Markovian Spatio-Temporal Pattern Variation and Prediction of Green Financial Development in China's Economic Belt Based on ADABOOST Algorithm

This paper constructs a comprehensive index system, measures Chinas provincial G-FINANCING INDEX using the VIKOR algorithm, explores the spatial and temporal distribution pattern of green financial development, hotspot regional migration and discrete trends by establishing a Markov spatial transfer matrix, and predicts the future pattern of green financial development in Chinas three major economic belts based on the machine-learning algorithm ADABOOST, which finds that: (1) the central region maintains a strong development momentum. The development of green finance in the region shows a scale effect, and there is a positive transmission trend of development momentum between cities. The right side of the nuclear density curve in the western region has a clear trailing trend, and the overall degree of improvement is high. The phenomenon of multi-polarisation exists among city clusters. The city clusters in the eastern region as a whole show extremely high development momentum. (2) Geographic background plays an important role in the process of transferring G-FINANCING INDEX in Chinese cities, and the level of regional G-FINANCING INDEX is the result of the joint action of desired outputs and non-desired outputs, which is dominated by the economic activities in the region, and is specifically expressed in the spatial spillover effect of regional G-FINANCING INDEX is the spatial spillover effect of regional economic activities. (3) The evolution of the G-FINANCING INDEX in the study region has obvious spatial spillover effects, and is synergistic with the type of regional G-FINANCING INDEX, showing the phenomenon of club convergence.