在中国新能源汽车产业的发展进程中,产业政策在发展方向引领、资源要素分配等方面发挥了至关重要的作用。但已有研究对于产业政策变迁过程和特征分析的维度较为单一。因此本文从政策变迁视角出发,提出了基于产业政策目的性、方向性和导向性的三维分析框架。基于计算文本量化分析方法分析了2007年到2023年颁布的1783条新能源汽车产业政策。采用无监督式机器学习LDA主题建模刻画了中国新能源汽车产业政策的变迁过程,使用监督式学习随机森林算法对政策类型进行预测划分并总结政策分布的阶段性特征。研究发现:(1)中国新能源汽车产业政策经历了由初始阶段到快速推广阶段再到高质量发展阶段的变迁过程,政策分布从初期的分散和不平衡过渡到更加集中和均衡的状态;(2)产业政策整体以选择性、区域性和市场导向为主,但政策变化呈现出向功能性、行业性、技术导向逐渐转移的趋势;(3)政策变迁过程表现出市场准入制度趋于成熟、财政补贴精准化、基础设施建设日益重视、信息化与智能化趋势加强的特征。本文拓展了政策变迁研究的理论视角,完善了政策工具的分析框架,丰富了产业政策变迁特征和规律的分析方法,同时亦为产业政策优化提供实践启示。

Industrial policy plays a crucial role in leading the development direction and allocating resource elements of China's NEV industry. However, the dimensions of existing studies on industrial policy change and characterization are relatively single. Therefore, this paper proposed a three-dimensional analytical framework based on the purposefulness, direction and orientation of industrial policies from the perspective of policy evolution. The 1783 NEV industrial policies enacted from 2007 to 2023 are analyzed based on the computational text quantitative analysis method. Unsupervised machine learning LDA topic modeling was used to portray the evolution process of China's NEV industrial policies, and the supervised learning random forest algorithm was used to predict and classify the policy types and summarize the stage characteristics of policy distribution. The study found that: (1) China's NEV industrial policy has gone through a process of change from the initial stage to the rapid promotion stage to the high-quality development stage, and the distribution of policies has transitioned from the initial fragmentation and imbalance to a more centralized and balanced state. (2) The policies are mainly selective, regional and market-oriented, but the policy changes show a trend of gradual transfer to functional, industrial and technology-oriented. (3) The process of policy change shows the characteristics of market access system tends to be mature, financial subsidies oriented to be precise, infrastructure construction is increasingly important, and the trend of informationization and intelligence is strengthened. This paper has expanded the theoretical perspective of policy change research, improved the analytical framework of policy tools, enriched the analytical methods of industrial policy change characteristics and laws, and it will provide some practical insights for industrial policy optimization.