Dataset for the paper “The impact of China's digital economy on urban land use performance and the mechanisms involved from the perspective of geographical factors”

This study employs Principal Component Analysis and the Entropy Weight Method to measure the level of digital economy and urban land use performance of 280 prefecture-level and above cities in China, respectively. Based on the above, we construct a panel dataset covering 280 Chinese cities from 2006 to 2022 and utilize various econometric models to evaluate the impact of China’s digital economy on urban land use performance from the perspective of geographical factors.In the model, the explanatory variable is the level of digital economy development, the explained variable is urban land use performance, and the control variables include traffic network density, government regulation intensity, economic development level, and population size. Geographical and climatic condition variables consist of annual sunshine duration, average temperature, and average precipitation, while socio-economic factors cover talent dividend, industrial structure, and policy agglomeration. Additionally, the dataset classifies cities into northern and southern groups, eastern, central, and western groups, transportation hub cities and non-hub cities, as well as pilot cities of the "Broadband China" strategy.Socio-economic data are primarily sourced from the China Urban Statistical Yearbook, China Urban Construction Statistical Yearbook, China Labor Statistical Yearbook, and official websites of statistical bureaus of various cities. Data on natural factors such as sunshine duration, temperature, and precipitation are obtained from the National Earth System Science Data Center. Policy-related data are mainly derived from the Peking University Legal Database (Pkulaw). To address missing data, the linear interpolation method is adopted for data imputation.