A Text-Mining-Based Evaluation of Data Element Policies in China: Integrating the LDA and PMC Models in the Context of Green Development

In the context of green development, promoting the development of data elements is crucial for advancing the green and low-carbon transition and achieving China’s “dual-carbon” targets. This study quantitatively evaluates China’s data element policies to identify their strengths and weaknesses and to assess their alignment with green development objectives. In this study, we examine 15 representative data element policy texts, evaluating their quality by integrating the Latent Dirichlet Allocation (LDA) topic model with the PMC-Index model. The LDA analysis identifies five core themes within the policy texts: the data element industry, data resource management, data element trading systems, service platform construction, and e-governments. The evaluation results show an average PMC-Index score of 6.03 for the 15 policies, with 9 rated as “Good” and 6 as “Acceptable”. This indicates that while the overall design of the current policy system is acceptable, there remains substantial room for improvement. Based on the average scores for the primary indicators, the policies perform relatively poorly in terms of green development assessment, policy timeliness, policy nature, and policy guarantee. Drawing from these findings, we propose recommendations to enhance China’s data element policies, offering insights for policymakers.