新质生产力、人工智能与区域经济韧性
New Quality Productivity,Artificial Intelligence,and Regional Economic Resilience
蔡咏梅 1郭津吟1
作者信息
摘要
本文基于2012-2022 年中国30 个省份的面板数据,采用双向固定效应模型、门槛模型和空间杜宾模型,研究新质生产力对区域经济韧性的影响及其传导机制.研究结果表明,新质生产力能够有效促进区域经济韧性的提升,并对本地区及邻近地区产生正向的空间溢出效应.进一步分析发现,人工智能发展水平在新质生产力对区域经济韧性影响中呈现非线性关系.异质性分析表明,新质生产力对区域经济韧性的促进作用受区位条件和创新能力发展水平的影响,相比于内陆地区和创新能力较弱的地区,沿海地区及创新能力较强的地区的促进作用更加显著.在此基础上,提出了推进新质生产力提升经济韧性的政策建议.政府应当全面培育和发展新质生产力,推动区域差异化治理、人工智能赋能和产业协同发展,提升区域经济韧性,实现经济高质量和可持续发展.
Abstract
Based on panel data from 30 provinces in China between 2012 and 2022,this paper employs the two-way fixed effects model,threshold model,and spatial Durbin model to examine the impact of new productive forces on regional economic resilience and its transmission mechanisms.The results show that new productive forces effectively promote regional economic resilience and generate positive spatial spillover effects both locally and in neighboring areas.Further analysis reveals that the level of artificial intelligence development exhibits a nonlinear relationship in the effect of new productive forces on regional economic re-silience.Heterogeneity analysis indicates that the promotion effect of new productive forces on regional eco-nomic resilience is influenced by location and innovation capability levels.Compared to inland regions and regions with weaker innovation capacity,the effect is more significant in coastal areas and regions with stron-ger innovation capabilities.Based on these findings,policy recommendations are proposed to enhance region-al economic resilience through the promotion of new productive forces.The government should comprehen-sively cultivate and develop new productive forces,promote differentiated regional governance,empower arti-ficial intelligence,and foster industrial collaboration to improve regional economic resilience and achieve high-quality and sustainable economic development.
关键词
新质生产力/区域经济韧性/人工智能/空间杜宾模型/门槛效应Key words
New Quality Productivity/Regional Economic Resilience/Artificial Intelligence/Spatial Durbin Model/Threshold Effect引用本文复制引用
出版年
2025
NETL
NSTL
万方数据