Research on the Impact of the Synergy Between Financial Technology and Green Finance on Environmental Efficiency

Abstract

With the continuous development of the economy and technology, environmental problems in China have become increasingly prominent. Researching how financial technology and green finance impact environmental efficiency is crucial when it comes to finding methods to lower the environmental cost generated by economic activities. This study measured the development level of financial technology and green finance by using the text mining method and the entropy weight TOPSIS. Meanwhile, this study empirically analyzed the influence of financial technology and green finance on environmental efficiency and their mechanisms of action by using the fixed-effect model and the moderation effect model. The results show the following: (1) Financial technology and green finance directly promote environmental efficiency. (2) Financial technology can synergize with green finance to promote improvements in environmental efficiency. (3) Heterogeneity analysis shows that there is a “path dependence” effect in the development of financial technology and green finance. Fintech and green finance had a stronger synergistic effect in improving environmental efficiency in the eastern region with strong technological innovation transformation capabilities, higher physical capital investment, and a larger regional population density. Our research results lay a foundation for the Chinese government to formulate policies related to financial technology, green finance, and environmental protection.

1. Introduction

At present, China is in the convergence period of the transformation of old and new economic drivers for its economic structure. How to maintain the dynamic balance among the economy, society, and the environment is the main problem that China urgently needs to solve. With the booming development of green finance, China’s industrial structure is constantly upgrading. However, leaning more towards green development has affected economic efficiency to some extent. The rapid rise of financial technology has profoundly changed the financial landscape and become a new driving force for economic development. To facilitate the green transformation of the real economy, the People’s Bank of China released the “Fintech Development Plan (2022–2025)” in 2022. It pointed out that the deep integration of fintech and green finance should be strengthened, and digital green finance should be innovatively developed.

The need to maintain a dynamic balance among the economy, society, and the environment is not unique to China. It is a universal challenge that countries around the world are grappling with. In Europe, for example, many countries have been pioneers in promoting green financial products like green bonds. These financial instruments have attracted a large amount of capital into environmentally friendly projects, such as renewable energy development and sustainable transportation. Green finance has also been growing steadily in the United States, with a focus on private-sector-led initiatives in areas like energy-efficient building financing. In emerging economies like India, fintech has revolutionized the payment system, making financial services more accessible to the masses. In developed economies such as the UK, fintech is being used to enhance risk management in the financial sector. Mahmood et al. [1] explores the role of green finance in promoting sustainable infrastructure, innovation in green technology, corporate social responsibility, economic stability, and environmental conservation within the framework of the Belt and Road initiative (BRI), with a specific focus on the China–Pakistan Economic Corridor (CPEC) initiatives. This global context further emphasizes the significance of strengthening the integration of fintech and green finance for achieving sustainable development goals.

The existing literature primarily focuses on the impacts of financial technology and green finance on economic development, industrial structure, and industrial upgrading but neglects the impact on environmental efficiency [2,3]. Environmental efficiency (EE) primarily measures the environmental consequences of economic operations, illustrating the ecological expense associated with these activities. It is quantified by the relationship between economic productivity and environmental footprint. EE primarily involves five environmental indicators, namely carbon dioxide emissions, waste disposal and landfilling, COD, NO_x emissions, and PRTE emissions and movement. Thus, EE can reflect urban environmental quality more comprehensively. These five environmental metrics assess pollution levels across five key dimensions: climate change, waste generation, the contamination of water resources, the degradation of air quality, and soil pollution. This indicator offers a more comprehensive reflection of the environmental pollution scenario compared to the index of an individual pollutant. In addition, it seamlessly integrates the economic dimension with environmental pollution, thereby functioning as an indicator for sustainable development when considering both economic and environmental aspects. This integration not only enables a more holistic understanding of the complex relationship between economic activities and environmental quality but also provides a more accurate assessment framework for gauging the progress towards sustainable development goals. Such an approach acknowledges the intertwined nature of economic growth and environmental protection, highlighting the importance of a balanced strategy that maximizes economic benefits while minimizing environmental degradation.

Drawing on both theoretical frameworks and real-world contexts, the present study raises the following inquiries: (1) Can fintech and green finance, respectively, provide a strong impetus for improving environmental efficiency? (2) Is there a synergistic relationship where fintech amplifies the effectiveness of green finance in boosting environmental efficiency? (3) Do regional differences exist in the impact of fintech and green finance on environmental efficiency? Exploring these questions can provide policymakers with critical insights to craft financial strategies that support sustainable development.

The contributions of this paper to the existing literature are as follows: (1) Our study centered on the influence of financial technology and green finance on environmental efficiency, seamlessly combining financial, economic, and environmental resource constraints to highlight their role in promoting environmental sustainability, thereby addressing a gap in the current literature in this field. (2) We introduced resource optimization allocation, industrial structure adjustment, and technological innovation mechanisms into the research framework, theoretically revealing the three main pathways through which financial technology and green finance synergistically impact environmental efficiency. (3) We discussed the moderating role of financial technology in regulating the environmental efficiency of green finance, supplementing existing research and providing empirical evidence for emerging technology-driven green finance to support sustainable economic and environmental development. (4) Theoretically, this study enriches the research on the relationship between finance, technology, and the environment. It provides a theoretical basis for explaining how fintech can optimize the allocation of green financial resources, enhance risk management in green projects, and promote the development of the green economy.

The rest of this paper is structured as follows. Section 2 provides a literature review and outlines the hypotheses. Section 3 details the methodology, variables, and summary statistics. Section 4 presents the empirical results testing the hypotheses. Section 5 concludes with findings and policy implications.

2. Literature Review and Hypotheses

2.1. Literature Review

In the field of research on the financial impact on the environment, green finance supporting environmental development is an “old topic”. A widely accepted view in this field is that green finance can guide funds towards green industries and the greening of industries. However, the rapid development of digital technology is reshaping the service model of the entire financial system. Can fintech promote improvements in environmental efficiency? Can fintech enhance environmental efficiency by fostering the growth of green finance? Existing research seldom directly explores these issues. The current research mainly focuses on the following four aspects.

The first category is the related research on financial technology and environmental pollution. Such studies mainly explore the impact of financial technology on environmental pollution and the mechanisms. Some studies focus on technological progress and argue that financial technology such as big data and cloud computing can effectively alleviate the external financing constraints of green enterprises. Therefore, it can promote the innovative incentive effects of information-disadvantaged entities such as green enterprises and ultimately achieve the goal of reducing environmental pollution [4,5]. There are also studies that focus on corporate competition, which show that the development of new technologies such as big data and artificial intelligence will lead to effective competition in green innovation, encouraging corporations to develop in the green industry and forcing the adjustment of industrial structure to achieve the goal of reducing environmental pollution [6,7,8,9]. In addition to enterprise competition, enterprise structure and network financial technology will also impact the cash holdings of enterprises and thereby affect their green behaviors [10].

The second category is the related research on green finance and environmental pollution. The early research mainly focuses on qualitative discussion and puts forward the idea that green finance has the important function of improving the effective utilization rate of resources and realizing ecological environmental protection and sustainable economic development [11]. Initial studies in this area primarily concentrate on qualitative analysis, suggesting that green finance plays a crucial role in enhancing the efficient utilization of resources and achieving ecological environmental protection alongside sustainable economic development. With the development of green finance, a large number of empirical studies have proposed that green finance promotes the healthy development of the green industry and reduces environmental pollution by guiding technologies, goods, and labor to transfer to the green industry [12,13,14]. In conclusion, current research predominantly examines the direct influence of green finance on environmental pollution, overlooking the reality that technological innovation in the digital economy era propels the advancement of green finance, which in turn affects environmental efficiency.

The third category is the theoretical and empirical research on the development of financial technology and green finance. In recent years, with the gradual enrichment of green financial technology practices, the relationship between financial technology and green finance has also received more and more attention [15]. The existing research mainly explores the mechanisms of financial technology’s impact on the development of green finance from a theoretical perspective. Meanwhile, some scholars argue that financial technology can collaborate with green finance to promote industrial upgrading [16]. However, existing studies have neglected the synergistic impact of financial technology and green finance on environmental efficiency.

The fourth category is the related research on financial technology, green finance, and environmental pollution. In recent years, the intersection of financial technology (fintech), green finance, and environmental efficiency has garnered significant attention from scholars and policymakers worldwide. This interdisciplinary research explores how technological advancements in finance and sustainable financial practices can collectively enhance environmental outcomes while fostering economic growth. Lee and Lee [3] investigated the role of green finance in improving green total factor productivity (GTFP) in China. Their study highlighted that green finance mechanisms, such as green credit and bonds, significantly contribute to reducing carbon emissions and enhancing resource efficiency. By employing econometric models, they demonstrated that regions with robust green finance policies exhibit higher environmental efficiency, underscoring the importance of policy frameworks in driving sustainable development. Tao et al. [6] examined the global impact of fintech on the transition to a low-carbon economy. Using cross-country data, they found that fintech development correlates with reduced carbon intensity, particularly in nations with strong regulatory support for green initiatives. Their work underscored the transformative potential of fintech in aligning financial systems with environmental goals. Yang et al. [8] analyzed the nexus between green finance, fintech, and high-quality economic development in China. Their research provided empirical evidence that digital financial tools, such as blockchain and big data analytics, enhance transparency and accountability in green investments. In the innovation systems theory, synergy acts as a new driving force. It integrates financial innovation with technological innovation in the environmental domain. This combination not only promotes the development of new green financial products but also encourages cross-sectoral cooperation. It spurs innovation in areas like blockchain-based green finance platforms, which improve the transparency and traceability of green transactions, thus expanding the boundaries of what is considered innovative in environment-related financial activities.

The papers address the intersection of financial technology (fintech), green finance, and environment, reflecting a shared focus on how financial innovations can contribute to sustainable development. Both this study and studies in the literature have found that financial technology (fintech) and green finance can mitigate environmental pollution by improving resource allocation, risk management, and transparency.

While the existing literature offers a robust theoretical framework for our research, certain gaps and limitations remain. (1) Current research predominantly emphasizes the impact of green finance on environmental pollution, while largely overlooking the interplay between fintech, green finance, and environmental efficiency. When examining pollutants, these studies often fail to integrate economic and environmental perspectives, thus inadequately capturing the environmental costs associated with economic activities. (2) Previous research has not adequately explored the synergistic relationship between financial technology and green finance. Additionally, it has not clearly delineated the pathways through which financial technology collaborates with green finance to enhance environmental efficiency. (3) Existing literature has largely overlooked the critical dimension of heterogeneity analysis in this domain. Although a limited number of studies have acknowledged that regional disparities within China may significantly moderate the efficacy of green finance and financial technology, these contentions remain largely unexplored, lacking rigorous theoretical elaboration or robust empirical validation.

2.2. Hypotheses

Financial technology, as the core of modern financial innovation and development, plays a role in rationally allocating funds among various departments. It can affect not only economic development but also the environment. Specifically, first of all, financial technology can raise funds more efficiently for environmental protection projects and sustainable development policies through innovative financial tools and technologies such as green bonds and carbon trading platforms [6,8]. This will drive the development of clean energy, resource recycling, and other fields [17,18]. Finally, financial technology can drive the intelligent management of energy, helping energy companies to better detect and manage energy consumption, optimize energy allocation, and improve energy utilization efficiency [19]. Therefore, we developed the following hypothesis:

Hypothesis 1 (H1). 

The development of financial technology can promote improvements in environmental efficiency.

Green finance represents a suite of green economic activities strategically formulated to enhance environmental quality, address the challenges posed by climate change, and promote the efficient and sustainable management of natural resources. It encompasses the provision of financial services, such as investments, project financing, operational assistance, and risk management, across various sectors including environmental conservation, energy efficiency, renewable energy, sustainable transport, and environmentally friendly construction. By doing so, it enhances economic performance while minimizing environmental harm. Specifically, firstly, green finance can direct funds towards environmentally friendly, clean, and sustainable projects [14]. It can reduce the banks’ support for high-pollution, high-energy-consuming industries, thereby driving the country’s economic structure towards green industries and ultimately improving environmental efficiency [20]. Secondly, green finance provides financing convenience for green projects such as the construction of renewable energy facilities and ecological restoration projects, helping them to be implemented smoothly. The promotion of these projects contributes to improving the overall environmental quality and enhancing environmental efficiency [21]. Thirdly, by using the standards and requirements of green finance, we can monitor and constrain the environmental performance of enterprises, thereby encouraging them to optimize their production processes, adopt clean technologies, and improve their environmental efficiency [22,23]. Fourthly, green finance can prompt enterprises to pay more attention to environmental risks through a reasonable risk pricing mechanism and adopt more active environmental protection measures to reduce the risk premium [24,25]. Therefore, we developed the following hypothesis:

Hypothesis 2 (H2). 

The development of green finance can promote improvements in environmental efficiency.

The synergy between financial technology and green finance can impact environmental efficiency through multiple paths. The schematic diagram of the mechanism of action is shown in Figure 1. First, financial technology can develop new green credit, green bonds, and green funds, among other green financial products and tools, to direct capital towards environmentally friendly, clean energy projects and other green projects [26]. This will drive the development of related industries and reduce the proportion of high-pollution and high-energy-consumption industries [27]. Second, financial technology can utilize big data and advanced analysis techniques to assess environment-related risks more accurately. This can enable regulatory agencies and financial institutions to better consider environmental factors when making decisions and avoid excessive support for environmentally unfriendly projects. Third, with the help of the efficient information-processing ability of financial technology, resources can be allocated to fields with high environmental benefits more effectively, promoting the rational utilization of green finance [28]. Fourth, financial technology can be utilized to build an efficient green finance trading platform. At the same time, technologies such as blockchain can be utilized to ensure the transparency, security, and traceability of transactions. This can promote the circulation and trading of green financial products, increase market activity, and attract more funds to participate [29]. Fifth, financial technology can be utilized to strengthen risk management in green finance and enable the real-time monitoring of environmental risk indicators. From this, financial technology can provide an early warning of potential risks and ensure the stable operation of green financial business. Sixth, green finance concepts and product information can be widely disseminated through mobile financial applications to enhance public awareness and participation. Finally, financial technology can break geographical limitations, enhance cross-border green finance cooperation and information sharing, and jointly promote environmental efficiency improvements and the achievement of sustainable development goals. Therefore, we developed the following hypothesis:

Hypothesis 3 (H3). 

Financial technology has a positive moderating effect in the process of green finance promoting improvements in environmental efficiency. Financial technology can cooperate with green finance to promote improvements in environmental efficiency.

3. Methodology and Variables

This study focused on the impact of the synergy between financial technology and green finance regarding environmental efficiency, and applied a variety of methods and models in different research stages (

Table 1. Methods and models.

Stage	Methods and Models
Construction of Fintech Index	Python; The Entropy Weight Method
Construction of Green Finance Index	The Entropy Weight Method
Measurement of Environmental Efficiency	The Super-SBM Model
Empirical Analysis Stage	Fixed-Effect Model; Model with Interaction Term
Testing Stage	Robustness Check; Endogeneity Test; Heterogeneity Test

):

3.1. Model Establishment

We constructed the following fixed-effect model for empirical research:

$$E{E}_{it}={\theta }_0+{\theta }_{1}F{I}_{it}+{\theta }_{2}G{F}_{it}+{\theta }_c{X}_{it}+{\mu }_i+{\delta }_t+{\epsilon }_{it}$$

(1)

In this model, i and t represent the variables for region and time, respectively. EE_it represents the environmental efficiency of each region. FI_it represents the level of financial technology development in the region. GF_it represents the level of green finance development in the region. X_it are control variables. ${\epsilon }_{it}$ is the error term.

Based on Model (1), we introduced the interaction term between fintech and green finance to construct Model (2). Model (2) can examine the synergistic effect of financial technology and green finance on environmental efficiency.

$$E{E}_{it}={\theta }_0+{\theta }_{1}F{I}_{it}+{\theta }_{2}G{F}_{it}+{\theta }_3(F{I}_{it}\times G{F}_{it})+{\theta }_c{X}_{it}+{\mu }_i+{\delta }_t+{\epsilon }_{it}$$

(2)

In Model (2), the coefficient θ₃ of the interaction term FI_it × GF_it is of primary importance. It characterizes the moderating effect of financial technology on the impact of green finance on environmental efficiency. If θ₃ is negative and significant, it suggests that financial technology diminishes (negatively moderates) the effect of green finance, possibly due to inefficiencies or misallocation of resources. If θ₃ is insignificant, financial technology does not play a meaningful moderating role in this relationship.

3.2. Variables

3.2.1. Explained Variable: Environmental Efficiency

Following Ye et al. [30], this study adopted the Super-SBM model. The SBM model is an extension of data envelopment analysis (DEA). It is employed to measure environmental efficiency because it addresses two critical limitations of traditional DEA models: (1) the inability to discriminate among efficient decision-making units (DMUs) with efficiency scores of 1, and (2) the neglect of slack variables in input/output optimization. The Super-SBM model incorporates undesirable outputs and calculates efficiency scores exceeding 1, enabling a finer ranking of high-performing regions. This is particularly suited to environmental efficiency assessment, where economic outputs and environmental costs must be balanced.

• Super-efficiency SBM model

The DEA method is a linear programming technique that can deal with the evaluation problems of multiple input and output indices. Among many decision-making units, DEA can identify the optimal decision-making unit and derive the efficiency value [31]. On this basis, Tone [32] proposed the super slack-based measure (SBM) model. It optimizes environmental efficiency measurement by treating desired and undesired outputs. The model is constructed as follows:

The production system has n decision-making units, and each decision-making unit uses m inputs to produce (including the desired output p and undesired output q). We denote the input, expected output, and undesired output by vectors.

The production system comprises n decision-making units (DMUs), each utilizing m inputs to generate outputs, which include p desirable outputs and q undesirable outputs. The input vector, desirable output vector, and undesirable output vector are denoted as $x\in R^m$, $y\in R^p$, and $y^{ud}\in R^q$, respectively. We define the matrices X, $Y^d$, and $Y^{ud}$ as follows:

$$X=[x_1,x_2\cdots ,x_n]\in R^{m\times n}$$

(3)

$$Y^d=[y_1^d,\cdots ,y_n^d]\in R^{p\times n}$$

(4)

$$Y^{ud}=[y_1^{ud},\cdots ,y_n^{ud}]\in R^{q\times n}$$

(5)

Accordingly, the production set is expressed as follows:

$$P(x)=\left\{(x,y^d,y^{ud})\left|x\ge X\lambda ,y^d\le Y^d\lambda ,y^{ud}\ge Y^{ud}\lambda ,\lambda \ge 0\right.\right\}$$

(6)

where λ is a non-negative weight vector, and a further improvement in the SBM model produces the following [33]:

$$\begin{array}{c}\beta =\min {\displaystyle \frac{1-\frac{1}{m}{\displaystyle \sum _{i=1}^m\frac{s_i^{-}}{x_{i0}}}}{1+\frac{1}{p+q}\left({\displaystyle \sum _{r=1}^p\frac{s_r^d}{y_{r0}^d}}+{\displaystyle \sum _{t=1}^q\frac{s_t^{ud}}{y_{t0}^{ud}}}\right)}}\\ x_0={\displaystyle \sum _{j=1}^n{x}_{ij}}{\lambda }_j+s_i^{-}\\ y_0^d={\displaystyle \sum _{j=1}^n{y}_{tj}^{ud}}{\lambda }_j-s_r^d\\ \mathit{s}\mathbf{.}\mathit{t}\mathbf{.}\\ y_0^{ud}={\displaystyle \sum _{j=1}^n{y}_{tj}^{ud}}{\lambda }_j+s_t^{ud}\\ s_i^{-}\ge 0,s_r^d\ge 0,s_t^{ud}\ge 0,{\lambda }_j\ge 0\\ i=1,2,\cdots ,m;j=1,2,\cdots ,n;r=1,2,\cdots ,p;t=1,2,\cdots ,q\end{array}$$

(7)

In Formula (7), the value of the objective function β ranges from 0 to 1. $s_r^d$, $s_i^{-}$, and $s_t^{ud}$ denote the slack variables of the desired output, input, and undesired output, respectively. The subscript 0 denotes the vector being evaluated. If $\beta$ = 1 and $s^{-}$ = $s^d$ = $s^{ud}$ = 0, then the decision-making unit SBM is considered valid. If $\beta$ > 1, it is invalid, and the input and output need to be improved. Assuming that ${DMU}_k\left(x_k,y_k^d,y_k^{ud}\right)$ is valid, the corresponding SBM model is shown in Equation (8).

$$\begin{array}{c}{\beta }_{SE}=\min {\displaystyle \frac{1+\frac{1}{m}{\displaystyle \sum _{i=1}^m\frac{s_i^{-}}{x_{ik}}}}{1-\frac{1}{p+q}\left({\displaystyle \sum _{r=1}^p\frac{s_r^d}{y_{rk}^d}}+{\displaystyle \sum _{t=1}^q\frac{s_t^{ud}}{y_{tk}^{ud}}}\right)}}\\ x_i^k={\displaystyle \sum _{j=1,j\ne k}^n{x}_{ij}}{\lambda }_j-s_i^{-}\\ y_{rk}^d={\displaystyle \sum _{j=1}^n{y}_{rj}^d}{\lambda }_j+s_r^d\\ \mathit{s}\mathbf{.}\mathit{t}\mathbf{.}\\ y_{tk}^{ud}={\displaystyle \sum _{j=1,j\ne k}^n{y}_{tj}^{ud}}{\lambda }_j+s_t^{ud}\\ 1-{\displaystyle \frac{1}{p+q}}\left({\displaystyle \sum _{r=1}^p\frac{y_r^d}{y_{rk}^d}}+{\displaystyle \sum _{t=1}^q\frac{y_t^{ud}}{y_{tk}^{ud}}}\right)>0\\ s_i^{-}\ge 0,s^{+}\ge 0,\lambda \ge 0\\ i=1,2,\cdots ,m;j=1,2,\cdots ,n(j\ne k);r=1,2,\cdots ,p;t=1,2,\cdots ,q\end{array}$$

(8)

${\beta }_{SE}$ is the objective function value, which can be greater than 1. The meanings of the remaining variables are identical to those in Equation (7).

2.

Environmental efficiency measurement

Environmental efficiency focuses on assessing the impact of resource inputs on the environment while driving economic growth over a specific period. A higher degree of environmental efficiency signifies achieving optimal economic output with minimal inputs, such as labor and natural resources, while simultaneously reducing negative environmental consequences to the greatest extent possible. Based on the existing body of research,

Table 2. Input–output indices of environmental efficiency and interpretation of the indices.

First-Level Evaluation Index	Second-Level Evaluation Index	Indicator Interpretation
Input	Capital input	Fixed asset accumulation
	Human input	Year-end employee headcount
	Energy input	Total societal electricity usage
Desired output	Economic output	Regional GDP
Undesired output	Three industrial wastes	SO2 emissions from industrial sources
		Industrial effluent release
		Industrial particulate emissions

details the metrics employed for the evaluation of environmental efficiency. Meanwhile,

Table 3. The descriptive statistics of input and output indices.

Variable/Unit	N	Mean	SD	Min	Max
Fixed asset accumulation (RMB 100 million)	4692	829.158	1079.2	6.9367	8352.5
Number of employees at the end of the year (10,000 people)	4692	42.0458	43.9379	4.84	593.552
Electricity consumption of the whole society (MWh)	4692	64,793.9	96,861.1	224.8	1,503,528
Gross regional product (RMB 100 million)	4692	1316.04	1839.74	20.6425	22,490.1
Industrial sulfur dioxide emissions (tons)	4692	53,619.8	48,641.5	2	496,377
Industrial wastewater discharge (million tons)	4692	6801.18	8492.64	7	86,804
Industrial smoke (dust) emissions (tons)	4692	31,187.2	111,512	34	5,168,812

presents the descriptive statistics of the input and output variables. The environmental efficiency scores were computed using the DEA-Solver Pro5 software.

3.2.2. Core Explanatory Variable

• Financial technology (FI)

This study adopted the methods of Zhan et al. [2] to extract relevant word frequencies through text mining and synthesize a financial technology index accordingly. Firstly, drawing on the existing body of literature, we identified and categorized financial technology terminology across four distinct levels: foundational technologies, payment and settlement systems, intermediary services, and direct addressing mechanisms. The specific keywords are listed in

Table 4. Fintech keyword vocabulary.

Four Levels	Keywords
Basic technology	artificial intelligence, big data, cloud computing, blockchain, biometric identification, Internet of Things
Payment and settlement	third-party payment, online payment, mobile payment, QR code payment, mobile phone payment, online payment
Intermediary services	online lending, online loan, online banking, online bank, e-bank, Internet insurance, Internet wealth management, online wealth management, mobile banking, Internet bank, direct banking, intelligent customer service
Direct addressing	Internet finance, financial technology, fintech

. Secondly, Python (version 3.80) was utilized to scrape the word frequency of the predefined vocabulary by province. Thirdly, the daily search frequencies of the single-word frequencies obtained from the crawled Google Index were aggregated. These frequencies were then grouped by year to synthesize the annual word-frequency data. Finally, the word-frequency weights were determined using the entropy method, and then, the financial technology index was synthesized. The financial technology index serves as a measure of the development level of financial technology across different regions. For clarity and ease of interpretation, we scaled down the aggregated financial technology index by a factor of 10,000 in our analysis.

2.

Green finance (GF)

Referring to the relevant studies by Wang and Wu [34], this study constructed an integrated index of green finance using six aspects of green finance activities, namely green credit, green securities, green insurance, green investment, carbon finance, and government environmental expenditures. Following the approach of Wang and Wu [34], the weights of each indicator in the system were calculated using the entropy weight method (

Table 5. Indicator rating system of green finance development level.

Target	Criterion	Indicator	Indicator Definition	Weight
The development level of green finance	Green credit	The proportion of green credit scale	The credit balance of listed environmental protection enterprises/the loan balance of financial institutions.	14.21
		The proportion of interest expenditure of high-energy-consuming industries	The interest expense of six major high energy-consuming industrial industries/the total interest expense of industrial industries.	3.62
	Green securities	The proportion of market value of environmental enterprises	The market value of A-share environmental protection enterprises/the total market value of A-share listed enterprises.	11.23
		The proportion of market value of high-energy-consuming enterprises	The market value of A-share high energy-consuming enterprises/the total market value of A-share listed enterprises.	1.52
	Green insurance	The proportion of environmental pollution liability insurance scale	The premium income of agricultural insurance/the premium income of property insurance.	17.29
		The proportion of environmental pollution liability insurance claims payment	The compensation expense of agricultural insurance/the premium income of agricultural insurance.	3.24
	Green investment	The proportion of public spending on environmental protection	The fiscal expenditure on energy conservation and environmental protection industries/the total fiscal expenditure.	3.19
		The proportion of environmental investment	The investment amount in environmental pollution control/GDP.	6.24
	Carbon finance	The proportion of clean development mechanism project transactions	The number of clean development mechanism projects/the total number of environmental protection projects.	14.97
		The intensity of carbon emissions loans	The loan balance of financial institutions/the carbon dioxide emissions.	18.31
	Environmental support	The proportion of environmental protection expenditure	The fiscal expenditure on environmental protection/the general budget expenditure of the government.	6.18

).

3.2.3. Control Variables

This study selected the following control variables to account for and mitigate the influence of factors affecting environmental efficiency (

Table 6. List of control variables and descriptions.

Variable	The Variable Name	Description
Agdp	GDP per capita	The proportion of the urban population at the end of the year relative to the urban GDP
Asct	Government R & D investment	The proportion of government spending on science and technology relative to the gross domestic product
rdp	The number of R & D personnel	The data utilized in this study were sourced from the China Urban Statistical Yearbook.
Popdensity	Urban population density	The ratio of urban population to urban area
Agov	The level of government intervention	The ratio of government expenditure to the gross regional product
Afdi	The degree of openness to the outside world	The ratio of foreign investment to GDP
Astr2	The industrial structure	The proportion of the secondary sector’s added value in GDP

). (1) The degree of affluence (Agdp) is proxied by the per capita GDP of each city. Theoretically, as a city’s economic development progresses, it establishes a more robust financial foundation for environmental protection initiatives, thereby potentially improving environmental efficiency. (2) R The level of technological advancement is captured by two indicators: the share of scientific and technological expenditures in government spending relative to the regional GDP (Asct), and the number of research and development (R & D) personnel (rdp). (3) Urban population density (Popdensity) measures the concentration of residents within urban areas, and its variations can exert significant impacts on environmental conditions. (4) The degree of government intervention (Agov) is operationalized as the ratio of government expenditure to the regional GDP, reflecting the scale of governmental involvement in the economy. In theory, when government expenditures are directed towards public goods and aim to enhance resource allocation by addressing market inefficiencies, they can contribute to lowering environmental pollution and boosting economic productivity. (5) The openness index (Afdi) is derived by dividing the total foreign investment by the GDP of the region. (6) The industrial structure, denoted as Astr2 and encompassing both industry and construction sectors, is measured by the proportion of the secondary industry’s value-added relative to the overall gross domestic product (GDP).

3.3. Data and Summary Statistics

This study took 30 Chinese provinces from 2003 to 2023 as the research objects, with a total of 330 observations. Due to the constraints in data accessibility, the regions of Tibet, Hong Kong, Macau, and Taiwan were omitted from the scope of this study.

The data on financial technology came from the Google search index and were obtained using web crawler technology. The data for green credit came from the China Financial Yearbook and the Guotai-An database. The data for regional environmental protection listed companies were obtained through the six stock sectors of environmental protection, air treatment, sewage treatment, garbage classification, wind and sand treatment, and energy conservation in the Tongxinda financial terminal software. The dataset pertaining to green securities was derived from the Guotai-An database, whereas the information on green insurance was extracted from the China Insurance Yearbook. The data on green investment were procured from the China Environmental Statistics Yearbook. Carbon finance data were sourced from the China Clean Development Mechanism Network and the National Bureau of Statistics. In instances where data were absent, the gray prediction method was employed to estimate them and complete the dataset. Additional data were primarily obtained from the Wind Database, the China Statistical Yearbook, the China Environmental Statistics Yearbook, and the official websites of regional statistical bureaus.

Table 7. Summary statistics.

Variable	N	Mean	SD	Min	Max
EE	330	0.381	0.250	0.051	1.452
lnAgdp	330	9.883	0.720	7.392	12.494
Asct	330	2.145	1.022	−1.003	4.992
lnrdp	330	1.901	0.722	−0.194	3.891
lnpopsensity	330	−2.819	0.718	−5.213	−2.194
lnAgov	330	8.512	0.914	6.924	10.424
lnAfdi	330	3.104	1.112	−1.213	6.294
lnAstr2	330	3.991	0.102	3.103	4.993

presents the variables’ summary statistics.

4. Empirical Results and Analysis

4.1. Baseline Results

Table 8. Baseline regression results.

Variables	(1)	(2)	(3)	(4)
FI	0.938 ***		0.726 ***	0.992 ***
	(4.294)		(4.213)	(3.012)
GF		3.113 ***	2.873 ***	2.137 ***
		(3.942)	(3.723)	(3.903)
FI×GF				0.713 ***
				(5.124)
lnAgdp	0.353 ***	0.699 ***	0.321 ***	0.287 ***
	(3.441)	(3.153)	(4.239)	(3.110)
lnAsct	0.428 ***	0.201 *	0.287 *	0.364 ***
	(5.186)	(1.767)	(1.835)	(4.244)
lnrdp	0.021 ***	0.018 *	0.014 ***	0.016 *
	(4.249)	(1.703)	(4.521)	(1.824)
lnpopdensity	0.035 *	0.014 *	0.046 *	0.022 *
	(1.795)	(1.802)	(1.904)	(1.851)
lnAgov	0.017 **	0.053 **	0.018 **	0.024 *
	(2.433)	(2.345)	(2.152)	(1.729)
lnAfdi	−0.102 *	−0.094 *	−0.154 **	−0.082 **
	(1.703)	(1.893)	(2.461)	(2.234)
lnAstr2	−0.194 **	−0.158 *	−0.039 **	−0.042 **
	(2.408)	(1.753)	(2.256)	(2.314)
Year FE	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes
Cons	0.184 ***	1.458 ***	4.294 **	1.383 *
	(4.294)	(3.291)	(2.173)	(1.807)
N	330	330	330	330

Note: The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. The corresponding t-statistics are provided in parentheses beneath the estimated coefficients.

shows the independent promotion effect and synergy effect of fintech and green finance in improving environmental efficiency. The regression results presented in Table 8, column (1), reveal that the coefficient associated with fintech development is statistically significant and positive at the 1% level. This finding suggests that the advancement of fintech has a substantial and positive impact on enhancing environmental efficiency. Consequently, Hypothesis 1 is empirically supported by the data. The regression result (2) reveals that the coefficient of green finance is statistically significant at the 1% level, demonstrating a positive relationship between green finance and the enhancement of environmental efficiency. This finding provides empirical support for Hypothesis 2, confirming that green finance exerts a significant promoting effect on environmental efficiency. The results underscore the pivotal role of financial technology and green finance as critical drivers in advancing environmental efficiency in the contemporary era.

Table 8 (3) and (4) provide empirical evidence of the synergistic interplay between financial technology and green finance in driving environmental efficiency improvements. Table 8 (3) presents the estimation results for Model (1). The findings indicate that the regression coefficients for both fintech and green finance are statistically significant and positively associated with the enhancement of environmental efficiency at the 1% significance level. Table 8 (4) displays the regression outcomes for Model (2). The empirical findings demonstrate that the coefficient of the interaction term between fintech and green finance is statistically significant at the 1% level, exhibiting a positive sign. This suggests that fintech exerts a significant moderating effect on the relationship between green finance and environmental efficiency enhancement. Specifically, the integration of fintech applications appears to amplify the positive impact of green finance initiatives on environmental performance indicators, thereby facilitating more effective promotion of environmental efficiency improvements through green financial mechanisms. Hypothesis 3 was verified.

The empirical findings in Table 8 demonstrate that both fintech and green finance independently and synergistically enhance environmental efficiency. Fintech solutions enhance transparency in green investments. By improving access to real-time environmental, social, and governance (ESG) data, fintech helps investors and policymakers to allocate capital more efficiently toward sustainable projects [35]. Digital platforms reduce the cost of financing green initiatives, enabling small and medium enterprises (SMEs) to participate in sustainability projects that were previously inaccessible due to high intermediation fees [36]. Green bonds, carbon trading, and preferential loan policies incentivize firms to adopt low-carbon technologies, directly improving regional environmental efficiency [37].

The interaction term’s positive significance (Hypothesis 3) reveals a moderating effect, which prior studies rarely explored. Fintech enables the real-time assessment of environmental risks, allowing green financial products to be priced more accurately. IoT and blockchain ensure that green funds are used as intended, reducing “greenwashing” and improving accountability. Digital crowdfunding platforms lower the barrier to green investments, accelerate the adoption of renewable energy projects, and thereby enhance environmental efficiency.

4.2. Robustness Check

In this section, we describe a series of regression analyses that were performed to rigorously validate the robustness of prior findings concerning the relationship between financial technology (fintech), green finance, and environmental efficiency.

Firstly, there may have been systematic differences between municipalities and other cities. Referring to the relevant studies by Qian et al. [38], a benchmark regression was conducted using a sample excluding Beijing, Shanghai, Tianjin, and Chongqing, the four municipalities directly under the central government. From the results in

Table 9. Robustness check.

Variable	Excluding Municipalities Directly Under the Central Government		Increasing Controlled Variables
	(1)	(2)	(3)	(4)
FI	0.632 ***	0.432 ***	0.927 ***	0.538 ***
	(3.108)	(3.642)	(5.265)	(4.264)
GF	2.463 ***	3.981 ***	4.018 ***	3.297 ***
	(4.193)	(4.247)	(5.664)	(4.991)
FI×GF		0.966 ***		0.813 ***
		(4.201)		(4.645)
lnAgdp	0.629 ***	0.928 ***	0.716 ***	0.971 ***
	(4.269)	(5.642)	(5.208)	(3.297)
lnAsct	0.355 ***	0.165 **	0.452 *	0.854 ***
	(4.193)	(2.367)	(1.742)	(3.208)
lnrdp	0.041 ***	0.053 *	0.062 ***	0.031 *
	(3.018)	(1.821)	(3.194)	(1.728)
lnpopdensity	0.029 *	0.019 *	0.031 *	0.013 *
	(1.810)	(1.781)	(1.864)	(1.904)
lnAgov	0.082 **	0.053 **	0.028 **	0.073 *
	(2.311)	(2.422)	(2.194)	(1.839)
lnAfdi	−0.128 *	−0.082 *	−0.145 **	−0.131 ***
	(1.814)	(1.913)	(2.319)	(3.633)
lnAstr2	−0.152 **	−0.101 *	−0.071 **	−0.018 **
	(2.391)	(1.829)	(2.363)	(2.401)
ER			0.182 **	0.153 **
			(2.413)	(2.129)
URB			−0.982 *	−0.462 *
			(1.872)	(1.702)
Time FE	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes
Cons	0.524 ***	0.952 ***	1.482 ***	1.192 **
	(3.111)	(4.184)	(3.177)	(2.239)
N	286	286	330	330

Note: The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. The corresponding t-statistics are provided in parentheses beneath the estimated coefficients.

(1) and (2), it can be observed that, after excluding the samples from the municipality regions, the regression results for fintech and green finance’s impact on environmental efficiency are still positive and significant. Meanwhile, the interaction term coefficient of fintech and green finance is still significant. Thus, the regression results are robust.

Secondly, referring to the research of Ye et al. [30] on environmental efficiency, two control variables, environmental regulation intensity (ER) and urbanization level (URB), were added on the basis of the existing control variables. ER is defined as the ratio of investment in environmental pollution control to GDP, and the URB is the proportion of urban population to total population. As evidenced by the regression outputs presented in Table 9, columns (3) and (4), the inclusion of these control variables does not alter the fundamental relationship between fintech, green finance, and environmental efficiency. Furthermore, the interaction term between fintech and green finance continues to demonstrate a significant positive coefficient, thereby confirming the robustness of our baseline regression results.

4.3. Endogeneity Test

To mitigate potential endogeneity issues arising from omitted variables and measurement errors, we utilized the instrumental variables approach as a robust solution. Referring to the relevant studies by Zhang and Chen [39], we took the postal and telecommunications business volume per hundred people in each province in 1984, the development of green finance, and the lagged term of their interaction terms as the instrumental variables of fintech, green finance, and their interaction terms, respectively. On the one hand, after controlling for regional economic levels, technological levels, etc., there was no direct correlation between the per capita postal and telecommunications business volume in each province in 1984 and environmental efficiency. Consequently, the choice of historical per capita postal and telecommunications business volume as an instrumental variable for fintech satisfies the exclusion restriction criterion. Furthermore, the lagged one-period development level of green finance also satisfies the two fundamental criteria for a valid instrumental variable, namely correlation and exogeneity. The results obtained from the instrumental variable regression analysis are presented in

Table 10. Endogeneity test.

Variable	2SLS
	(1)	(2)
FI	0.339 ***	0.211 ***
	(4.646)	(3.052)
GF	3.002 ***	0.492 ***
	(3.294)	(4.164)
FI×GF		0.393 ***
		(5.243)
Controlled variables	Yes	Yes
R2	0.899	0.931
Kleibergen–Paap rk LM statistic p	p = 0.0013	p = 0.0021
Cragg–Donald Wald F	19.361	11.535

Note: The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. The corresponding t-statistics are provided in parentheses beneath the estimated coefficients.

. As shown in Table 10 (1), the p-value of the Kleibergen–Paap LM statistic is 0.0013, which strongly refutes the null hypothesis of the “underidentification of instrumental variables” at conventional significance levels. Therefore, the selected instrumental variables satisfied the validity conditions, and the core findings of the study were empirically substantiated.

4.4. Heterogeneity Test

Owing to the heterogeneity in economic growth and the uneven development of financial systems across various regions in China, the impact of financial technology and green finance on environmental efficiency exhibits significant regional variations. Based on the classification criteria outlined in the National Economic Accounting Method, we categorized the 30 provinces and municipalities in China into three distinct regions—the eastern region, central region, and western region—as detailed in

Table 11. Regional classification table.

Region	Provinces
Eastern region	Beijing, Tianjin, Shanghai, Hebei Province, Shandong Province, Jiangsu Province, Zhejiang Province, Fujian Province, Guangdong Province, Hainan Province, Liaoning Province
Central region	Shanxi Province, Henan Province, Hubei Province, Anhui Province, Hunan Province, Jiangxi Province, Heilongjiang Province, Jilin Province
Western region	Inner Mongolia Autonomous Region, Xinjiang Uygur Autonomous Region, Ningxia Hui Autonomous Region, Shaanxi Province, Gansu Province, Chongqing, Sichuan Province, Guizhou Province, Yunnan Province, Guangxi Zhuang Autonomous Region

. Building on this framework, this study conducted an empirical analysis of the regional heterogeneity in the effects of financial technology (fintech) and green finance on environmental efficiency across China.

Table 12. Heterogeneity test.

Variable	Eastern Region		Central Region		Western Region
	(1)	(2)	(3)	(4)	(5)	(6)
FI	0.8134 ***	0.723 ***	0.318 ***	0.487 ***	0.251 ***	0.312 ***
	(4.056)	(3.974)	(3.184)	(4.676)	(5.666)	(3.012)
GF	3.783 ***	3.998 **	1.845 ***	1.983 ***	1.003 ***	1.093 ***
	(3.174)	(2.309)	(4.108)	(4.643)	(4.145)	(4.508)
FI×GF		1.082 ***		0.912 ***		0.610 ***
		(5.103)		(4.111)		(4.199)
lnAgdp	0.274 ***	0.813 ***	0.914 ***	0.188 ***	0.153 ***	0.122 ***
	(3.155)	(4.161)	(5.082)	(4.697)	(5.194)	(4.208)
lnAsct	0.716 ***	0.794 *	0.881 *	0.730 ***	0.194 *	0.205 ***
	(4.439)	(1.881)	(1.719)	(5.197)	(1.710)	(5.108)
lnrdp	0.033 ***	0.089 *	0.052 ***	0.078 *	0.029 ***	0.034 *
	(4.146)	(1.888)	(5.588)	(1.771)	(5.108)	(1.782)
lnpopdensity	0.049 *	0.052 *	0.034 *	0.081 *	0.015 *	0.019 *
	(1.813)	(1.841)	(1.850)	(1.718)	(1.814)	(1.901)
lnAgov	0.019 **	0.052 **	0.014 **	0.041 *	0.023 **	0.016 *
	(2.322)	(2.428)	(2.483)	(1.872)	(2.362)	(1.891)
lnAfdi	−0.175 *	−0.071 *	−0.184 **	−0.151 **	−0.231 **	−0.111 **
	(1.871)	(1.704)	(2.135)	(2.319)	(2.5110	(2.341)
lnAstr2	−0.247 **	−0.461 *	−0.194 **	−0.145 **	−0.200 **	−0.522 **
	(2.381)	(1.811)	(2.152)	(2.110)	(2.871)	(2.051)
Time FE	Yes	Yes	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes	Yes	Yes
Cons	0.319 ***	1.663 **	2.405 **	2.477 *	2.108 **	1.411 *
	(3.274)	(2.536)	(2.500)	(1.718)	(2.019)	(1.799)
N	21	21	88	88	121	121

Note: The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. The corresponding t-statistics are provided in parentheses beneath the estimated coefficients.

presents the outcomes of the re-regression analysis conducted after segmenting the samples by region. Specifically, columns (1), (3), and (5) of Table 12 demonstrate that the coefficients for both fintech and green finance are statistically significant at the 1% level, indicating a robust positive relationship. The interplay between financial technology (fintech), green finance, and environmental efficiency remains consistent irrespective of regional development disparities. Table 12, columns (2), (4), and (6), demonstrates that the coefficients of the interaction terms are statistically significant at the 1% level across the eastern, central, and western regions. The coefficient of the interaction term is the highest in the eastern region, while it is the lowest in the western region, indicating regional heterogeneity in the magnitude of the effect. This suggests that the synergistic effect of fintech and green finance on environmental efficiency is most pronounced in the eastern region. The synergistic effect of fintech and green finance on environmental efficiency is the smallest in the western region. The findings of the study suggest that disparities in regional economic development and the maturity of financial infrastructure play a critical role in determining the extent to which fintech and green finance contribute to the enhancement of environmental efficiency.

5. Conclusions and Policy Implications

With the sustained expansion of China’s economic landscape, environmental concerns have emerged as a critical area of focus, necessitating comprehensive analysis and strategic intervention. Hence, enhancing environmental efficiency has become an imperative in the current context. The synergistic development of financial technology and green finance holds substantial significance for advancing environmental efficiency, offering a promising pathway toward sustainable economic and ecological outcomes. Therefore, the main purpose of this study was to explore whether fintech, green finance, and the synergy of fintech and green finance could contribute to enhancing environmental efficiency. Utilizing panel data spanning 30 provincial-level regions from 2003 to 2023, we constructed a fintech index and a comprehensive green finance index to quantify the development and integration of these domains. Then, we explored their relationships with environmental efficiency, respectively, from the perspectives of fintech, green finance, and the synergy of fintech and green finance. This study offers a methodological framework for more accurately assessing the impact of fintech and green finance on both environmental and economic dimensions within the Chinese context. The following conclusions were derived from the research findings:

(1)

Fintech and green finance exhibit a statistically significant and direct positive impact in enhancing environmental efficiency. This conclusion remains robust and statistically valid even after a series of rigorous robustness checks, including adjustments to the sample period, the incorporation of additional control variables, and the utilization of instrumental variable techniques to mitigate potential endogeneity concerns.

(2)

The integration of financial technology (fintech) with green finance demonstrates significant potential to enhance environmental efficiency. This conclusion retains its validity even after rigorous robustness checks and endogeneity tests, underscoring the robustness and reliability of the findings.

(3)

Heterogeneity analysis reveals the presence of “path dependence” in the development of fintech and green finance. The synergistic effect of fintech and green finance in enhancing environmental efficiency is particularly pronounced in regions with higher levels of economic development, highlighting the influence of regional economic conditions on the efficacy of such collaborations.

When comparing the conclusions of this study on the impact of fintech and green finance on environmental efficiency with similar research in other countries, both similarities and differences emerge. Some international studies also demonstrate a positive influence of fintech and green finance on environmental protection. For example, research in developed economies has found that fintech can improve the efficiency of green finance resource allocation. Digital platforms help to direct funds to environmentally friendly projects more accurately, which is consistent with the finding in this study that fintech and green finance have a positive impact on environmental efficiency. However, differences exist due to varying economic structures, financial development levels, and environmental policies. In some developing countries, the synergistic effect of fintech and green finance on environmental efficiency might be less significant. Their financial markets are less mature, and the application of fintech in green finance is more limited. In contrast, this study in the Chinese context shows a strong synergistic effect, especially in more developed regions. The Chinese government’s active policies promoting green development and fintech innovation play a crucial role.

Based on the aforementioned conclusions, the following policy recommendations are proposed:

(1)

The government should bolster the capacity of fintech to support the advancement of green finance and establish a multifaceted mechanism aimed at enhancing environmental efficiency across various dimensions. Firstly, a national-level green finance data platform that integrates data from multiple sources such as environmental regulatory agencies, financial institutions, and corporate sustainability reports should be developed. Using machine learning algorithms, this platform will be able to detect patterns of potential “fraudulent green finance” and “misrepresented green financial practices”. It will require financial institutions to report on their fintech-based risk management measures quarterly. Secondly, the government can cultivate a technologically driven ecosystem anchored on a robust fintech infrastructure. A specific annual budget should be allocated to support the building of a fintech infrastructure for green finance. A national fintech innovation center focused on green finance should be established. This could offer training programs for financial institution employees on how to use new fintech tools for green finance, such as blockchain-based systems for the transparent tracking of green funds. This will offer comprehensive data support for the green identification and risk management of financial institutions, thereby significantly reducing the operational costs associated with risk management in green finance. Finally, a government-led innovation fund for green financial products should be set up. Financial institutions will be able to apply for grants from this fund to develop new fintech-enabled green financial products. At the same time, the government should collaborate with industry associations to develop industry-wide standards for fintech-enabled green financial products. These standards should cover aspects such as product transparency, performance measurement, and client suitability.

(2)

Technological innovation plays a pivotal role in advancing the fields of fintech and green finance. Consequently, it is imperative for the government to prioritize and enhance efforts in fostering technological innovation. The government ought to allocate resources towards the research and development of fundamental technologies, including artificial intelligence, big data, blockchain, and cloud computing. Public–private partnerships for fundamental technology R&D should be established. The government could offer tax breaks and subsidies to private companies that collaborate with research institutions on green finance-related fintech research. At the same time, it should constantly explore application scenarios and solutions combining fintech and green finance. A task force consisting of government representatives, financial experts, and technology professionals should be created to identify potential application scenarios for fintech in green finance. This task force should conduct market research and publish an annual report on emerging application scenarios. An example would be exploring how AI can be used for early-warning systems in green bond investments, detecting potential environmental risks that could affect bond performance.

(3)

The government should adopt tailored fintech and green finance policies that account for the heterogeneous characteristics and developmental disparities across different regions. The government in the eastern region should allocate more resources to support the innovation of fintech in the context of green finance. This includes promoting the development of advanced digital platforms for green financial transactions. The eastern region could share its successful experiences in fintech–green finance integration, offer technical support, and conduct personnel training for the central and western regions. The government of the eastern region, which is a more open region, should encourage fintech and green finance institutions to participate in international cooperation. This could help to introduce international advanced concepts, technologies, and management models, and, at the same time, promote China’s fintech–green finance experience on the international stage, enhancing China’s influence in global sustainable finance. For the central and western regions, the government should actively apply fintech to assist green finance in vigorously developing economic and environmental protection projects and strive to improve environmental efficiency. Preferential policies should be introduced to attract fintech and green finance enterprises to invest and develop in the central and western regions. Tax incentives, land-use concessions, and financial subsidies should be provided for enterprises engaged in green finance projects with the support of fintech. These policies could encourage more market participants to enter the green finance market, promoting the growth of related industries and improvements in environmental efficiency.

6. Limitations and Future Research

Despite the contributions of this study, several limitations should be acknowledged to enable a more comprehensive understanding of the research findings and to guide future research directions. The research relied on panel data from 30 provincial-level regions in China over the period from 2003 to 2023. Although this covers a significant portion of the country, it does not include all regions, such as Hong Kong, Macau, and Taiwan. These regions have unique economic, financial, and environmental characteristics, and their omission might limit the generalizability of the results. Additionally, the data at the provincial level are aggregated, which may mask the heterogeneity within each province. Sub-provincial data, such as city-level data, could provide more detailed insights into the relationships between fintech, green finance, and environmental efficiency, as economic development, financial infrastructure, and environmental policies can vary greatly even within the same province. Moreover, the measurement of variables might present certain inaccuracies. For example, constructing the fintech index and green finance index involved text mining and entropy weight TOPSIS methods. These methods rely on specific data sources and algorithms. If the data sources do not comprehensively cover all aspects of fintech and green finance development, or if the algorithms have limitations in capturing the true nature of these complex concepts, the constructed indices may not precisely reflect the actual development levels. Despite using instrumental variable techniques to address endogeneity concerns, the problem may not have been completely resolved. Economic development is an important factor related to fintech, green finance, and environmental efficiency. While some control variables related to economic development were included, it is possible that there were still unobserved aspects of economic development that were correlated with the error term in the model, causing endogeneity. In conclusion, these limitations highlight the need for future research to expand data coverage, improve variable measurement, consider more potential influencing factors, and develop more sophisticated methods to deal with endogeneity in order to gain a more in-depth and accurate understanding of the relationships among fintech, green finance, and environmental efficiency. In addition, future research directions can be further expanded, for instance, by examining the role of specific financial technology innovations (such as blockchain) in green finance.