Impact of artificial intelligence technology innovation on total factor productivity: an empirical study based on provincial panel data in China

被引：6
作者：
Luo, Shuang [1 ]
Lei, Wenting [2 ]
Hou, Peng [1 ]
机构：
[1] Beijing Forestry Univ, Sch Econ & Management, Beijing 100083, Peoples R China
[2] Univ Int Business & Econ, Sch Stat, Beijing 100029, Peoples R China
来源：
NATIONAL ACCOUNTING REVIEW | 2024年 / 6卷 / 02期
关键词：
Total factor productivity; artificial intelligence technology innovation; sustainable economic development; industrial structure upgrading; human capital; RENEWABLE ENERGY; GROWTH;
D O I：
10.3934/NAR.2024008
中图分类号：
F8 [财政、金融];
学科分类号：
0202 ;
摘要：
As the focus of the new round of technological revolution, it is crucial to explore the role of artificial intelligence (AI) technology innovation in improving total factor productivity (TFP). Based on the data from 30 Chinese provinces from 2003 to 2021, this article measured AI innovation using the number of patent applications and empirically investigated the effects of AI technology innovation on TFP. The results demonstrated that AI technology innovation exerts significantly positive influences on the TFP. The mechanism analyses revealed that AI technology innovation improves TFP by upgrading industrial structures and promoting human capital. The subsample results indicated that the promotion effect of AI technology innovation on TFP is significant only in areas with high levels of marketization, financial development, and digital infrastructure. The panel quantile regression results indicated that as the TFP increases, the promoting effect of AI technology innovation on TFP gradually strengthens. This study offers comprehensive empirical evidence for understanding the impacts of AI technology innovation on TFP, giving a reference for further enhancing the level of AI development and promoting a sustainable economic development.
引用
收藏
页码：172 / 194
页数：23
相关论文
共 50 条
[21]   Study of total factor productivity on China's science and technology service industry: provincial efficiency comparatively using 2010-2019 data [J].
Guo, Lu ;
Dai, Zhimin .
APPLIED ECONOMICS, 2021, 53 (51) :5917-5928
[22]   The impact of the digital economy on the total factor productivity of manufacturing firms: Empirical evidence from China [J].
Li, Huanjie ;
Zhang, Yuan ;
Li, Yan .
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2024, 207
[23]   The impact of industrial structure upgrades on the urban-rural income gap: An empirical study based on China's provincial panel data [J].
Zhou, Qiuyang ;
Li, Zhenqiang .
GROWTH AND CHANGE, 2021, 52 (03) :1761-1782
[24]   An Empirical Study on Externality and Total Factor Productivity of Manufacturing Industry in China [J].
Zhang, Yiyao .
2017 5TH INTERNATIONAL CONFERENCE ON PHYSICAL EDUCATION AND SOCIETY MANAGEMENT (ICPESM 2017), VOL. 2, 2017, 71 :286-292
[25]   Intelligence and Green Total Factor Productivity Based on China's Province-Level Manufacturing Data [J].
Zhang, Yining ;
Wu, Zhong .
SUSTAINABILITY, 2021, 13 (09)
[26]   The Influence of the Internet on Regional Economic Development-An Empirical Study Based on China's Provincial Panel Data [J].
Wang, Yan ;
Dai, Youyu ;
Wang, Zhengyin .
SUSTAINABILITY, 2022, 14 (19)
[27]   How does artificial intelligence development affect green technology innovation in China? Evidence from dynamic panel data analysis [J].
Yin, Kedong ;
Cai, Fangfang ;
Huang, Chong .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (10) :28066-28090
[28]   Inclusive finance, industrial structure upgrading and farmers' income: Empirical analysis based on provincial panel data in China [J].
Liu, Guibo ;
Fang, Huimin ;
Gong, Xiaoxian ;
Wang, Feifei .
PLOS ONE, 2021, 16 (10)
[29]   The Total Factor Productivity and Influencing Factors of China's Science and Technology Service Industry--Based on Panel Threshold Regression Model [J].
Xia Fang ;
Du Xiao-chun ;
Ji Hong .
2018 25TH ANNUAL INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING, 2018, :200-205
[30]   The Impact of New Energy Enterprises' Digital Transformation on Their Total Factor Productivity: Empirical Evidence from China [J].
Ren, Yangjun ;
Zhang, Xin ;
Chen, Hui .
SUSTAINABILITY, 2022, 14 (21)
12345