县域数字经济网络与全域旅游发展的互动演化及驱动因素研究——以福建省为例

Research on the Interactive Evolution and Driving Factors of the County Digital Economy Network and All-for-one Tourism Development: Taking Fujian Province as an Example

  • 摘要: 县域作为文化数字化战略的基本单元,探究其数字经济与全域旅游发展的互动演化及驱动因素,对于促进文化传播效能,实现县域旅游新业态具有重要意义。文章构建全域旅游与数字经济评价指标体系,利用网络中心度模型测算数字经济的空间特征;通过耦合协调度模型和冷热点分析,对福建省2015—2022年全域旅游发展水平和数字经济耦合协调发展的时空演变特征进行实证研究,并采用地理探测器模型诊断耦合水平的关键影响因素。研究表明:1)福建省乡村全域旅游水平与数字经济网络水平整体呈波动上升态势,乡村全域旅游和数字经济耦合协调度呈中心增长极辐射边缘区域的时空演变格局。2)全局Moran's I从0.421上升至0.453,冷点区与热点区数量均有所增加,整体上呈现“空间极化”趋势。3)经济因素和文化教育是空间分异地主要驱动因子,因子交互探测结果均为双因子增强与非线性增强。

     

    Abstract: As the fundamental unit of the cultural digitization strategy, exploring the interactive evolution and driving factors between the digital economy and all-for-one tourism development at the county level is of great significance in enhancing the effectiveness of cultural dissemination and realizing new forms of tourism in county areas. This article constructs an evaluation index system for all-for-one tourism and the digital economy and uses the network centrality model to measure the spatial characteristics of the digital economy. Through the coupling coordination degree model and cold spot-hot spot analysis, an empirical study is conducted on the spatiotemporal evolution characteristics of the coupling and coordinated development between the levels of all-for-one tourism development and the digital economy in Fujian Province from 2015 to 2022. Additionally, the geographic detector model is employed to diagnose the key influencing factors of the coupling level. The research indicates that: 1) The overall levels of rural all-for-one tourism and the digital economy network in Fujian Province show a fluctuating upward trend, with the coupling coordination degree between rural all-for-one tourism and the digital economy demonstrating a spatiotemporal evolution pattern where a central growth pole radiates towards peripheral regions. 2) The global Moran's I increased from 0.421 to 0.453, with an increase in the number of both cold spot and hot spot areas, indicating an overall trend of 'spatial polarization'. 3) Economic factors and cultural education are the primary driving factors for spatial differentiation, and the results of factor interaction detection show both dual-factor enhancement and nonlinear enhancement.

     

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