Abstract: To investigate the driving mechanisms of airport throughput under different regional characteristics, this papercarries out an analysis using an integrated approach combining Pearson correlation coefficients with entropy-weighted grey relational degree. First, the data of passenger as well as cargo and mail throughput data from trans-port airports in the 31 provincial-level administrative center cities of China between 2006 and 2023 were col-lected. Second, 9 categories of primary driving factor indicators, including gross regional product (GRP), peo-ple's living standards, tourism, ete., and 19 secondary driving factor indicators were selected, and Pearson cor-relation coefficients were used to select driving factors highly correlated with airport throughput. Finally, the en-tropy-weighted grey relational degree method was applied to the selected driving factors for weighted analysisand relational ranking, and the top five primary driving factor indicators from the ranking results were identifiedas key driving factors of airport throughput, with Tianjin Binhai Intemational Airport serving as a case study tovalidate the model's effectiveness. The results showed that the impacts of various driving factors on airportthroughput differ significantly, indicating that airports in provincial-level administrative center cities across dif-ferent regions exhibit regional characteristics. The research results of this paper can provide reference for futureplanning and sustainable development of airports with different driving patterns.

Key words: airport throughput, Pearson correlation coefficient, entropy-weighted grey relational degree, driving factors