Dataset for effects of China's urban transition on tourism competitiveness

This article uses the entropy weight TOPSIS method to comprehensively evaluate the tourism competitiveness of Chinese prefecture level cities. The evaluation index system includes tourism resources, tourism There are a total of 28 indicators in four dimensions: tourism industry, social development support, and ecological environment support. Based on this, this article generates panel data for 272 prefecture level cities from 2005 to 2019, and estimates the impact of urban transformation on tourism competitiveness using a multi period DID method. In the DID model, the dependent variable is tourism competitiveness, the explanatory variable is the cross dummy variable of innovative cities and low-carbon city construction pilot projects, the control variables include economic development, foreign investment utilization, foreign trade, resident consumption, financial development, human capital, information technology, and population, and the mechanism variables are industrial structure rationalization, industrial structure upgrading, technological innovation input, technological innovation output, and environmental regulation. This dataset also distinguishes between eastern, central, and western cities, cities with different administrative levels, and whether they are resource-based cities. The control variable and mechanism variable data mainly come from the "China Urban Statistical Yearbook", and the environmental regulation data comes from the work reports of local municipal governments. Regarding the issue of missing data in some areas, we will supplement it using linear interpolation method.