Measurement and Influencing Factors Analysis of Regional Carbon Finance Development Level in China Based on ANN-RBF

Zhaojun Liu; Yongqin Ma

doi:10.54691/bcpbm.v26i.1931

Authors

Zhaojun Liu
Yongqin Ma

DOI:

https://doi.org/10.54691/bcpbm.v26i.1931

Keywords:

Carbon Finance; Financial Market; Green Finance; Green Development.

Abstract

The green development of carbon finance contributes to the rapid realization of the "double carbon strategy" goal. This paper uses the analytic hierarchy process-coefficient of variation (AHP-CVM) coupling method to measure the level of carbon finance development in China’s provinces from 2018 to 2020, and uses the artificial neural network-radial basis function (ANN-RBF) method to analyze the influence of different factors on the level of carbon finance development. The results show that the development of regional carbon finance in China is affected by the new coronavirus. However, the overall trend is still rising, and there are differences in the development level of carbon finance between regions. From the perspective of influencing factors, carbon loan intensity, the proportion of added value of financial industry, the proportion of scientific research funds and the contribution rate of scientific and technological progress contribute greatly to the development of carbon finance, among which the intensity of carbon loans has the most prominent influence.

Downloads

Download data is not yet available.

References

Liu Yunzhe. (2014). Analysis and path of provincial carbon finance development level and influencing factors in China. Exploration of economic problems (07), 118-123.

Yilan, Li Chaopeng, Yang Li & Liu Jie. (2018). Comparative study on the development of seven carbon trading pilot projects in China. Population, resources and environment in China (02), 134-140.

Fan Dan, Wang Weiguo & Liang Peifeng. (2017). Policy effect analysis of China’s carbon emissions trading rights mechanism - based on the estimation of the difference-in-difference model. China Environmental Science (06), 2383-2392.

Wang Yong & Zhao Han. (2019). Impact of China’s carbon trading market on regional carbon emission efficiency. Population, resources and environment in China (01), 50-58.

Chen Zhiying, Xu Lin & Qian Chongxiu. (2020). Measurement and dynamic evolution of China’s carbon finance development level. Quantitative economic, technical and economic research (08), 62-82. doi: 10.13653/j.cnki.jqte.2020.08.004.

Zheng Qunzhe. (2022). Measurement of China’s carbon finance development level and analysis of its influencing factors. Technical economy and management research (02), 75-79.

Xu Xiaofei. (2020). Research on the evaluation system of China’s carbon financial market development. Economic Research Journal (02), 154-156.

Yang yu-sheng & Gao Min. (2021). Research on unbalanced quotation recognition based on AHM-coefficient of Variation coupling weighting. Engineering Economics (04), 63-67. doi: 10.19298/j.cnki. 1672-2442.202104063.

Huang sijie & Li Yingguo.(2021). Research on regional financial risk prediction in Jiangsu Province based on RBF neural network model. Doi: 10.16517/j.cnki.cn12-1034/ F.2021.05.036.

Wei wenxuan. (2013). Application of improved RBF neural network in stock market prediction. Statistics and decision (15), 70-72. The doi: 10.13546/j.carol carroll nki tjyjc. 2013.15.035.

Bai, L., Wang, Z., Wang, H., Huang, N., & Shi, H.. (2020). Prediction of multiproject resource conflict risk via an artificial neural network. Engineering Construction & Architectural Management, ahead-of-print(ahead-of-print).