Research on the Strategy of Industrial Structure Optimization Driven by Green Credit Distribution

Credit is an important means to promote economic development, while green credit is conducive to the sustainable development of industry. This paper aims to build a multiple linear regression model and a dynamic panel data GMM estimation model to analyze the important factors that affect the optimiz...

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Bibliographic Details
Published in:Sustainability 2022-08, Vol.14 (15), p.9360
Main Authors: Ding, Guoping, Hua, Jingqian, Duan, Juntao, Deng, Sixia, Zhang, Wenyu, Gong, Yifan, Sun, Huaping
Format: Article
Language:English
Subjects:
Coal
Correlation analysis
Economic development
Energy conservation
Energy consumption
Energy industry
Environmental impact
Environmental protection
Expenditures
GDP
Gross Domestic Product
Hypotheses
Industrial development
Interest rates
Neural networks
Optimization
Regions
Renewable resources
Subsidies
Sustainability
Sustainable development
Variables
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Summary:Credit is an important means to promote economic development, while green credit is conducive to the sustainable development of industry. This paper aims to build a multiple linear regression model and a dynamic panel data GMM estimation model to analyze the important factors that affect the optimization of the industrial structure. We then use an analytic hierarchy process to explore the relationship between green credit and industrial optimization. We compare this with the optimization rate of the industrial structure according to the real data, and then obtain the effectiveness of the hierarchical analysis of the three major industries in the eastern, central and western regions. Finally, neural networks are used to forecast the total amount and distribution of green credit in 2021. The final results show that there are regional and industrial differences in the influence of green credit on industrial structure optimization, and in the process of using green credit to promote the optimization and upgrading of industrial structure.
ISSN:2071-1050
2071-1050
DOI:10.3390/su14159360