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Science & Technology Strategy and Policy

Research on the Optimization Path of Artificial Intelligence Poli⁃ cies in China: Quantitative Analysis Based on the Framework of  "Tools—Implementing Subjects—Goals" 

Li Shangyue, Kang Tianshu 

(School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China)

Abstract: Artificial intelligence (AI) policies serve as a crucial driving force in guiding tech⁃nological innovation and industrial development, with their internal synergy being paramount to  policy effectiveness and international competitiveness. Currently, while China's AI policy sys⁃ tem is rapidly evolving, it still faces structural contradictions such as imbalanced application  of policy tools, mismatched rights and responsibilities among implementing subjects, lack of pub⁃ lic participation, and short-term orientation of policy goals. To systematically explore the syner⁃ gistic characteristics of China's AI policies and identify optimization pathways, this paper con⁃ structs a three-dimensional analytical framework of "Policy Tools—Implementing Subjects— Policy Goals". Taking 112 legally effective AI policy texts issued by central and local govern⁃ ments in China from 2017 to 2024 as samples, it employs content analysis and the grey relational  analysis model to quantitatively analyze the synergistic effects among policy tools, implementing  subjects, and policy goals. The results indicate: ①In the dimension of policy tools, there is a  structural imbalance characterized by "supply-side dominance, environmental-type supplemen⁃ tation, and demand-side deficiency". Key sub-tools such as information services, government  procurement, and tax incentives are underutilized. Furthermore, demand-side tools exhibit  weak synergy with goals like technological self-innovation and governance system construction, and talent cultivation shows insufficient synergy with application scenario expansion. ②In the  dimension of implementing subjects, enterprises, research institutes, and the government domi⁃ nate, while other social organizations, families, communities, and individuals demonstrate low  participation. Enterprises exhibit "selective collaboration"; research institutes show the lowest  relational degree with the goal of technological self-innovation, indicating imbalanced resource  allocation and ineffective industry-academia-research collaboration; the government's func⁃ tional performance is inadequate in certain areas; and the public lacks effective empowerment  mechanisms in public governance. ③In the dimension of policy goals, there is an excessive fo⁃ cus on short-term application scenario expansion, with insufficient attention paid to long-term  goals such as governance system construction, industrial digital transformation, and innovation  platform support. The implementation effectiveness of the technological self-innovation goal  needs strengthening; the goal of governance system construction lacks sufficient supporting mea⁃ sures, and systematic design is absent for risk assessment and regulatory mechanisms. Based on  the above conclusions, this paper proposes targeted improvement suggestions in three aspects: ① Optimize the combination of policy tools and construct a dynamically balanced policy tool sys⁃ tem; ②Refine the rights and responsibilities list for multiple implementing subjects and estab⁃ lish a subject synergy mechanism with matched rights and responsibilities; ③Prioritize risk gov⁃ ernance and foundational support to leverage the comprehensive and coordinated effects  of policy goals.

Key words: artificial intelligence; policy text; framework of "tools—implementing subjects— goals"; grey relational analysis; policy synergy