Spatio-Temporal Evolution and Transition Mechanisms of Municipal Digital Economy Development Level in China

In the context of global digital transformation, scientifically examining the spatio-temporal evolution patterns and transition mechanisms of the digital economy at the municipal level is crucial for promoting coordinated regional development. This study takes 281 prefecture-level cities in China from 2011 to 2023 as its research units. Exploratory Spatio-Temporal Data Analysis (ESTDA) is employed to analyze its spatio-temporal dynamics, while a panel quantile regression model nested with spatio-temporal transition types is used to reveal the driving mechanisms. The findings indicate that (1) the overall development level of China’s municipal digital economy has steadily increased, yet significant regional heterogeneity persists, characterized by a pattern of “eastern leading, central fastest-growing, and western lagging,” with considerable room for overall improvement. (2) The digital economy exhibits a significant positive spatial correlation. High–high agglomeration areas remain stable in the southeastern coast, whereas low–low agglomeration areas are concentrated in the central-western and northeastern regions. The spatial pattern demonstrates strong stability and path dependence. (3) LISA time paths reveal drastic changes in local spatial structures in provinces such as Heilongjiang, Inner Mongolia, Hubei, Guangdong, and Guangxi, while East and Central China remain relatively stable. Tortuosity analysis indicates that spatial linkages in the western region are becoming active yet unstable. (4) The quantile regression nested with transition types identifies four mechanisms: “Economic development-Technological innovation” serves as the fundamental driving mechanism across all regions. Low-quantile areas face a complex situation with dual suppression from “opening-up and urbanization” coexisting with drivers from “human capital, government intervention, and industrial structure.” High-quantile areas are synergistically driven by “urbanization, human capital, government intervention, and advanced industrial structure.” This study provides a decision-making reference for overcoming the dilemma of “low-level club convergence” in digital economy development and formulating differentiated regional policies.