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Exploring the spatial correlation network and formation mechanism of urban water resource utilisation efficiency in the Yellow River Basin is crucial for modernising national ecological security capacities and promoting high-quality economic development. It contributes to environmental protection, enhances model precision, and strengthens theoretical foundations. Based on panel data from 94 cities in the Yellow River Basin from 2010 to 2021, this study employs a modified gravity model, stochastic frontier analysis, kernel density estimation, social network analysis, and the Quadratic Assignment Procedure (QAP) regression to examine the characteristics and determinants of the spatial correlation network. The results reveal a consistent upward trend in water resource utilisation efficiency, with a spatial hierarchy of downstream > midstream > upstream. The network demonstrates significant spatial correlation effects, although its density slightly decreased from 0.1166 in 2010 to 0.1161 in 2021, indicating notable regional spillover effects. Geographical proximity, industrial structure, and informatisation significantly promote the formation of the spatial correlation network, while per capita GDP, openness to the outside world, and technological innovation have negative impacts. The findings provide important insights and policy implications for improving water resource governance in the region.

In the context of regional integration, it is more than crucial to compare and analyze the spatial correlation network structure and formation mechanism of high-quality economic development in the Yangtze River Economic Belt and the Yellow River Basin urban cities as an attempt to strengthen collaborative work on high-quality economic development in both river basins. The paper measured high-quality economic development of the Yangtze River Economic Belt and the Yellow River Basin from 2010 to 2021. Then, it employed social network analysis and the QAP method to study the network structure’s characteristics and formation mechanism. The conclusion of the research illustrates a few points clearly that first, the high-quality economic development of the two rivers presents a complex and multithreaded network structure. Although the network structure is hold at a comparatively stable state, the correlation degree needs improvement. Second, cities such as Chongqing, Wuhan, Hefei, Nanjing, Hangzhou, Shanghai, and Changsha and cities like Zhengzhou, Xi’an, Luoyang, Yulin, Hulunbuir, Ordos, and Nanyang are at the very central as well as central position of the network. The spatial correlation networks of the Yangtze River Economic Belt and the Yellow River Basin can be divided into four plates: “agent plate,” “main outflow plate,” “bidirectional spillover plate,” and “main inflow plate.” Third, reverse geographical distance and differences in the digital economy attach great significance to the spatial correlation networks of the two basins. The difference in urbanization level makes significant impacts only on the spatial correlation network of the Yangtze River Economic Belt, while the difference in environmental regulation and material capital accumulation only significantly influences the spatial correlation network of the Yellow River Basin.

While tourism eco-efficiency has been analyzed actively within tourism research, there is an extant dearth of research on the spatial network structure of provincial-scale tourism eco-efficiency. The Super-SBM was used to evaluate the tourism eco-efficiency of 30 provinces (excluding Tibet, Hong Kong, Macao and Taiwan). Then, social network analysis was employed to examine the evolution characteristics regarding the spatial network structure of tourism eco-efficiency. The main results are shown as follows. Firstly, tourism eco-efficiency of more than two thirds’ provinces witnessed an increasing trend. Secondly, the spatial network structure of tourism eco-efficiency was still loose and unstable during the sample period. Thirdly, there existed the multidimensional nested and fused spatial factions and condensed subsets in the spatial network structure of tourism eco-efficiency. However, there was still a lack of low-carbon tourism cooperation among second or third sub-groups. These conclusions can provide references for policymakers who expect to reduce carbon emissions from the tourism industry and to achieve sustainable tourism development.

With the rapid acceleration of industrialization and urbanization in China, PM2.5 pollution has emerged as a major challenge to public health and sustainable development of the society and economy. At the interprovincial level, PM2.5 exhibits a complex spatial correlation network structure. Using data from 31 provinces in China from 2000 to 2023, this study constructed a spatial correlation network of PM2.5 and analyzed its structural characteristics and formation mechanisms. The results reveal that China’s PM2.5 spatial correlation network is both complex and stable, underscoring the severity of the pollution problem. The network demonstrates a distinct ‘core–periphery’ distribution, with provinces such as Jiangsu, Shandong, and Henan occupying central positions and functioning as critical bridges. Block model analysis showed a clear role of differentiation among provinces in the diffusion of pollution. Temporal exponential random graph model suggests that geographical proximity, industrial structure, vehicle ownership, and government intervention are key factors shaping the network. Geographically adjacent provinces are more likely to form close connections, whereas environmental regulation and vehicle ownership tend to constrain the spread of pollution. This study provides a novel theoretical framework for understanding the spatial diffusion pathways of PM2.5 pollution and offers important policy implications for optimizing and implementing cross-regional air quality governance strategies in China.

Analyzing the driving factors and mechanisms of urban carbon emission correlation networks can provide effective carbon reduction decision-making support for Shandong Province and other regions with similar industrial characteristics. Based on industrial carbon emission data from various cities in Shandong Province from 2013 to 2021, the spatial correlation network of carbon emission was established by using a modified gravity model. The characteristics of the network were explored by using the Social Network Analysis (SNA) method, and significant factors affecting the network were identified through Quadratic Assignment Procedure (QAP) correlation analysis and motif analysis. The driving mechanism of the carbon emission correlation network was analyzed by using Temporal Exponential Random Graph Models (TERGMs). The results show that: (1) The spatial correlation network of urban carbon emission in Shandong Province exhibits multi-threaded complex network correlations with a relatively stable structure, overcoming geographical distance limitations. (2) Qingdao, Jinan, and Rizhao have high degree centrality, betweenness centrality, and closeness centrality in the network, with Qingdao and Jinan being relatively central. (3) Shandong Province can be spatially clustered into four regions, each with distinct roles, displaying a certain “neighboring clustering” phenomenon. (4) Endogenous network structures such as Mutual, Ctriple, and Gwesp significantly impact the formation and evolution of the network, while Twopath does not show the expected impact; FDI can promote the generation of carbon emission reception relationships in the spatial correlation network; IR can promote the generation of carbon emission spillover relationships in the spatial correlation network; GS, differences in GDP, differences in EI, and similarities of IR can promote the generation of organic correlations within the network; on the temporal level, the spatial correlation network of urban carbon emission in Shandong Province has shown significant stability during the study period.

The rational allocation of chemical fertilizer resources is of strategic importance in mitigating agricultural source pollution and achieving agricultural green development. The spatiotemporal correlation of chemical fertilizer supply/demand and its determinants remains unclear. In this study, based on the inter-provincial chemical fertilizer supply/demand panel data of China from 1994 to 2018, an improved gravity model was employed to determine provincial chemical fertilizer supply/demand correlations. Finally, the chemical fertilizer supply/demand evolution and its driving factors were analyzed using social network analysis and a quadratic assignment procedure. The results indicated that (1) the intensity of the spatial relationship of inter-provincial chemical fertilizer supply/demand increased in a fluctuating fashion, but there was still room for improvement. The network structure presented good stability, and the spillover effect exhibited multiple superposition characteristics; (2) the spatial correlation network of inter-provincial chemical fertilizer supply/demand presented a “core-periphery” distribution pattern of the supply, demand, and balance areas. The division of blocks in the network changed in time and space, and some provinces changed their roles and positions in the network during development; (3) chemical fertilizer-related policies (e.g., Exemption Agricultural Tax, Notice on the resumption of value added tax policy on fertilizers, and Rural Revitalization Strategy) have played a positive role in the formation and development of the interprovincial spatial correlation network of chemical fertilizer supply/demand in China; (4) natural conditions and socioeconomic factors interact to promote the formation of the spatial correlation network of chemical fertilizer supply/demand. The differences in the scale of the rural labor force, the scale of agricultural mechanization, the agricultural planting structure, the populations, and urbanization levels all had a significant impact on it. The identification of the spatial characteristics of chemical fertilizer supply/demand correlation networks offers a new perspective on taking various measures to realize the cross-regional coordination of chemical fertilizer resources, strengthen the protection and utilization of agricultural resources, and promote green agricultural development.

Accurately portraying the spatially linked network characteristics of digital infrastructure and exploring its energy-saving effects are highly valuable for enhancing the synergy in digital infrastructure development and expanding its network spillover effects on energy conservation. This paper uses panel data at the city level in China and employs a modified gravity model to calculate the centrality of digital infrastructure spatial correlation network nodes. Based on this, an econometric model is constructed, incorporating variables such as digital infrastructure spatial correlation network node centrality and urban green total factor energy efficiency. The model is used to analyze the effects and transmission paths of digital infrastructure network node centrality on urban green total factor energy efficiency. The analysis yields the following conclusions: (1) Digital infrastructure spatial correlation network node centrality significantly improves urban green total factor energy efficiency, with considerable variability due to city geographic location, city scale, and city attributes. (2) Nonlinear testing results indicate that as digital infrastructure construction advances, its impact on urban green total factor energy efficiency shifts from inhibitory to promotional. (3) The impact mechanism shows that digital infrastructure node centrality enhances urban green total factor energy efficiency through green technology innovation. Additionally, it promotes advanced industrial structures and reduces capital mismatch, further influencing energy efficiency. (4) Digital infrastructure node centrality not only boosts urban green total factor energy efficiency but also facilitates regional convergence, increasing the convergence rate from 0.094 to 0.170%. The findings of the research offer policy guidance for the government on advancing digital transformation initiatives and enhancing energy efficiency.

Clarifying the spatial correlation network structure of green development efficiency (GDE) is of great significance for realizing coordinated and sustainable development in China. By constructing the evaluation index system of GDE, this study used the super epsilon-based measure (EBM) model that considers undesirable output to measure the GDE of China from 2000 to 2018, based on which the characteristics of the spatial correlation network characteristics and influencing factors were analyzed using social network analysis (SNA) and a geographical detector. The results indicated that: (1) The GDE of China as a whole remained relatively stable, and there was a significant spatial spillover effect of GDE between provinces; the spatial correlation network demonstrated complex and dense characteristics, and the closeness and stability of the network gradually increased. However, the strict hierarchical structure of the network still existed. (2) The eastern coastal provinces exhibited significant spillover effects and connectivity functions, while the northeastern and central-western provinces are located at the edges of the spatial correlation network. (3) The GDE spatial correlation network is divided into a leader subgroup, bridge subgroup and net benefit subgroup, with no isolated subgroup. (4) The economic development level, urbanization and financial development have a decisive impact on the formation of the GDE spatial correlation network.

Based on the perspective of the value chain of agricultural science and technology innovation, in this paper, we divided the process of agricultural science and technology innovation into two stages: the Research and Development (R&D) of agricultural technology and the application of agricultural technology. We took the efficiency of agricultural science and technology innovation of the two stages as a comprehensive index measure for the development of agricultural science and technology innovation in China. On this basis, we used social network analysis to establish a two-stage spatial correlation network for the innovation development of agricultural science and technology in China. The spatial-temporal evolution trends, structural characteristics, and influencing factors of the network were analyzed from the three aspects of the overall, local, and individual network structure. The results show that: a. The development of agricultural science and technology innovation in China demonstrated a clear spatial correlation and spillover effect, and the spatial correlation network was in a connected state. b. The network had the distribution characteristics of ‘core-edge’ and strong stability, and the hierarchical structure of the members of each province in the network was gradually broken. c. The differences at the market level in agricultural science and technology, the differences in government support for agriculture, the geographically adjacent relationships, and the level of agricultural economic development were important factors affecting the spatial correlation of agricultural science and technology innovation. This study provides a policy reference to use a cross-regional coordinated development mechanism to solve the uneven and asymmetry problem of the distribution of elements in various regions in China.