International Review of Economics and Finance 103 (2025) 104481
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International Review of Economics and Finance
journal homepage: www.elsevier.com/locate/iref
Green credit, bank competitiveness and bank risk-taking
Meixuan Wu
Department of Finance, Shandong Technology and Business University, Yantai, 264003, China
A R T I C L E I N F O
A B S T R A C T
Keywords:
Green credit
Risk taking
Competitiveness
Mediation effect modeling
With the gradual improvement of people’s environmental awareness, the government and various
social groups have begun to realize the importance of green credit. Green credit is an important
tool of green finance and a bridge to introduce commercial banks into environmental control,
while enhancing core competitiveness is the intrinsic motivation for commercial banks to carry
out green credit business. The article takes 22 commercial banks as research samples, selects data
from 2009 to 2023, constructs commercial bank competitiveness scores, explores the relationship
between green credit and commercial bank competitiveness, and applies the mediation effect
model to test the role of risk-taking in it. It is found that green credit can significantly enhance the
core competitiveness of commercial banks, and the risk-taking indicator produces a significant
mediating effect. Therefore, the development of green credit has become a powerful weapon for
commercial banks to enhance their competitiveness.
1. Introduction
Along with the development of human society and the growth of human demand for environmental resources, the global envi­
ronment is facing problems such as climate deterioration, air and water pollution and loss of biodiversity, and these increasingly
serious problems are exacerbating environmental degradation and indelibly affecting human social and economic activities, with an
urgent need to take action to intervene. Promoting economic and social development in a green and low-carbon manner, and focusing
on promoting green and low-carbon production and lifestyles, is an important part of realizing high-quality development. Considering
that the realization of a green transformation of the economic model requires the investment of large amounts of capital, it has become
particularly urgent for financial institutions to implement a green credit model. Green credit, often referred to as sustainable financing
or environmental financing, mainly refers to loans issued by financial institutions to borrowing enterprises for investment in green
environmental protection, clean energy, circular economy, infrastructure and green upgrading and services for traditional industries.
In order to transform the mode of economic development and resolve the contradiction between economic development and ecological
and environmental protection, it is necessary to establish a sound green credit system (Table 1 and 2 and 11).
To this end, China has introduced and formulated a series of relevant policies.In July 2007, the national environmental regulatory
authorities, the People’s Bank of China and the China Banking Regulatory Commission jointly issued the Opinions on Implementing
Environmental Protection Policies and Regulations to Standardize Credit Risks, which treats green credit as a policy system and ar­
ranges for guidance on the reasonable layout of commercial banks’ credit investment. In February 2012, the China Banking Regulatory
Commission issued the Green Credit Guidelines, which put forward clear requirements for banking financial institutions to effectively
carry out green credit, vigorously promote energy conservation, emission reduction and environmental protection, effectively guard
against environmental and social risks, and better serve the real economy. The 2016 Guiding Opinions on the Construction of a Green
E-mail address: sdcjmeiyi@126.com.
https://doi.org/10.1016/j.iref.2025.104481
Received 10 April 2025; Received in revised form 3 June 2025; Accepted 25 July 2025
Available online 25 July 2025
1059-0560/© 2025 The Author.
Published by Elsevier Inc.
This is an open access article under the CC BY-NC license
(http://creativecommons.org/licenses/by-nc/4.0/).
International Review of Economics and Finance 103 (2025) 104481
M. Wu
Table 1
Selection of indicators.
Indicators
Observational indicators
Meaning of the indicator
Indicator
properties
Size
Total assets
Revenues
Deposit-taking
The larger the size of a bank’s total assets
Revenue from main operations + other operations
Total deposits taken by banks
forward
forward
forward
Profitability
Operating profit margin
Return on total assets
Return on net assets
Cost-to-income ratio
Operating profit/operating income
Net Profit/average total assets
Net profit/average net worth,
Operating costs/operating income
forward
forward
forward
negativity
Net interest margin
non-interest income
Intermediate business
income ratio
Net interest income/amount of interest-earning assets
Non-interest income/operating income
(Fee and commission income− expenses)/operating income
forward
forward
Forward
Safety
Provision coverage ratio
Non-performing loan ratio
Capital adequacy ratio
Allowance for loan losses/non-performing loan balance
Non-performing loans/total loans
Total capitalization/risk-weighted assets
forward
negativity
forward
Liquidity
Loan-to-deposit ratio
Liquidity ratio
Asset-liability ratio
Equity ratio
Loans/deposits
Current assets/current liabilities
Total liabilities/total assets
Liabilities/shareholders’ equity
negativity
forward
negativity
negativity
Growth
Net profit growth rate
Revenue growth rate
Total asset growth rate
EPS growth rate
Net profit is profit after income tax is deducted from gross profit
(Current operating income− previous operating income)/previous operating income
Indicators for examining the future development of banks
(Earnings per share for the current period− Earnings per share for the previous period)/
Earnings per share for the previous period
forward
forward
forward
forward
Business
Return on operating cash
flow
Net cash flow to net profit
ratio
Ratio of net cash flow to
liabilities
Increase in net cash flows for the period/net flows at the beginning of the period
forward
Net cash flow/total net profit
forward
Net cash flow/total liabilities
forward
Table 2
Validity check.
KMO quantity of sample suitability
Bartlett’s test of sphericity
0.847
approximate chi-square
degrees of freedom
significance
8282.492
276
0.000
Financial System further elevated the importance of green credit, and in 2017, the People’s Bank of China (PBOC) incorporated green
credit into its macro-prudential assessment system and started to evaluate and assess the implementation of green credit policies of
commercial banks.
As far as competitiveness is concerned, the traditional competitiveness of commercial banks is their safety, liquidity. Profitability
under the principle of sustainable development, through the interaction of internal resources and external environment, which is
superior to the competitors (Xiaoyan & Ge, 2018). In the operating activities of commercial banks, green credit is mainly manifested in
the principle of prudent management, through the formulation of green credit-related policies, the introduction of professionals in the
field of green economy, the development of green credit innovation products and other initiatives to support energy conservation and
environmental protection, renewable energy, clean projects and green high-tech industries, and to assume social responsibility with a
view to realizing its own sustainable development (Lingyun et al., 2018). Theoretically, the implementation of green credit may
directly or indirectly affect the competitiveness of commercial banks. Based on this, this paper attempts to theoretically clarify the
mechanism of the impact of green credit on the competitiveness of commercial banks, as well as how it affects bank competitiveness
through the risk-taking factor, in order to affirm the policy orientation of green credit and provide a reference for banking financial
institutions to correctly grasp the opportunities and challenges of green credit.
2. Literature review
2.1. Bank competitiveness
Regarding the definition of competitiveness, Wenqiao et al. (2019) pointed out that the competitiveness of commercial banks refers
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to the ability of banks to survive and continue to develop relative to their competitors by utilizing their own resources and providing
financial products and services while taking into account their social responsibilities. Na and Haifeng (2011), Yuexiang and Yongdong
(2011) argued that banks need to achieve a balanced development of the principle of trinity in the process of operation, need to
maximize profits while controlling risks, achieve sustainable development in the areas including corporate governance, management
status, innovative technology, business structure, and brand impact, and be able to respond in a timely and flexible manner in the face
of changes in the domestic and international environment. Ruopeng (2018) indicated that improving the construction of risk man­
agement system and strengthening data management became an important means to enhance the international competitiveness of
Chinese banks.A study conducted by Irfan, A. I et al. (2017) on top management found that from the management’s point of view,
innovation management, corporate resource management, and technology adoption, are more beneficial to the development of banks’
competitive strategies.
Regarding the selection of evaluation indexes and model construction, Junshan and Xudong (2021) proposed that the trade-offs
between various types of business means of commercial banks can have a great impact on the profitability level of commercial
banks, so the net return on total assets can be used as a proxy variable for the competitiveness level of commercial banks. Biying (2019)
proposed that the scale factor has been an important factor affecting the competitiveness of commercial banks, but scale growth is
characterized by diminishing marginal efficiency, and over time, the contradiction between scale expansion and bank efficiency will be
highlighted. Combined with the current macro background, Li (2018) proposed that accelerating the development of innovative
financial technology is a new enhancement point of competitiveness, and the index of the proportion of intermediate business and
non-interest income should be increased. Zhao (2019) takes the three items of scale, profitability and risk control ability as the
first-level indicators, and argues that profitability has the greatest impact on the competitiveness of commercial banks. Lixia et al.
(2022) selected 10 indicators from four levels of profitability, safety, growth and innovation and used factor analysis to measure the
comprehensive competitiveness indicators of commercial banks.
Xianming et al. (2014) and Yuman (2018) used factor analysis to empirically analyze the competitiveness of China’s commercial
banks in terms of scale and adequacy, and the results showed that large state-owned commercial banks ranked high in this regard. Hu
et al. (2016), JianKang and YuJuan (2017) empirically analyzed the competitiveness of China’s commercial banks using the data
envelopment method. Nan and Zhongchao (2012) constructed a competitiveness evaluation model of rural commercial banks based on
hierarchical analysis and studied the competitive ability of agricultural and commercial banks in East, Central and West China. Hang
(2015) evaluated the competitiveness of commercial banks with the help of fuzzy neural network method by selecting nine input
variables such as profitability.
2.2. Green credit and bank competitiveness
One view is that the implementation of green credit by commercial banks is conducive to competitiveness. Aupperle, K and John, D.
H. (1985) pointed out that companies that actively undertake social responsibility have a more significant competitive advantage
compared with other companies that do not. Ralph, Thomas, and Connel (2022) argued that enterprises vigorously develop green
finance can increase the economic value of the enterprise while at the same time be able to effectively solve the problem of moral
hazard and improve the efficiency of business management. Cilliers, E. J et al. (2010) and Ezikanyi, D. N (2014) are of the opinion that
green credit can effectively improve the business performance of commercial banks and promote sustainable development. The study
of Debasish, B (2016), on the other hand, found that in the long run green credit policy has a positive effect on banks’ asset quality and
rate of return, advocating for banks to go green and include environmental and ecological aspects as part of their lending principles.
Xiaoyan and Ge (2018) argue that green credit can create market opportunities and enhance bank competitiveness through
reputation effects. Wanlian and Xiaolian (2017), Chuanlin and Yongsheng (2019), and Hui et al. (2021) argue that assuming social
responsibility brings positive externalities for banks, and a bank that wins a higher sense of social acceptance and trust can form a
higher switching cost in comparison with other banks, which promotes competitiveness. Ping and Haifeng (2009), Yun et al. (2019),
and Qingmin et al. (2016) found that green credit has a positive contribution to the financial performance of commercial banks, and it
improves its market competitiveness while fulfilling its social responsibility. Hao (2021) introduced the perspective of risk manage­
ment culture into green credit and empirically examined the correlation between green credit and development quality of Chinese
commercial banks using a panel SVAR model, and the results showed that there is a positive correlation between the profitability and
risk management level of commercial banks and the green credit business of commercial banks.
Another view is that green credit can adversely affect bank competitiveness. Mathuva, D. M and Kiweu, J. M (2016) et al. analyzed
the relationship between social and environmental disclosure and financial performance of 212 financial institutions in Kenya and
found that those financial institutions that have enthusiastically implemented green credit and are active in social responsibility have
lower return on assets i.e. poorer profitability. Scholtens, B and Dam, L. (2007) analyzed the operations of two types of banks with and
without an Equator Principles policy and concluded that socially responsible behavior enhances bank reputation but comes at a cost in
terms of performance. Rongcai and Wenqiong (2016), Cheng, L et al. (2016), and Xiaoning and Guangyin (2017) indicated that green
credit business increases promotion, maintenance and management costs, increases sunk costs, and negatively affects the performance
of commercial banks, thus weakening on the core competitiveness of commercial banks.
2.3. Green credit and bank risk-taking
Most scholars believe that green credit will alleviate the level of bank risk-taking. Cui et al. (2016) point out that commercial banks
can minimize the bad debts caused by environmental risks by granting loans to environmentally friendly enterprises. Clarkson, P. M
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M. Wu
et al. (2008) believe that commercial banks can minimize environmental risks by carrying out a detailed pre-credit review as well as
post-credit tracking, and terminate the loan in time if the enterprise performs poorly, which can minimize the environmental risk.
Haifeng and Huiping (2023) empirically analyze that an increase in the degree of banks’ fulfillment of social responsibility reduces the
risk borne by banks, and that the fulfillment of social responsibility inhibits the risk bearing of joint-stock banks more strongly than
that of state-owned banks and city commercial banks. Li (2023) argues that green credit behavior can help commercial banks control
environmental risks and reduce risk costs. Jialan and Yuxin (2023) point out that green credit has a lower probability of default
compared to traditional credit, and the chances of bank losses have been reduced, and that increasing the proportion of green business
is important for controlling the proportion of non-performing loans, improving asset quality, and reducing risks. Xin and Ying (2021)
are of the view that the clear guideline nature of green credit funds can effectively restrain the rent-seeking motivation of enterprise
management and provide an information channel for banks to better supervise the lending enterprises.
Some scholars put forward the opposite point of view, such as Pan et al. (2022), who believe that green project construction cycle is
long, unsecured, and large investment finance, and there is a greater uncertainty compared with the mature traditional industry, and
the banks carrying out green credit are instead faced with higher risks and smaller profit margins. Xiaoying (2018) and Wei et al.
(2019) state that due to the current imperfect information disclosure system in China, the environmental information disclosure rules
are not uniform, the disclosure form is not standardized, and the quantitative performance indicator disclosure is not sufficiently
described, so it is easy for enterprises to generate the motive of “drifting to green” in order to obtain credit support, which will increase
the bank’s environmental responsibility and risk.
3. Theoretical analysis
Commercial banks, as special financial enterprises, have their own uniqueness in terms of competitiveness. According to this paper,
competitiveness is the sum of commercial banks’ ability to outperform their competitors’ ability to survive, their ability to create value
on a sustained basis, and their ability to obtain their own sustainable development by utilizing the resources and technologies and
other capabilities accumulated in the course of operation and management in conjunction with the advantages of the external
environment.
3.1. The impact of green credit on the competitiveness of commercial banks
(1) External economic environment. According to the theory of sustainable financial development, finance, as a country’s
strategic resource, can address the irrationality of the economic structure through the rational allocation of financial resources,
thereby improving economic efficiency. By continuously improving efficiency and reducing financial vulnerability, a benign
ecological cycle is formed, realizing the sustainable development of finance and economy. In the process of green development,
financial sustainability is its core and green credit is its tool. China’s commercial banks will carry the concept of sustainable
finance throughout their business activities, take the initiative to assume more social and environmental responsibility, and
better play their role in the economy, which can also promote the virtuous cycle of the economy to a certain extent and reduce
the degree of systemic risk. Green credit policy creates a favorable economic and financial environment for commercial banks to
enhance their competitiveness by guiding commercial bank funds to flow into environmentally friendly and energy-saving
projects, promoting green transformation of industries and a virtuous cycle of the economy (Wei et al., 2021). The imple­
mentation of green credit policy also strengthens social supervision to a certain extent, and reduces the speculative behavior of
some commercial banks to avoid social responsibility for the sake of economic benefits. It is precisely because the external
environment of the bank is becoming more standardized and fair that the green project funding can form a large-scale effect,
which boosts the core competitiveness of the bank and reduces the level of risk-taking.
(2) Profitability. By developing various new financial products and innovating financial business models, commercial banks
promote diversification and expand business income channels. For example, in the process of promoting green credit business,
issuing green financial bonds, green mortgages and other bank-type environmental financial products (Zhijian, 2021) or in the
process of banks reviewing loans for corporate environmental protection projects, launching account management, financial
consultants and other ancillary businesses. These measures have enriched and deepened the income source channels and risk
structure of commercial banks, promoted the diversification of services and operations, expanded brand-new customer groups
as well as the channels of assets and liabilities, and formed a differentiated competitive advantage in the current market
environment of serious homogenization and competition to expand the market share and achieve revenue growth and enhance
their competitiveness.
(3) Aspects of corporate social responsibility. While generating profits and assuming legal responsibility for shareholders and
employees, enterprises also have to assume responsibility for consumers, communities and the environment. At present, the
fulfillment of social responsibility has become an important aspect of the business activities of large and medium-sized en­
terprises and has become one of their competitive advantages. Through the allocation of green credit funds, commercial banks
support the development of green industries or projects from the source, and restrain the ”two highs and one leftover” industries
or projects, which is an active fulfillment of social and environmental responsibility (Ruowei & Tongtong, 2021). By actively
disclosing their development strategies, socio-economic performance and social responsibility concepts to stakeholders, com­
mercial banks spread the concept of green development in the whole society and promote the participation of the general public
in the protection of the environment, so as to build up a good social image and enhance the bank’s reputation. A good image and
the transmission of positive information will enhance customer confidence, obtain a higher sense of social identity and trust,
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M. Wu
help the bank attract more customers, and effectively reduce the cost of bank financing, alleviate financing constraints, which is
conducive to promoting the sound operation of the bank to enhance its competitiveness (Jialan & Yuxin, 2023; Ping & Haifeng,
2009).
H1. Implementation of green credit policy has a positive impact on the competitiveness of commercial banks.
3.2. The impact of green credit on the competitiveness of commercial banks through the risk-taking channel
(1) Green credit reduces bank credit risk. The investment and operation of the “two highs and one leftover” industries have been
in a state of low efficiency for a long time, and the reduction of demand and overcapacity have led to the compression of
enterprise profit margins and increased the difficulty of repaying bank loans. The green credit policy effectively prevents credit
risks by clearly defining the scope and standards of bank credit operations. The differentiated pricing standards and risk
management measures adopted by commercial banks are conducive to restricting the inflow of funds into the “two high and one
leftover” industries, alleviating the overinvestment in inefficient industries, optimizing the allocation efficiency of credit re­
sources, and reducing the level of risk bearing by banks (Guanglin et al., 2017; Haifeng & Huiping, 2023). In order to solve the
problem of financing constraints and cross the “high threshold” of green credit, high-polluting and high-energy-consuming
enterprises must accelerate the clean transformation and actively carry out green technological innovation. This active
“greening” behavior can improve the productivity of enterprises and enhance their solvency. In addition, the government has
introduced a number of supportive policies, including subsidized interest rates and guarantees for green projects. The gov­
ernment’s support and endorsement plays a credit enhancement role and also serves the purpose of reducing the default risk of
commercial banks (Yao et al., 2019).
(2) Green credit reduces banks’ reputation risk and social risk. Loan companies that fail to meet environmental standards will
face penalties of fines, rectification, or even closure (Dongwei & Lili, 2018), and commercial banks that provide credit and
consulting services to companies will face serious reputational and social risks. The projects supported by green credit are often
related to environmentally friendly and sustainable development, and usually the projects have already considered resource
utilization efficiency, energy consumption, and emission reduction in the design and operation stages, and the risk of green
credit-invested enterprises being subject to additional expenditures such as shutdowns and rectification, and compensation for
losses due to environmental pollution exceeding the standards is low, and the probability that commercial banks will bear joint
and several liabilities as a result is small. In addition, through the full disclosure of the environmental information of the loan
project, the bank improves the transparency and traceability of the loan project and strengthens the bank’s understanding of the
environmental risk, while the green credit investment releases the positive signal of protecting the environment to the stake­
holders, and to a certain extent, it can obtain the good evaluation of the news media and the positive attention of the public,
increase its social influence, and reduce the reputation risk of the commercial bank and social risks.
H2. Green credit policies enhance their competitiveness by reducing the level of risk taking.
4. Empirical research design
4.1. Sample selection and data sources
In 2007, the State Environmental Protection Administration (SEPA), the People’s Bank of China (PBOC) and the China Banking
Regulatory Commission (CBRC) jointly issued the Opinions on the Implementation of Environmental Protection Policies and Regu­
lations for the Prevention of Credit Risks, which required commercial banks to disclose green credit-related information in their Social
Responsibility Reports, and green credit formally entered China’s main battleground of anti-pollution and governance with an eco­
nomic means. Most banks began to disclose complete green credit data from 2009 onwards (Qingmin & Jing, 2022; Ruowei &
Tongtong, 2021). Because most banks began disclosing green credit balances later, this study selects annual panel data from 22 Chinese
banks between 2009 and2023.1
4.2. Variable selection
Explained variable is bank competitiveness, constructing competitiveness indicators based on principal component analysis.
1
6 large state-owned banks: Industrial and Commercial Bank of China, Agricultural Bank of China, Construction Bank of China, Bank of China,
Bank of Communications, Postal Savings Bank; 8 joint-stock commercial banks: Industrial Bank of China, Pudong Development Bank, Ping An Bank,
China Merchants Bank, CITIC Bank, Everbright Bank, Minsheng Bank, and Huaxia Bank; and 8 city commercial banks: Bank of Ningbo, Bank of
Jiangsu, Bank of Nanjing, Bank of Beijing, Bank of Shanghai, Bank of Qingdao, Bank of Hangzhou, and Bank of Guiyang.
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(1) Selection of indicators. Considering data availability and comprehensiveness, 24 indicators were identified to measure the
competitiveness of banks in six areas: size, profitability, safety, liquidity, growth and business.
Scale indicators reflect the size of the bank’s business, reflecting the bank’s overall competitiveness; profitability indicators reflect
the ability of commercial banks to convert capital into profit, which is a necessary performance indicator for evaluating the level of
commercial banks’ business; safety indicators reflect the ability of commercial banks to maintain normal operations in the face of
possible risk of loss of funds, which can also be regarded as the ability to tolerate errors in business decision making Liquidity indicators
reflect whether commercial banks have the ability to recover loans issued and whether there are enough funds for depositors to
withdraw, indirectly reflecting the bank’s business status; growth indicators can be used to analyze the capital turnover of commercial
banks, reflecting the growth trend and sustainable development of the main business of commercial banks; and business indicators
reflect the strength of the bank’s ability to operate its business.
Due to differences in economic significance and expression, the quantitative indicators are not comparable with each other,
therefore, in order to make a scientific and comprehensive evaluation of each quantitative indicator, it is necessary to standardize each
indicator:
Standardized treatment of positive indicators: xi=(Xi-minX)/(maxX-minX)
Standardized treatment of negative indicators: xi=(maxX-Xi)/(maxX-minX)
(2) Testing the suitability of variables for factor analysis. KMO and Bartlett method were used to test the validity of the data,
and the KMO was obtained to be 0.847, which was greater than 0.5, and the original variables could be analyzed by factor
analysis; the P of Bartlett’s spherical test was 0.000, which indicated that the variables had a certain degree of independence.
(3) Solve for the initial factor. The principal component factors are extracted according to the principle that the eigenvalue is
greater than 1. In this paper, a total of six factors are extracted, and the cumulative contribution rate is 75.39 %, which is more
than 70 %, representing that the six extracted factors can basically cover the effective information of the original data.
Composite metrics were generated according to the weight of the variance contribution of each principal component to the cu­
mulative variance contribution of the extracted principal components. Using the variance contribution ratios after rotation before and
after rotation as weights, they were calculated as follows:
F = F1 × 28.16 ÷ 75.39 + F2 × 13.08 ÷ 75.39 + F3 × 9.69 ÷ 75.39 + F4 × 8.75 ÷ 75.39 + F5 × 8.14 ÷ 75.39 + F6 × 7.56
÷ 75.39
The core explanatory variable is green credit. Based on bank social responsibility reports, green credit data are mainly compiled
from the Cathay Pacific database, quarterly and annual social responsibility reports, sustainability reports, and environmental
disclosure reports of each bank. In view of the late start of China’s green credit policy, the small scale of green credit business, the trend
of change in the proportion of green credit is not obvious, compared to the proportion of green credit, the green credit balance can
more directly reflect the scale of the development of banks’ green credit business and the absolute investment of banks in green credit
(Yonggang et al., 2024). Therefore, this paper refers to the research design of Haifeng and Qianqian (2023), and chooses the annual
balance of green credit as the representative variable of the scale of green credit business of each sample bank and as the explanatory
variable of the model. Because the green credit balance data are different from other variables, in order to smooth the data, the
empirical analysis is also logarithmic treatment of the green credit sample data.
The mediating variable is risk taking. In the research literature on the correlation between green credit and commercial bank risktaking, most of the risk indicators used are non-performing loan ratio and Z-value. In this paper, we choose the Z-value to measure bank
risk-taking and use the non-performing loan ratio for robustness testing.
Z = [(ROA + ER) / σ(ROA)]
(1)
ER denotes the bank equity ratio, i.e., total capital/total assets; ROA represents the bank’s return on total assets, and σROA is the
rolling standard deviation of the bank’s return on total assets in the last three years (Jingyu & Ying, 2021). Considering that the Z-value
has sharp peaks and thick tails characteristics, the Z-value is processed by natural logarithm in order to alleviate the problem of
heteroskedasticity (Chuanlin & Yongsheng, 2020; Jingyu & Ying, 2021; Yifei and Zilong, 2022).
4.2.1. Control variables include
The rate of economic growth. Banks’ risk prevention and control capabilities are related to the macroeconomic environment. GDP
per capita as a relative scale indicator better reflects the economic development level of a country or region than total GDP (Xiaoyao,
2016). GDP and population data are obtained from the National Bureau of Statistics (NBS), successive years of China Statistical
Yearbooks, and national economic and social development statistical bulletins issued by provincial statistical bureaus Large
state-owned commercial banks and joint-stock commercial banks are matched with GDP per capita at the national level, and urban
commercial banks are matched with GDP per capita at the provincial level where they are located (Hongtao, 2022).
The rate of inflation. The CPI index was calculated using the previous year as the base (100) (Guojin et al., 2020; Ning et al., 2020;
Ye & Jing, 2017).
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Table 3
Rotated factor loading matrix.
Ingredient
F1
F2
F3
F4
F5
F6
Initial eigenvalue
Extract the sum of the squares of the loads
Rotational load sum of squares
total
variance%
accumulate%
total
variance%
accumulate%
total
variance%
accumulate%
9.922
2.374
2.066
1.436
1.217
1.076
41.34
9.90
8.61
5.99
5.07
4.48
41.34
51.24
59.85
65.83
70.90
75.39
9.922
2.374
2.066
1.436
1.217
1.076
41.34
9.90
8.61
5.99
5.07
4.48
41.34
51.24
59.85
65.83
70.90
75.39
6.759
3.138
2.324
2.100
1.954
1.815
28.16
13.08
9.69
8.75
8.14
7.56
28.16
41.24
50.93
59.68
67.82
75.39
Table 4
Variable definitions.
Variable Type
Variable Name
Variable Symbol
Variable Interpretation
Explanatory variable
Explanatory variable
Intermediary variable
​
Bank competitiveness
Green credit
Bank risk-taking level
Economic growth rate
SOCRE
GC
RISK
​
GDPP
Comprehensive indicators,F
Natural logarithm of green credit balances
lnZ = ln[(ROA + ER)/σ(ROA)]
​
GDP per capita growth rate
Control variable
​
​
​
​
​
Inflation rate
Money supply growth rate
Banking sector sentiment index
CPI
M2R
BCI
Calculation of the CPI index on the basis of the previous year (100)
(Current year M2− previous year M2)/previous year M2
The People’s Bank of China’s Banker’s Questionnaire Report
The rate of money supply growth. (Current year M2− previous year M2)/previous year M2. on the one hand, excess liquidity makes
the yield of traditional financial assets low, and the social excess funds irrationally flow into the real estate and stock markets, resulting
in asset price bubbles; on the other hand, excessive liquidity leads to excessive competition among banks, irrationally floating lending
rates and lowering lending terms and conditions, increasing credit risk and interest rate risk (Ning et al., 2020; Tianyu & Yuping,
2013).
The banking sector sentiment index. The Banking Sector Prosperity Index is derived from the People’s Bank of China’s Banker’s
Questionnaire Report.
4.3. Econometric modeling
4.3.1. Baseline regression
In order to explore the relationship between green credit and bank competitiveness, this paper constructs the following benchmark
regression model with green credit balance as the explanatory variable and bank competitiveness as the explanatory variable:
SCOREit = α1 + β1 GCit + η1 Xit + λi + εit
(2)
i and t denote bank and year, respectively; SCOREit denotes the level of bank operational risk; GCit denotes green credit, Xit is a control
variable, λi is an individual fixed effect, and εit is a random perturbation term.
4.3.2. Mediating effects model
In order to test the mechanism and theoretical assumptions of the previous paper, this paper constructs the following mediation
effect model for mediation effect test analysis. The test of the mediating effect model adopts the stepwise regression method to test the
significance of the corresponding coefficients.
SCOREit = α1 + β1 GCit + η1 Xit + λi + εit
(3)
RISKit = α2 + β2 GCit + η2 Xit + λi + εit
(4)
SCOREit = α3 + φRISKit + β3 GCit + η3 Xit + λi + εit
(5)
5. Analysis of empirical results
5.1. Descriptive statistics
As can be seen through Table 4, the mean value of the explanatory variable SOCRE is 0.0004, the standard deviation is 0.4592, the
minimum value is − 1.56 and the maximum value is 0.84, which indicates that there are large differences in the competitiveness of
different banks (see Table 3). The mean value of the core explanatory variable green credit balance GC is 10.7702, the standard
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International Review of Economics and Finance 103 (2025) 104481
M. Wu
Table 5
Descriptive statistics.
Variable
Average Value
Standard Deviation
Maximum Values
Minimum Value
Standard deviation
SOCRE
GC
RISK
GDPP
CPI
M2R
BCI
0.0004
10.7702
4.8903
8.9823
102.0636
13.7733
70.28
0.055
10.975
4.86
8.405
102.1
12.6
68.7
0.84
15.17
6.88
19.12
105.4
26
85.4
− 1.56
4.17
3.07
1.99
98.7
6.4
60.5
0.4592
2.3085
0.8566
4.2086
1.3499
4.7428
6.0466
Table 6
Baseline regression results.
Variable
SOCRE
SOCRE
SOCRE
SOCRE
SOCRE
GC
0.1435***
(5.44)
0.1669***
(5.91)
0.0164***
(2.93)
0.1649***
(5.49)
− 0.0025
(− 0.46)
0.1242***
(6.11)
01751***
(4.61)
− 0.0027
(− 0.52)
0.1283***
(7.12)
0.0045
(0.48)
0.1736***
(4.57)
− 0.0039
(− 0.68)
0.1125***
(5.82)
0.0015
(0.15)
0.0059
(1.48)
− 13.7532***
330
0.3438
GDPP
​
CPI
​
​
M2R
​
​
​
BCI
​
​
​
​
C
N
adj. R2
− 1.5451***
330
0.2029
− 1.9443***
330
0.2216
− 14.4357***
330
0.3393
− 15.0234***
330
0.3407
Note: Values in parentheses indicate t-values, and *, **, and *** indicate 10 %, 5 %, and 1 % significance levels, respectively.
Table 7
Robustness test results.
Variable
SCORE(1)
GC
0.0348***
(3.04)
L.GC
GDPP
CPI
M2R
BCI
C
N
adj. R2
​
− 0.0131**
(− 2.38)
0.1031***
(4.99)
− 0.0185**
(− 2.15)
0.0070*
(1.77)
− 10.8321***
330
0.2641
SCORE(1)
​
0.1321***
(3.38)
− 0.0032
(-0.58)
0.0150
(0.68)
0.0259***
(3.21)
0.0060
(1.57)
− 3.5946
330
0.1932
Note: Values in parentheses indicate t-values, and *, **, and *** indicate 10 %, 5 %, and
1 % significance levels, respectively.
deviation is 2.3085, the minimum value is 4.17 and the maximum value is 15.17, which can be seen that some banks are more negative
about the development of green credit, some banks have more rapid development of green credit business, and there is an uneven
phenomenon in the overall development of green credit business in China (see Table 5).
5.2. Baseline regression results
In order to test the robustness of green credit affecting bank competitiveness, this paper takes the way of adding control variables
one by one to carry out regression. According to the regression results in Table 6, it can be seen that the coefficient of the impact of
green credit policy on bank competitiveness is always positive and passes the significance test at the 1 % level, indicating that green
credit has a promotional effect on the competitiveness of China’s commercial banks, which preliminarily verifies the H1 of this paper
(see Table 7).
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International Review of Economics and Finance 103 (2025) 104481
M. Wu
Table 8
Heterogeneity test.
Variable
State-Owned Large Banks
Joint-Stock Commercial Banks
Urban Commercial Banks
GC
0.2560**
(3.81)
− 0.0019
(− 0.18)
0.0982***
(4.82)
− 0.0127
(− 0.82)
0 .0097
(1.70)
− 13.7455***
90
0.4825
0.2324***
(3.64)
− 0.0113
(− 1.47)
0.1633***
(4.80)
− 0.0012
(− 0.08)
0.0067
(0.89)
− 19.5314***
120
0.4957
0.1721**
(2.82)
0.0147
(1.77)
0.1012*
(2.35)
0.0302
(1.53)
0.0002
(0.03)
− 12.5278**
120
0.2642
GDPP
CPI
M2R
BCI
C
N
adj. R2
Note: Values in parentheses indicate t-values, and *, **, and *** indicate 10 %, 5 %, and 1 % significance levels, respectively.
Table 9
Intermediation test results.
Variable
Model(3)
Model(4)
Model(5)
GC
0.1736***
(4.57)
0.2659***
(7.04)
​
​
− 0.0039
(− 0.68)
0.1125***
(5.82)
0.0015
(0.15)
0.0059
(1.48)
− 13.7532***
330
0.3438
− 0.0517***
(− 8.21)
0.0659*
(1.99)
0.0036
(0.24)
0.0184***
(2.90)
− 5.5805
330
0.2936
0.1495***
(3.49)
0.0904**
(2.65)
0.0008
(0.14)
0.1065***
(5.75)
0.0012
(0.11)
0.0043
(1.08)
− 13.2484***
330
0.3637
RISK
GDPP
CPI
M2R
BCI
C
N
adj. R2
Note: Values in parentheses indicate t-values, and *, **, and *** indicate 10 %, 5 %, and 1 % significance levels, respectively.
5.3. Robustness check
1. Alternative variable, the logarithm of green credit is replaced by green credit ratio, i.e. green credit/total loans. The empirical
results show that the regression coefficient is significantly positive, i.e., green credit has a positive effect on bank competitiveness,
verifying H1.
2. Lagged by one period, taking the log of green credit lagged by one period as the explanatory variable. The regression results are still
significantly positive and have good robustness.
5.4. Heterogeneity analysis
The regression results in Table 8 show that green credit has a positive impact on the competitiveness of different types of com­
mercial banks. Compared with urban commercial banks, green credit has a greater positive impact on the competitiveness of large
state-owned banks and joint-stock banks.
6. Further analysis
6.1. Mechanism of action tests
According to the requirements of the mediation effect model, three-step regressions were conducted according to models (3), (4)
and (5), respectively. From the regression results in Table 9, it can be seen that β1 in model (3) is significantly positive through, and the
total effect of green credit on the competitiveness of commercial banks is 0.1736. β2 and φ in models (4) and (5) are both significantly
positive, indicating that green credit will improve the competitiveness of commercial banks by reducing their risk-taking level. β3 in
model (5) is significantly positive, but the value is reduced compared with model (3), the risk-taking level, as a channel through which
green credit has an effect on the competitiveness of commercial banks, assumes an intermediary role in the effect of green credit on
improving the competitiveness of banks, and the intermediary effect is β2 φ = 0.0240, accounting for 13.85 % of the total, and H2 is
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International Review of Economics and Finance 103 (2025) 104481
M. Wu
Table 10
Heterogeneity test.
Variable
Model(3)
Model(4)
Model(5)
GC
0.1736***
(4.57)
− 0.0448**
(− 2.19)
​
​
− 0.0039
(− 0.68)
0.1125***
(5.82)
0.0015
(0.15)
0.0059
(1.48)
− 13.7532***
330
0.3438
− 0.0119***
(− 3.60)
− 0.0400***
(− 5.40)
− 0.0318***
(− 4.38)
− 0.0190***
(− 7.54)
7.6410***
330
0.4808
0.1551***
(4.63)
− 0.4129***
(− 3.73)
− 0.0088
(− 1.53)
0.0960***
(4.39)
− 0.0115
(− 1.02)
− 0.0019
(− 0.58)
− 10.5983***
330
0.3953
RISK
GDPP
CPI
M2R
BCI
C
N
adj. R2
Note: Values in parentheses indicate t-values, and *, **, and *** indicate 10 %, 5 %, and 1 % significance levels, respectively.
Table 11
Heterogeneity test.
Variable
Model(3)
Model(4)
Model(5)
L.GC
0.1321***
(3.47)
0.2660***
(7.19)
​
​
− 0.0032
(− 0.58)
0.0150
(0.68)
0.0259***
(3.21)
0.0060
(1.57)
− 3.5946
308
0.1932
− 0.0456***
(− 7.82)
0.0025
(0.07)
0.0211
(1.32)
0.0189***
(3.08)
0.6755
308
0.2655
0.1150***
(2.87)
0.0643**
(2.29)
− 0.0003
(− 0.05)
0.0148
(0.69)
0.0245***
(2.84)
0.0048
(1.22)
− 3.6380
308
0.2101
RISK
GDPP
CPI
M2R
BCI
C
N
adj. R2
Note: Values in parentheses indicate t-values, and *, **, and *** indicate 10 %, 5 %, and 1 % significance levels,
respectively.
verified (see Table 10).
6.2. Robustness tests
1. The Z-value is replaced by the NPL ratio. There is a mediating effect on the level of risk-taking, and the findings are consistent with
the previous section and have good robustness.
2. The explanatory variables are lagged by one period, again testing H2.
7. Conclusions
Through the study, it can be seen that the implementation of green credit business can significantly enhance the core competi­
tiveness of commercial banks; green credit can serve the purpose of enhancing the competitiveness of banks by reducing the level of
risk-taking, and risk-taking has an intermediary effect of 13.85 %. Based on this, the following suggestions are put forward: first,
continuously expand the scale of green credit, strengthen the depth and breadth of green credit implementation by commercial banks,
and expand the comprehensive competitiveness of commercial banks. Second, continuously improve the audit and supervision
mechanism, update and popularize the knowledge of green credit, focus on the training of relevant personnel, avoid the emergence of
“bleaching green” and “greenwashing” behaviors, prevent polluting enterprises from exploiting loopholes, and maximize the scope of
the implementation of the policy to ensure that the strength and effectiveness of the policy. Third, promote green credit business
innovation and performance system construction, optimize the green credit business processing structure system.
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International Review of Economics and Finance 103 (2025) 104481
M. Wu
Author statement
I would like to declare that there is no conflict of interest exits in the submission of this manuscript, and the manuscript is approved
by author for publication. The work described was original research that has not been published previously.
Compliance with ethical standards
The author has no relevant financial interests to disclose.
Declaration of competing interest
None.
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