Measurement of digitalization level and spatial disequilibrium of tourism industry in China

Identifying the spatial non-equilibrium characteristics of digitalization development in the tourism industry can provide scientific basis for bridging the “digital divide” in the tourism industry and optimizing the spatial layout of new quality productivity in the tourism industry. Based on the connotation characteristics of digitalization in the tourism industry, a comprehensive evaluation system was constructed, and the entropy weight TOPSIS method, Dagum Gini coefficient, Kernel density estimation, and Markov chain analysis were used to explore the spatial non-equilibrium characteristics and dynamic evolution trends of tourism digitalization level from 2011 to 2022 in China. The findings indicate that: 1) According to the entropy weight TOPSIS method, it can be concluded that the digitalization level of tourism industry has maintained a steady upward trend from 2011 to 2022 in China, with eastern provinces and cities such as Shanghai, Beijing, and Guangdong leading the way. The spatial differentiation characteristics of the digitalization level of tourism industry in China were gradually emerging, roughly showing a gradient distribution state of “high in the east and low in the west” and “high in the south and low in the north”. The spatial imbalance phenomenon of the digitalization level of tourism industry among provinces in China was significant. 2) In terms of regional differences and decomposition, the overall difference in the digitalization level of the tourism industry shows a fluctuating downward trend, and the imbalance between regions shows a shrinking trend. The overall regional differences were in a state of “northeast>east>west>central”, but inter provincial differences have become the main source of the “digital divide” in the tourism industry, and narrowing regional differences has become a key direction to bridge the digital level gap of the tourism industry in China. 3) According to the Kernel density estimation method, it can be concluded that the conditional probability density curve was distributed along a 45° diagonal, and the dynamic distribution of digitalization level in the tourism industry was relatively stable. After taking into account geographical spatial factors, the digitalization level of tourism industry in China showed a shrinking trend during the research period. 4) According to the Markov chain method, it can be inferred that if geographical spatial effects are not taken into account, the digitalization level of tourism industry in China shows the phenomenon of “Matthew effect” and “club convergence”; After incorporating geographic spatial effects, as the level of spatial lag in neighboring provinces increases, the digitalization level of local tourism industry was significantly affected by the spatial spillover effects of neighboring provinces.