Quantitative evaluation of China’s artificial intelligence policies: A PMC index-based modeling approach

Xia Liu, Xuan Zhuang, Hongfeng Zhang, Han Zhang, Yuli Wang and Juntao Chen

PLOS ONE, 2026, vol. 21, issue 2, 1-24

Abstract: With the rapid development of artificial intelligence (AI), various countries have introduced policies to address the social, economic, and ethical challenges brought by technological advancements. This study systematically evaluates the effectiveness of China’s AI policies based on the Policy Model Consistency (PMC) method and conducts a comparative analysis with policies from developed countries in Europe and the United States. By constructing a multi-dimensional quantitative assessment system that encompasses indicators such as policy types, timeliness, content, fields, evaluation, tools, and effectiveness levels, this study fills a gap in the existing research on quantitative evaluation. Text mining and high-frequency word analysis revealed the core themes and focus areas of the policies, laying the groundwork for subsequent quantitative analysis. The study finds that China’s AI policies have achieved significant results in promoting technological innovation, industrial development, and social transformation; however, shortcomings remain in legal protection, ethical regulation, cross-domain collaboration, and sustainable development issues. Further cross-national comparisons indicate that there are differences between China and developed countries in Europe and the United States in terms of AI policy design and implementation, particularly regarding the application of policy tools and the driving forces behind international collaboration. Based on the empirical analysis results using the PMC index model, this study proposes targeted policy optimization suggestions aimed at enhancing policy execution and adaptability. This study not only provides an innovative framework for the quantitative evaluation of AI policies but also offers theoretical support for the collaborative development of global AI policies.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0335423

DOI: 10.1371/journal.pone.0335423

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