## 2 Literature Review
[30] used a multi-prediction model, [PERSON], [PERSON] [31] used a multiple regression analysis model and time series ARIMA model, [PERSON], [PERSON] [32] utilized nonadditive forecast combination with gray prediction, [PERSON], [PERSON] et al. [33] applied the GM (1, 1) gray prediction model and the GM (2, 1) gray prediction model and [PERSON], [PERSON] et al. [34] used nonlinear autoregression (NARX) to predict air passenger flow. [PERSON], [PERSON] et al. [35] developed a hybrid framework integrating machine learning algorithms to predict passengers. [PERSON], [PERSON] et al. [36] utilized search engine data (SED) to improve air passenger demand forecasting. Scholars have also studied the coordinated development of air transport, economy and industry. [PERSON], [PERSON] [37] introduced the concept of adaptability in the scale economy of airport construction and analyzed the conditions for realizing the best scale of airport construction in line with social and economic factors. [PERSON], [PERSON] et al. [38] investigated the correlation between air transportation and economic development. [PERSON]; [PERSON] et al. [39] suggested that the air transport industry promoted economic growth. [PERSON], [PERSON] et al. [40] adopted a panel VAR model to deeply investigate the coupling relationship between the air transport industry and tourism development. [PERSON], [PERSON] et al. [41] conducted an analysis on the coupling coordination relationship and spatial effects between China's aviation and tourism industries by employing coupling coordination degree metrics alongside a spatial econometric model. [PERSON], [PERSON] et al. [42] studied the spatio-temporal relationship between civil aviation transportation and tourism in western China. There are still some limitations in the previous scholars' research. (1) Scholars mostly perform qualitative and quantitative research on one side of the supply or demand factors of civil aviation passenger transport. There are relatively few examples in the literature that study the supply and demand of civil aviation passenger transport as a system. (2) There are few systematic studies on the coupling and coordination of supply-demand of civil aviation passenger transport. Moreover, there is no clear understanding of the factors affecting the coupling and coordination of supply-demand, and there is a lack of a quantitative basis for guiding policy formulation. (3) The study on the regional characteristics of the coupling and coordination of provincial passenger transport supply-demand in space is neglected. This study puts both the supply (supply side) and the demand (demand side) of civil aviation passenger transport in an overall framework. The civil aviation subsystem is taken as an organic whole to make up for the limitation of separating the civil aviation subsystem from the previous research to one-sidedly characterize the development status of civil aviation passenger transport. Focusing on data from 31 province-level administrative divisions (excluding Hong Kong, Macao, and Taiwan) in China between 2004 and 2019, this study evaluates the levels of supply and demand in civil aviation passenger transport, assesses the stages of coupling and coordinated development and investigates the spatio-temporal evolution of these dynamics. Additionally, it analyzes the factors influencing the degree of coupling coordination. The aim of this study is to establish a theoretical and practical foundation for enhancing the coordinated development between the supply and demand of civil aviation passenger transport. There are two innovations in this study. Firstly, it develops an evaluation system designed to assess the coupling and coordination between the supply and demand within the civil aviation passenger transport sector. Secondly, it illustrates the spatio-temporal evolution of the degree of coupling coordinationbetween supply and demand in civil aviation passenger transport and conducts an analysis of the influencing factors.