, and comprehensive scarcity types. The research provides scientific guidance for promoting sustainable development through the rational flow of total factors and offers valuable insights for similar countries. Full article

Green supply chain finance is a new financing method that focuses on corporate restructuring and promotes corporate capital flow and the development of environmental protection. This paper used BP neural network technology to study the green financing of the supply chain under the sustainable ecological environment. The method played an important role in the trial. Due to the more uncertain factors faced and the more complex environment, the risks of green supply chain finance are more hidden, diverse, and complex. The BP neural network is relatively mature in both network theory and performance. Its outstanding advantages are its strong nonlinear mapping ability and flexible network structure. The positive effect of BP neural network on green financial risk management is verified by experiments. Green supply chain finance is an innovative model of green finance. This experiment studies the risk management of green finance in supply chain and the evaluation index of green finance risk management through BP neural network method, and shows that the evaluation results are highly scientific. In addition, based on the green supply chain model, the historical data of different regions provide a scientific basis for the sustainable ecological development of the region. This paper provides guidance for the sustainable development of green finance in the supply chain and makes contributions to promoting the development of green economy. In order to control the risks of supply chain financing business, the risks of supply chain financing business are classified and analyzed, and specific project risk levels and points are determined to propose control measures to ensure effective control of the business risks. Full article