产业链供应链现代化的区域异质性与驱动因素研究

Research on the Regional Heterogeneity and Driving Factors of Industrial Chain and Supply Chain Modernization

产业链供应链现代化是我国经济循环畅通的关键,其空间分布及驱动因素研究一直是学者们关注的热点问题,但是对其中的区域差异及驱动因素的研究相对较少,而这些是认识中国产业链供应链现代化的重要基础。运用熵值法、Dagum基尼系数和地理探测器模型,探究我国产业链供应链现代化区域异质性及驱动因子。主要研究结论有:产业链供应链现代化发展水平的总体差异呈现振荡上升变化趋势,从区域差异大小来看,东部与西部>东部与中部>中部与西部;产业链供应链现代化的核心影响因子包括政府支持、数字化和金融支持;8个变量(科技支撑、经济发展、科学技术创新、对外开放、绿色转型、政府支持、数字化、金融支持)之间进行交互探测后均出现双因子增强现象,说明产业链供应链发展水平的空间异质性实际上是多种因素共同影响的结果。

The modernization of industrial chain and supply chain is a key component of smoothing China's economic circulation,the research on its spatial distribution and driving factors has always been a hot topic in domestic and international academic communities.However,there are relatively few studies on its spatiotemporal pattern changes,regional differences and driving factors,which constitute an important basis for understanding the modernization of China's industrial chain and supply chain.In this study,the spatiotemporal variations of China's industrial chain and supply chain modernization and its associated driving factors are examined through entropy method,Dagum Gini coefficient and geographical detector model(Geodetector).The findings reveal:The overall difference in the modernization development level of industrial chain and supply chain presents a trend of rising.In terms of regional disparities,the eastern and western regions>the northeast and central regions>the central and western regions;The major driving factors of the modernization of industrial chain and supply chain are government support,digitization and financial support;The interaction detection between the eight independent variables(technological support,economic development,scientific and technological innovation,opening up to the outside world,green transformation,government support,digitalization,financial support)shows a dual-factor enhancement effect,indicating that the spatial heterogeneity of the development level of industrial chain and supply chain is actually influenced by multiple factors.