Jiahan Hu

Artificial intelligence (AI) medical devices have become an important technological foundation for enhancing healthcare system resilience and promoting the equitable allocation of medical innovation resources. However, limited attention has been paid to the distribution of upstream innovation resources and the spatial organization of this industry in the wake of the pandemic. Using AI medical device patent collaboration data from the Yangtze River Delta during 2018–2025, this study constructs local and external collaborative innovation networks and applies social network analysis and the Geodetector to examine their spatial evolution and associated factors across different phases of the COVID-19 pandemic. The results show that all innovation networks expanded continuously, particularly the external collaborative network. Nevertheless, cross-regional collaboration remained concentrated in a limited number of core cities, resulting in a persistent core–periphery structure. Regional GDP, retail market size, higher education resources, science expenditure, and financial support consistently exhibited strong explanatory power for collaboration intensity, while GDP growth and openness became increasingly associated with network differentiation following the COVID-19 shock. Moreover, the explanatory power of most variables was stronger for external collaboration than for local collaboration, suggesting that cross-regional knowledge exchange relies more heavily on comprehensive innovation capacity. This study advances the understanding of the spatial organization of AI medical innovation and provides empirical evidence for promoting a more balanced allocation of cross-regional medical R&D resources.

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.

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.

Urban energy intensity measures the energy required per unit of economic output and is closely linked to energy-related emissions that shape air quality and climate-related health risks. Using panel data from 273 prefecture-level Chinese cities (2006–2021), we estimate the causal impact of China's Low-Carbon City Pilot Scheme (LCCPS) on urban energy intensity. We apply a partially linear double/debiased machine learning framework with city and year fixed effects, enabling flexible adjustment for high-dimensional confounders. The LCCPS lowers urban energy intensity by about 0.11 units, and the result is robust to alternative specifications. Channel analyses suggest that pilot designation strengthens fiscal support intensity and stimulates enterprise green innovation, which together contribute to reduced energy intensity. Effects are strongest in large and coastal cities and in resource-scarce regions, but weaker in small and medium-sized cities, inland areas, and resource-rich regions. By reducing the energy required for economic activity, low-carbon pilots may also generate public-health co-benefits through cleaner urban environments.

Mitigation and adaptation are recognized as essential pillars for urban sustainability; however, the degree to which adaptation oriented policies contribute to broader environmental improvements remains an unresolved empirical question. This study treats the Climate Adaptive Pilot Cities (CAPC) initiative as a quasi natural experiment to evaluate its integrated impacts on urban pollution and carbon reduction. Utilizing a multi period staggered difference in differences framework on a comprehensive panel of 273 Chinese municipalities from 2011 to 2023, we find that the CAPC initiative significantly improves environmental outcomes, evidenced by a 0.284 decline in aggregate emission intensity. Mechanism analysis reveals that this reduction is driven by informal regulation via heightened public climate risk perception and innovation compensation via green technology advancement. Specifically, the policy initiates a state to society vertical diffusion of environmental norms, where pilot cities function as social amplification stations to foster a broad based social consensus. Notably, policy efficacy depends on local conditions: significant abatement effects are concentrated in high income cities with robust fiscal capacity, non-resource based cities free from carbon lock in, and central or western regions with vast untapped marginal abatement potential. These findings underscore the necessity of dedicated fiscal support and structural reforms to bridge urban resilience with low carbon transitions to ensure inclusive environmental governance.

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.

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.

The spatial configuration and collaborative networks of research and development (R&D) enterprises are continuously reshaping the innovation landscape of strategic industries. Clarifying this evolution is crucial for advancing China’s lithium-ion battery (LIB) sector. Leveraging a unique dataset of corporate LIB patents, this study examines the co-evolution of spatial agglomeration and inter-city collaborative networks within China’s LIB industry. Integrating spatial statistics, social network analysis, and geographic detectors across four sub-periods, we systematically track this reshaping process and identify its driving forces. Our findings reveal a dual trajectory of restructuring. Spatially, LIB R&D enterprises exhibit persistent east-west disparities, with innovation hotspots concentrated in coastal urban clusters, the Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei region. However, standard deviation ellipse analysis indicates a significant northward shift in the distribution center toward Ji’an, Jiangxi Province, suggesting gradual restructuring toward inland regions. In the network dimension, inter-city collaboration has expanded from 2 to 157 cities, yet overall connectivity remains low and fragmented. Notably, high-level connections concentrate among core cities unconstrained by geographical distance, indicating network structure is reshaped by node hierarchy rather than spatial proximity. Further analysis reveals that regional economic openness, industrial agglomeration, technological innovation capabilities, and policy support collectively shape enterprise distribution and network positions. By integrating spatial and network perspectives, these findings advance understanding of how strategic industries reshape regional innovation landscapes and provide evidence-based implications for fostering a more balanced and connected technological innovation landscape in China.