Innovation Ecosystems in Transition: The Dynamic Evolution and Knowledge Flows of Industry-Education Integration in China's City Clusters

This study positions Industry-Education Integration as a key engine for regional innovation and knowledge-driven development. Within the context of national-level city clusters, we construct a comprehensive spatial analysis framework integrating the Entropy Weight-TOPSIS method, Dagum Gini coefficient, Kernel density estimation, and the Geographical Detector. Utilizing panel data from China's national-level city clusters from 2014 to 2023, we assess the efficacy of Industry-Education Integration. The findings reveal that although the overall level of Industry-Education Integration has improved, its regional disparities are significant, with over 54% of the inequality stemming from inter-regional differences, highlighting spatial misallocation of knowledge innovation resources. Spatial analysis further delineates a distinct "core-periphery" structure: "High-High" agglomeration zones characterised by high-efficiency knowledge innovation coexist with "Low-Low" depressions trapped in developmental difficulties, signalling a potential risk of innovation polarisation. The Geographical Detector identifies New Product Development (q=0.52) as the most critical driving factor, whose influence is significantly greater than other key elements such as R Funding: 0.49), International Cooperation (0.48), and Patent Output (0.47). The standard deviation ellipse indicates that the centre of gravity for Industry-Education Integration has shifted approximately 47.1 kilometres toward the southwest. Spatial effect decomposition further reveals that industry-education integration jointly drives high-quality regional economic development through both direct effects (0.210) and spatial spillover effects (1.107), with a total effect of 1.317. This study’s core innovation is the utilization of a mediating effect model to reveal that Industry-Education Integration promotes high-quality regional economic development through multiple channels, including three crucial mediating pathways: catalysing employment structure diversification, enhancing regional openness, and accelerating industrial upgrading. Consequently, this research not only provides precise targets for optimising China's vocational education and regional innovation policies but also offers a replicable paradigm for developing countries undergoing critical periods of industrialisation and digital transformation to overcome knowledge innovation resources fragmentation and achieve synergistic development.