Search Results (85)

Background: Combinatorial optimization problems (COPs) are central to Logistics and Supply Chain decision making, yet their NP-hardness prevents exact optimal solutions in reasonable time. Methods: This work addresses that limitation by developing a novel ternary network flow linear programming (LP) model of the assignment problem (AP) polytope. The model is very large scale (with $\mathsf{\Theta }\left(m^9\right)$ variables and $\mathsf{\Theta }\left(m^8\right)$ constraints, where m is the number of assignments). Although not intended to compete with conventional two-dimensional formulations of the AP with respect to solution procedures, it enables hard COPs to be solved exactly as “strict” (integrality requirements-free) LPs through simple transformations of their cost functions. Illustrations are given for the quadratic assignment problem (QAP) and the traveling salesman problem (TSP). Results: Because the proposed LP model is polynomial-sized and there exist polynomial-time algorithms for solving LPs, it affirms “$P=NP$.” A separable substructure of the model shows promise for practical-scale instances due to its suitability for large-scale optimization techniques such as Dantzig–Wolfe Decomposition, Column Generation, and Lagrangian Relaxation. The formulation also has greater robustness relative to standard network flow models. Conclusions: Overall, the approach provides a systematic, modeling-barrier-free framework for representing NP-complete problems as polynomial-sized LPs, with clear theoretical interest and practical potential for medium to large-scale Logistics and other COP-intensive applications. Full article

This study analyzes 1441 industry–university–research (I-U-R) collaborative invention patents (2004–2023) in China’s new energy vehicle (NEV) industry using social network analysis. We propose the “Proximity–Industry Life Cycle” Fit Theory to systematically investigate the influence mechanisms of industrial proximity, geographical proximity, and technological proximity on the evolution of the industry–university–research collaborative innovation network of the new energy vehicle industry across three industry life cycle stages. Key findings include: (1) the network scale expanded significantly while density declined; (2) State Grid Corporation emerged as the core node after 2010; (3) all three proximity dimensions positively influence network evolution, with varying effects across stages—industrial proximity dominates in the emergent stage, while technological proximity becomes the primary driver in later stages. Policy implications: Governments should formulate stage-differentiated policies—encouraging industrial chain collaboration in early stages while promoting technology alliances in mature stages. Core enterprises should be supported to strengthen I-U-R collaboration, and cross-regional innovation platforms should be established to optimize proximity-driven knowledge transfer. Full article

The international wheat trade serves as a vital pathway for balancing the global food supply and demand while facilitating the cross-regional allocation of cropland resources. Based on the telecoupling framework, this study constructed a global virtual-cropland-flow network using wheat trade data from eight time points between 1995 and 2023. Social network analysis and quadratic assignment procedure regression were applied to examine its structural evolution and driving factors. The findings reveal that (1) while growing in connectivity, the virtual cropland network exhibits structural vulnerability and evolutionary complexity. (2) The network demonstrated a clear telecoupled structure, with the sending system shifting from U.S.–Canada dominance towards multipolarity, and the receiving system centered in Asia, Africa, and Latin America, with China at its core. The United States and France are major spillover systems. (3) Economic development and foreign demand significantly promote the establishment and intensification of trade relationships between countries. Geographical distance has a dual effect: it strongly negatively influences trade initiation but can be overcome by high complementarity between countries during trade deepening. (4) International wheat trade contributes to global cropland savings but also introduces systemic risks and environmental spillovers in some countries. The results provide theoretical support for building sustainable food trade and agricultural resource governance systems and offer important insights for advancing SDG 2 (Zero Hunger), SDG 12 (Responsible Consumption and Production), sustainable land systems, and the optimization of global land governance. Full article

As the “brain” of the information industry and modern manufacturing, chips have emerged as a focal point in global competition over critical technologies. Based on global chip trade data from 2010 to 2023, this study employs social network analysis to investigate the structural evolution of the chip trade network and applies the quadratic assignment procedure (QAP) to examine the driving mechanisms of network reconstruction. The findings are as follows: First, the global chip trade network exhibits a loosely connected core-periphery structure, characterized by clustering and polarization, with a pronounced short-term deglobalization trend. Second, China, the United States, Germany, France, South Korea, and Singapore have long dominated central positions in competitive dynamics, while developing economies such as Mexico, Malaysia, and the Philippines have significantly risen in prominence in recent years. Third, the network takes on a core–subcore–periphery configuration with clearly delineated trade communities, reflecting a community-based, multi-centric, and hierarchical pattern. Fourth, political relations serve as a key driver of network restructuring, with their promotional effect on chip trade being negatively moderated by technological distance yet positively moderated by economic-complexity distance. Full article

Innovation in the new energy industry serves not only as a key accelerator for the global green and low-carbon energy transition but also as a core driving force of the ongoing energy revolution. This study utilizes data on publications, patents, and the spatial distribution of representative innovation enterprises in the new energy industry of the Yangtze River Delta urban agglomeration from 2009 to 2023 to construct a multilayer science–technology–industry innovation network. Social network analysis is employed to examine its evolutionary dynamics and structural characteristics, and the Quadratic Assignment Procedure (QAP) is used to investigate the factors shaping intercity innovation linkages. The results reveal that the multilayer innovation network has continuously expanded in scale, gradually forming a multi-core radiative structure with Shanghai, Nanjing, and Hangzhou at the center. At the cohesive subgroup level, the scientific and technological layers exhibit clear hierarchical differentiation, where core cities tend to engage in strong mutual collaborations, while the industrial layer shows a hub-and-spoke pattern combining large, medium, and small cities. In terms of layer relationships, the centrality of the scientific layer increasingly surpasses that of the technological and industrial layers. Inter-layer degree correlations and overlaps also display a strengthening trend. Furthermore, differences in regional higher education scale, urban economic density, and geographic proximity are found to exert significant influences on scientific, technological, and industrial innovation linkages among cities. In response, this study recommends enhancing the leadership role of core cities, leveraging the bridging and intermediary functions of peripheral cities, and promoting application-driven cross-regional innovation collaboration, thereby building efficient science–technology–industry networks and enhancing intercity innovation linkages and the flow of innovation resources, and ultimately promoting the high-quality development of the regional new energy industry. Full article

Amid global digital transformation, digital service trade has become a transformative force reshaping international economies. We employ an innovative combination of Social Network Analysis (SNA) and Quadratic Assignment Procedure (QAP) to simultaneously dissect the macroscopic structure and microscopic determinants of the global digital service trade network. Key findings reveal: (1) The global digital service trade network exhibits distinct scale-free and small-world characteristics, reflecting deepening globalization. (2) The global hierarchy demonstrates structural rigidity, wherein core nations persistently reinforce their dominance despite selective upward mobility achieved by certain emerging economies. (3) Clear community differentiation emerges, featuring stable European subgroups, dynamic Asia-Pacific reorganization, and marginalized yet internally diverging Africa-Latin America clusters. (4) QAP regression identifies technological gaps and economic disparities as primary enablers, whereas geographical distance, internet development asymmetries and digital infrastructure divides constitute significant barriers, with linguistic commonality exerting positive effects. Based on empirical findings, we propose policy suggestion from four aspects: multilateral coordination for digital trade rules, digital infrastructure development, regional digital integration, and cross-civilizational digital communities. The study enriches analytical approaches to digital trade networks and provides theoretical foundations and policy insights for constructing an inclusive global digital economy framework. Full article

The rational utilization of cultivated land resources is central to ensuring both ecological and food security in the Yangtze River Economic Belt (YREB), holding strategic significance for regional sustainable development. Using panel data from 2010 to 2023 for 130 cities in the YREB, this study examines a spatial correlation network (SCN) for non-grain land use (NGLU) and its driving forces via a modified gravity model, social network analysis (SNA), and quadratic assignment procedure regression. The results show the following: (1) The risk of NGLU continues to increase, with the spatial pattern evolving from a “single-peak right deviation” pattern to a “multi-peak coexistence” pattern featuring three-level polarization and gradient transmission, primarily driven by economic potential disparities. (2) The SCN has increased in density, but its pathways are relatively singular. Node functions exhibit significant differentiation, with high-degree nodes forming “control poles”, high-intermediate nodes dominating cross-regional risk transmission, and low-proximity nodes experiencing “protective marginalization”. Node centrality distribution is highly connected with the regional development gradient. (3) The formation of the spatial network is jointly driven by multiple factors. Geographical proximity, economic potential differences, comparative benefit differences, non-agricultural employment differences, and factor mobility all positively contribute to the spillover effect. Conversely, implementing cultivated land protection policies and the regional imbalance in local industrial development path dependence significantly inhibit the non-grain trend. This study further reveals that a synergistic governance system characterized by “axial management, node classification, and edge support” should be recommended to prevent the gradient risk transmission induced by economic disparities, providing a scientific basis for achieving sustainable use of regional cultivated land resources and coordinated governance of food security. Full article

Driven by the global energy transition and the pursuit of dual carbon goals (carbon peaking and carbon neutrality), the innovation network of the new energy vehicle (NEV) industry, composed of enterprises, universities, and research institutes, has become a key driver of sustainable industrial development. The evolution of this network is jointly shaped by both supply chain networks (SCNs) and executive networks (ENs), representing formal and informal relational structures, respectively. To systematically explore these dynamics, this study analyzes panel data from Chinese A-share-listed NEV firms covering the period 2003–2024. Employing social network analysis (SNA) and Quadratic Assignment Procedure (QAP) regression, we investigate how SCNs and ENs influence the formation and structural evolution of innovation networks. The results reveal that although all three networks exhibit sparse connectivity, they differ substantially in their structural characteristics. Moreover, both SCNs and ENs have statistically significant positive effects on innovation network development. Building on these findings, we propose an integrative policy framework to strategically enhance the innovation ecosystem of China’s NEV industry. This study not only provides practical guidance for fostering collaborative innovation but also offers theoretical insights by integrating formal and informal network perspectives, thereby advancing the understanding of multi-network interactions in complex industrial systems. Full article

Against the backdrop of carbon reduction and sustainable development, cities play a central role in carbon emissions. These emissions are interconnected through economic, demographic, technological, and other factors, forming a complex network. This study investigates the structural characteristics and driving factors of carbon emission linkages among Chinese cities, with the aim of providing theoretical support and practical guidance for the development of sound regional carbon reduction policies. An improved gravity model was used to measure both the presence and intensity of linkages between cities. Social Network Analysis (SNA) was applied to examine network features such as density, centrality, and hierarchical structure. In addition, the Quadratic Assignment Procedure (QAP) was employed to test the effects of geographical proximity, economic disparities, demographic differences, and technological gaps on carbon emission linkages. Based on these methods, the study constructs the Chinese Carbon Emission Correlation Network (CECN), which shows high connectivity, a clear hierarchical structure, and a strengthened role of core cities. Cities with extensive linkages are mainly located in the eastern coastal region and political centers, forming a spatial pattern with stronger connections in the east than in the west, and more along the coast than inland. The network can also be divided into five distinct sub-groups. Moreover, geographical proximity, population differences, economic affluence, and technological disparities were found to significantly shape the spatial correlation of carbon emissions. These findings offer valuable guidance for designing targeted carbon reduction policies, which are essential for fostering regional coordination and advancing sustainable urban development. Full article

Digital city planning increasingly relies on data-driven approaches to address complex urban sustainability challenges through innovative network analysis methodologies. This study introduces a comprehensive spatiotemporal network framework to examine digital innovation networks as fundamental infrastructure for urban sustainable development, focusing on the Yellow River Basin as a representative case study. Utilizing digital patent data as innovation indicators across 57 urban centers, we employ advanced network analysis techniques including Social Network Analysis (SNA) and the Quadratic Assignment Procedure (QAP) to investigate the spatiotemporal evolution patterns and underlying driving mechanisms of regional digital innovation networks. The methodology integrates big data analytics with urban planning applications to provide evidence-based insights for digital city planning strategies. Our empirical findings reveal three critical dimensions of urban sustainable development through digital innovation networks: First, the region demonstrated significant enhancement in digital innovation capacity from 2012 to 2022, with accelerated growth patterns post 2020, indicating robust urban resilience and adaptive capacity for sustainable transformation. Second, the spatial network configuration exhibited increasing interconnectivity characterized by strengthened urban–rural linkages and enhanced cross-regional innovation flows, forming a hierarchical centrality pattern where major metropolitan centers (Xi’an, Zhengzhou, Jinan, and Lanzhou) serve as innovation hubs driving coordinated regional development. Third, analysis of network formation mechanisms indicates that spatial proximity, market dynamics, and industrial foundations negatively correlate with network density, suggesting that regional heterogeneity in these characteristics promotes innovation diffusion and strengthens inter-urban connections, while technical human capital and governmental interventions show limited influence on network evolution. This research contributes to the digital city planning literature by demonstrating how data-driven network analysis can inform sustainable urban development strategies, providing valuable insights for policymakers and urban planners implementing AI technologies and big data applications in regional development planning. Full article

As global climate change intensifies and the transition to clean energy accelerates, lithium-ion batteries—critical components of electric vehicles—are becoming increasingly vital in international trade networks. This study investigates the structural evolution and determinants of the electric vehicle lithium-ion battery trade network among European Union (EU) member states from 2012 to 2023, employing social network analysis and the multiple regression quadratic assignment procedure method. The findings demonstrate the transformation of the network from a centralized and loosely connected structure, with Germany as the dominant hub, to a more interconnected and decentralized system in which Poland and Hungary emerge as the leading players. Key network metrics, such as the density, clustering coefficients, and average path lengths, reveal increased regional trade connectivity and enhanced supply chain efficiency. The analysis identifies geographic and economic proximity, logistics performance, labor cost differentials, energy resource availability, and venture capital investment as significant drivers of trade flows, highlighting the interaction among spatial, economic, and infrastructural factors in shaping the network. Based on these findings, this study underscores the need for targeted policy measures to support Central and Eastern European countries, including investment in logistics infrastructure, technological innovation, and regional cooperation initiatives, to strengthen their integration into the supply chain and bolster their export capacity. Furthermore, fostering balanced inter-regional collaborations is essential in building a resilient trade network. Continued investment in transportation infrastructure and innovation is recommended to sustain the EU’s competitive advantage in the global electric vehicle lithium-ion battery supply chain. Full article

New infrastructure construction (NIC) is pivotal for advancing China’s sustainable development, yet the spatial interdependencies between NIC and inclusive green growth (IGG) remain critically underexplored. This study quantifies provincial-level NIC–IGG coordination dynamics across China (2011–2023) using a novel coupling coordination model. We further dissect regional disparities through Dagum Gini decomposition and identify causal drivers via QAP regression analysis. Key findings reveal: (1) Despite a gradual upward trend, overall NIC–IGG coordination remains suboptimal, hindering sustainable transition; (2) Regional disparities follow a “U-shaped” trajectory, primarily driven by inter-regional imbalances; (3) Uneven marketization is the dominant factor fragmenting spatial coordination. Our results expose systemic barriers to regionally integrated sustainable development and provide actionable pathways for place-based policies that synchronize NIC investment with IGG objectives. Full article

Understanding the spatial association network structure and carbon balance zoning of land-use carbon emissions (LUCEs) is essential for guiding regional environmental management. This study constructs a LUCE spatial association network for Hubei Province using a modified gravity model to uncover the spatial linkages in carbon emissions. Carbon balance zones are delineated by integrating LUCE network characteristics with economic and ecological indicators. To further examine the network dynamics, link prediction algorithms are employed to anticipate potential emission connections, while quadratic assignment procedure (QAP) regression analyzes how intercity differences in socioeconomic, ecological, and land-use attributes influence LUCE connectivity. The results reveal a pronounced core–periphery structure, with potential carbon spillover pathways extending toward both eastern and western cities. Based on the carbon balance analysis, six functional zones are identified, each aligned with targeted collaborative mitigation strategies. The QAP results indicate that intercity differences in innovation capacity, industrial structure, and economic development are positively associated with the formation of LUCE spatial networks, whereas disparities in urbanization level, government expenditure, and construction land use are negatively associated with LUCE connectivity. This study provides a differentiated governance framework to address the dual challenges of carbon emissions and land-use transformation in agro-urban regions. Full article

The Yellow River Basin (YRB) is an important ecological security barrier in China, playing an irreplaceable role in soil and water conservation, climate regulation, and biodiversity maintenance, and it is related to the stability and security of the ecosystem. Exploring the spatial correlation networks and factors influencing ecological security in the YRB can provide new ideas for cross-domain collaborative governance, promote efficient cooperation among regions, and optimize resource allocation. Using a quantitative approach to assess the YRB’s ecological security, we employed an adjusted gravity model, social network analysis, and quadratic assignment procedure analysis to understand the spatial connection dynamics. The results indicate the following: (1) Ecological security in the YRB continued to improve from 2005 to 2019, but the overall level was low. The degree of the dispersion of the ecological security status among cities constantly increased, and there were significant regional differences in the level of ecological security in the YRB. (2) From 2005 to 2019, the number and density of network connections among cities within the YRB increased significantly, and the ecological security links gradually strengthened. The Shandong Peninsula city cluster and the Hubao–Eyu City cluster are not only located at the core of the network but also play the role of “bridge intermediary”, exhibiting strong control. (3) Among all variables, economic development and geographic proximity increased significantly in terms of their correlation with the YRB’s ecological security. The study of spatial connectivity networks and their influencing factors in the YRB provides new ideas for inter-regional collaborative governance. Full article

The digital economy has become an important engine for global economic development by promoting optimal resource allocation and advancing industrial restructuring. Based on the panel data from 279 prefecture-level cities in China from 2012 to 2021, this paper constructs the spatial association networks of urban digital economy using a modified gravity model and analyzes the complex network characteristics and driving factors of urban digital economy growth by the social network analysis methods and the Quadratic Assignment Procedure (QAP). This study finds that (1) the level of urban digital economy in China shows a rising trend year by year and displays an uneven spatial distribution. (2) Spatial association networks of urban digital economy are relatively well-connected, with increasing density and stability of spatial associations, yet some hierarchical structure remains, and overall connectivity still needs to be improved. (3) Most cities in the east region occupy the core positions within the complex network, significantly influencing the overall complex network through a “siphon effect”, while cities in the central region play more of a “bridge” role in the spatial association network. In contrast, cities in the northwest, northeast, and southwest regions are situated on the periphery of this spatial association network. (4) The economic development level, informatization level, technological innovation, urbanization level, industrial structure, and human capital contribute to the formation of the spatial association network of the digital economy. Based on these conclusions, specific policy implications for the future development of the spatial association network of the urban digital economy are proposed. Full article