Proceedings of the 5th International Conference on Economic Management and Big Data Application (ICEMBDA 2024)

Study on the Development Path of Green Finance to Support Low-carbon Economy Based on Neural Network Model

Authors
Xiande Wang1, *
1Beijing Royal High School, Changpin District, Beijing, China
*Corresponding author. Email: 1280538273@qq.com
Corresponding Author
Xiande Wang
Available Online 30 December 2024.
DOI
10.2991/978-94-6463-638-3_2How to use a DOI?
Keywords
Green Finance; Low Carbon Economy; Double Carbon; SPSS; Database; Growth Rate of Credit Balance; Green Financial Credit Balance Month on Month Growth; Neural Network Model
Abstract

Promoting high-quality economic development and improving the ecological environment is China’s development goal. Continuously promoting green financial practices, guiding and leveraging financial resources to lean towards low-carbon industries, and promoting energy conservation and emission reduction are important guarantees for achieving the “dual carbon” goal. At present, the research on finance, economy and environment focuses more on the specific impact of finance on the ecological environment and the relationship between green finance and economic growth. The scope of research is basically limited to the real economy and the research topic is generally a one-way impact of a variable. There is a lack of research on green finance to support the dynamic development of low-carbon economy from an economic perspective. To this end, this paper adopts the method of literature and data analysis, calls the corresponding data from the database, preprocesses and cleans the data, and calculates the relevant index, including Growth Rate of Credit Balance, Green Financial Credit Balance Month on Month Growth based on SPSS, quantifies the content of the analysis in the form of data, and finally analyzes the green finance to support the status quo and solve existing problems of low-carbon economic development using Neural Network Model. Through analysis, this paper finds that there are such problems as incomplete green financial policies and regulations, perfect green financial products, perfect environmental information disclosure system, and uncleared role of government and market. Finally, this paper proposes strategies such as improving green finance laws and regulations, innovating products and services, correctly handling the relationship between the government and the market, and clarifying the division of labor among various entities, which is conducive to better realizing the contribution of green finance.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 5th International Conference on Economic Management and Big Data Application (ICEMBDA 2024)
Series
Advances in Economics, Business and Management Research
Publication Date
30 December 2024
ISBN
978-94-6463-638-3
ISSN
2352-5428
DOI
10.2991/978-94-6463-638-3_2How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Xiande Wang
PY  - 2024
DA  - 2024/12/30
TI  - Study on the Development Path of Green Finance to Support Low-carbon Economy Based on Neural Network Model
BT  - Proceedings of the 5th International Conference on Economic Management and Big Data Application (ICEMBDA 2024)
PB  - Atlantis Press
SP  - 4
EP  - 14
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-638-3_2
DO  - 10.2991/978-94-6463-638-3_2
ID  - Wang2024
ER  -