The measurement of green finance index and the development forecast of green finance in China

Abstract

This paper proposes a green finance index that may help policymakers and investors take more favorable actions based on the development of green finance. After analysis and organization of the development process of green finance and related green finance and index concepts, this paper uses the improved fuzzy comprehensive evaluation method to construct a measurement model suitable for measuring the development level of green finance based on the principle of fuzzy mathematics. The index weight adopts the entropy method and improved Analytic Hierarchy Process (AHP) joint determination. At the same time, using the relevant statistical indicators of China's green credit from 2011 to 2019, and using the constructed model, the level of China's green finance development during this period was evaluated. Finally, the obtained data and classical gray model methods were used to predict China's green development level from 2020 to 2024. The research results show that: This model is a good measure of the level of development of green finance, and China's green finance index has generally shown a rapid growth trend over the past nine years, with the fastest growth rate between 2013 and 2014. From the perspective of the weight of each index affecting the green financial index, the weight of new energy, green transportation projects and new energy vehicles ranked in the top three, and the impact of these three indexes on China's green financial index is significant. In the future, China's green financial development level will continue to improve.

Appendices

Appendix A

The 21 major banking institutions include: China Development Bank, Export Import Bank of China, Agricultural Development Bank of China, Industrial and Commercial Bank of China, Agricultural Bank of China, Bank of China, China Construction Bank, Bank of Communications, China CITIC Bank, China Everbright Bank, Huaxia Bank, Guangdong Development Bank, Ping An Bank, China Merchants Bank, Pudong Development Bank, Industrial Bank, China Minsheng Bank, Evergrowing bank, China Zheshang Bank, China Bohai Bank and Post Savings Bank of China.

Appendix B

With the help of MATLAB software, the judgment matrix is processed to make the calculation easier.

Start the MATLAB software and input in the command window:

clc.

clear all.

%%Consistency test and weight vector calculation.

[n,n] = size(B);

[v,d] = eig(B);

r = d(1,1);

CI = (r-n)/(n-1);

RI = [0 0 0.52 0.89 1.12 1.24 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 1.59];

CR = CI/RI(n);

if CR < 0.1

CR_Result = 'Pass';

else.

CR_result = 'Fail';

end.

%%Weight vector calculation.

w = v(:,1)/sum(v(:,1));

w = w';

%%Result output.

disp('Calculation report of weight vector of the judgment matrix:');

disp(['Consistency indicators:',num2str(CI)]);

disp(['Consistency ratio:',num2str(CR)]);

disp(['Consistency test results:',CR_Result]);

disp(['Eigenvalues:',num2str(r)]);

disp(['Weight vector:',num2str(w)]);

Output results:

Calculation report of weight vector of the judgment matrix:

Consistency index: 0.0074337.

Consistency ratio: 0.0046753.

Consistency test result: passed.

Characteristic value: 15.1041.

Weight vector: 0.066149 0.069312 0.029018 0.10879 0.065566 0.053291 0.030963 0.059657 0.12215 0.043634 0.048855 0.071576 0.019772 0.056393 0.15487.

That is to say, the weight $W_{A}$ of the improved AHP is

$$\begin{gathered} W_{A} = (0.0661,0.0693,0.0291,0.1088,0.0656,0.0533,0.0310,0.0597, \hfill \\ {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} 0.1222,0.0436,0.0489,0.0716,0.0198,0.0564,0.1549) \hfill \\ \end{gathered}$$

Appendix C

Also use MATLAB software for prediction.

Start the MATLAB software and input in the command window:

X = [2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024];

Y = [2011,2012,2013,2014,2015,2016,2017,2018,2019];

B = [2838.007872,3306.99793,3694.050601,4664.444747,5591.202845,6239.958816,7163.469794,8484.417873,9908.6653];

A = [2838.007872,3349.710125,3909.546787,4562.948884,5325.55399,6215.613213,7254.427922,8466.859612,9881.924868,11,533.48981,13,461.08062,15,710.82945,18,336.57853,21,401.16876];

plot(Y,B,'-*b');

hold on.

plot(X,A,'-or');

legend('real','prediction');

xlabel('Time(y)');ylabel('Index');