Pollution transfer across regions has become an important source of environmental health risks and spatial health inequalities, yet its underlying transfer mechanisms and structural pathways remain insufficiently understood. This study investigates the spatiotemporal evolution of pollution transfer in China’s Yangtze River Delta (YRD) from both national and local perspectives. Using off-site environmental penalty data from large-scale enterprises between 2012 and 2023, we construct pollution transfer networks and apply social network analysis and the geographical detector method to identify structural characteristics and multidimensional driving mechanisms. This study addresses three interrelated challenges: identifying pollution transfer relationships from regulatory records, disentangling multidimensional proximity mechanisms and their interaction effects, and assessing whether pollution transfer structurally reinforces environmental health inequality across cities, which are tasks that are methodologically demanding due to the hidden nature of transfer processes and the nonlinearity of proximity effects. The results reveal that the pollution transfer network in the YRD has continuously expanded, exhibiting a pronounced core–periphery structure centered on Shanghai, Hangzhou, Nanjing, and other core cities, with strong internal linkages and outward diffusion at the national scale. Pollution transfer intensity has increased over time and displays significant proximity effects. Multidimensional proximity factors jointly shape pollution transfer, with economic proximity dominating intra-regional transfers, while institutional and technological proximity play a more critical role in extra-regional transfers. Notably, public health proximity exhibits limited independent explanatory power but significantly enhances pollution transfer when interacting with economic and institutional factors, suggesting a structural linkage between pollution redistribution and environmental health inequality. Scientifically, this study advances the understanding of pollution transfer as a networked and interaction-driven process. Practically, the findings provide quantitative support for regional collaborative governance and integrated environmental and health management in the YRD.

The recycling of lithium batteries not only requires government guidance and support but also necessitates the deep involvement of enterprises to ensure sustainable production and promote circular economic development. Improper disposal and informal recycling of waste lithium batteries can release heavy metals and toxic electrolytes, posing potential threats to environmental safety and public health. Based on data related to lithium battery recycling enterprises in China, this paper utilizes spatial statistical methods and geographic detectors to analyze the spatial distribution patterns and influencing factors of these enterprises. The results indicated the following: (1) the overall spatial pattern of lithium battery recycling enterprises in China is characterized as having a “tiered” structure, with the eastern region consistently having the highest number of recycling enterprises, and a standard deviation ellipse shows the general temporal and spatial distribution trend in a south–north direction; (2) the spatial distribution of lithium battery recycling enterprises in China exhibits a significant positive spatial correlation, with a basically stable spatial pattern of cold and hot spots; Meanwhile, kernel density analysis indicates a gradual expansion of the spatial distribution, albeit with significant geographic polarization characteristics; (3) since 2013, lithium battery recycling enterprises have rapidly expanded, showing marked spatial differentiation within city clusters; and (4) the spatial distribution of these enterprises is significantly influenced by factors such as consumer spending levels and regional economic development, with all interaction factors showing a strong bi-factor enhancement effect. The findings provide guidance for developing smart waste-management systems to strengthen environmental governance and mitigate potential public health risks associated with lithium battery recycling.

Against the backdrop of rapid global advances in artificial intelligence (AI), growing concerns surrounding public health and stressful environmental conditions, this study examines the Yangtze River Delta (YRD) of China by using social network analysis, a geographical detector model, and a coupling coordination model to investigate the spatiotemporal evolution and factors associated with rehabilitation assistive technology transfer networks before and after the COVID-19 pandemic (2016–2023). The results reveal that the YRD’s technology transfer network has continued to expand, transforming from a “single-center” structure dominated by Shanghai to a “multicenter” network jointly anchored by Shanghai, Hangzhou, and Suzhou. The results of the coupling coordination analysis indicate that the synergistic relationship between public health systems and technology transfer networks improved during the pandemic. In contrast, the synergistic potential of AI technology innovation has yet to be fully unleashed. The geographical detector results show that economic development, technological innovation, and financial development are the core drivers of technology transfer. Moreover, interactions between public health or AI inventive capacity and other determinants exhibit pronounced “bifactor enhancement” effects. This study offers a new perspective on the sustainable development of the rehabilitation assistive devices industry within the context of AI integration and environmental pressures.

Climate change and carbon-intensive development models pose potential long-term risks to public health by exacerbating exposure to air pollution. Promoting low-carbon technological innovation through regional collaboration has become one of the critical pathways to mitigate environmental health risks and to improve public health governance. Based on enterprise low-carbon patent data in the Yangtze River Delta (YRD) from 2014 to 2023, this study integrates spatial statistical analysis, social network analysis, and geographic detector methods to systematically investigate the spatiotemporal evolution, structural characteristics, and driving factors of low-carbon technology collaborative innovation networks. The results show that: (1) The number of low-carbon innovation enterprises in the YRD has grown substantially, with obvious spatial agglomeration. The collaborative innovation network continues to expand, while regional development disparities are widening; (2) Core cities, including Shanghai, Ningbo, Hangzhou, and Nanjing, consistently maintain central positions within the network, serving as critical hubs for low-carbon technology diffusion and regional carbon governance. Meanwhile, cities such as Hefei and Suzhou have achieved a significant increase in network centrality. (3) Economic scale, financial support, technological innovation capacity, and policy investment are the core driving factors shaping network structure and evolutionary patterns, which collectively shape urban carbon emission reduction performance and environmental health improvement. The findings provide empirical evidence to strengthen innovation-driven carbon governance and promote more equitable and resilient regional health systems under the low-carbon transition.

The pressures of economic development and environmental governance are growing increasingly intense. This study uses 2014–2023 off-site environmental penalty data of Chinese listed firms to construct weighted intercity networks of cross-regional environmental violation linkages and compares the spatial patterns between firms in polluting industries and those in other industries via social network analysis, spatial statistics, and geographical detector methods. The results show that cross-regional environmental violation linkages have become increasingly dense, with firms in other industries accounting for a fast-growing share of cross-city violations, and these linkages exhibit significant spatial clustering patterns. Specifically, levels of economic development, industrial structure, and financial capacity are positively and significantly associated with cities’ network centrality and intensity, while environmental regulation and public health factors show no significant associations. These findings contribute to the literature by offering a behavioral and outcome-based perspective on cross-regional environmental violations and highlighting the need for unified governance frameworks.

Against the backdrop of China’s rapid expansion of artificial intelligence (AI), and intelligent healthcare policies, the integration of AI into medical devices is reshaping healthcare delivery and innovation systems. Based on patent data on industry–university–research cooperation in the China AI medical device industry from 2019 to 2025, this study adopts social network analysis and geographical detector methods to systematically examine the spatiotemporal evolution and influencing factors of the innovation network across three dimensions. The findings reveal that at the microagent level, the early dominant pattern of enterprises has been gradually reconstructed by the participation of universities, research institutes, and medical institutions, with medical institutions rising from marginal participants to core intermediaries. At the provincial level, the network exhibits prominent unbalanced agglomeration characteristics. Beijing’s status as a national hub has been continuously strengthened, and a multipolar spatial pattern has taken initial shape. At the level of the cooperation type, intraprovincial cooperation is dominated by industry–industry collaboration, while interprovincial cooperation has formed a cross-regional synergy framework with Beijing as the core, radiating to the Yangtze River Delta and the Pearl River Delta. The value added of the financial industry, the volume of import and export trade, and the transaction volume of the technology market are revealed as the core factors explaining the network status of provinces, whereas the explanatory power of the proportion of education expenditure remains weak. This study contributes to a deeper understanding of China’s AI medical device industry and offers practical insights regarding public health governance.