| 温兴春,徐友博,张伊华,龚六堂.公共数据开放、人工智能创新与经济高质量发展——基于“Data+AI”加速器的理论分析[J].数量经济技术经济研究,2026,(2):30-53 | | 公共数据开放、人工智能创新与经济高质量发展——基于“Data+AI”加速器的理论分析 | | Public Data Access, Artificial Intelligence Innovation, and High-quality Economic Development: Theoretical Analysis Based on “Data+AI” | | | | DOI： | | 中文关键词: 公共数据开放 人工智能创新 经济高质量发展 “Data+AI”加速器 动态一般均衡模型 | | 英文关键词: Public Data Access Artificial Intelligence Innovation High-quality Economic Development “Data+AI” Accelerator Dynamic General Equilibrium Model | | 基金项目: | | | 中文摘要: | | 人工智能是经济高质量发展的新动能，数据要素是人工智能创新发展的基础性资源，开放具有高共享性的政府公共数据将对经济发展有何种影响？为此，本文在梳理基本事实的基础上构建了将数据要素纳入人工智能内生研发过程的动态一般均衡模型，首次提出“Data+AI”加速器效应，分析公共数据开放通过促进人工智能创新推动经济高质量发展的具体机制。公共数据开放的正向冲击将通过增加社会数据要素供给，降低数据要素价格和人工智能研发成本，进而提高知识价值和转化速度，从而生成更多的人工智能技术，这使生产效率提高和总产出增加并进一步产生更多的数据，由此形成以“数据要素—知识价值—转化速度”为核心的“Data+AI”加速器效应，最终大幅提升总产出。在政策设计方面，加强公共数据平台建设和提供数据要素价格补贴会增强加速器的效果，而且这二者的协调搭配将更加充分地发挥数据要素的放大、叠加、倍增效应，从而更好地推动经济高质量发展。 | | 英文摘要: | | Artificial intelligence (AI), as a strategic technology leading the future, is the core driving force of the new round of scientific and technological revolution and industrial change and is bound to have a far-reaching impact on global economic and social development. Therefore, understanding how to grasp the new wave of AI technology revolution is of great significance for achieving high-quality economic development. In recent years, along with the rapid iteration of AI technology, the role played by data elements in it has become increasingly prominent. For China, data resources are mainly concentrated in government departments, and the public data held by the government is highly shared. Therefore, how to give full play to the role of public data resources is key to building a new national competitive advantage. In this context, it is crucial to clarify the impact and specific mechanism of the opening of government public data on the development of AI innovation and find the key influencing factors in this process to enhance the level of AI innovation.This study first collects city-level panel data from China and preliminarily observes a positive correlation between government public data openness and AI innovation, as well as between AI innovation and economic growth. Then, through the construction of a dynamic stochastic general equilibrium model that includes “Data+AI” accelerator, the AI research and development process is endogenized and data elements are introduced to analyze the role and influence channels of the government’s openness to public data on AI innovation. Finally, based on counterfactual numerical simulation, this study evaluates the effects and mechanisms of different policies, such as strengthening the construction of public data platforms and subsidizing the price of data factors, and puts forward corresponding policy recommendations.Based on empirical and theoretical analyses, this study draws the following conclusions: first, the positive impact of government public data opening will reduce the price of data elements and the cost of AI research and development by increasing the supply of social data elements. Then, it will increase the value of knowledge and the speed of transformation to generate more AI technologies, which will improve productivity and increase total output, and further generate more data. This will lead to an accelerator effect of “Data+AI” centered on data-knowledge value-transformation speed. Second, innovative human capital, data factor endowment, enterprise digitalization, computing power capacity, and marginal contribution rate of data significantly strengthen the role of government public data openness in promoting AI innovation by acting as a “Data+AI” accelerator. Finally, strengthening the construction of public data platforms and providing data factor price subsidies will enhance the accelerator’s effect, and the coordination of the two will give fuller play to the amplification, superposition, and multiplication effects of data factors, thereby better promoting high-quality economic development.Compared with the existing studies, the contributions of this study are as follows: First, the accelerator effect of “Data+AI” is proposed for the first time to provide a fundamental theoretical analysis framework for the integration of data elements and AI, which is a significant addition to the existing literature and provides an important reference for clarifying the relationship between data elements and AI. Second, unlike the existing literature, this study internalizes AI innovation in the framework of a dynamic stochastic general equilibrium model, which clearly portrays and analyzes the research and development process of AI, and provides a benchmarking analytical framework for the subsequent related studies. Third, the role of data element sharing on AI innovation is discussed with public data openness as an entry point, thereby providing a new research perspective for related fields. | | 查看全文 相关附件： 下载数据代码附录 |
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