Spatial distribution and influencing factors of data centers in China: An empirical analysis based on the geodetector model

Abstract

Data centers are vital infrastructure for the digital economy’s growth. Analyzing the spatial distribution of data centers and the factors influencing this distribution can guide their sustainable and regionally balanced development. Using data from Chinese data centers between 2016 and 2022, this study employs the nearest neighbor index, geographic concentration index, imbalance index, kernel density estimation, and Anselin Local Moran’s I to quantitatively analyze the spatial distribution characteristics of data centers. Additionally, Geodetector and Pearson correlation analysis are used to identify factors that significantly correlate with the spatial distribution of data centers. The results indicate that: (1) Data centers exhibit clear agglomeration characteristics, forming a “dense east and sparse west” distribution pattern, with three cores in the Beijing-Tianjin-Hebei region, the Yangtze River Delta, and the Pearl River Delta. (2) Provincially, the spatial distribution of data centers shows a significant imbalance, with “high-low” clustering observed in Guangzhou and “high-high” clustering in Shanghai. (3) Multiple factors influence the spatial distribution, with computing demand and economic development showing the strongest correlations. Furthermore, data center distribution is shifting from solely pursuing economic benefits to taking into account both economic and environmental benefits. (4) Regional variations exist in influencing factors. In the eastern region, computing demand and economic development levels show the strongest correlations, while in the central and western regions, government financial support is more significantly correlated. Based on the analysis results, this study proposes specific recommendations for the development and distribution of data centers across various regions of China from the perspectives of policymakers and data center operators.