The Impact of Fintech Development on Green Transformation of Private Enterprises—Empirical Evidence from China

Abstract

In the context of Chinese-style modernization, the promotion of comprehensive green development within the economy is a crucial aspect of achieving high-quality development. The private economy plays an irreplaceable role in China’s economic framework, and the advancement of green development is intrinsically linked to the green transformation of private enterprises. However, many of these enterprises encounter many challenges that significantly hinder their green transformation efforts. At the same time, the rapid growth of Fintech offers a compelling impetus for transforming and upgrading the financial industry. Therefore, in the pursuit of high-quality development, it is particularly vital to accelerate the green transformation of private enterprises by leveraging the supportive role of the financial system. This paper analyzes A-share listed private enterprises in China and the city-level Fintech development index from 2011 to 2022, utilizing a two-way fixed-effects model for empirical analysis. The findings indicate that Fintech development facilitates private enterprises’ green transformations. Furthermore, the mechanism analysis reveals that the primary pathways through which this effect occurs are the alleviation of financing constraints and the enhancement of corporate innovation abilities. Notably, this effect is more pronounced among enterprises located in eastern regions and within heavily polluting industries. Based on these conclusions, this paper offers relevant recommendations for the government and private enterprises, aiming to foster the green transformation of private enterprises and contribute to sustainable societal development.

1. Introduction

As the world’s second-largest economy, China’s transition to a green development approach is significant for its domestic ecological environment and economic development and has a far-reaching impact on global sustainable development. China has made important contributions to the reduction of carbon emissions and in addressing climate change. Pushing forward the green transition not only helps to realize the goals of the Paris Agreement, but also provides an experience for other countries to learn from, promotes global green economy cooperation, and jointly builds a future in which human beings and nature coexist in harmony.

In recent years, China has vigorously advocated accelerating the green transformation of the development mode. The Central Government has proposed implementing the concept of green development, promoting high-quality economic growth, and building a modernization in which human beings coexist harmoniously with nature; accelerating the green transformation of the mode of development; implementing a comprehensive conservation strategy; developing green and low-carbon industries; advocating green consumption; and promoting the formation of green and low-carbon modes of production and lifestyles. In the process of Chinese-style modernization, the promotion of a comprehensive green transformation of economic and social development is an intrinsic requirement for the sound development of a green, low-carbon, and recycling economy and the realization of sustainable economic development.

The private economy plays an indispensable and important role in China’s economic structure. By the end of 2023, the number of private enterprises nationwide exceeded 53 million, contributing more than 90% of the number of enterprises, and accounting for more than 80% of urban labor employment, more than 70% of all technological innovations, more than 60% of the GDP, and more than 50% of tax revenues [1]. The full implementation of sustainable development and the promotion of green production cannot be separated from the green transformation of private enterprises. However, since green transformation itself involves a large amount of investment, a long recovery cycle, and the dual risks of technological and environmental spillovers, coupled with the imbalance in the structure of China’s traditional financial services and the irrational allocation of financial resources, private enterprises are faced with many obstacles to the obtaining of green credit and green financing. The lack of green financing channels and the high cost of capital have become the main bottlenecks restricting sustainable development [2].

At present, in order to support and encourage private enterprises to carry out green transformation, the government has introduced a series of guidance and support policies, such as putting forward the carbon peaking and carbon neutrality goals and developing green finance, etc., to provide a policy framework for the green transformation of private enterprises; the “14th Five-Year Plan for Industrial Green Development” even explicitly proposes to support the green transformation of small and medium-sized enterprises. China’s private enterprises have also actively responded to the call and have achieved discrete results: some large-scale private enterprises are at the forefront of green transformation and have realized significant environmental benefits through technological innovation and green supply chain management [3]. The Social Responsibility Report of Chinese Private Enterprises (2024), recently released by the All-China Federation of Industry and Commerce, shows that in 2023, the R&D expenses of the top 1000 private enterprises in terms of R&D investment amounted to 1.39 trillion yuan, and private enterprises accounted for 95% of the specialized and innovative enterprises. In terms of promoting ESG, the top 500 private enterprises have also shown a positive attitude, with 20% of them having standardized the disclosure of ESG information [1]. However, many small and medium-sized private enterprises are still making slow progress in green transformation, due to limited resources, and their efforts are mainly focused on basic areas such as the upgrading of environmental protection equipment and energy-saving renovation.

Specifically, although the overall green transformation of China’s private enterprises has risen in fluctuation, there is still a gap between them and state-owned enterprises: the green transformation levels of state-owned enterprises in all categories are above those of private enterprises, and each year are significantly higher than those of private enterprises. Moreover, within private enterprises, their green transformation levels reveal another big difference, which is their uneven nature. In terms of different scales, large-scale enterprises take the lead, medium-scale enterprises take second place, and small-scale enterprises lag behind; in terms of industries, the green transformation of heavily polluting enterprises has been stagnant, while c have maintained a rising trend as a whole [4,5,6].

The green transformation of private enterprises faces many practical obstacles. Comparatively speaking, private enterprises are generally smaller in size, lack standardized management, have backward internal governance, and have stronger profitability goals that lead to more short-term behaviors, all of which will have negative impacts on their green transformation [7]. In terms of internal problems, the majority of small and medium-sized private enterprises are not strong in innovation, their ability to attract and retain talent is inadequate, and the management of the enterprise lacks standardization. In the external environment of these enterprises, the market demand is insufficient, the production chain and supply chain are not smooth enough, and the long-term technical reform funds are in even tighter supply; additionally, the direct financing channels are limited, and the proportion of credit loans in indirect financing is very low [8,9]. To sum up, subject to the limitations of capital, technology, and internal management, as well as other factors, many private enterprises are still facing difficulties in not daring to transform and not being able to transform.

As a new product relating to the deep integration of traditional finance and cutting-edge technology, Fintech has shown vigorous development in the era of the digital economy. This emerging field has promoted innovation, upgraded the financial industry, and reshaped modern financial services’ supply mode by organically combining cutting-edge information technology, such as AI, big data, and cloud computing, with traditional financial services [10]. Therefore, in the process of high-quality sustainable development, how to promote the green transformation of private enterprises is a key and difficult issue worthy of attention. Fintech, supported by advanced technology and possessing significant technological advantages that are difficult to obtain by using traditional finance, has presented an important issue for research and practice, specifically, whether it can accelerate the green transformation of private enterprises by fully utilizing the enabling role of the financial system.

This paper adopted the data of A-share listed private enterprises and the Fintech development index at the city level during 2011–2022 as samples for empirical analysis. First, we carried out descriptive statistical analysis and correlation analysis for each variable to ensure that the variables could subsequently be analyzed. Then, we constructed a two-way fixed-effects model to conduct a benchmark regression on the main effect. To ensure the reliability of the results, we adopted the method of replacing the explanatory variable and the samples in order to conduct a robustness test. In order to clarify the influence path, we also constructed a mechanism test model to conduct mechanism tests on financing constraints and enterprise innovation capacity, aiming to derive the influence path of the main effect. Finally, this paper also distinguishes regional and industry-based heterogeneity to explore these differential impacts under different classifications. After completing all the above analyses and tests, we arrived at the research conclusions of this paper and made targeted policy recommendations accordingly.

2. Literature Review

2.1. Research on Green Transformation

2.1.1. Connotations of Green Transformation

“Green transformation” was initially derived from the term “green economy”, which was proposed by Pearce et al. in their report [11]; since then, scholars have carried out a series of research investigations concerning it, and its connotations have gradually evolved into the present sense of the change from the traditional rough development mode to the low-carbon and clean sustainable development mode, in order to realize the goals of resource conservation, environmental protection, and promotion of sustainable development. According to Janson A, the reason green transformation can improve the efficiency of resource utilization and reduce pollution emissions is that it reduces to a certain extent the problems of dependence on resources and damage to the environment inherited from the traditional development mode, emphasizes the relationship between development and the environment, and achieves the harmonious coexistence of human beings and the environment [12]. Xie and Han described green transformation as the green development approach of enterprises, guided by the green development concept and centered on green innovation [13].

2.1.2. Factors Influencing the Green Transformation

The existing studies have primarily analyzed the factors influencing enterprise-level green transformation at both the macro and micro perspectives.

At the macro level, the driving factors can be summarized as national policies and environmental regulations. In terms of national policies, Huang pointed out that the reduction of the tax burden can effectively promote the green transformation of enterprises in heavily polluting industries and can be realized through the mechanisms of improving the financial situation, reducing the financing constraints, and increasing the investment in research and development within enterprises [5]. Wan et al. used the difference-in-difference model to test the impact of the Cleaner Production Promotion Law implemented in China in 2003, and the results show that the implementation of the policy reduced pollutant emissions and enhanced productivity, promoting the green transformation of manufacturing enterprises [14]. There is no unanimous conclusion relating to the impacts of environmental regulation: Tang et al. confirmed that three types of environmental regulation, namely, environmental administrative regulation, environmental economic regulation, and environmental soft constraints, can significantly promote the green transformation of manufacturing enterprises in China [15]. Zhu and Ren found that China’s industrial green transformation shows a U-shaped trend with the increase in the intensity of environmental regulation. They also found that China’s current environmental regulation intensity hurts the industrial green transformation [16].

At the micro level, the existing literature primarily examines the topics of technological innovation, digital transformation, and the characteristics of executive teams. In regard to technological innovation, research by Yue et al. indicates that both independent innovation and the introduction of new technology play significant roles in advancing the green development of China’s industrial sector [17]. Similarly, Xu demonstrated that green technological innovation effectively fosters the green transformation of manufacturing enterprises, particularly in heavily polluting industries [18]. In terms of digital transformation, the findings of Dai and Yang reveal that digitalization can facilitate the green transformation of enterprises, primarily through scale effects and technology effects [19]. Additionally, Cao et al. highlighted that the digitization process within manufacturing enterprises has evolved through four distinct phases, thereby aiding organizations in achieving their sustainable green transformation [20]. Regarding the characteristics of executive teams, Li’s study emphasizes that executives with extensive academic experience positively influence the green transformation of manufacturing enterprises. Such executives are generally more adept at understanding and evaluating new technologies, making these executives more inclined toward green development strategies [21]. Furthermore, research by Li et al. found that the foreign experience of executive teams significantly enhances corporate environmental responsibility, leading to increased investment in environmental protection and a reduced occurrence of environmental violations, which in turn supports green transformation [22].

2.2. Research on Fintech

2.2.1. Connotations of Fintech

In recent years, the rapid progress of science and technology, along with their increasing integration into the financial sector, has made Fintech a prominent topic for scholars, both domestically and internationally. However, within the academic community, there remains a lack of consensus on its definition.

First, there is the perspective of technological innovation. Scholars who hold this view believe that Fintech is technology-enabled finance, with its core essence rooted in technological advancement. Chishti et al. believe that Fintech refers to the technological progress attained by technology companies engaged in the financial sector that utilize scientific and technological innovations to launch new types of financial products [23]. Ma and Liu, as well as Chen and Wu, believe that the series of technologies that can influence financial transactions and promote the high-quality development of financial services are what is collectively referred to as Fintech [24,25]. Gomber defines Fintech as the intersection of modern science and technology with the business practices of the financial industry [26].

Secondly, from a financial services perspective, Zhu’s research defines Fintech as the application of new technologies in order to enhance the transformation and innovation of financial products and business models [27]. Thakur emphasizes that the essence of Fintech lies in improvements in financial services, with the objective of reducing transaction costs for both parties involved. In this context, technology serves merely as a means to achieve this goal [28].

Third, there is the convergence perspective of technology and finance. In 2016, the Financial Stability Board (FSB) defined Fintech for the first time: Fintech refers to technology-induced financial innovations that create new business models, applications, processes, or products that can have a substantial impact on financial markets and financial institutions, and on the ways in which financial services are provided [29,30]. Ba and Bai pointed out that Fintech utilizes technological innovation in applications within the financial sector that serve the general public and improve the efficiency of the industry, thus realizing the deep integration of finance and technology [31].

2.2.2. Economic Consequences of Fintech

The current academic research on the economic consequences of Fintech focuses on both the macro and micro levels.

At the macro level, Fintech offers more advanced and comprehensive financial services that contribute to the development of the real economy, playing a crucial role in fostering both economic and green development. Xue and Hu emphasized that Fintech can effectively allocate resources, promote innovation and transformation in financial services, and enhance the capability to support the real economy, thereby providing vital support for high-quality economic development in the New Normal [32]. Furthermore, Tang et al. employed a Spatial Dubin Model for empirical analysis and found that Fintech, along with its emerging financial sector, significantly boosts total factor productivity in regions and promotes positive spillover effects in neighboring areas [33]. From an environmental perspective, Tan and Shu concluded that Fintech facilitates the green transformation of industries by strengthening the overall innovation capacities of cities [34]. Additionally, Hui’s research indicates that Fintech can markedly enhance the green total factor productivity of Chinese cities, primarily through pathways that improve innovation levels, upgrade industrial structures, and alleviate resource mismatches [35].

At the micro level, Fintech mainly has a large impact on two micro subjects: banks and enterprises. As for banks, the findings of Meng et al. show that Fintech helps promote healthy competition in the regional banking industry, and there are significant spatial geographic effects and industrial competition effects that result [36]. In addition, the development of Fintech also has positive effects, such as the optimization of bank profitability and operational performance [37] and the enhancement of bank risk-taking ability [38]. On the enterprise side, findings by Li et al. reveal that the number of patent applications from New Third Board-listed enterprises rises in correlation with the level of Fintech development in their respective cities [39]. Furthermore, Guo et al. argued that Fintech development positively influences the enhancement of enterprises’ green innovation capacities [40]. Regarding total factor productivity, Fintech is shown to have significantly boosted the total factor productivity of enterprises [41]. This effect is primarily driven by reductions in information asymmetry and the expansion of financing channels, ultimately promoting an improvement in overall productivity.

2.3. Impact of Fintech on the Green Transformation

While research on the impact of Fintech on the green transformation of enterprises is still evolving, several scholars have conducted studies that generally agree on the positive correlation between the two. Xu et al. (2024) [42] empirically analyzed data from A-share listed companies in Shanghai and Shenzhen. Their findings indicate that enhancing the alignment between Fintech and the real economy significantly boosts corporate green transformation. This improved alignment can alleviate financial constraints faced by corporations, increase expenditures on environmental protection, and enhance the capacities of these corporations to engage in proactive risk management. Wu et al. (2024) [43] employed text analysis to assess the intensity of green transformation within enterprises. Their empirical analysis concluded that Fintech can significantly facilitate this transition, with particularly strong incentives noted for high-tech firms, non-state-owned enterprises, and heavily polluting enterprises. They identified three mechanisms through which Fintech empowers green transformation: improving financial access, smoothing risk, and incentivizing green innovation.

2.4. The Literature Gap

Based on the above sorting and analysis, there is a wealth of research on the concepts, economic consequences, and influencing factors associated with Fintech and green transformation in academia, and there is an intense collision of academic viewpoints. As for the research on the economic consequences of Fintech, most scholars focus on its ability to promote economic growth at the macro level, enhance commercial banks’ and companies’ business performance and competitiveness at the micro level, and promote the improvement of corporate innovation, total factor productivity, etc. In investigating the factors influencing enterprises’ green transformations, the macro-level considerations of national policy and environmental regulation, together with corporate technological innovation, executive team characteristics, and digital development at the micro level, are the hotspots of attention. In addition, the research works engaging with the above two topics involve mostly samples from all enterprises or industrial enterprises listed in Shanghai and Shenzhen A-share organizations in China, with relatively little attention being given to private enterprises.

Fintech, as a rapidly growing sector within the financial industry, is playing a crucial role in empowering China’s economy, enabling it to foster high-quality, sustainable development. The private economy, which serves as the backbone of China’s economic structure, is increasingly recognized for its strategic importance in the green transformation of private enterprises, particularly within the background considerations of the “dual-carbon” goal and the transition toward a greener economy and society. Currently, there is a notable gap in the literature addressing the impact of Fintech on the green transformation of enterprises, with studies concentrating on private enterprises being especially limited. Thus, investigating the relationship between Fintech and the green transformation within the context of private enterprises not only has the potential to enhance the academic discourse in this area but also holds significant practical implications for advancing the establishment of a robust financial nation and achieving sustainable economic development.

3. Theoretical Foundations and Research Hypotheses

3.1. Theoretical Foundations

3.1.1. The Asymmetric Information Theory

The information asymmetry theory, a foundational concept in information economics introduced by Akerlof in 1970, posits that market participants may have differing levels of information available to them [44]. Typically, one party possesses sufficient information to gain a competitive advantage, while another party lacks the necessary details, placing them at a disadvantage. This disparity allows the better-informed party to leverage their knowledge to make more favorable decisions during transactions. Consequently, the differences in information access can significantly influence the investment decision-making behaviors of enterprises. Additionally, a similar information asymmetry exists between investors and publicly listed companies. Investors usually rely only on the information that companies publicly disclose; additionally, the companies make it challenging to access internal data. This results in the levels of information varying between investors and listed companies, ultimately impacting the effectiveness of investors’ decision-making processes.

The development and application of Fintech improves information transparency [45], prompting enterprises to improve environmental performance and information quality; in addition, financial institutions can obtain more authentic and reliable information and make more reasonable and accurate assessments of enterprises, so that the financial institutions can allocate credit resources more scientifically and reduce the phenomenon of credit mismatches. Moreover, the sources of information for enterprises have also been expanded, which reverses the unfavorable situation of enterprises on the disadvantaged side of the information asymmetry and helps to optimize decision-making behavior. This provides a new solution to the problem of information asymmetry.

3.1.2. The Long-Tail Theory

The concept of the “long tail” was first introduced by Chris Anderson to analyze the profitability and business models of e-commerce platforms like Amazon and Netflix [46]. He observed that, in contrast to traditional brick-and-mortar stores, where producers typically focus on the top 20% of best-selling products and overlook those at the tail end of the sales volume, Internet-based business models have the capacity to better serve the long-tail market. This advantage arises from the Internet’s lower sales costs and the rapid circulation of goods, which creates an environment conducive to the development of long-tail products. Anderson encapsulated the Long-Tail Theory by noting that in models driven by the Internet economy, when the costs associated with product stocking and distribution are minimal, the combined market shares of a multitude of previously overlooked products can surpass those of a few popular items, thereby generating benefits comparable to those of the mainstream market.

The long-tail market implies a huge potential market demand. In the past, financial institutions generally favored lending entities such as state-owned enterprises or large corporations due to factors such as soundness. Currently, with the development of Fintech, the financial inclusion has been significantly improved, and many small enterprises and micro-enterprises that originally were associated with the long-tail customers are also able to get financial support. For example, in Malaysia, Fintech has redefined inclusive finance through crowdfunding, providing a comparative perspective to assess the role of Fintech in bridging the funding gap for SMEs in emerging economies [47].

3.1.3. The Sustainable Development Theory

The World Conservation Strategy, published in 1980, can be traced back to the idea of sustainable development, which holds that “the biosphere should be managed in such a way that it not only meets the needs of the present human race to the maximum extent possible but also ensures that the needs of future generations will continue to be met”. The primary aim of sustainable development is to enable a certain country or region to cultivate and accumulate resources that are coordinated, efficient, balanced, sustainable, and energy-conserving [48]. This approach seeks to minimize the impact on the ecological environment while ultimately achieving a “high-quality GDP” through the reduction of natural, social, institutional, and management costs.

At present, the idea of sustainable development has reached a point of broad consensus in the world; countries are actively dealing with global environmental problems, and the exploration of the relationships inherent in balancing environmental protection and economic efficiency has become more and more in-depth, and how to promote green economic growth has gradually become a global issue. The concept of green development is consistent with the concept of sustainable development, which focuses on the realization of the organic combinations and coordination between efficient economic development, sustainable social progress, and natural environmental protection [16]. In the context of China’s increasing emphasis on ecological environmental protection, how to promote the green transformation of China’s enterprises under the guidance of the concept of sustainable development and green development has become a key issue to be solved.

3.2. Research Hypothesis

3.2.1. Research Hypotheses for Benchmark Regression

The green transformation of enterprises requires huge amounts of credit. However, those enterprises facing financing challenges not only lack the motivation for such transformation, but also struggle with the capacity to undertake it. On the supply side, the traditional bank-driven financial system suffers from credit distortions and inefficiencies [33], resulting in many enterprises encountering difficulties in securing affordable financing. This situation hinders their ability to pursue green transformation and achieve high-quality development. As an important tool for the digital and intelligent development of China’s economy, Fintech plays an important role in promoting the green transformation of enterprises. Studies have shown that Fintech has a powerful capital-aggregation function, one which can gather small-scale funds from “long-tail” investment groups into large-scale funds that can then be lent to enterprises in need of external financing, thus realizing the incremental replenishment of capital supply [22].

From the perspective of internal incentives, Fintech—an innovation driven by information technology—reduces the redundant costs associated with the financing process, improves the financing environment for enterprises, and provides critical credit support for those seeking to embark on green transformation but struggling with funding constraints [43]. With the enabling role of Fintech, enterprises are no longer confined to traditional production and operational models and actively transform towards intelligence and digitalization. Additionally, Fintech has introduced a range of innovative green financing products and services, such as green bonds, renewable energy financing, and carbon emissions trading. These offerings provide enterprises with more accessible and cost-effective financing options to support their environmental protection investments and the development of green projects. This, in turn, helps alleviate the financial constraints faced by enterprises and creates opportunities for their green transformation. Consequently, we propose the hypothesis H1:

H1. 

The development of Fintech is conducive to promoting the green transformation of private enterprises.

3.2.2. Research Hypotheses for Mechanisms Analysis

The issue of information asymmetry often leads firms to conceal unfavorable information in order to secure more favorable loan terms during traditional processes such as loan applications and negotiations. Financial institutions, particularly banks, typically require businesses to provide adequate collateral or guarantees to safeguard the lenders’ interests and mitigate the risk of loan defaults [49]. Consequently, this creates challenges in meeting the financing needs of enterprises through traditional financial services. However, the rise of Fintech has prompted banks and other financial institutions to leverage advanced technologies such as blockchain and cloud computing. This enables a deeper analysis of enterprise-level information, thereby alleviating the issues of information asymmetry and reducing the collateral requirements for credit transactions. As a result, the barriers for enterprises seeking credit are lowered. Furthermore, Fintech enhances the dissemination of information within the credit market and improves the sharing of information. This aggregation of credit data significantly reduces the screening and monitoring costs for financial institutions, further mitigating information asymmetry and lowering the overall cost of credit financing [50].

When enterprises encounter financing constraints, it becomes challenging for them to meet their financial needs, secure external funding for their production and operations, and select an optimal capital structure with the lowest cost of capital. These obstacles can hinder their operational efficiency and impede their development in innovation [51]. The alleviation of financing constraints allows enterprises to access more resources for project investments and research and development in innovation, thereby enhancing their operational and innovation performance. This improvement ultimately contributes to advancing the level of green transformation within private enterprises. Consequently, we propose the following hypothesis H2a:

H2a. 

The development of Fintech can facilitate the green transformation of private enterprises by alleviating their financing constraints.

As a product of the convergence of innovative technology and finance, Fintech inherently possesses transformative characteristics [23]. With advancements in information technology, Fintech plays a crucial role in fostering innovation. Firstly, in terms of its scope, Fintech, through deep integration with Internet platforms, transcends temporal and geographical limitations, facilitating information sharing and collaboration among enterprises and their upstream and downstream partners. This connectivity enables businesses to utilize external resources more effectively, accelerating technological breakthroughs and product innovation [52]. Secondly, regarding the breadth of services, a variety of integrated offerings, such as digital payments, have led to innovative business models for financial service institutions. This trend of innovation not only reduces various costs but also lowers the financing expenses of enterprises, thereby providing financial support essential for their technological innovation efforts [52]. Furthermore, financial technology is increasingly digital and intelligent, contributing to enhancements in the digitalization of enterprises. This evolution improves management efficiency, allows for more accurate assessments of market demand and risks, optimizes resource allocation, and guides companies in identifying high-potential innovation-based projects, ultimately reducing the possibility of failure.

Innovation ability is the key to maintaining enterprises’ core competitiveness and is indispensable in promoting green transformation. Green innovation capacity, in particular, has emerged as an essential technical foundation which can advance the shift towards environmentally friendly production practices [53]. This capacity can enhance and upgrade existing production technologies, making them more energy-saving, environmentally sustainable, high-quality, and efficient, ultimately leading to the greening of production processes and effective post-event governance. Moreover, when the effects of innovation permeate various links and departments within an enterprise, it fosters the establishment of a comprehensive green management system. This system spans the entire value chain and encompasses all aspects of green management—from decision-making and production processes to product sales. It also strengthens staff awareness of green development and enhances the overall level of green transformation within the enterprise. In summary, we propose the following hypothesis:

H2b. 

The development of Fintech can facilitate the green transformation of private enterprises by enhancing their innovation capabilities.

3.2.3. Research Hypotheses for Heterogeneity Analysis

The economic development across China’s three regions—the East, Central, and West—varies significantly, and the rise of Fintech has not progressed uniformly in each area. Therefore, it is essential to analyze the development of Fintech in detail based on regional differences.

Since the reform and opening-up policies, the eastern region has benefited from superior resource endowments, leading it to its present position as a leader in total economic volume, technological innovation, and openness to global markets. This region is home to a large number of Fintech enterprises and organizations. Companies in the East are at the forefront of adopting and developing Fintech solutions, actively recruiting and nurturing talent in this field. Additionally, the government has implemented various supportive policies to promote Fintech initiatives, employing measures that leverage Fintech to address financing challenges and mitigate the risk of business failures. Conversely, the central and western regions face constraints related to terrain, population density, economic structure, and infrastructure. These challenges result in fewer opportunities and resources for Fintech development. In these areas, the primary industry occupies a larger share of the economic structure, limiting the potential for Fintech to drive expansion in the tertiary sector [18]. Consequently, the central and western regions struggle to break free from their reliance on traditional financial systems, and a robust Fintech-led growth dynamic has yet to emerge. Based on this, we propose Hypothesis H3a:

H3a. 

Compared to private enterprises in the central and western regions, Fintech has a more significant role in facilitating the green transformation of private enterprises in the eastern region.

Heavily polluting industries face stricter environmental regulations, such as carbon quotas and emission permits, leading to a significant green financing gap as traditional financing channels are becoming less accessible due to environmental risk aversion. In order to reduce costs, heavily polluting enterprises are more motivated to utilize Fintech to improve their abilities to obtain funds and alleviate financing constraints.

In addition, Fintech can effectively alleviate the problem of information asymmetry [45], which can motivate heavy polluters to better deliver environmental protection information through digital marketing and public-relations strategies and promote the green transformation of enterprises. Moreover, Fintech can also provide environmental data analysis and monitoring technology, prompting heavily polluting enterprises to better understand their own environmental impacts and formulate targeted governance programs. Therefore, Fintech has a significant role in promoting the green transformation of heavily polluting enterprises, which helps them better cope with environmental challenges, reduces environmental protection costs, and improves the level of environmental governance and sustainable development ability. As a result, we propose hypothesis H3b:

H3b. 

Compared to private enterprises which are not heavily polluting, FinTech has a more significant role in facilitating the green transformation of heavily polluting private enterprises.

4. Research Design and Empirical Analysis

4.1. Research Design

4.1.1. Sample Selection and Data Sources

We selected the listed private enterprises with A-shares and the index of Fintech development at the city level as the samples, ranging from 2011 to 2022, in order to study the impact of Fintech development on the green transformation of private enterprises. The firm-level data utilized in this study were sourced from the Wind database and the CSMAR database. The sample data were refined through application of the following criteria:

(1)

Exclusion of enterprises facing delisting risks (ST/ST*) and those that were delisted during the study period.

(2)

Exclusion of firms within the financial industry.

(3)

Exclusion of samples with unavailable, missing, or anomalous key indicators.

After applying these screening criteria, a total of 14,834 data entries were obtained, and the data processing was conducted using Stata 17.0.

4.1.2. Variable Measurement

(a)

Dependent Variable

Drawing on the study by Loughran and McDonald [54], this paper employs the textual data found in annual reports to assess the transition towards corporate greening. There are two central reasons for selecting annual reports as our texts of interest: first, green transformation is a critical strategic aspect for publicly listed companies, one which is typically disclosed in these reports; second, the mandatory nature of annual report disclosures, governed by stringent wording specifications, enhances the accuracy of keyword matching. In this research, we identified 113 keywords related to corporate greening transformation across five categories: publicity initiatives, strategic concepts, technological innovations, emissions governance, and monitoring management; this selection was informed by relevant policy documents and previous studies [13,14]. To illustrate the green transformation of enterprises, we calculated the frequency of each keyword in the annual reports of listed companies, adding 1 to each frequency before applying the natural logarithm.

(b)

Independent Variable

The level of regional Fintech development is utilized as the primary independent variable in this study. Drawing on the studies of Guo et al. and Gao et al. [55,56], we have selected the digital financial inclusion index from the Digital Finance Research Center of Peking University to serve as the indicator of Fintech development. This index effectively highlights the advantages of Fintech in terms of accessibility, popularity, efficiency, and convenience, while also offering a thorough assessment of the present status of Fintech development in Chinese society. As such, it is frequently employed as a proxy measure for assessing the level of Fintech advancement. To enhance the comparability of the variable coefficients, this paper standardizes the variable.

(c)

Mechanism Variables

Drawing on the research of Kaplan and Zingales [57], this paper adopts the KZ index to represent the degree of financing constraints faced by firms. The KZ index has the advantages of being more relevant to China’s market and more comprehensive in its measurement dimensions, compared to other measures of financing constraints [58]. A higher KZ index indicates that enterprises face a greater degree of financing constraints. This paper also draws on the practice of Zhu and Chen [59] to take the number of invention patents applied for by enterprises in the current year as a proxy variable used to measure innovation ability. Invention patents embody a greater technological content relative to innovation, compared to other patent types, thereby providing a more accurate representation of enterprises’ innovation output. In terms of variable configuration, the total number of invention patents applied for in the current year by each enterprise, plus one, is subjected to natural logarithmic processing.

(d)

Control Variables

Referring to the existing studies [19,33,43], the following control variables were selected in this paper: Percentage of tangible assets, Long-term capital gearing ratio, Gross profit, Shareholding concentration, Firm age, Institutional investor shareholding ratio and Loss or not.

The main variables in this paper are shown in

Table 1. Variable definitions.

Variable Symbol	Variable Name	Definitions
Dependent Variable
Green	Level of green transformation of private enterprises	Natural logarithm of the number of green transformation words in corporate annual reports plus 1.
Independent Variable
Fintech	Fintech development level	Digital Financial Inclusion Index compiled by the Digital Finance Research Center of Peking University.
Mechanism Variables
KZ	Degree of financing constraints of enterprises	KZ index.
Innovation	Enterprise innovation capacity	Natural logarithm of the number of patents filed by enterprises plus 1.
Control Variables
Tangible	Percentage of tangible assets	(Total Assets − Net Intangible Assets − Net Goodwill)/Total Assets.
DLCR	Long-term capital gearing ratio	Non-current liabilities/(Equity + non-current liabilities).
GProfit	Gross profit	(Operating Income − Operating Costs)/Operating Income.
Top1	Shareholding concentration	Number of shares held by the largest shareholder/Total number of shares.
FAge	Years of establishment of the enterprise	Current year minus year of incorporation plus 1 in natural logarithm.
INST	Institutional investor shareholding ratio	Total number of shares held by institutional investors/total number of shares.
Loss	Whether the enterprise is loss-making	A dummy variable, taking 1 for a net profit of less than zero in the previous year and 0 otherwise.

.

4.1.3. Model Setting

(a)

Benchmark Regression

To verify the hypotheses presented in this paper and examine the influence of Fintech on the green transformation of private enterprises, we constructed a two-way fixed-effects model for the main effect regression, drawing on the relevant literature. Individual effects must be fixed due to the existence and inexhaustibility of variables that are correlated with the explanatory variables and do not vary over time, and not all such variables can be included in the model. At the same time, time-based effects need to be fixed because all individuals are jointly exposed to time trend shocks. Therefore, it is reasonable to use a two-way fixed-effects model in the article. The benchmark regression model is established as follows:

$${Green}_{i,t}=\alpha +\beta {Fintech}_{i,t}+\gamma {Controls}_{i,t}+{\delta }_i+{\tau }_t+{\epsilon }_{i,t}$$

(1)

In Equation (1), the subscript $i$ denotes listed companies, while $t$ corresponds to the respective year. The variable ${Green}_{i,t}$ serves as the primary explanatory variable in this paper, representing the extent of green transformation for company $i$ in year $t$. The variable ${Fintech}_{i,t}$ is the core explanatory variable, reflecting the level of Fintech development at the city level, in the location where company $i$ is situated during year $t$. The term ${Controls}_{i,t}$ comprises a range of control variables, specifically including the following: the ratio of tangible assets ${Tangible}_{i,t}$, the long-term capital debt ratio ${DLCR}_{i,t}$, the gross profit rate ${GProfit}_{i,t}$, the shareholding ratio of the largest shareholder ${Top1}_{i,t}$, the years of the firm’s establishment ${FAge}_{i,t}$, institutional investors shareholding ratio ${INST}_{i,t}$, and whether or not there is a loss ${Loss}_{i,t}$. The term $\alpha$ represents a constant, while ${\epsilon }_{i,t}$ denotes a random perturbation term. Additionally, ${\delta }_i$ and ${\tau }_t$ capture the individual effect and the time effect, respectively. The coefficient $\beta$ preceding the core explanatory variable ${Fintech}_{i,t}$ indicates the direction and magnitude of the impact that the level of Fintech development has on the green transformation of private enterprises. Based on the theoretical analysis provided in this paper, it is anticipated that $\beta$ will be significantly positive.

(b)

Mechanism Analysis Model

To investigate how Fintech specifically influences the green transformation of private enterprises, this paper adopts an approach inspired by Jiang’s methodology [60]. This approach highlights the significant endogeneity issues associated with the traditional three-step method used for testing mediation effects. Therefore, the following model is utilized for mechanism analysis. First, we examine the regression coefficients $\beta$ and $\theta$ before Fintech in Equations (2) and (3). If the signs and significance of these coefficients are meaningful, further analysis can be conducted. Additionally, since the earlier theoretical analysis has addressed the impact of each intermediary variable on the green transformation of private enterprises, this completes the mechanism test. The mechanism test model is structured as follows:

$${Gre}_{i,t}=\alpha +\beta {Fintech}_{i,t}+\gamma {Controls}_{i,t}+{\delta }_i+{\tau }_t+{\epsilon }_{i,t}$$

(2)

$${Mediator}_{i,t}=\alpha +\theta {Fintech}_{i,t}+\mu {Controls}_{i,t}+{\delta }_i+{\tau }_t+{\epsilon }_{i,t}$$

(3)

In Equation (3), ${Mediator}_{i,t}$ represents the mechanisms at play, with this paper focusing on financing constraints (KZ) and enterprise innovation capacity (Innovation) as the selected variables. Specifically, the primary regression analysis demonstrates that the development of financial technology can facilitate the green transformation of private enterprises. Taking financing constraints as an example, a significantly negative coefficient $\theta$ would indicate that financial technology effectively alleviates the financing limitations faced by enterprises. The previous part of the paper has already thoroughly explored the impact of financing constraints on green transformation, thus allowing us to examine how financial technology reduces these constraints and subsequently fosters green transformation in private enterprises. The principles used in assessing the mechanism of enterprise-level innovation capacity follow a similar rationale and will not be reiterated. However, it is important to note that, in the expected results, both the coefficient $\beta$ and coefficient $\theta$ should show significant positive values.

4.2. Empirical Analysis

4.2.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variable	N	Mean	SD	Min	Max
Green	14,834	1.873	0.822	0.000	3.970
Fintech	14,834	2.175	0.696	0.178	3.611
Tangible	14,834	0.632	0.422	0.000	1.000
DLCR	14,834	0.114	0.141	0.000	0.996
GProfit	14,834	0.312	0.182	−0.554	3.764
Top1	14,834	31.61	13.86	1.844	89.99
FAge	14,834	2.892	0.345	1.099	4.174
INST	14,834	36.73	25.30	0.000	463.5
Loss	14,834	0.124	0.330	0.000	1.000
KZ	14,834	1.208	2.215	−11.980	11.980
Inno	14,834	0.465	0.813	0.000	5.252

presents the results of descriptive statistics analysis for the main variables discussed in this paper.

The explanatory variable, which is the green transformation of private enterprises (Green), exhibits a standard mean value of 1.873 and a deviation of 0.822, with a minimum value of 0 and a maximum value of 3.970. This substantial range indicates a significant disparity in the levels of greening transformation among different private enterprises. Meanwhile, the core explanatory variable, regional financial technology development (Fintech), has a mean value of 2.175, a standard deviation of 0.696, a minimum value of 0.178, and a maximum value of 3.611. This suggests that financial technology in China displays considerable dispersion and highlights the issue of inter-regional imbalance, as well as notable differences in development levels between cities. The descriptive statistics for the other control and mechanism variables fall within normal ranges and will not be analyzed individually.

4.2.2. Correlation Analysis

The results presented in

Table 3. Pearson correlation coefficient matrix.

	Green	Fintech	Tangible	DLCR	GProfit	Top1	FAge	INST	Loss
Green	1
Fintech	0.326 ***	1
Tangible	−0.026 ***	−0.171 ***	1
DLCR	0.113 ***	0.067 ***	−0.003	1
GProfit	−0.079 ***	0.038 ***	−0.169 ***	−0.147 ***	1
Top1	−0.032 ***	−0.085 ***	0.143 ***	−0.012	0.036 ***	1
FAge	0.160 ***	0.394 ***	−0.069 ***	0.177 ***	−0.038 ***	−0.108 ***	1
INST	−0.007	−0.067 ***	0.052 ***	0.126 ***	0.017 **	0.395 ***	0.002	1
Loss	0.014 *	0.112 ***	−0.028 ***	0.159 ***	−0.161 ***	−0.119 ***	0.086 ***	−0.089 ***	1

Note: Significance level * p < 0.1, ** p < 0.05, *** p < 0.01. The same applies to the tables below.

indicate a significant positive correlation between the core explanatory variable Fintech and the explanatory variable considering the green transformation level of private enterprises (Green), at the 1% significance level, with a coefficient of 0.326. This suggests that the degree of green transformation in private enterprises is positively associated with the level of Fintech development in their respective regions. Consequently, a higher level of regional Fintech development correlates with better performance in the green transformation of private enterprises, thereby initially validating hypothesis H1.

4.2.3. Benchmark Regression

In line with the empirical plan outlined in the previous section, this paper employs Stata 17.0 to conduct full-sample regression using models with year-based and individual-based fixed effects. The results are presented in

Table 4. Benchmark regression result.

	(1)	(2)
Variables	Green	Green
Fintech	0.135 *	0.208 **
	(0.075)	(0.090)
Tangible		0.090
		(0.078)
DLCR		0.018
		(0.059)
GProfit		−0.152 **
		(0.066)
Top1		0.004 ***
		(0.001)
FAge		−0.165 *
		(0.086)
INST		0.001 *
		(0.001)
Loss		−0.093 ***
		(0.018)
Constant	1.573 ***	1.669 ***
	(0.162)	(0.319)
Year-FE	YES	YES
Individual-FE	YES	YES
N	14,318	10,005
R-squared	0.726	0.724

. Model (1) illustrates the regression outcomes without any control variables, while Model (2) displays the results after the addition of all control variables.

The table indicates that the coefficient for financial technology (Fintech), when analyzed directly, stands at 0.135, which is significant at the 10% level. As control variables are added, the coefficient increases to 0.208, with the significance level improving to 5%. Consequently, hypothesis H1 is supported.

4.2.4. Robustness Tests

(a)

Substitution of Dependent Variable

The number of green patent applications submitted by enterprises serves as an indicator of the green transformation strategies of these enterprises and their commitment to green initiatives. The patent application process is rigorous, meaning that even if a final application is not approved, companies can still leverage the resulting innovations in their production processes. Numerous studies have employed patent applications as a measure of corporate green transformation [59]. Consequently, this paper utilizes this number (GPatent) as a proxy variable for the explanatory factors in a robustness test. The measurement method is to take the natural logarithm of the number of green patent applications across all sample enterprises, adding one to the count beforehand.

Column (1) of

Table 5. Robustness test results.

	(1)	(2)
Variables	GPatent	Green
Fintech	0.208 **	0.305 ***
	(0.090)	(0.098)
Constant	1.669 ***	1.569 ***
	(0.319)	(0.336)
Controls	YES	YES
Year-FE	YES	YES
Individual-FE	YES	YES
Observations	10,005	8416
R-squared	0.724	0.720

presents the regression results. The coefficient for Fintech is 0.208, which is significantly positive at the 5% significance level. This indicates that even after the adjustments made to the explanatory variables, the research hypotheses remain valid, reinforcing the core conclusion that the advancement of Fintech can effectively encourage the green transformation of private firms.

(b)

Winsorization

To mitigate the impact of extreme values on the regression results, this study trims the tails of all continuous variables at the 1% level and subsequently re-runs the regression analysis. The results, presented in column (2) of Table 5, reveal a coefficient of 0.214 for Fintech, which is significantly positive at the 5% significance level, further reinforcing the primary conclusions of this paper.

4.2.5. Endogeneity Analysis

In order to ensure the determination of the causal relationship between Fintech development and the green transformation of private enterprises, and to solve the endogeneity problem caused by issues such as omitted variables and reverse causation, this paper adopts the instrumental variable method to conduct an endogeneity test.

Referring to Zhang et al. [61], this paper takes the spherical distance from the enterprise’s city to Hangzhou as the instrumental variable. First of all, typical Fintech enterprises represented by Alipay originate from Hangzhou, and it can be expected that under the influence of Hangzhou’s proximity-driven effect, the closer the city to Hangzhou, the higher the level of Fintech development, which satisfies the correlation requirement of the instrumental variables. On the other hand, the green transition decisions of enterprises are not related to geographic location, a factor which can satisfy the assumption of exogeneity. In addition, in order to make the instrumental variables have time-varying effects, this paper integrates the spherical distance with the average value of Fintech development for the whole country (except the city) as an instrumental variable (DistanceIV) for 2SLS estimation. The regression results are shown in

Table 6. Instrumental variable regression results.

	(1)	(2)
	First Stage	Second Stage
Variables	Fintech	Green
DistanceIV	0.000 ***
	(11.02)
Fintech		0.325 ***
		(2.94)
Constant	0.527 ***	0.298
	(3.28)	(1.44)
Controls	YES	YES
Anderson LM Statistic	120.816 ***
Cragg–Donald Statistic	121.508 ***

.

Column (1) presents the regression results for the first stage, the regression coefficient before DistanceIV is significantly positive at the 1% level. Both the Anderson LM statistic and the Cragg–Donald statistic are significant at the 1% level, indicating that there is no problem of under-identification of instrumental variables and weak instrumental variables, and the instrumental variable selection is valid. Column (2) presents the results of the second-stage regression, and the coefficient of Fintech remains significantly positive at the 1% level, again validating the results of the benchmark regression.

In summary, after using instrumental variables to solve the endogeneity problem, the conclusion that Fintech development can promote the green transformation of enterprises still holds.

4.2.6. Mechanism Analysis

(a)

Financing Constraints

In this paper, the KZ index serves as a measure of financing constraints faced by enterprises. As shown in column (1) of

Table 7. Mechanism tests results.

	(1)	(2)	(3)
Variables	KZ	SA	Innovation
Fintech	−0.896 ***	−0.031 *	0.176 *
	(0.252)	(0.174)	(0.101)
Constant	−1.108	−3.782 ***	−0.986 ***
	(0.888)	(0.036)	(0.357)
Controls	YES	YES	YES
Year-FE	YES	YES	YES
Individual-FE	YES	YES	YES
Observations	10,005	10,005	10,005
R-squared	0.689	0.882	0.666

, the regression coefficient for Fintech on the KZ index is −0.896, which is statistically significant at the 1% level. This finding suggests that the advancement of Fintech can significantly help alleviate the financing constraints encountered by private enterprises. In column (2), the paper also selects the SA index for further validation and arrives at the same conclusion as when using the KZ index.

The advancement of Fintech has significantly enhanced the capacities of banks and financial institutions to acquire, analyze, and process information. This development has mitigated the issue of information asymmetry between these institutions and enterprises, allowing for more favorable credit approval conditions for businesses. Consequently, the financing channels available to enterprises have expanded, and the financing constraints they face have been alleviated. The existing literature and theoretical analyses indicate that reducing these financing constraints enables private enterprises to secure the additional funds that are essential for their production, operations, and innovative research and development activities. This support not only ensures the sustainability of production investments and R&D initiatives, but also markedly enhances the business performance and innovation capacities of enterprises. In turn, this reflects positively on their green transformation efforts. Thus, the mechanism of “Fintech development–easing financing constraints–enhancing the level of green transformation” is validated, confirming Hypothesis H2.

(b)

Innovation Capability

This paper employs the natural logarithm of the number of patents applied for by an enterprise in a given year, plus one, as a measure of innovation level. In column (2) of Table 7, the regression coefficient for Fintech concerning the innovation capacities of enterprises is 0.176, which is statistically significant at the 10% level. This finding suggests that the advancement of Fintech positively influences the innovation capabilities of private enterprises, as evidenced by the increase in the numbers of patents they applied for.

Fintech can foster enterprise-level innovation in several key ways.

First, it enables a comprehensive analysis of enterprise-level information, allowing for precise assessments of credit situations and business performance. This, in turn, provides robust financial guarantees for the execution of innovation-based projects and effectively addresses the financing challenges and the high costs faced by some businesses, particularly small and medium-sized private enterprises.

Second, Fintech enhances the efficiency of fund approval processes, ensuring timely access to financial resources for innovation and R&D initiatives. This maintenance of a stable capital chain accelerates the pace of technological advancement.

Lastly, Fintech broadens the service scope for financial institutions. By leveraging Fintech capabilities, these institutions are better equipped to focus on “long-tail” groups and projects, thereby invigorating innovation among those whose financing needs are often overlooked by traditional financial services.

The enhancement of enterprise-based innovation capabilities, as previously analyzed, can lead to the green transformation of private enterprises. This transformation is fueled by increased awareness of green development, the attraction of green technology talents, and the establishment of a green development framework. Thus, the pathway of “Fintech development–improved innovation capacity of private enterprises–promotion of the green transformation of private enterprises” illustrates the crucial role of Fintech in this process, supporting the validity of hypothesis H3.

4.2.7. Heterogeneity Analysis

(a)

Regional Heterogeneity

China’s vast territory showcases significant disparities in resource distribution, levels of economic development, and infrastructure across various regions, all of which impact the development of Fintech. In this paper, we build upon the work of Hu by categorizing private enterprises into three groups based on their location: eastern, central, and western regions [62]. This classification allows us to investigate the differential effects of Fintech on the green transformation of private enterprises across these regions. The regression results are presented in

Table 8. Regional heterogeneity regression results.

	Easter Region	Central Region	Western Region
Variables	Green	Green	Green
Fintech	0.183 *	0.314	0.253
	(0.108)	(0.334)	(0.302)
Constant	1.621 ***	0.775	0.804
	(0.363)	(1.243)	(1.066)
Controls	YES	YES	YES
Year-FE	YES	YES	YES
Individual-FE	YES	YES	YES
Observations	8166	822	1013
R-squared	0.718	0.746	0.779

.

The findings from the regression analysis indicate that within the eastern region, the regression coefficient for Fintech is 0.183, which is significant at the 10% level. In contrast, while the regression coefficients for the central and western regions are positive, they do not reach statistical significance. This suggests that Fintech is more effective in facilitating the green transformations of private enterprises in the eastern region compared to those in the central and western regions.

The disparity between the regions may stem from the eastern region’s notable advantages in economic development, financial infrastructure, and talent concentration compared to the central and western regions. With significant resource endowment and a dynamic financial market, the eastern region benefits from a range of Fintech-supportive government policies. First-tier cities, such as Beijing and Shanghai, boast a high concentration of financial institutions and abundant financial resources. This results in a more advanced level of Fintech development and a pronounced effect radiating outward to the surrounding areas, leading to a situation in which private enterprises in the eastern region are able to access more convenient and efficient Fintech services. Consequently, the positive feedback received by these enterprises tends to be significantly more pronounced.

(b)

Industrial Heterogeneity

According with the Guidelines for Industry Classification of Listed Companies issued by the China Securities Regulatory Commission (CSRC), the Industry Classification and Management Directory for Environmental Verification of Listed Companies and Guidelines for Environmental Information Disclosure of Listed Companies formulated by the Ministry of Environmental Protection (MEP), and drawing on existing research [63], this paper categorizes enterprises into two groups based on their pollution levels: heavily polluting industries, such as coal, mining, tanning, and another 13 industries; and industries that are not heavily polluting. The regression results are presented in

Table 9. Industrial heterogeneity regression results.

	Heavily Polluting Enterprises	Non-Heavily Polluting Enterprises
Variables	Green	Green
Fintech	0.759 ***	0.031
	(0.198)	(0.104)
Constant	0.427	1.876 ***
	(0.697)	(0.367)
Controls	YES	YES
Year-FE	YES	YES
Individual-FE	YES	YES
Observations	1761	8216
R-squared	0.739	0.715

.

Within the group of heavily polluting enterprises, the coefficient for Fintech is 0.759, which is statistically significant at the 1% level. In contrast, the coefficient for enterprises which are not heavily polluting is not statistically significant. This suggests that the development of Fintech plays a more pivotal role in facilitating the green transformation of heavily polluting private enterprises, while no such effect is observed in the case of enterprises which are not heavily polluting.

One possible explanation is that, in contrast to enterprises which are not heavily polluting, heavily polluting industries are characterized by high energy consumption, significant pollution levels, and elevated carbon emissions, coupled with a relatively low level of green transformation. The presence of numerous pollutants complicates management efforts, thereby intensifying the urgency for heavily polluting enterprises to undergo green transformation. In light of China’s current push towards high-quality development, these industries are increasingly motivated to leverage the opportunities and support provided by advancements in financial technology to drive technological innovation and pollution control. This approach not only facilitates their green transformation but also enhances their corporate image and improves market competitiveness.

5. Conclusions and Recommendations

5.1. Research Conclusions

This paper empirically examines the impact of Fintech development on the green transformation of private enterprises by analyzing data from A-share listed private companies in Shanghai and Shenzhen, alongside Fintech data collected at the city level from 2011 to 2022. The study reaches several key conclusions.

First, the development of Fintech significantly enhances the green transformation of private enterprises. This conclusion remains robust even after conducting tests that involve the substitution of explanatory variables and the application of data reduction techniques. Second, regarding the mechanisms of impact, Fintech facilitates the green transformation of private enterprises primarily through two channels: the alleviation of financing constraints and the enhancement of the innovation capacities of enterprises. Third, notable disparities exist in the effects of Fintech on the green transformation of private enterprises; the influence of Fintech is particularly pronounced among firms located in the eastern region and those in heavily polluted areas.

This research provides empirical evidence that Fintech drives the green transformation of private enterprises and offers policy insights which are important for achieving the goal of sustainable green development.

5.2. Recommendations

Firstly, it is essential to actively promote the high-quality development of Fintech and enhance the financing support system that assists enterprises in their green transformation. Encouraging Fintech companies to create green financing tools, such as green bonds and green loans, is vital. Providing private enterprises with accessible green financing channels will further facilitate their transition to sustainable practices. Government departments should conform to the trends of Fintech innovation, introduce relevant policies to support and encourage the development of Fintech by financial institutions and enterprises, and establish unified and perfected Fintech access standards along with the relevant laws and regulations. Additionally, strengthening the construction of the financial infrastructure will create external conditions which are favorable for the development of Fintech, enabling companies to access digitized sustainable financial services. This includes offerings such as green energy financing, environmental protection project financing, carbon emissions trading, and more; these offerings promote the application of Fintech in supporting green transformation initiatives. At the same time, the government and relevant institutions should actively explore the green service model of Fintech, make full use of digital technologies to effectively identify and regulate green projects, provide enterprises with green governance advice and sustainable development strategies, and promote the green transformation of enterprises.

Secondly, differentiated policies should be tailored for various regions and types of enterprises. Fintech support initiatives need to be targeted, aligned with local conditions and specific enterprises, and constructed with precision. On one hand, due to the uneven distribution of financial resources and disparities in economic levels across different regions, the relevant authorities should design and execute financial technology development programs that align with local economic growth, taking into account the unique circumstances of each area. Given that the level of Fintech development in the eastern region is higher than the levels in the central and western regions, a higher-level and more flexible Fintech development program can accordingly be implemented in the eastern region, encouraging it to explore the in-depth application of AI in cross-border payments, supply chain finance and other cutting-edge technologies. In the central and western regions, priority should be given to promoting the popularization and improvement of Fintech infrastructure and supporting the use of Fintech to solve the problem of insufficient coverage of traditional financial services, such as by enhancing the accessibility of inclusive finance through tools such as mobile payment and digital credit, so as to prevent the further widening of interregional differences.

On the other hand, given that the impact of financial technology development on the green transformation of private enterprises varies by individual enterprise and industry, the allocation of Fintech resources should be more precise. This will enhance the ability to maximize the benefits of financial technology in promoting the green transformation of private enterprises. For heavily polluting private enterprises, policymaking should focus on building a “Fintech–environmental protection” deep synergy mechanism. Environmental protection departments and financial regulators can establish a mandatory environmental data sharing platform, focusing real-time enterprise emissions monitoring, energy consumption, and other related data within a Fintech risk-control system, in order to provide financial institutions with a dynamic risk assessment basis; at the same time, the central bank can provide special refinancing support for the use of green technology for heavily polluting enterprises and encourage the development of carbon asset pledge financing tools based on smart contracts, to address the long-term financial matching problems faced by such enterprises in green transformation, and to deepen the positive role of Fintech in promoting green transformation. For private enterprises in non-polluting industries, the focus of policy should shift to lowering the threshold of green certification and fostering market demand, and enterprises’ existing e-commerce platform transaction data, logistics information, etc. can be utilized as a substitute for traditional collaterals to solve the financing dilemma of SMEs, which is caused by a lack of data.

Thirdly, private enterprises are encouraged to proactively align with national policies and strategies, enhancing their investments in green innovation and governance to boost their capacities for sustainable development and environmental protection. As key players in energy conservation, emission reduction, and the advancement of a green economy, private enterprises should develop long-term strategies for their production and operations. By leveraging financial technology to enhance the efficiency of green energy development and utilization, they can actively engage in green innovation and governance, thereby facilitating not only their own transformation but also contributing to a broader shift toward sustainability.

5.3. Limitation and Prospects

This paper focuses exclusively on A-share listed private enterprises due to data availability issues. Consequently, it does not include the unlisted small and micro private enterprises, which account for over 90% of the total number, and this limits the comprehensiveness of the sample. In future research, it would be beneficial to expand the data sources in order to capture the dynamics of the unlisted small and medium-sized private enterprises by combining survey data or case studies, and thus give greater attention to the influence of Fintech development on the green transformation of small and micro private enterprises, in order to provide a more holistic view of the overall impact. It is important to note that listed private enterprises typically possess greater resource integration capabilities, more advanced technological reserves, and better-structured internal management systems. As a result, the effectiveness and pathways of their green transformation may differ from those of micro-, small-, and medium-sized enterprises. Furthermore, at a time when global economic connection is growing, cross-border Fintech flows such as international green Fintech platforms may hasten the green transition even more. Scholars may incorporate this into their future studies.

As to the measurement of dependent variables, although this paper refers to the mainstream literature in relying on the frequency of keywords in annual reports to measure green transformation and using green patent applications as a robustness test, it still cannot completely ignore the concern of potential “greenwashing” behavior. Therefore, in future research, scholars may consider introducing third-party environmental performance indicators or emissions data for additional verification, which would further strengthen the scientific validity and effectiveness of the study.

Fintech is becoming increasingly vital to China’s economic development. The potential impact of Fintech on all private enterprises, and indeed on all businesses within the country, remains a topic of significant research interest. There is ample opportunity for further exploration regarding more heterogeneity issues, such as different industries and scales, the various mechanisms of influence, and the synergistic effects among these pathways. Through enhanced research, we aspire to offer insights and wisdom from China that can contribute to sustainable development globally; at the same time, due to the special characteristics of China’s economy and capital market, each country should take into account its own national conditions and formulate policies and measures in line with its own development.