Against the dual backdrop of the global industrial and technological revolution and China’s transition toward high-quality economic development, deepening the integration of industry and education has become a key pathway for resolving the structura...
Against the dual backdrop of the global industrial and technological revolution and China’s transition toward high-quality economic development, deepening the integration of industry and education has become a key pathway for resolving the structural mismatch between educational supply and industrial demand. This study examines strategic industrial clusters in Yichun—a resource-based city in central China—including lithium new energy, intelligent equipment manufacturing, and biomedicine. Integrating multidisciplinary perspectives from educational economics, complex adaptive systems theory, and new structural economics, the study constructs a triadic theoretical framework of “actor synergy–process optimization–outcome emergence,” providing a systematic explanation of the intrinsic mechanisms and practical pathways through which industry–education integration promotes high-quality regional economic development.
China’s industrial economy currently faces the dual challenges of “low-end positioning of high-end industries” and structural shortages of skilled talent. As a core growth pole of the Ganxi region and home to Asia’s largest lithium mica reserves, Yichun is striving to build itself into the “Lithium Capital of Asia.” However, its development has been hindered by multiple bottlenecks, including insufficient talent density, delayed educational response, weak school–enterprise cooperation, and inadequate financial support. These issues reveal systemic shortcomings of traditional industry–education integration models in terms of synergy mechanisms, adaptive capacity, and value co-creation. Accordingly, this study addresses a central question: under conditions of accelerated technological iteration and incremental institutional change, how can mechanism innovation be leveraged to achieve synergistic resonance between talent cultivation and industrial upgrading?
At the theoretical level, the study advances beyond the traditional dualistic linear paradigm of “education–economy” by proposing a four-chain collaborative model comprising the education chain, talent chain, industry chain, and innovation chain. It elucidates the transmission mechanisms that ensure alignment between competency standards and occupational requirements, synchronization between curriculum content and technological progress, and coherence between technological problem-solving and industrial needs. By introducing the concepts of “institutional friction coefficient” and “knowledge stickiness index,” the study develops a modified Cobb–Douglas function that explains the impact of educational investment on total factor productivity (TFP), thereby shifting theoretical analysis from a static to a dynamic perspective. Methodologically, the research integrates Q methodology, social network analysis, system dynamics, difference-in-differences, and deep learning to construct an “Industry–Education Integration Maturity Index,” enabling systematic evaluation and dynamic monitoring of integration effectiveness.
Using panel data from Yichun spanning 2015–2025 and in-depth investigations of 12 leading enterprises, the empirical findings reveal the following. First, collaboration among the four chains generates a distinct multiplier effect: following the pathway of “improved educational quality → optimized talent structure → accelerated technological innovation → industrial upgrading,” the integration yields a 1:5.3 economic amplification effect. Second, a golden-ratio interval exists between educational response speed and technological iteration cycle (0.8 ≤ TCL/TRL ≤ 1.2), and a critical policy-support threshold is identified (ICMM ≥ 0.72), beyond which enterprise participation increases exponentially. Third, industry–education integration reshapes spatial structures, forming a 50 km “efficiency zone,” within which the elasticity coefficient of educational investment’s impact on industrial upgrading reaches 0.53.
On the practical side, the research outcomes have been translated into three application systems. First, a lithium industry talent-demand early warning platform and a policy toolkit were established to enhance the scientific basis of policymaking. Second, a “four-stage training model” led by competency standards and a “triadic resource-sharing platform” were developed to advance education supply-side reform. Third, a seven-dimensional collaborative governance paradigm integrating“government–industry–academia–research–finance–services–employment” was introduced, enabling bidirectional optimization of curricula and production processes through a digital-twin laboratory.
The study makes three major contributions. Theoretically, it breaks through the limitations of traditional linear human capital analysis. Methodologically, it establishes a multidisciplinary, cross-integrated analytical system. Practically, it proposes a replicable solution for the transformation of resource-based cities. The findings provide quantitative evidence for industry–education integration policies and present a systematic pathway for upgrading regional industrial clusters and university transformation. Future studies should further explore the emerging fusion paradigm of “AI + Education + Industry” under the digital economy, providing sustained support for industry–education collaborative innovation within the broader process of Chinese modernization.