中国人工智能效率及其影响因素——基于DEA-Malmquist和Tobit回归模型

IF 1.4 Q3 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Yan-Yan Dong, Dong-Qiang Wang
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引用次数: 1

摘要

人工智能(AI)的扩散已经成为评估一个国家技术进步的关键指标,也是中国区域经济协调和高质量发展的关键指标。基于2006 - 2021年中国31个省份的面板数据,采用DEA-Malmquist指数模型和面板Tobit模型,考察了中国人工智能资源配置的规模、分布属性及其影响因素。结果表明,中国人工智能资源配置效率总体提高,技术效率产生“拉动效应”,推动全要素生产率增速高于技术进步。此外,非沿海地区的人工智能效率超过沿海地区,全要素生产率的增长源于技术进步速度的大幅提高。区域经济发展、劳动力需求、对外开放程度和人力资本水平对提高人工智能资源配置效率起着举足轻重的作用。基于这些研究结果,我们提出了一套旨在提高中国人工智能资源配置效率的策略,包括加大政府引导、增加研发投入、提升经济发展水平、促进有形经济的发展和加强、吸引和培养高素质的科研人才。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
China's artificial intelligence efficiency and its influencing factors: Based on DEA-Malmquist and Tobit regression model
The proliferation of artificial intelligence (AI) has emerged as a critical metric for assessing a country's technological advancement, but also for regional economic coordination and high-quality development in China. Based on panel data collected from 31 provinces between 2006 and 2021, this study employs the DEA-Malmquist index model and panel Tobit model to examine the scale, distributional attributes, and influencing factors of AI resource allocation. Results indicate that China's AI resource allocation efficiency has generally increased, with technical efficiency generating a “pull effect” that propels total factor productivity growth rates higher than those attributable to technological progress. Furthermore, AI efficiency in non-coastal regions outstrips that in coastal areas, with total factor productivity growth arising from a substantial increase in technological progress rates. Regional economic development, labor demand, openness to foreign participation, and human capital level exert pivotal roles in enhancing AI resource allocation efficiency. Based on these findings, we suggest a set of strategies aimed at enhancing China's AI resource allocation efficiency, including amplifying government guidance, increasing R&D investments, upgrading economic development levels, fostering the development and strengthening of tangible economy, and attracting and nurturing high-quality scientific research talent.
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来源期刊
Decision Science Letters
Decision Science Letters Decision Sciences-Decision Sciences (all)
CiteScore
3.40
自引率
5.30%
发文量
49
审稿时长
20 weeks
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