In the context of “space of flows”, city-based innovation correlation in driving economic growth is no longer limited to the traditional hierarchical structure. It is of great significance to explore Chinese cities innovation association network from the perspective of high-tech zones which gather a large number of innovation resources. Here our report is to provide new ideas for improving the innovation capability of high-tech zones and accelerating the construction of Chinese high-quality innovation system. Here we take 142 cities with high-tech zones as research samples, and explore the characteristics and influencing factors of spatial network of city-based innovation correlation in China, through modified gravity modelsocial, network analysis and QAP analysis. The results show that city-based innovation network is not closely connected, the number of redundant connection channels is low efficiency, showing a four-level spatial pattern of “Z” shaped spindle. Among them, degree centrality of cities in eastern China is higher than that in the western region, the core cities in central China play a bridging role, and western remote cities are easily affected by related cities. Moreover, there are four innovation cohesion subgroups, including the northern hinterland subgroup, the eastern coastal subgroup, the southern subgroup and the western cooperation subgroup. Furthermore, the results of the influencing factors analysis show the differences in administrative level, economic development level, openness to the outside world, and investment in technology are conducive to the innovation association between cities, while the similarities in spatial adjacency and industrial structure will promote the strong innovation association between cities.

In the era of the global knowledge economy, the rapid advancement of new-generation information technologies has positioned digital industry at the forefront of socio-economic development. Digital industrial clusters, comprising key enterprises and related institutions, have emerged as global hubs for technological innovation, drawing recognition from governments and international organizations. However, China’s digital industry faces challenges characterized by being “large but not strong” and experiencing “industrial structure softening.” This paper explores the temporal evolution and spatial distribution of China’s digital industry, shedding light on its unique patterns. This study reveals that China’s digital industry has progressed through four distinct phases, evolving from manufacturing-oriented to service-oriented, and experiencing dynamic stability. In terms of spatial distribution, it exhibits a pattern known as the “One Core, Three Poles, and Multiple Strongholds,” with Beijing as the core and Shanghai, Shenzhen, Hangzhou, among others, serving as growth poles. The study identifies factors influencing spatial distribution, including software development, patent authorizations, mobile phone penetration, industrial parks, digital finance, and foreign investment. Manufacturing-based digital industries prioritize innovation and IP protection, while service-based sectors rely on software development, innovation, and international collaboration. The GWR model demonstrates spatial variation, emphasizing the importance of tailored development strategies. This research contributes to understanding the dynamics of China’s digital industry and provides valuable insights for policymakers aiming to create an internationally competitive digital industry cluster.

The digital economy serves as a catalyst for global economic growth, making its interaction with carbon emissions critical for sustainable development and green transformation. Using panel data from 110 cities in the Yangtze River Economic Belt from 2012 to 2021, this study evaluates DE and CE levels through the entropy weight TOPSIS method and analyzes their coupling and influencing factors via the coupling coordination and the diamond model. Results indicate that, first, DE and CE exhibit similar spatiotemporal evolution, characterized by annual growth and significant regional disparities. The downstream region grows 6% faster than the overall study area, while the CE growth rate in the midstream region is 7% higher. Second, coupling coordination has been improved, with low-coupling and micro-imbalanced regions as the dominant types. Notably, the upstream region records the fastest coupling coordination degree growth, reaching 9%. Finally, innovation, government policies, and environmental pressures are identified as the main drivers of spatial coordination, with innovation exerting the most extensive influence. This study yields actionable insights into DE-CE dynamics and provides valuable guidance for sustainable development strategies at both regional and global scales.