The Impact of Policy Thematic Differences on Industrial Development: An Empirical Study Based on China's Electric Vehicle Industry Policies at the Central and Local Levels

Since the 21st century, the electric vehicle (EV) industry has become a key driver of global transformation, with increasing emphasis on the study and evaluation of industrial policies across nations. However, traditional frameworks struggle to capture the dynamic interactions between policies at different government levels or effectively analyze large volumes of policy texts. This study adopted a central-local policy interaction perspective, employing the BERT deep semantic learning model and a threshold regression model to investigate the impact of policy differences on industrial development. The findings reveal an inverted U-shaped relationship between central-local policy thematic similarity and EV market penetration, with the optimal similarity shifting as policy volume increases. This suggests the necessity of dynamically allocating central and local policies to balance national consistency with regional flexibility and promote synergy among regions. Recommendations include optimizing multi-level coordination, maintaining a balance between uniformity and specialization, strengthening policy error tolerance mechanisms, and fostering innovation. By integrating text analysis with econometric modeling, this study offers a novel framework aligned with China's political system, providing insights into central-local policy interactions and serving as a reference for other countries seeking to refine their industrial strategies.