数据要素与人工智能技术融合对实体经济韧性的影响效应检验OACHSSCD
Emprircal Test of The Impact of the Integration of Data Elements and Artificial Intelligence Technology on the Resilience of the Real Economy
数据作为新型生产要素,与人工智能技术融合可产生价值倍增效应,为增强实体经济韧性提供新动能.文章采用2013-2024年中国221个地级及以上城市的面板数据,重点考察数据要素与人工智能技术融合对实体经济韧性的影响及作用机制.结果表明:数据要素与人工智能技术融合能够显著增强实体经济韧性,该结论在经过一系列稳健性检验和内生性检验后仍然成立.异质性分析结果表明,相较于非省会城市及中部、西部和东北地区城市,数据要素与人工智能技术融合对省会城市和东部地区城市的影响更为显著.作用机制检验结果表明,数据要素与人工智能技术融合不仅能够通过促进新质生产力发展增强实体经济韧性,而且能够通过缓解资源错配增强实体经济韧性.
As a new type of production factor,data can produce a value-multiplying effect through its integration with artifi-cial intelligence technology,providing new impetus for enhancing the resilience of the real economy.This paper uses the panel da-ta of 221 prefecture-level and above cities in China from 2013 to 2024 to focus on examining the impact of the integration of data elements and artificial intelligence technology on the resilience of the real economy and its mechanism.The results show that the integration of data elements and artificial intelligence technology can significantly enhance the resilience of the real economy,and this conclusion still holds after a series of robustness tests and endogeneity tests.The results of the heterogeneity analysis show that,compared with non-prvincial-capital cities and cities in the central,western and northeastern regions,the impact of the inte-gration of data elements and artificial intelligence technology on the resilience of the real economy is more significant in provincial capital cities and cities in the eastern region.The results of the mechanism test show that the integration of data elements and arti-ficial intelligence technology can not only enhance the resilience of the real economy by promoting the development of new quality productivity,but also enhance the resilience of the real economy by alleviating resource misallocation.
霍晓艳
吉林财经大学 工商管理学院,长春 130000
管理科学
数据要素人工智能技术实体经济韧性新质生产力资源错配
data elementsartificial intelligence technologyresilience of the real economynew quality productivityre-source misallocation
《统计与决策》 2026 (15)
5-11,7
吉林省教育厅人文社科研究项目(20250079)
评论