1. Introduction
Green credit, a vital component of green finance, refers to loans provided by financial institutions that prioritize environmental protection and resource conservation [
1,
2]. These loans support projects and enterprises that contribute to environmental sustainability, climate change mitigation, and efficient resource use. The main goal of green credit is to drive sustainable development through financial incentives. While definitions may vary by region, the core principle of promoting environmental protection through financing activities remains consistent globally.
China’s green credit market, despite its relatively recent development, has rapidly developed and is now the largest in the world [
3]. By the end of 2022, China’s green loans reached CNY 22.3 trillion (approximately USD 3.2 trillion), reflecting a 38.5% year-on-year increase from 2021. This growth outpaced total loan growth by 28.1 percentage points, with green credit accounting for 10.8% of the total loan balance, up 0.5 percentage points from 2021. The impact of green credit has been substantial, supporting the saving of over 600 million tons of coal and reducing more than 1 billion tons of CO
2 emissions annually [
4]. The Chinese banking sector has established a comprehensive green credit system and developed unique strategies to promote sustainable finance [
5,
6].
Why should banks engage in green credit? Some may argue that the recent growth of green credit in China is primarily driven by government initiatives. However, if green credit depends solely on government support, its sustainability could be at risk if that support diminishes in the future. If green credit proves to be profitable, banks are more likely to adopt these practices, which would promote sustainable development and create a positive feedback loop. Therefore, understanding the impact of green credit on bank performance is crucial. However, the existing literature on the relationship between green credit and bank performance is extensive but inconclusive. While most studies suggest a positive impact [
7,
8,
9], others indicate a negative effect [
10,
11,
12], leading to continuing debate among scholars. Further studies are needed to explore the reasons behind these differing results.
In recent years, the rapid advancement of FinTech has transformed the financial industry. Banks now use artificial intelligence, blockchain, cloud computing, big data, and mobile internet technologies to enhance service efficiency and quality. The impact of FinTech on the banking sector is deep and comprehensive, changing not only the way banks operate but also their business models, risk management, customer relationships, and market positioning [
13,
14]. Firstly, integrating advanced digital technologies allows banks to offer faster and more secure services. Secondly, automation and technological optimization help reduce operational costs and improve efficiency, particularly in credit approval and risk management. Additionally, FinTech significantly enhances the user experience by providing convenient and specialized banking services, thereby increasing customer loyalty and satisfaction [
15]. However, FinTech also introduces new challenges, such as data security and privacy issues, and competitive pressure from emerging tech companies [
16]. Overall, FinTech is driving the banking industry towards greater efficiency, security, and customer friendliness.
With the application of FinTech in green credit businesses [
17], a natural question arises: Does FinTech influence the impact of green credit on bank performance? Can FinTech explain the divergent views in the literature regarding the relationship between green credit and bank performance? These are the primary issues this article aims to explore. While previous studies have examined how FinTech promotes green credit growth [
18,
19,
20], the interaction between FinTech, green credit, and bank performance remains insufficiently studied. This study addresses this gap by investigating the synergistic effects of FinTech and green credit on bank performance, providing valuable insights for banks in developing appropriate FinTech and green credit strategies.
The potential marginal contributions of this study are as follows. First, while the existing literature has examined how FinTech influences green credit, few studies have explored the joint impact of FinTech and green credit on bank performance. This paper attempts to fill this gap and verifies that FinTech indeed affects the impact of green credit on bank performance. Additionally, it reveals that the impact of green credit on bank performance varies under different levels of FinTech, partially explaining the differing opinions about the impact of green credit on bank performance in the existing literature.
Secondly, this study confirms three channels through which FinTech influences the impact of green credit on performance: reducing costs, enhancing reputation, and mitigating risks. This conclusion helps to understand the specific mechanisms of how FinTech promotes the development of sustainable finance.
Thirdly, the heterogeneity analysis reveals that FinTech has a stronger impact on the performance of green credit for city commercial banks and banks with higher levels of green credit development, as well as during periods of lower banking sector prosperity. This provides theoretical evidence for banks to determine the extent to which they should develop FinTech based on their own and the industry’s environmental conditions.
Lastly, this paper constructs a more comprehensive dataset of green credit in China. Previous studies on Chinese green credit have primarily focused on listed banks. In contrast, we collected and integrated data from multiple databases and manually verified green credit data from approximately 127 commercial banks, including many non-listed city and rural commercial banks. As a result, we can explore the impact of green credit on non-listed banks, thereby improving the generalizability and reliability of the conclusions in this paper.
The remainder of this paper is structured as follows:
Section 2 provides the literature review and research hypotheses.
Section 3 outlines the research design, including the empirical model and data sources.
Section 4 presents the main empirical results.
Section 5 offers the heterogeneity analysis.
Section 6 discusses the robustness tests. Finally,
Section 7 concludes with a summary of the findings, policy recommendations, and directions for future research.
3. Methodology and Data
3.1. Econometric Modeling
To test hypotheses H1 and H2, we constructed the following two regression models:
where
i represents the bank,
t represents time, the dependent variable
is the bank’s performance indicator,
is the bank’s green credit indicator,
is the bank’s FinTech indicator, and
represents control variables. Furthermore, to mitigate potential estimation biases caused by unobserved individual and temporal factors, we introduce individual fixed effects
and time fixed effects
into the regression model.
According to Hypothesis H1, if green credit improves bank performance, the coefficient in the regression results of Equation (1) should be significantly positive. According to Hypothesis H2, if the development of FinTech further enhances the positive impact of green credit on bank performance, the coefficient in the regression results of Equation (2) should also be significantly positive. Additionally, since the inclusion of interaction terms alters the meaning of the main effect coefficient in Equation (2), we adjust the green credit and FinTech variables by subtracting their respective sample means before regression. After this adjustment, the main effect coefficient indicates the marginal impact of green credit on bank performance when FinTech is at its average level.
Next, we use different methods to examine the three channels through which FinTech enhances the impact of green credit on bank performance. To test the cost reduction channel (Hypothesis H3), we constructed the following regression equation:
Equation (3) mirrors Equation (1), except that the dependent variable has been replaced by the bank cost indicator . The equation suggests that the marginal cost of green credit is . The derivative of the marginal cost of green credit with respect to FinTech, denoted by , indicates the effect of FinTech on the marginal cost of green credit. According to the Hypothesis H3, if FinTech reduces the costs associated with green credit businesses, then as the level of FinTech increases, the negative impact of green credit on costs will become more significant. Therefore, the coefficient in Equation (3) is expected to be significantly negative.
Regarding the reputation enhancement channel (Hypothesis H4), we employ a grouped regression method for testing. Green reputation is often difficult to quantify since it refers to the public image and perception of a bank regarding environmental protection and sustainable development. If a bank’s FinTech can enhance its green reputation, customers will have greater confidence in the bank’s capabilities, leading them to consume more non-interest services at the bank. Using the median as the dividing line, we split the entire sample into two groups based on the proportion of fee and commission income. We then perform grouped regression by Equation (2). If Hypothesis H4 holds, the sample with a higher proportion of fee and commission income (more reliant on non-interest business) will show a more pronounced effect of FinTech in strengthening the impact of green credit on bank performance; i.e., will be larger. It is important to note that this paper does not group based on the non-interest income ratio because several components of non-interest income, such as investment income, fair value gains, exchange gains, and other business income, are unrelated to green reputation.
We employ a similar method to examine the risk mitigation channel (Hypothesis H5), categorizing banks into high-risk and low-risk groups based on their risk indicators and using Equation (2) for grouped regression. According to Hypothesis H5, FinTech can reduce pre-loan and post-loan risks in green credit businesses. Therefore, we believe that the risk-mitigating effect of FinTech is more pronounced in high-risk banks. In other words, in the high-risk group, the effect of FinTech in enhancing the impact of green credit on bank performance will be stronger, as indicated by a larger .
3.2. Variable Definitions
First, we select bank performance indicators. In this study, we assess the financial performance of commercial banks using three widely recognized indicators: Return on Assets (ROA), Return on Equity (ROE), and Net Profit Margin (NPM). These indicators were selected because they provide a comprehensive view of bank performance from different financial dimensions. Specifically, ROA reflects the efficiency with which a bank utilizes its total assets to generate profits, offering insights into overall asset management. ROE, on the other hand, measures the return generated on shareholders’ equity, which is crucial for assessing the value delivered to investors. NPM provides a perspective on the bank’s operational efficiency by evaluating the profitability relative to revenue. These indicators are commonly used in the literature to capture different aspects of financial performance, ensuring a robust evaluation. In our empirical analysis, we conduct separate regressions using each of these indicators as dependent variables to comprehensively explore the relationship between green credit, FinTech, and financial performance.
Next is the selection of the bank green credit indicator (GC). The existing literature mainly employs two indicators: the logarithmic value of the green credit balance and the ratio of green credit to total loans. The logarithmic value of the green credit balance (lnGC) represents the scale of a bank’s green credit business, while the green credit ratio (GCR) measures the proportion of green credit relative to the total loan portfolio, providing a scale-independent perspective. However, preliminary data analysis revealed significant fluctuations in the green credit ratio for small banks. In these banks, issuing or recovering a single green credit loan can cause large fluctuations in the green credit ratio due to their smaller loan portfolios. These fluctuations might misleadingly suggest substantial changes in the bank’s green credit business when no such change has occurred. To mitigate this risk and provide a more stable measure, we have chosen the logarithmic value of the green credit balance (lnGC) as the primary indicator. This choice is further supported by the observation that banks with larger green credit balances generally exhibit stronger development in their green credit businesses. To ensure the robustness of our results, we also include the green credit ratio (GCR) in subsequent robustness checks, thereby validating the consistency of our findings across different measurement approaches.
Regarding bank FinTech, we employ three indicators for measurement, detailed as follows:
The news-based FinTech index (FTNews): The existing literature supports the use of internet search indices to measure banks’ FinTech levels [
85,
86]. Since banks frequently advertise their FinTech capabilities and involvement in FinTech projects, a higher number of reports related to a bank’s FinTech activities generally indicates a higher level of FinTech expertise. In China, Baidu News is a prominent news aggregation platform that sources information from over 500 authoritative websites, providing a broad spectrum of coverage. We employed web crawling technology to conduct searches combining each commercial bank’s name with FinTech-related keywords. This methodology allowed us to quantify the number of news pages referencing a bank’s FinTech activities annually. We then summed the news articles containing FinTech keywords for each bank within a given year and took the logarithm of this total to derive the annual FinTech index for each bank. For selecting FinTech keywords, we referred to significant documents such as the “13th Five-Year National Science and Technology Innovation Plan″ and the “China FinTech Development Report (2018)”, along with relevant key news and conferences. These keywords span fundamental FinTech technologies and applications under the “ABCD+” framework [
87], with a total of 48 keywords listed in
Table 1. The FTNews index captures the bank’s public-facing FinTech efforts, which are indicative of its FinTech development and market presence.
The Peking University China Commercial Bank Digital Transformation Index (FTDigital): The FTDigital index, developed by Peking University, provides a comprehensive depiction of digital transformation trends among Chinese commercial banks. This index evaluates banks across three dimensions and eight aspects, offering a nuanced measure of FinTech development [
88]. It has been utilized in previous studies as a reliable proxy for bank FinTech engagement [
19].
The patent application-based FinTech Index (FTPatent): We constructed a FinTech index based on bank FinTech patent application data. First, we collected patent application information from the China National Intellectual Property Administration’s patent search database for banks up to 2022. We collected a total of 38,407 patent application records. Each record includes details such as the patent applicant, application date, and a patent abstract, which discloses the primary purpose and technology of the patent. Second, we matched the patent applicants with commercial bank samples. Specifically, patent applications from bank branches were treated as the whole bank’s applications. Third, we searched each patent application abstract for FinTech-related keywords from
Table 1. If an abstract contained any of these keywords, the patent was classified as a FinTech patent. Finally, we calculated the cumulative number of FinTech patents applied for by each bank and took the logarithm of this count as the FinTech index. A higher index reflects better development and application in FinTech by the bank.
To summarize, these three FinTech variables cover various aspects of financial technology, each providing a comprehensive reflection of the overall level of FinTech development in banks. However, due to differing construction methods, these variables vary in data volume. The Peking University China Commercial Bank Digital Transformation Index, based on external data such as annual reports and patent information from commercial banks, offers extensive coverage and includes many non-listed banks in China. However, its data update slowly, with the most recent update only extending to 2021. The patent application-based FinTech index directly captures a bank’s FinTech achievements, but since some commercial banks have not applied for FinTech patents, this index may not fully represent their actual level of FinTech development. In contrast, the news-based FinTech index is more comprehensive in terms of data coverage, as most banks are featured in relevant news reports, offering a more accurate representation of their FinTech development. Considering these factors, we select the news-based FinTech index as the primary indicator of FinTech, while the other two indices are used for robustness analysis.
We include a range of control variables that capture both bank-specific characteristics and macroeconomic conditions, as these factors are commonly recognized in the literature as significant determinants of bank performance. Bank-specific characteristic variables include bank size (Size), capital-to-asset ratio (CAP), interest-generating asset ratio (IGAR), total asset turnover ratio (TAT), loan concentration Herfindahl–Hirschman index (LoanHHI), and short-term asset liquidity ratio (LIQ). Macroeconomic variables include the real GDP growth rate (RGDP) and the consumer price index (CPI). Note that the two macroeconomic control variables in this study are provincial-level indicators, so they are not absorbed by time fixed effects.
Table 2 illustrates the definitions and calculation methods of the primary variables.
In our mechanism analysis, we carefully selected variables corresponding to different channels. For the cost channel, we used the cost-to-income ratio (CIR) and the administrative cost ratio (AdminCost) as key indicators. CIR measures a bank’s operational efficiency by comparing operating costs to operating income, with a lower CIR indicating better efficiency. AdminCost reflects the proportion of operating expenses attributable to administrative activities, highlighting the bank’s ability to control overhead costs. For the reputation channel, we used the fee and commission income ratio (FeeIncome) to evaluate a bank’s reliance on non-interest income activities. A higher FeeIncome ratio enables the bank to derive greater performance benefits from its reputation. For the risk channel, we incorporated three risk indicators: the non-performing loan ratio (NPLR), the overdue loan ratio (ODLR), and the risk-weighted asset ratio (RWAR). NPLR measures the proportion of loans not generating income due to borrowers’ failure to repay. ODLR represents the percentage of loans overdue but not yet classified as non-performing. RWAR reflects the level of risk in the bank’s asset portfolio, considering the varying risk levels of different asset types. These variables comprehensively reflect the bank’s risk profile.
3.3. Data Sources and Sample Selection
The fundamental information and financial data of banks, along with macroeconomic variables, are derived from the Wind database. For green credit data, we used a combination of database extraction and manual verification. The Wind, CSMAR, and CNRDS databases, which are considered authoritative economic and financial sources in China, include green credit data of Chinese banks. However, these databases exhibit variations in sample coverage and frequency. They may occasionally show inconsistencies in data for the same bank within the same year. Therefore, we combined green credit data from these three databases and conducted manual checks and modifications based on annual reports and ESG disclosures from banks.
Based on the available public data, this study’s sample includes 127 commercial banks, covering the period from 2007 to 2022. This timeframe aligns with significant developments in green credit and FinTech in China. The sample consists of 6 state-owned commercial banks, 9 joint-stock commercial banks, 80 city commercial banks, 29 rural commercial banks, and 3 private banks. The number of banks in this sample is substantial compared to previous studies. During the sample period, the total assets of these commercial banks accounted for 85% to 92% of the total assets of all commercial banks in China, demonstrating the strong representativeness of the sample. Furthermore, to mitigate the negative effects of outliers on regression analysis, variables containing outliers were winsorized at the top and bottom 1%.
3.4. Descriptive Statistics
Figure 2 illustrates the trends in green credit balances across different types of banks from 2007 to 2022. The y-axis uses a logarithmic scale to better display data covering multiple levels of magnitude. From 2009 to 2022, different types of banks experienced rapid growth in green credit. The Compound Annual Growth Rate of green credit balances (CAGR) was 24.67% for state-owned banks, 35.81% for joint-stock banks, 71.57% for city commercial banks, and 91.23% for rural commercial banks. Private banks, starting from a smaller base in 2016, also showed a robust CAGR of 49.65% through 2022.
By the end of 2022, state-owned banks held the largest share of green credit at 74.03%. Joint-stock banks followed with 16.51%, and city commercial banks accounted for 8.12%. Although smaller, rural commercial banks and private banks held 1.33% and 0.01%, respectively. This distribution highlights the leading role of state-owned banks in China’s green finance sector, while smaller banks are gradually increasing their participation.
Figure 3 shows the changes in the ratio of green credit to total loans among various types of banks from 2007 to 2022. Starting in 2007, most bank types exhibited a steady increase in the proportion of green credit within their total loan portfolios. State-owned banks led, reaching a ratio of 13.05% by 2022, followed by joint-stock banks at 7.06%. City commercial banks and rural commercial banks also raised their green credit ratios to 5.87% and 3.65%, respectively. Private banks initially experienced growth in green credit, but their ratio declined over time, falling to 0.58% in 2022. This decrease is due to the slower growth of green credit compared to the rapid expansion of their total loan portfolios.
Overall, the banking sector in China is rapidly developing its green credit business. The involvement of various banks highlights their strong commitment to promoting sustainable economic growth and contributing to China’s environmental goals.
Table 3 presents the descriptive statistics of the primary variables in this study. The results indicate that all variable values fall within a reasonable range. The average Return on Assets (ROA) is 0.77%, the average Return on Equity (ROE) is 11.27%, and the average Net Profit Margin (NPM) is 29.73%. There are significant numerical discrepancies among performance indicators due to their different denominators. Moreover, there is a notable disparity in the scale and proportion of green credit among different banks. The natural logarithm of the green credit balance (lnGC) ranges from 14.51 to 26.71, indicating that the green credit balance varies from approximately CNY 20 million to nearly CNY 400 billion. The green credit ratio (GCR) fluctuates between 0.04% and 28.47%. Despite the rapid development of green credit in China’s banking industry, its proportion remains relatively low, with an average value of only 3.72%. This suggests considerable room for growth and market opportunities in China’s green credit sector.
Regarding the control variables, the average value of CAP is 7.21%, indicating a high leverage ratio in the banking industry. The average IGAR is 88.65%, while FeeIncome has an average of 8.80%, demonstrating that the Chinese banking sector still heavily relies on traditional interest-based business. LoanHHI averages at 0.195, suggesting a high level of diversification and low concentration in loan industries. The average value of NPLR is 1.53%, with a maximum value of 4.69%. According to Chinese regulations, the non-performing loan ratio should be below 5%, indicating that the credit risk in the Chinese banking industry is controlled and remains at a manageable level.
Table 4 lists the correlation coefficients of the key variables. First, the correlation coefficients among indicators of the same type are relatively high. For instance, the performance indicators of banks are significantly positively correlated, with all correlation coefficients exceeding 0.7. Second, there is a significant positive correlation between bank performance and the scale of green credit. In contrast, the positive correlation between bank performance and the ratio of green credit is weaker. This may indicate that expanding the scale of green credit is more effective in enhancing bank performance than increasing the proportion. Furthermore, the relationship between FinTech and bank performance is not clearly defined. Some FinTech indicators are positively correlated with performance, while others are negatively correlated. This suggests that the impact of FinTech on bank performance may depend on the nature of the technology and its implementation within bank operations. Lastly, there is a positive correlation between FinTech and green credit. This indicates that banks with higher levels of FinTech tend to have better development in green credit business, consistent with existing literature findings [
19,
20].
5. Heterogeneity Analysis
Different bank characteristics and macroeconomic environments can lead to significant differences in their operational behaviors. These differences affect how FinTech enhances the impact of green credit on bank performance. In this section, we discuss the heterogeneity analysis of bank types, green credit development, and the prosperity of the banking industry.
5.1. Bank Types
In China, there are many types of banks, each exhibiting significant differences in scale, customer base, strategic positioning, business capabilities, and risk management. Consequently, it is essential to analyze the heterogeneous effects of FinTech across different types of banks to gain a nuanced understanding of its impact. In this section, we conducted regression analyses on samples from national commercial banks, city commercial banks, and rural commercial banks. For clarity, we grouped state-owned and joint-stock commercial banks under the category of national commercial banks. Due to the small sample size and distinct characteristics of private banks, they were excluded from this analysis.
Table 9 presents the heterogeneity results for different bank types. Unlike previous tables, each panel here represents different bank performance indicators, while the columns represent the different types of banks. The results reveal that the interaction between FinTech and green credit is not significant for national and rural commercial banks. However, for city commercial banks, the interaction term between FinTech and green credit shows a significantly positive coefficient.
National commercial banks are the most developed entities within China’s banking system. These banks have made substantial investments in FinTech and have achieved a high level of technological integration. As a result, FinTech is likely already deeply embedded in their green credit businesses, which means that its marginal contribution is relatively limited. This saturation effect explains the insignificant interaction coefficient between FinTech and green credit in these banks.
City commercial banks, on the other hand, occupy a middle ground between national and rural commercial banks in terms of scale and competitiveness. They primarily serve local customers who demand high-quality financial services. In recent years, these banks have made significant progress in both green credit and FinTech. By integrating FinTech into their green credit businesses, they have effectively reduced costs and risks, built strong green reputations, and enhanced their overall performance. Unlike national commercial banks, city commercial banks have not yet reached a stage where the marginal benefits of FinTech diminish, which explains the significantly positive interaction coefficient observed.
Rural commercial banks lag behind other types of banks in FinTech and green credit development, and the integration of FinTech and green credit is insufficient. Their primary clientele consists of individuals and enterprises in rural areas, where the demand for green credit is relatively low. Given the limited volume of business, adopting high-cost FinTech solutions is not financially justified for these banks. Traditional credit approval and management methods remain more profitable. As a result, FinTech struggles to achieve economies of scale in rural commercial banks, leading to an insignificant interaction effect between FinTech and green credit.
In summary, the impact of FinTech on green credit businesses has reached a saturation point for national commercial banks, while city commercial banks continue to experience positive effects. However, rural commercial banks have not yet seen a significant impact. In the long term, as green credit businesses evolve, the demand for FinTech is expected to grow. This will help address the saturation of FinTech efficiency in national commercial banks, and rural commercial banks are likely to adopt FinTech more extensively. Consequently, the positive impact of FinTech on green credit businesses is expected to become significant across all types of banks.
5.2. Green Credit Development
We are also interested in whether FinTech’s impact on enhancing green credit’s influence on bank performance varies at different levels of green credit development. We selected two indicators to assess green credit development levels: the size of the bank’s green credit (lnGC) and the green credit index (GCI) of the bank’s region. The GCI represents the ratio of total environmental project credit to total credit in the province where the bank is located. The first indicator measures the bank’s green credit development, while the second assesses regional green credit development. Examining these two dimensions provides a more comprehensive perspective on the question we proposed. Next, we conduct grouped regressions based on these two indicators separately.
Table 10 presents the grouped regression results based on the development level of green credit. The results indicate that, whether from the perspective of the bank itself or its region, a higher level of green credit development increases FinTech’s impact on enhancing green credit’s influence on bank performance. For banks, FinTech exhibits a “scale effect” in the green credit business. As the scale of green credit expands, the average technological cost per unit of green loan decreases, thus improving the bank’s performance. For regions, a higher level of green credit development indicates a larger green credit market, greater policy support and encouragement, and a more mature green financial ecosystem. In such markets, the numerous green projects more significantly require FinTech to empower green credit businesses. Therefore, FinTech plays a more important role in enhancing the impact of green credit on bank performance.
In conclusion, from both the banks’ and the regional perspectives on green credit development, there is also a synergistic effect between FinTech and green credit. To maximize the benefits of green credit, banks and the local markets must collaborate to create a favorable green financial environment.
5.3. The Prosperity of the Banking Industry
The prosperity of the banking industry reflects its overall operational status, covering aspects such as asset quality, profitability, and risk management. Examining the heterogeneous effects of banking prosperity can deepen our understanding of FinTech’s impact on improving green credit performance across different banking cycles. This insight can guide banks on when to enhance their FinTech investments for greater benefits. We grouped the data based on the Banking Prosperity Index (BPI) into downturn and upturn states. The results of the grouped regression are shown in
Table 11. The results indicate that whether the banking industry is in an upturn or downturn, the coefficient of the interaction term between FinTech and green credit is significantly positive. Notably, this coefficient is higher during a downturn, suggesting that FinTech more effectively enhances the impact of green credit on bank performance during downturns.
The results can be interpreted from a risk aversion perspective. During prosperous periods in the banking industry, the market maintains a positive outlook, and participants’ risk tolerance increases. With abundant capital, banks may take on higher risks, reducing the demand for FinTech’s risk management and prudent assessment in green credit businesses.
However, during a downturn, market participants’ risk awareness rises significantly. Banks then emphasize risk management and prefer lower-risk green projects. At this time, FinTech becomes crucial. It helps banks more effectively evaluate and manage green credit risks, improve asset allocation efficiency, and reduce potential losses. Therefore, during banking industry downturns, FinTech can more significantly enhance the positive impact of green credit on bank performance.
7. Conclusions
Green credit, as a financial product supporting environmental protection and sustainable development projects, has a complex impact on bank performance. The existing literature presents diverse viewpoints on this impact. In recent years, FinTech has gradually spread into various banking domains, demonstrating its unique value. With the ongoing integration of FinTech and green credit, this paper explores whether FinTech influences the impact of green credit on bank performance. Theoretically, we try to explain the diverse viewpoints in the existing literature regarding the effect of green credit on bank performance. Practically, we study the extent to which FinTech can enhance the sustainability of banks’ green financial businesses. This paper provides theoretical foundations and practical guidance for banks to apply FinTech in their green finance businesses.
Based on an empirical analysis of data from Chinese commercial banks, this paper finds that green credit generally enhances bank performance, and FinTech further amplifies this positive effect. Marginal effect analysis indicates that when a bank’s FinTech level is low, the impact of green credit on bank performance is negative but not significant. However, when the FinTech level is high, the impact is significantly positive. This suggests that FinTech can explain the conflicting views in the existing literature, as differences in the level of bank FinTech lead to different conclusions about the impact of green credit on bank performance. This study offers a new perspective for understanding the relationship between FinTech and green credit, enriching relevant research. While the existing literature points out that FinTech can influence the level of green credit, this paper proposes that FinTech can synergize with green credit to affect bank performance.
We propose three mechanisms through which FinTech influences the impact of green credit on bank performance. First, FinTech reduces costs associated with green credit business, such as approval, maintenance, and external agency expenses. Second, FinTech enhances the reputation of green credit by improving customer experience and loyalty through precise marketing and personalized services. Third, FinTech mitigates the risks of green credit by improving the accuracy of risk assessments and strengthening dynamic information monitoring and early warning systems. Empirical results validate these mechanisms.
Furthermore, heterogeneity analysis reveals that the strengthening effect of FinTech on the impact of green credit on bank performance is more pronounced in city commercial banks. Additionally, as green credit development increases and during periods of banking industry downturns, FinTech demonstrates greater advantages, making the impact of green credit on bank performance more apparent. Finally, this paper conducts various robustness tests, including replacing green credit and FinTech indicators, using lagged explanatory variables, and employing the DID method. All these tests support the above conclusions.
Based on this study, we propose the following policy recommendations:
First, the regulator should continue to support the development of green credit in banks. This can be achieved by collaborating with relevant institutions to establish scientific and objective standards for evaluating green credit, enabling banks to better assess the risks and returns of green projects. Additionally, the regulator should revise policy documents to provide clear guidelines for banks, including specific details on operational processes, approval standards, and post-loan management. These measures will ensure the compliance and effectiveness of green credit activities. Furthermore, the regulator can organize green finance training to help banks improve their management capabilities, particularly in project evaluation and risk control.
Second, the government should support the development of banking FinTech through various policy measures. It can encourage banks to invest more in FinTech and acquire advanced equipment and technologies by offering tax deductions. Additionally, the government can provide financial support for FinTech infrastructure, such as big data processing centers, cloud computing service centers, and blockchain technology platforms. These infrastructures will ensure that financial institutions receive the necessary technical support, thereby enhancing their service efficiency.
At last, banks should enhance the development of FinTech and actively integrate it into their green credit business. They need to establish dedicated FinTech departments to drive innovation across various business areas. Rural commercial banks should increase investment in FinTech to achieve synergy with green credit services. State-owned and joint-stock commercial banks should maintain their current pace of FinTech development and explore new profit opportunities in green finance. Additionally, banks can accelerate FinTech advancement by collaborating with tech companies and research institutions, utilizing their expertise and resources. The widespread application of FinTech can enhance the economic and environmental benefits of banks, significantly contributing to sustainable finance.
Future research could expand on this study by exploring the impact of specific FinTech components on green credit activities. With the wide variety of FinTech technologies available, analyzing how individual elements like blockchain, artificial intelligence, and mobile payments enhance the effectiveness of green credit could offer deeper insights into FinTech adoption in banking. Currently, the development of green credit businesses is still in its early stages, and detailed data, such as revenues, costs, and risks associated with each bank’s green credit activities, remain limited. As more comprehensive data become available in the future, more in-depth research on the impact of green credit on bank performance will be possible. While the core principles of green credit and FinTech are generally consistent across countries, the findings of this study may provide valuable guidance for their development in other nations. However, due to variations in economic development, financial regulation, and cultural factors, the effects of green credit and FinTech may differ between countries, requiring further research to explore the influence of these country-specific factors.