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Original Research
Green finance, fintech and environmental protection: Evidence from
China
Tadiwanashe Muganyi
a
,
b
, Linnan Yan
a
, Hua-ping Sun
c
,
*
a
Shanghai University School of Economics, Baoshan, Shanghai, People's republic of China
b
Warwick Business School, The University of Warwick, Coventry, CV4 7AL, UK
c
Jiangsu University, Zhejiang, People's republic of China
article info
Article history:
Received 24 March 2021
Received in revised form
17 June 2021
Accepted 18 June 2021
Keywords:
Green finance
Fintech
Environmental protection
Green consumption
abstract
This paper is one of the first to offer a comprehensive analysis of the impact of green finance related
policies in China, utilizing text analysis and panel data from 290 cities between 2011 and 2018.
Employing the Semi-parametric Difference-in-Differences (SDID) we show that overall China's green
finance related policies have led to a significant reduction in industrial gas emissions in the review
period. Additionally, we found that Fintech development contributes to the depletion of sulphur dioxide
emissions and has a positive impact on environmental protection investment initiatives. China is poised
to be a global leader in green finance policy implementation and regulators need to accelerate the
formulation of green finance products and enhance the capacity of financial institutions to offer green
credit. While minimizing the systemic risk fintech poses, policy makers should encourage fintechs to
actively participate in environmental protection initiatives that promote green consumption.
©2021 The Author(s). Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences,
Harbin Institute of Technology, Chinese Research Academy of Environmental Sciences. This is an open
access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
China's green finance industry is growing rapidly, transforming
the country's financial sector in the process. Although green finance
has been a hot topic amongst researchers in recent years, it remains
conceptually unclear (Dayong [1]. Green finance refers to financial
investments targeted at environmental protection initiatives [2].
There are three main categories of green finance namely green-
asset finance, credit &investments [3]. Green finance seeks to
engage the private sector in the funding of environmental projects
to bridge the gap left by insufficient public budgets (see Fig. 1).
Developing countries face many credit constraints that increase
the likelihood of poor environmental performance, hence the need
for active green finance policies [4]. It is the prerogative of gov-
ernments in developing countries to develop and implement pol-
icies that promote green finance systems [5]. Financial instruments
like green bonds are now being employed to ensure environmental
projects are funded in a sustainable way. Green bonds are fixed
income instruments aimed at supporting environmental projects.
To increase uptake and narrow the green finance gap, these bonds
often have several tax incentives attached to them [6].
Green finance has become a key policy concern for emerging
economies. China's 13th Five Year Plan proposed the creation of a
green financial system that encourages the private sector to play a
more active role in sustainable development. Green bonds are
favored by shareholders as they have the potential to increase firm
value in the long run (J [7]. Although green finance is an increas-
ingly important policy issue in China several barriers still exist at
the operational micro and meso levels (M [8].
While there is still a need for greater coordination between
policy makers and key stakeholders, green finance in China is
already yielding positive results. There is an inverse relationship
between green investment and CO
2
emissions [9]. Green financing
alone cannot guarantee successful environmental protection ini-
tiatives, it has to be augmented by significant investment in both
human capital and technological innovation [10]. Green finance is
expected to play an important role in the attainment of climate
change goals outlined in the Paris Agreement. China is at the
forefront of green finance initiatives globally with most benefi-
ciaries being developing countries with a stable political environ-
ment with significantly high levels of credit risk [11].
Green finance initiatives face complex risk dimensions that affect
their overall performance. For example, carbon financing includes
*Corresponding author.
E-mail addresses: shp@ujs.edu.cn,shp797@163.com (H.-p. Sun).
Contents lists available at ScienceDirect
Environmental Science and Ecotechnology
journal homepage: www.journals.elsevier.com/environmental-science-and-
ecotechnology/
https://doi.org/10.1016/j.ese.2021.100107
2666-4984/©2021 The Author(s). Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences, Harbin Institute of Technology, Chinese Research
Academy of Environmental Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Environmental Science and Ecotechnology 7 (2021) 100107
significant risks for financial institutions. To ensure the long-term
sustainability of green finance projects, financial institutions need
to hedge against associated risks (X [12]. Although green finance
significantly reduces emissions, over the short-term it has a negative
effect on highly polluting firm performance (D [13].
2. China's green finance gap
Green finance policies play a guiding role in credit supply to
enterprises in areas with less developed financial ecosystems as
well as state owned enterprises [14]. China is a global leader in
green credit policy implementation. It is important for green
finance policy to leverage technology and preexisting relationships
between banks and enterprises (F [15].
Green finance policies that are based on strict carbon emission
regulation often lead to a win-win scenario for both manufacturers
and suppliers [16]. Green finance policies also have a positive
impact on the manufacturing industry [17 ]. showed a positive
correlation between green finance instruments and firm innova-
tion, further concluding that green finance has the potential to play
an imperative role in China's transition to intelligent and sustain-
able manufacturing. Increased scrutiny on green bonds in global
capital markets is facilitating the development of a more dynamic
green finance ecosystem in China [18].
In most emerging markets including China, the effectiveness of
green finance initiatives is dependent on the level of economic
development. Developed regions are more significantly impacted
by bank green finance activities (X [19]. Green finance is essential
for sustainable and clean production within the economy. By
strengthening the integrity of green finance systems, governments
can achieve their sustainable development goals [20]. Green
finance policies require coordination amongst all stakeholders to
improve effectiveness and guarantee continuity which is not al-
ways the case [21].
Commercial banks in China greatly value green management.
Firms with comprehensive green management strategies can get
access to significantly larger lines of credit [22]. Chinese state-
owned banks are playing an increasingly leading role in the allo-
cation of green finance in credit markets. This edge has come at a
cost, as green finance initiatives tend to be more profitable for
private banks than for state owned banks. State own banks are
forced to take on greater risk to meet green finance policy objec-
tives [23]. Banks should focus on encouraging green finance tar-
geted at achieving green transitions based on stable prices and
mature technology to achieve sustainable environmental outcomes
(C [24]. To achieve optimal results, green finance policies should
also consider the technology inter-dependencies which influence
the whole financial system and value chains [25].
For green finance to achieve its objectives there is need for
stronger regulations that remedy information asymmetry and
moral hazards, while ensuring an optimal balance between ecology
and finance (Y [26]. To close the green finance gap, there is need to
develop sustainable investment vehicles based on long-term policy
perspectives [27]. China's green transition financing model is one of
the most comprehensive in the world and it is on track to be the
global leader in green finance [28].
Predicated on the theoretical analysis presented above, we
developed the following hypothesis:
I. Green finance policies promote positive environmental
outcomes
2.1. Role of fintech in green finance
Fintech is poised to play a leading role in the provision of green
finance through leveraging big data analytics and artificial intelli-
gence to foster a green transition amongst consumers and small
and medium-sized enterprises (SMEs) [29]. There is a research gap
regarding the involvement of fintech in environmental protection
efforts in China. This gap is attributed to many fintech companies
not being actively involved in these efforts, except for Ant Groups'
bespoke Ant Forest initiative. Ant forest is a model example of how
fintech platforms can encourage consumers to actively participate
in green finance projects. The award-winning initiative encourages
a sustainable green transition amongst consumers and SMEs (H
[30]. The Ant forest mini program encourages Alipay's consumers
to reduce their carbon footprint by rewarding green behaviors such
as walking, cycling, taking public transport, and paying utilities
online. Users can accumulate these carbon savings and earn green
energy which they can use to grow a virtual tree, which Alipay will
eventually turn into a real tree as part of a desert reclamation
project.
Other fintech companies are actively incorporating “green
financial system”measures aimed at using technology to reduce
carbon emissions and facilitate efficient resource utilization. Fin-
tech has been credited with promoting the adoption of green
agricultural practices in China by ensuring credit availability,
addressing information asymmetry, and increasing trust amongst
farming communities [31]. Empirical evidence shows that China's
internet development has had a noticeable negative impact on
energy consumption through promoting financial development
and industrial upgrading [21].
As China's fintech ecosystem continues to expand, it is expected
to play an important role in the country's transition to a new green
financial system. Fintech platforms accelerate both the procure-
ment and deployment of funds earmarked for environmental pro-
jects [32]. Green bonds can help boost the financial performance of
fintech firms while providing a conduit for long term green in-
vestment [33]. Fintechs have the potential to accelerate China's
transition to a new green financial system that will promote cleaner
production through intelligent manufacturing and other green
management processes.
Subsequently, we developed a 2nd and 3rd hypothesis.
II. Fintech development has a negative impact on SO
2
emissions
in China
III. Fintech enhances environmental investment initiatives
3. Data and methodology
This study utilizes the research policy text analysis database to
identify when related green finance policies were affected across
Fig. 1. Number of cities with a green finance related policy initiative
Source: Ruiyan database.
T. Muganyi, L. Yan and H.-p. Sun Environmental Science and Ecotechnology 7 (2021) 100107
2
290 cities in China. The study period ranges from the year
2011e2018. To identify the relevant treatment effects, we utilized
the following keywords: green finance, green bonds, green credit,
green operations, and carbon finance. These green finance policy
initiatives vary across each city, however they all have the same
goal which is to promote environment protection initiatives.
Regulators in China are keen on achieving their climate and
environmental protection targets and green finance has been
identified as a key priority area. China is accelerating its efforts to
create a green financial system aimed at helping the country meet
its emission reduction targets and transition into a more sustain-
able model of industrial production. The visualization below shows
how rapidly green finance policy initiatives have been enacted
across China's cities.
3.1. Base model
Green finance policies in China include several follow up ini-
tiatives to ensure set targets are met. To analyze our first hypothesis
a semi parametric difference-in-differences (SDID) model is uti-
lized. SDID is best suited for the analysis of treatment effects based
on non-experimental panel data with multiple rounds of policy
measures [34]. The average treatment effect (ATT) on Chinese cities
can be represented by equation (1) below.
ATT≡Eðy
1t
y
0t
jd
t
¼1Þ(1)
Where, y
1t
denotes the environmental variable of interest after
green finance related policies are put into effect at time t and y
0t
represents the same variable before the treatment effect, d
t
shows
whether a city enacted a green finance related policy at time t.
When ts0 after the baseline dis equal to 1.
To address the unevenness of characteristics between treated
and untreated groups [35], suggests a reweighted estimate of the
ATT given in equation (2).
Ey
t
y
z
Pðd
t
¼1Þd
t
p
ðx
z
Þ
1
p
ðx
z
Þ(2)
Abadie's weighted estimate (2) is unbiased if the following
equations (3) and (4) hold.
Eðy
1t
y
0t
jd
t
¼1;x
z
Þ¼Eðy
0t
y
0z
jd
t
¼0;x
z
Þ(3)
Pðd
t
¼1Þ>0and
p
ðx
z
Þ<1 (4)
Where, y
t
y
z
is the change in our environment interest variable
between baseline z and time t. Baseline characteristics of the con-
trol variables are represented by x
z
. The propensity score which
represents the conditional probability of being part of the policy
treatment group is denoted by the following identity:
p
ðx
z
Þ≡
Pðd
t
¼1jx
z
Þ. The propensity score for the SDID estimator can be
estimated using a linear probability model (LPM), as suggested by
Ref. [35] or a series logit estimator (SLE) postulated by Ref. [36].
The LPM propensity score can be derived from equation (5).
b
p
ðx
z
Þ¼b
g
0
þb
g
1
x
1
þX
k
i¼1
b
g
2i
x
i
2
(5)
Where, parameters are estimated using the ordinary least squares
(OLS) method and x
1
is a binary variable. The order of polynomial
function used to approximate the propensity score for LPM is given
as k and x
i
2
is a continuous variable represented by the following
expression: x
i
2
¼Q
i
j¼1
x
2
.
The SLE model produces the following propensity score:
b
p
ðx
z
Þ¼∧ b
g
0
þb
g
1
x
1
þX
k
k¼1
b
g
2k
x
k
2
!(6)
Where, ∧ðxÞis represented by the logistic function (7).
∧ðxÞ¼ e
x
1þe
x
(7)
To measure the impact of green finance related policies this
paper focuses on three environmental interest variables. The
dependent variables are highlighted in the following expression;
y
it
½ln ESO2
it
;ln DSD
it
and lnPSO2
it
, where ESO2
it
denotes emis-
sions of industrial sulphur dioxide (SO
2
);represents DSD
it
discharge of industrial smoke and dust and PSO2
it
is the volume of
industrial SO
2
produced. The environmental interest variables are
measured in tonnes for each city, I, at time t. The binary treatment
variable or time variable in our SDID model takes the value of 1
when a green finance related policy is affected and 0 at baseline (for
cities without green finance related policies).
3.2. Control variables
The control variables or x
it
employed in the model are the level
of industrialization in each city, Ind
it
, measured as a secondary
industry value added as a percentage of gross domestic product
(GDP). To control for the level of economic activity and size, we
employed the GDP per capita represented by GDPpc
it
and the level
of trade openness in each city, TOP
it
.
3.3. Impact of fintech on environmental protection
China's fintech industry has grown expeditiously in the period
under review. Our second hypothesis is dependent on the
assumption that the fintech sector growth has contributed signif-
icantly to the decline of industrial gas emissions in Chinese cities.
Fintech is central to a few green initiatives including the sharing
economy. To test our second hypothesis, we utilize the econometric
model specified below.
SO2
it
¼
a
0
þ
a
1
Fintech
it1
þ
a
c
Control
it1
þε
it
(8)
Where, the criterion variable SO2
it
represents SO
2
emissions in city,
I, at time t and Fintech
it1
is the lagged fintech variable and it is
based on the Peking University digital financial inclusion index
(PKU-DFIC) [37]. The Index utilizes Ant Group's comprehensive
data on fintech usage across China including digitalization levels,
coverage breath, usage depth and other key indicators. How these
factors are weighted is included in the appendix section. Lagged
control variables include city GDP per capita (GDPpc
it1
), level of
trade openness ( TOP
it1
), and industrialization level (Ind
it1
). To
capture the state of pollution within each city we also include the
current level of discharge of industrial smoke and dust which is
represented by DSD
it
.
SO2
it
¼
a
0
þ
a
1
Fintech
it1
þ
a
2
DSD
it
þ
a
3
GDPpc
it1
þ
a
4
Ind
it1
þ
a
5
TOP
it1
þε
it
(9)
To test our 3rd and final hypothesis, we utilize provincial data on
environmental protection investment. Fintech is expected to have a
positive impact on environmental protection investment. The
following econometric model is employed:
T. Muganyi, L. Yan and H.-p. Sun Environmental Science and Ecotechnology 7 (2021) 100107
3
EPI
jt
¼
a
0
þ
a
1
Fintech
jt1
þ
a
c
Control
jt1
þε
jt
(10)
Where, the dependent variable is environmental prevention in-
vestment denoted by EPI
jt
for Chinese province, j, at time t. The
level of fintech development in each province is given as Fintech
jt1
which is a lagged variable. Control variables are similar to those
employed for city level data with the addition of the rate of ur-
banization which is denoted by Urb
jt1
. We also expand our esti-
mation model to include other environmental control variables
namely, RRI
jt
, which represents the recycling rate of solid industrial
wastes and IWG
jt
denoting the industrial waste gas emissions of in
each province.
EPI
jt
¼
a
0
þ
a
1
Fintech
jt1
þ
a
2
RRI
jt
þ
a
3
IWG
jt
þ
a
4
GDPpc
jt1
þ
a
5
Ind
jt1
þ
a
6
TOP
jt1
þ
a
7
Urb
jt1
þε
jt
(11)
3.4. Cross sectional dependency
Panel data analysis cross sectional dependency can lead to
spurious estimation results [38]. This study will conduct the
Pesaran CD test which holds for fixed values of either T and N [39].
The Pesaran CD statistic is calculated using equation (12).
CD ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2T
NðN1Þ
s0
@X
N1
i¼1
X
N
J¼iþ1
b
r
ij
1
A/
d
Nð0;1Þ(12)
Where, b
r
ij
the error term pairwise-correlation sample estimate.
When T/∞and N/∞the tends to a normal distribution.
4. Results and discussions
This study utilizes City panel data from 290 Chinese cities be-
tween the years 2011 and 2018. The research policy text analysis
database offers a comprehensive compendium of green finance
related policies used as a treatment effect in this study. The text
analysis database utilized in this study is the Ruiyan database
(2020), published by China's main administrative authorities and
regulatory bodies, at the national, provincial, and municipal levels.
To determine our treatment group, we collated the following key-
words from the database “green credit”,“green bond”,“green
operation”, and “carbon finance”. Treatment group cities are shown
in Table A1 in the appendix section. The earliest time of policy
appearance is regarded as the policy initiation point. The table
below shows the city data characteristics.
Green finance policies are expected to result in lower industrial
gas emissions and lead to more investment in environmental
protection projects within China's cities (see Table 1). Our SDID
approach seeks to capture how these policies affect environmental
outcomes across China. Table 2 below highlights the characteristics
of our entire sample.
4.1. Green finance and environmental protection
To test whether or not green finance policies implemented
across China's city led to positive environmental outcomes, we use
the SDID estimator for both the LPM and SLE approaches. The LPM
employed is of order 4. Dependent variables include SO
2
emissions
and volume produced as well as discharge of industrial smoke and
dust. SDID estimation results for both LPM and SLE are presented in
Table 3.
SDID estimation results confirm our first hypothesis that green
finance related policies leads to positive environmental outcomes.
For both LPM and SLE models, our environmental interest variables
have negative significant coefficients. Green finance related policies
implemented between the period 2011 to 2018 in Chinese cities
have had an overall negative impact on industrial gas emissions.
Overall, using SLE results, green finance policies have led to a 38%
decline in SO
2
emissions, a 28% decline in industrial gas and smoke
discharge, and a 20% decline in the volume of SO
2
produced in
China's cities in the period under review.
4.2. Fintech and environmental protection
To analyze the impact of fintech growth on industrial gas
emissions outlined in our second hypothesis, we estimate equa-
tions (8) and (9) using the fixed effects (FE) model. Hausman test
Table 1
Data characteristics.
Variable Measurement Source Obs Mean SD Min Max
Sulphur Dioxide Emissions ( ESO2
)
Emissions of industrial Sulphur Dioxide in tonnes (log) EPS (2019) 2,447 10.226 1.154 0.693 13.25
Smoke and Dust Discharge ( DSDÞDischarge of industrial smoke and dust in tonnes (log) EPS (2019) 2,186 9.781 1.168 2.398 15.458
Volume of Sulphur Dioxide
(PSO2)
Volume of industrial Sulphur Dioxide in tonnes savings
(log)
EPS (2019) 1,682 11.505 1.298 0.693 14.569
Fintech Index (Fintech)PKU-DFIC (log) Institute of Digital Finance, PKU
(2019)
2,311 4.936 0.514 2.834 5.714
GDP per capita (GDPpc) GDP per capita in RMB (log) China City Yearbook (2010e2018) 2,528 10.601 0.593 8.576 13.056
Trade Openness (TOP) Actual foreign investment USD 10,000 (log) EPS (2019) 2,149 10.177 1.728 0.693 14.9469
Industrialization (Ind)Secondary industry value added as a % of GDP (log) EPS (2019) 2,585 3.843 0.271 2.402 4.497
Green Finance Related policy
(DID)
Binary variable 1 for cities with policy &0 for cities without Research Policy text analysis database 2,601 0.204 0.403 0 1
Table 2
City policy data characteristics.
Entire
Sample
policy Non-policy diff.
DiD 0.20
[0.40]
ES02 10.23 9.84 10.32 0.48***
[1.16] [1.20] [1.12] (0.06)
DSD 9.78 9.56 9.84 0.28***
[1.17] [1.20] [1.15] (0.06)
PSO2 11.51 11.37 11.53 0.16*
[1.30] [1.26] [1.30] (0.08)
Ind 3.84 3.8 3.85 0.05***
[0.27] [0.25] [0.27] (0.01)
TOP 10.18 10.85 10.02 0.83***
[1.73] [1.68] [1.70] (0.09)
GDPpc 10.60 10.70 10.58 0.12***
[0.59] [0.61] [0.59] (0.03)
observations 2601 530 2071 2601
Notes: Standard errors are in parentheses. Significance levels are denoted as fol-
lows: *p<0.10, **p<0.05, and ***p<0.01.
T. Muganyi, L. Yan and H.-p. Sun Environmental Science and Ecotechnology 7 (2021) 100107
4
results confirmed the suitability of our model. We adopted a similar
approach to estimate equations (10) and (11) to determine the
impact of fintech on environmental protection investment across
China's provinces. Provincial data summary statistics are given in
the appendix section. We conducted the Pesaran CD test for both
models and results are reported in Table 4.
Pesaran CD results reject the null hypothesis for model A.
Showing that city panel data errors are not weakly cross sectionally
dependent. This result can be attributed to structural factors and
economic shocks that affected industrial gas emissions in China's
cities during the study period. To account for cross sectional de-
pendency in city data analysis, we report our FE model estimation
results with clustered standard errors. Model B shows that for
provincial data analysis, we fail to reject the null hypothesis. Esti-
mation results for both city data analysis are shown in Table 5 (see
Table 6).
Our estimation results show that fintech development in China's
cities has a significant negative impact on industrial gas emissions.
Taking model (2) results from Table 5, the fintech variable has a
coefficient of 0.153. This values indicates that a 1% increase in
fintech development contributes to a 15% decline in SO
2
emissions
across China's 290 cities. This result is a novel finding and it points
to the role fintech can play in facilitating China's transition to a
green financial system. Fintech firms can play an integral role in the
provision of green finance and promote environmentally friendly
consumption. This result confirms our second hypothesis.
Provincial level estimation results show that fintech promotes
environmental protection investment. The fintech coefficient re-
ported above in provincial model (2) is 0.117, which indicates that a
1% increase in fintech development at the provincial level enhances
environmental protection investment by 11%. These results show
that despite the systemic risks fintech in China poses, it has the
potential to promote green finance initiatives that channel the
much needed financial resources to fund environmental protection
and prevention projects.
5. Conclusion and policy implications
Green finance is not just a global trend, but it has become an
important channel for industrialized nations to achieve sustainable
growth. China is at the forefront of green finance policy investiga-
tion and implementation. Financial regulators in China are inves-
tigating how to include green credit in macro prudential policy
Table 3
Effect of Green Finance related policies on environmental interest variables.
Environmental variable (ATT)
LPM SLE
(1) (2) (3) (4) (5) (6)
ESO2 DSD PSO2 ESO2 DSD PS02
Constant 0.432*** 0.349*** 0.211** 0.381*** 0.281*** 0.199**
(0.074) (0.081) (0.090) (0.075) (0.078) (0.087)
Observations 2047 1771 1571 2066 1791 1590
Notes: Models (1), (2) and (3) are reported using an LPM of degree 4. Models (4), (5), and (6) are reported using default SLE. Standard errors are in parentheses. Significance
levels are denoted as follows: *p<0.10, **p<0.05, and ***p<0.01.
Table 4
Pesaran CD test results.
Model A (SO2
it
)Model B ðEPI
jt
Þ
CD statistic P value CD statistic P value
88.058 *** 0.0000 1.006 0.314
Note: H
0
errors are weakly cross-sectional dependent, CD Nð0;1Þand *** indicates significance at the 1% level.
Table 5
Impact of fintech on industrial gas emissions.
Variables Criterion (SO2
it
)
(1) (2)
Fintech
it1
0.166*** 0.153***
(0.060) (0.049)
DSD
it
0.471***
(0.052)
GDPpc
it1
1.172*** 0.832***
(0.179) (0.146)
TOP
it1
0.011 0.014***
(0.021) (0.018)
Ind
it1
1.491*** 0.999***
(0.237) (0.180)
City FE Yes Yes
Year FE Yes Yes
Observations 1,797 1787
F Statistic 116.54*** 133.16***
R
2
0.3774 0.5522
Notes: Standard errors are in parentheses. Model (1) and (2) report FE estimation
with clustered standard errors Significance levels are denoted as follows: *p<0.10,
**p<0.05, and ***p<0.01.
Table 6
Impact of fintech on provincial environmental protection investment.
Variables Criterion (EPI
jt
Þ
(1) (2)
Fintech
jt1
0.103** 0.117***
(0.042) (0.042)
RRI
jt
0.243**
(0.113)
IWG
jt
0.198
(0.122)
GDPpc
Jt1
0.054*** 0.050***
(0.017) (0.017)
TOP
Jt1
0.560** 0.456*
(0.255) (0.260)
Ind
jt1
0.022*** 0.016*
(0.008) (0.009)
Urb
jt1
0.067*** 0.054***
(0.014) (0.015)
Province FE Yes Yes
Year FE Yes Yes
Observations 248 248
F Statistic 16.40*** 12.79***
R
2
0.2789 0.2989
Notes: Standard errors are in parentheses. Significance levels are denoted as fol-
lows: *p<0.10, **p<0.05, and ***p<0.01.
T. Muganyi, L. Yan and H.-p. Sun Environmental Science and Ecotechnology 7 (2021) 100107
5
analysis. The People’Bank of China (PBOC) is working on devel-
oping more green finance products that will help accelerate the
green transformation of China's financial system. According to PWC
(2017), green finance has the potential to reduce credit risk, in-
crease financial openness, and encourage sustainable development.
This paper offers insights on how China is already implementing
a variety of green finance related policy initiatives and how that has
significantly impacted industrial gas emissions in the period under
review. Using the SDID model we proved that overall green finance
related policies lead to positive environmental outcomes (i.e.,
reduction in industrial emissions). Subsequently, the study in-
vestigates the role of fintech development in environmental pro-
tection. Our novel findings show that fintech development
contributes to reduced industrial gas emissions and augments
environmental protection investment initiatives. This finding gives
rise to the following policy implications:
Financial regulators need to accelerate the development of
green finance products and enhance the capacity of financial
institutions to offer green credit.
There is need for greater investment into fundamental research
on how green finance products can be implemented while
mitigating associated risks.
Regulators should encourage fintechs to actively participate in
green finance and environmental protection initiatives that
promote green consumption, while also minimizing the sys-
temic risk fintech poses.
Our study is not without a few caveats. Limited data availability
meant we could not exhaustively study the heterogenous factors
that affect our study questions including long-term impacts. The
lack of suitable instrumental variables limited our ability to address
endogeneity and simultaneity challenges. Despite these afore-
mentioned limitations, this paper offers important insights on how
green finance and fintech development can play a vital role in
promoting environmental protection and sustainable growth.
Fund
Key Program of National Social Science Fund of China
(21AZD067).
CRediT authorship contribution statement
Tadiwanashe Muganyi: Conceptualization, Methodology, Proj-
ect administration, Writing eoriginal draft, Software. Linnan Yan:
Data curation, Investigation, Formal analysis. Hua-ping Sun:
Methodology, Writing ereview &editing.
Declaration of competing interest
We confirm that this paper is our original work with all data
acquired by our investigation and there is no plagiarism in it. We
also confirm that this manuscript has not been published elsewhere
and is not under consideration by another journal. The authors have
no conflicts of interest to declare.
Acknowledgements
We are grateful to all independent reviewers who contributed to
this paper.
Appendix A. Supplementary data
Supplementary data to this article can be found online at
https://doi.org/10.1016/j.ese.2021.100107.
Appendix
A1
Treatment Group Cities
Treatment Group Cities
Anqing Huangshi Qingdao Xingtai
Anyang Huizhou Qingyuan Xuancheng
Baiyin Ji'an Qingyang Ya'an
Baise Jinan Quzhou Yancheng
Baoding Jiamusi Quanzhou Yangzhou
Beijing Jiayuguan Sanmenxia Yangjiang
Benxi Jinhua Sanya Yangquan
Bozhou Jingmen Shanwei Yichun
Cangzhou Jingzhou Shanghai Yichang
Changzhou Jingdezhen Shangrao Yiyang
Chaoyang Jiujiang Shenzhen Yongzhou
Chenzhou Jiuquan Shiyan Yulin
Chengde Kaifeng Shijiazhuang Yueyang
Chongzuo Lishui Shuangyashan Yunfu
Dazhou Liuan Suizhou Zhangye
Dezhou Longyan Taizhou Changchun
Dingxi Luohe Tangshan Zhenjiang
Dongguan Ma'anshan Tianjin Zhongwei
Ezhou Maoming Tianshui Chongqing
Fuzhou Meishan Tonghua Zhoukou
Fuyang Meizhou Tongren Zhuzhou
Ganzhou Nanchang Wulumuqi Zhumadian
Guangzhou Nanping Wuxi
Guigang Nanyang Wuzhong
Handan Ningbo Wuhu
Heyuan Ningde Wuhan
Heihe Pingliang Wuwei
Hengshui Pingxiang Xianning
Hengyang Puyang Xiangyang
Huanggang Qinzhou Xinxiang
Source : Ruiyan Database
T. Muganyi, L. Yan and H.-p. Sun Environmental Science and Ecotechnology 7 (2021) 100107
6
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A2
Provincial data summary statistics.
Variable Measurement Source Obs Mean Std. Dev. Min Max
Fintech PKU-DFI (log) Institute of Digital Finance, PKU (2019) 248 4.676796 1.038375 1.345218 5.819044
IWG Industrial waste gas emissions (100M cubic meters (Log) EPS (2019) 248 9.590395 1.137573 4.733563 11.50336
RRI Recycling rate of industrial solid waste EPS (2019) 248 4.065193 0.644419 0.405465 4.831793
EPI Environmental prevention investment 100 million Yuan (log) EPS (2019) 248 5.320464 0.927774 1.386294 7.050077
Ind Secondary industry value added as a % of GDP (log) EPS (2019) 248 45.53116 8.434547 19.01 59
TOP Actual foreign investment USD 10,000 (log) EPS (2019) 248 0.281506 0.329081 0.016706 1.58716
Urb Provincial urbanization rate (log ) China Statistical Yearbook (2010-2018) 248 55.00306 13.6115 22.67 89.6
GDPpc GDP per capita in RMB (log) China Statistical Yearbook (2010-2018) 248 9.780281 2.833834 -2.5 17.4
A3
Fintech Index weight vectors.
Dimension Weight Vector
Coverage Breadth 54%
Depth of Usage 30%
Level of Digitalization 16%
Depth of Usage Service Dimensions Weight Vector
Payment 4%
Money Funds 6%
Credit Investigation 10%
Investment 25%
Insurance 16%
Credit 38%
Digitalization Service Dimensions Weight Vector
Mobility 50%
Credit 10%
Convenience 16%
Adorability 25%
Source: The Peking University digital financial inclusion index (PKU-DFIC) (Guo
et al., 2019).
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