Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 7, No. 1, 2023 

 

47 

Does	Digital	Finance	Promote	Green	Economic	Growth?	
Yalan Xu 

School of Anhui Finance and Economics University, Bengbu, China 

 

Abstract: This paper constructs a balanced panel model of 285 cities in China to study the impact of digital finance on green 
economic growth. It is found that digital finance has a significant contribution to green economic growth. Green finance can 
increase green economic growth by promoting industrial structure upgrading. The contribution of green finance to green 
economic growth is higher in eastern cities and western cities than in the east. Digital finance has a significant threshold effect 
on green economic growth, and when the depth of digital finance exceeds the threshold value, the promotion effect of digital 
finance on green economic growth is greater. 

Keywords: Digital finance, Green economic growth, Mediating effect, Mechanism of action, Threshold model. 
 

1. Introduction 
In 2021, China's GDP will grow by 8.1% over the previous 

year, and China's GDP will exceed 114 trillion yuan. However, 
along with the rapid economic growth, a large amount of 
ecological space has been squeezed and the ecological 
environment continues to deteriorate, especially the 
concentrated outbreak of environmental problems under the 
crude development mode, which has put the issue of 
sustainable economic development in front of the government 
and the public. According to the China Ecological 
Environment Status Bulletin, in 2019,180 cities exceeded 
ambient air quality standards, accounting for 53.4%. 
Therefore, how to achieve economic growth while taking into 
account environmental protection and resource conservation, 
and promote the green development of China's economy has 
become a difficult problem that the government must face. 
Existing research on green economic growth has suggested 
that scientific and technological innovation (Lorek and 
Spangenberg, 2014; Padilla-Pérez and Gaudin, 2014), 
industry structure (Boschma et al., 2017), and carbon 
emissions (Jianhua Yin, et al., 2015; Acemoglu et al. ,2016; 
Sohag,et al.,2019; Khan & Ulucak, 2020) are important 
enforcing factors for the development of green economic 
growth. Therefore, Financial development can promote green 
economic growth by reducing carbon emissions (Jalil & 
Feridun,2011; Mahmood et al., 2013) optimizing industrial 
structure (Wurgle,2000; Michalopoulos, 2015)and improving 
science and technology innovation(Thorsten, et al,2002). 

The level of green development in China has been at a 
relatively low level. Several scholars at home and abroad have 
explored the reasons affecting green economic growth. The 
first explanation stems from the government's behavior under 
the Chinese fiscal decentralization system (Daming You, et al, 
2019), where the government's competitive behavior leads to 
serious resource allocation distortions and environmental 
pollution problems, and more seriously, the emergence of 
rent-seeking behavior that further inhibits green economic 
growth. Although rapid economic growth is achieved, 
environmental problems are ignored; the second explanation 
is called "Dutch disease", in which natural resource-rich 
regions inhibit green economic growth (Nagasaka, 1977), and 
resource-rich regions rely excessively on resource extraction 
to achieve economic growth, which leads to resource 
depletion and the formation of a "green economy". This leads 

to resource depletion, forming a "resource curse" and 
inhibiting green economic growth (Zhonghua Cheng, et 
al.,2020); the third explanation is: market segmentation. 
Market segmentation has a significant worsening effect on 
environmental pollution (Yuanchao Bian, et al. ,2019). The 
fourth explanation is carbon emissions. (Bailey, et al.,2014). 
There are also domestic and international studies on the 
relationship between digital finance and the economy. 
Wang,wanxin (2019) confirms that the application of 
digitalization can create certain benefits for venture capital; 
the application of digital technologies in the healthcare 
industry helps to reduce infrastructure costs, making 
healthcare services cheaper and more accessible, bringing 
benefits to people (Roberto and Donato, 2020). 

Through the above review, we found that digital finance 
has a big impact on the economy. Further, the contribution of 
digital finance to economic development is also significant. 
However, there are not many studies related to the impact of 
digital finance on the growth of green economy. Therefore, 
this paper establishes intermediary effect model and panel 
threshold model to analyze the influence of digital finance on 
China's green economic growth in detail. 

The marginal contribution of this paper lies in: in the first 
place, this paper constructs the mediation effect model to 
study the influencing mechanism of digital finance on green 
economic growth in China based on the effective 
measurement of China's green economic growth. Second, this 
paper analyzes in detail the heterogeneity of digital finance 
on green economic growth in eastern, central and western 
China. Third, this paper further investigates the threshold 
effect of digital finance on green economic growth in China.  

2. Data and Empirical Model 

2.1. Data sources 
The data in this paper come from two databases: digital 

finance from the "Digital Financial Inclusion Index of Peking 
University". The data of the explanatory variables, control 
variables and mediating variables are all from the China City 
Statistical Yearbook. Due to some missing data, the data in 
this paper include balanced panel data of 285 cities from 2011 
to 2016.. 



 

48 

2.2. Variables 
2.2.1. Dependent variable: Green economic growth  

This paper uses green total factor productivity as a proxy 
variable of green economic growth. The directional distance 
function (DDF) and Malmquist-Luenberger productivity 

index proposed by Chung et al. (1997) are used to measure 
total factor productivity under unintended output. The 
variables used to establish green economic growth in this 
paper are shown in Table 1. In this paper, the green economic 
growth variable is set as Green. 

 
Table 1. Green Total Factor Productivity Evaluation Index System 

category variable Data and Instructions unit 

Input 

Labor Number of people employed in each city that year thousands

capital stock 
The capital stock is established by the perpetual inventory method, which 

is 100 million yuan. Kt=Kt-1*(1-δ)+It 
billion 

energy 
Energy input is measured by the annual standard coal consumption of each 

city 
Tons 

Output GDP With each city in those days actual gross national product will measure billion 

Undesirable 
output 

solid waste Output of industrial solid waste in each city that year Tons 
SO2 The amount of sulfur dioxide produced in each city that year Tons 

wastewater The discharge of industrial waste water in each city that year Tons 

 

2.2.2. Explanatory variables: Digital finance 
The explanatory variables in this paper include the digital 

finance index (digfin) and its two dimensional indices. The 
two dimensional indices are the depth of digital finance usage 
index (digfin2) and the breadth of digital finance coverage 
index (digfin3). Two of the dimensional indices are used as 
data for robustness testing. The data were obtained from the 
Digital Financial Inclusion Index of Peking University (Guo 
Feng et al., 2020). 

2.2.3. Mediating variable 
The mediating variable in this paper is industrial structural 

upgrading (isu). The formula for industrial structure 
upgrading is as follows: 

 

   3 2isu S / S                (1) 

iS denotes the ratio of the output value of industry i to the 
total output value. This indicator mainly reflects the 
upgrading relationship among the three industries. a larger 
value of isu indicates a higher level of industrial structure 
development in the region, which also means a more 
advanced industrial structure in the region. 

2.2.4. Control variable 
In this paper, the following control variables are used: post 

revenue (the ratio of local post revenue level to GDP), foreign 
investment level (the ratio of foreign investment amount to 
local GDP), financial development level (the ratio of financial 
loan balance to GDP), and human capital (the ratio of students 
receiving higher education to local population). The above 
four control variables indicators are set as: post, fdi, loan, and 
humcap. 

2.3. Model 
First, we set up the panel regression model. The panel 

model is shown below. 
 

0 1 2    it it it itGreen a a digfin a control    (2) 
 

 itGreen  stands for green economic growth; itdigfin  
stands for digital finance; i denotes city and t denotes year. 

it
control   is the control variable, ε it is the error 
disturbance term . 

Equation (2) reflects the direct impact mechanism of digital 
finance on the growth of the green economy. This paper 
introduces the mediating variable Industrial Structural 
Upgrading (isu) to further examine the potential indirect 
impact mechanism of digital finance on green economic 
growth. The mediating effect model constructed in this paper 
can be expressed as: 

 
0 1 2     i t i t i t i tG r e e n a a d i g f i n a c o n t r o l  (3) 

 
0 1 2 o n t r     i t i t i t i ti b b d i g i t a l b c ou ls    (4) 

 
0 1 2 3     i t i t i t i t i tG r e e n d i g i t a l i s u c o n t r o l       (5) 

 
To test whether digital finance has a non-linear effect on 

green economic growth, this paper uses the panel threshold 
model established by Hansen (2000) to do a non-linear 
mechanism examination. In this paper, we use the depth of 
digital finance (digfin2) as the threshold variable to examine 
the non-linear impact of data finance on green economic 
growth. This paper further constructs the threshold model 
based on equation (2) as shown below. 

 
         0 1 2 3q   „   it it it it it it itGreen digfin digfin q cc c I c trolI c on   (6) 

 
I(·) is an indicative function, the value of which depends 

on the relationship between the threshold variable and the 
threshold value γ. When ≤γ holds, the function value is 1, 
otherwise it is 0. 

3. Empircal Results 

3.1. Baseline results 
Table 2 displays the regression results for the panel fixed 

effects and random effects of the model. Meanwhile, the 
Hausman test was conducted in this paper, and the p-value of 
the results of the Hausman test was 0.0006, indicating that the 
fixed-effects model is more appropriate. From Table 2, it can 
be seen that digital finance has a facilitating effect on green 



 

49 

economic growth at the 99% significance level. 
 

Table 2. Baseline results 
Variables fe re 

digfin 0.0430*** 0.0492*** 
 (0.0073) (0.0065) 

post 0.0154* 0.0263*** 
 (0.0089) (0.0072) 

loans -0.0252 -0.0668*** 
 (0.0171) (0.0121) 

humcap 0.000406 0.000909 
 (0.0031) (0.0029) 

fdi -0.142 -0.564** 
 (0.3168) (0.2591) 

Constant term 0.231*** 0.129** 
 (0.067) (0.0602) 

N 1710 1710 
Standarderrors in parentheses;* p<0.1, ** p<0.05, *** p<0.01 

 

3.2. Heterogeneity analysis 
In this paper, cities are divided into eastern, central and 

western cities for heterogeneity analysis of digital finance for 
green economy growth. Table 3 shows the results of the 

heterogeneity analysis. The contribution of digital finance to 
green economic growth in central cities is relatively small. 
Digital finance contributes relatively more to green economic 
growth in eastern and western cities. 

 
Table 3. Heterogeneous influence of digital finance on green economic growth 

digfin 0.0582** 0.0233** 0.0584*** 
 (0.0115) (0.0102) (0.0191) 

post 0.00976 0.0237* 0.0215 
 (0.0156) (0.0131) (0.0191) 

loans 0.0121 0.0369* -0.146*** 
 (0.0354) (0.0209) (0.0439) 

humcap 0.00139 -0.00558 0.00593 
 (0.0047) (0.0046) (0.0074) 

fdi 0.693** -1.016 -1.649 
 (0.3511) (0.7055) (1.7963) 

Constant term 0.199* 0.282*** 0.128 
 (0.1148) (.0999) (0.1485) 

Fixed Effect YES YES YES 
N 690 648 372 

R-sq 0.0843 0.1501 0.1363 
Standarderrors in parentheses;* p<0.1, ** p<0.05, *** p<0.01 

 
Table 4. Mediating effect test 

Variables 
(1) (2) (3) 

Step1 Step2 Step3 
digfin 0.0430*** 0.0662*** 0.0383*** 

 (0.0073) (0.0055) (0.0077) 
isu   0.0697** 

   (0.0349) 
post 0.0154* -0.00133 0.0155* 

 (0.0089) (0.0067) (0.0088) 
loans -0.0252 0.230*** -0.0413** 

 (0.0171) (0.013) (0.0188) 
humcap 0.000406 0.0260*** -0.00141 

 (0.0031) (0.0023) (0.0032) 
fdi -0.142 -0.329 -0.119 

 (0.3168) (0.2407) (0.3166) 
Constant term 0.231*** 3.510*** -0.0142 

 (0.067) (0.0509) (0.1396) 
Fixed Effect Yes Yes Yes 

N 1710 1710 1710 
R-sq 0.1447 0.5098 0.1422 

Standarderrors in parentheses; * p<0.1, ** p<0.05, *** p<0.01 
 



 

50 

 

3.3. Mechanism of inspection 
This paper constructs a mediating effect model to analyze 

whether digital finance can promote green economic growth 
by influencing industrial structure upgrading. Table 4 displays 
the regression results of the mediating effect model. Column 
(2) of Table 4 displays that digital finance has a positive 
influence on industrial structure upgrading at the 99% 
significance level. Column (3) of Table 4 displays that digital 
finance and industrial structure upgrading have a significant 
positive influence on green economic growth. This indicates 
that digital finance can promote green economic growth by 
influencing industrial structure upgrading. 

3.4. Threshold effect analysis 
Referring to Hansen (2000), this paper uses the depth of 

digital finance use as a threshold variable to construct a 
threshold model to test whether digital finance has a nonlinear 
effect on green economic growth. Table 5 demonstrates the 
results of the double threshold effect test. Table 5 
demonstrates that there is a single threshold effect at the 90% 
significance level. The threshold value is 5.1390. 

To better understand the estimation of the threshold value 
and the process of constructing confidence intervals, this can 
be done with the help of the likelihood ratio function plot, 
where the likelihood ratio function sequence LR( γ) is used as 
a trend plot for the threshold parameter, and when the 
likelihood ratio LR( γ) is 0, the threshold value is calculated 
as γ = 5.1390, as demonstrated in Figure 1, and the 95% 
confidence interval for γ is indicated below the dashed line. 

 
Table 5. Threshold effect test results 

Number of thresholds F-statistic P-value Threshold value 
Single Threshold 27.87 0.0867 5.1390 
Double Threshold 8.65 0.8433 4.2726 

 

 
Figure 1. Confidence interval of the single threshold model 

 
Table 6 shows the results of the panel threshold model 

regression. As can be seen from Table 6:Digital finance has a 
significant threshold effect on green economic growth when 
the depth of digital finance use (digifin2) is the threshold 
variable. Specifically, at the 99% significance level, the effect 
of digital finance on green economic growth is 0.0362 when 
the depth of digital finance use (digifin2) is less than 5.1390 
and 0.0431 when the depth of digital finance use (digifin 2) is 

greater than 5.1390.This indicates that when the depth of 
digital finance use (digifin2) is depth (digifin 2) is higher, the 
greater the contribution of digital finance to green economic 
growth. The threshold effect of digital finance on green 
economic growth arises because when the depth of digital 
finance use exceeds a certain threshold, digital finance has a 
better contribution to the upgrading of industrial structure and 
thus better promotes green economic growth. 

 
Table 6. Regression results of threshold model 

Variables Digfin2<5.1390 Digfin2>51390 
digfin 0.0362***  

 (0.0074)  
digfin  0.0431*** 

  (0.0072) 
post 0.0147* 0.0147* 

 (0.0088) (0.0088) 
loans 0.0346** 0.0346** 

 (0.017) (0.017) 
humcap 0.00445 0.00445 

 (0.0032) (0.0032) 
fdi 0.0341 0.0341 

 (0.3151) (0.3151) 
Fixed Effect YES YES 

N 1710 1710 
Constant term 0.229*** 0.229*** 

 -0.0665 -0.0665 



 

51 

 

3.5. Robustness checks 
In this paper, we use depth of digital financial usage 

(digfin2) and breadth of digital financial coverage (digfin3) 
to replace the core parsing variables for robustness testing. 

Table 7 demonstrates the results of the robustness tests. Both 
depth of financial usage (digfin2) and breadth of digital 
financial coverage (digfin3) have a significant contribution to 
green economic growth. This indicates that the results of this 
paper are robust. 

 
Table 7. Robustness test results 

Variables (1) (2) 
digfin2 0.0375***  

 (0.0075)  
digfin3  0.0299*** 

  (0.0064) 
Controls YES YES 

Fixed Effect YES YES 
Constant term 0.244*** 0.290*** 

 (0.0681) (0.0653) 
N 1710 1710 

R-sq 0.1524 0.155 
Standarderrors in parentheses; * p<0.1, ** p<0.05, *** p<0.01 

 

4. Conclusion 
This paper constructs a balanced panel model of 285 cities 

in China to study the impact of digital finance on green 
economic growth. It is found that: first, digital finance has a 
significant contribution to green economic growth. Second, 
green finance can enhance green economic growth by 
promoting industrial structure upgrading. Third, green 
finance has a higher contribution to green economic growth 
in eastern cities and western cities than in the east. Finally, 
digital finance has a significant threshold effect on green 
economic growth, and when the depth of digital finance 
exceeds the threshold, the promotion effect of digital finance 
on green economic growth is greater. 

The results of the study have the following policy 
implications: first, accelerate the development of digital 
finance, enhance the coverage of digital financial services, 
and promote a balanced distribution of financial resources. 
Second, the government should formulate policies to further 
improve the development of digital finance in central and 
western China. By formulating effective digital finance 
policies, it will in turn improve the level of green economic 
growth and high-quality economic development. 

References 
[1] Bailey, et al. " Making sense of the gren economy." 

Geografiska Annaler (2014). 

[2] Daming You, et al."Environmental regulation and firm eco-
innovation: Evidence of moderating effects of fiscal 
decentralization and political competition from listed Chinese 
industrial companies." Journal of Cleaner Production 
207.(2019):. doi:10.1016/j.jclepro.2018.10.106. 

[3] Daron Acemoglu, et al."Transition to Clean Technology."  
124.1(2016):. doi:10.1086/684511. 

[4] Guo, F., J. Wang, F. Wang, T. Kong, X. Zhang, and Z. Cheng. 
2020. “Measuring the Development of Digital Inclusive 
Finance in China: Index Compilation and Spatial 
Characteristics.” China Economic Quarterly 19 (4): 1401–1418. 

[5] Jalil Abdul,and Feridun Mete."The impact of growth, energy 
and financial development on the environment in China: A 
cointegration analysis." Energy Economics 33.2(2010):. 
doi:10.1016/j.eneco.2010.10.003. 

[6] Jianhua Yin, et al."The effects of environmental regulation and 
technical progress on CO 2 Kuznets curve: An evidence from 
China." Energy Policy 77.(2015):. 
doi:10.1016/j.enpol.2014.11.008. 

[7] Kazi Sohag, et al."Green economic growth, cleaner energy and 
militarization: Evidence from Turkey." Resources Policy 
63.(2019):. doi:10.1016/j.resourpol.2019.101407. 

[8] Khan D ,  Ulucak R . How do environmental technologies 
affect green growth? Evidence from BRICS economies[J]. 
Science of The Total Environment, 2020, 712:136504.  

[9] Michalopoulos, S. ,  R. Levine , and  L. A. Laeven . 
"Financial Innovation and Endogenous Growth." Journal of 
Financial Intermediation 24(2015). 

[10] Muhammad Shahbaz, et al."Does financial development 
reduce CO 2 emissions in Malaysian economy? A time series 
analysis." Economic Modelling 35.(2013):. 
doi:10.1016/j.econmod.2013.06.037. 

[11] Nagasaka. “The Economist,1977, November 26, pp.82-83. 

[12] Ramón Padilla-Pérez, ,and Yannick Gaudin."Science, 
technology and innovation policies in small and developing 
economies: The case of Central America." Research Policy 
43.4(2014):. doi:10.1016/j.respol.2013.10.011. 

[13] Roberto Moro Visconti, ,and Donato Morea. "Healthcare 
Digitalization and Pay-For-Performance Incentives in Smart 
Hospital Project Financing." International Journal of 
Environmental Research and Public Health 17.7(2020):. 
doi:10.3390/ijerph17072318. 

[14] Ron Boschma, et al."Towards a theory of regional 
diversification: combining insights from Evolutionary 
Economic Geography and Transition Studies." Regional 
Studies 51.1(2017):. doi:10.1080/00343404.2016.1258460. 

[15] Sylvia Lorek, ,and Joachim H. Spangenberg."Sustainable 
consumption within a sustainable economy – beyond green 
growth and green economies." Journal of Cleaner Production 
63.(2014):. doi:10.1016/j.jclepro.2013.08.045. 

[16] Thorsten, et al. "Industry growth and capital allocation:: does 
having a market- or bank-based system matter?." Journal of 
Financial Economics (2002). doi:10.1016/S0304-
405X(02)00074 

[17] Wanxin Wang, et al."The evolution of equity crowdfunding: 
Insights from co-investments of angels and the crowd." 
Research Policy .(2019):. doi:10.1016/j.respol.2019.01.003. 



 

52 

[18] Wurgler, J. A. . "Financial Markets And The Allocation Of 
Capital." Ssrn Electronic Journal (1999). 

[19] Y.H. Chung, et al."Productivity and Undesirable Outputs: A 
Directional Distance Function Approach." Journal of 
Environmental Management 51.3(1997). doi:10.1006/jema. 
1997.0146. 

[20] Yuanchao Bian, et al. "Market segmentation, resource 
misallocation and environmental pollution." Journal of Cleaner 
Production 228.(2019):. doi:10.1016/j.jclepro. 2019.04.286 

[21] Zhonghua Cheng, et al. "Natural resource abundance, resource 
industry dependence and economic green growth in China." 
Resources Policy 68.(2020):. 
doi:10.1016/j.resourpol.2020.101734.c