Advances in Economics, Management and Political Sciences

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Proceedings of the 2nd International Conference on Financial Technology and Business Analysis

Series Vol. 73 , 05 March 2024


Open Access | Article

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

Jiafei Yue * 1 , Zongnan Wu 2
1 Beibu Gulf University, Qinzhou, Guangxi
2 Beibu Gulf University, Qinzhou, Guangxi

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 73, 33-44
Published 05 March 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Jiafei Yue, Zongnan Wu. Markovian Spatio-Temporal Pattern Variation and Prediction of Green Financial Development in China's Economic Belt Based on ADABOOST Algorithm. AEMPS (2024) Vol. 73: 33-44. DOI: 10.54254/2754-1169/73/20230907.

Abstract

This paper constructs a comprehensive index system, measures China’s 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 China’s 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”.

Keywords

green finance, spatio-temporal pattern change, spatial correlation network, spatial Markov matrix

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
ISBN (Print)
978-1-83558-319-7
ISBN (Online)
978-1-83558-320-3
Published Date
05 March 2024
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/73/20230907
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated