Environmental Regulation, Digital Financial Inclusion, and Environmental Pollution: An Empirical Study Based on the Spatial Spillover Effect and Panel Threshold Effect

: Environmental regulation is a crucial tool for controlling environmental pollution. Digital ﬁnance is essential for the development of green ﬁnance. The relationship between environmental regulation and digital ﬁnance concerning environmental pollution is an issue worth exploring. This paper uses the spatial econometric model and the panel threshold model to empirically analyze the impact of environmental regulation and digital ﬁnancial inclusion on environmental pollution using panel data from 30 Chinese provinces between 2011 and 2019. It mainly discusses the independent impact and synergy of environmental regulation and digital inclusive ﬁnance on environmental pollution. The research results show that the improvement of the intensity of environmental regulation and the development level of digital-inclusive ﬁnance can effectively alleviate the problem of environmental pollution. Moreover, environmental regulation and digital inclusive ﬁnance can coordinately control environmental pollution. A panel threshold analysis shows that as the intensity of environmental regulation increases, digital ﬁnancial inclusion will reﬂect the function of environmental governance. Similarly, with the development of digital ﬁnancial inclusion, environmental regulation has shown a signiﬁcant inhibitory effect on environmental pollution. The results of a heterogeneity analysis show that the intensity of environmental regulation in the eastern region has a signiﬁcant inhibitory effect on environmental pollution. Digital ﬁnancial inclusion in the central region shows a strong environmental governance function. The intersection of environmental regulation and digital ﬁnancial inclusion has shown a signiﬁcant synergistic governance effect in the eastern region. Therefore, the government gives full play to the functions of environmental regulation and digital inclusive ﬁnance environmental governance to achieve coordinated governance of environmental pollution.


Introduction
According to the 2020 Global Environmental Performance Index Evaluation Report, China ranks 120th in the comprehensive environmental performance index. For air quality, it is ranked fourth from the bottom, and the associated environmental problems are severe, hindering sustainable and healthy economic development. Environmental regulation is an essential driving force for economic transformation and development and has been the primary policy tool for pollution prevention and control in China. China has continually improved environmental regulation intensity, from the "11th Five-Year Plan" to the "14th Five-Year Plan" [1]. However, finance is a vital force to serve the real economy [2]. Promoting economic development affects the level of environmental governance, changing the interaction between economic development and the ecological environment [3]. Furthermore, since the conception of financial inclusion by the United Nations, it has become a priority policy option for many countries to address financial exclusion. At present, the global practice of inclusive finance has been used for more than ten years. It has completed the development process of "small and micro finance-internet finance-digital inclusive finance". It has significantly contributed to global financial equity and sustainable development [4]. Digital financial inclusion is essentially a combination of digital finance and financial inclusion. It uses mobile internet, cloud computing, and big data to provide financial products and services digitally. In addition, digital financial inclusion has the advantages of wide coverage, low cost, and high efficiency. Digital financial inclusion has started to reshape the economic production pattern, influence residents' economic decisionmaking, and provide new opportunities for promoting the coordinated development of the economy and the ecological environment. However, in implementing environmental regulation policies, enterprises are required to improve the level of production technology and have sufficient funds to guarantee technological updates. Problems such as misallocation of financial resources and financial exclusion have always been crucial factors that inhibit the innovation output of enterprises [5]. The inclusivity of digital finance is conducive to alleviating financing discrimination in the traditional financial system, lowering the threshold of financial services, improving the efficiency and fairness of financial services, and improving the availability of financial resources. This alleviates the financial problems brought about by environmental regulation to a certain extent. Therefore, in this paper, we analyze the environmental pollution problems caused by environmental regulation and digital inclusive finance. See the following questions: does digital financial inclusion have an environmental governance function? What is the mechanism of environmental regulation and digital finance on environmental pollution? Are there synergies between them? How should the government formulate policies for environmental regulation and digital financial inclusion? In order to answer the above questions, this paper uses the spatial econometric model and the panel threshold model to analyze the spatial spillover effects and threshold effects of environmental regulation, digital financial inclusion, and environmental pollution. The main purpose of this article is to explore whether environmental regulation and digital inclusive finance have the function of collaborative governance of the problem of environmental pollution in order to provide a quantitative basis and decisionmaking support for the government to formulate environmental supervision policies and digital inclusive financial policies in the process of environmental pollution control.
The remainder of this paper is arranged as follows. Section 2 is devoted to a literature review. Section 3 details the research hypothesis. Section 4 explains the meaning of the indicators, describes the data, and introduces the model. Section 5 conducts an empirical analysis of the spatial econometric model and the panel threshold model. Section 6 presents research conclusions. Section 7 presents policy recommendations and research outlook.

Literature Review
The research literature related to environmental regulation, digital financial inclusion, and environmental pollution is primarily divided into the following categories. The first category encompasses research on the relationship between environmental regulation and environmental pollution. Poter postulates that strong environmental regulation stimulates technological innovation of enterprises and reduces environmental pollution [6]. Studies by Dasgupta et al. and Thiel et al. demonstrate that higher environmental pressure could promote the technological research and development of enterprises so that pollutants can be effectively treated [7,8]. The study by Magat et al. showed that the level of environmental regulation of the US EPA during 1982-1985 had an inhibitory effect on pollution emissions [9]. Laplante et al. also verified the above findings using Canadian data [10]. Berman et al. and Peuckert believed that effective environmental regulation can improve environmental quality and reduce environmental pollution [11,12]. In addition, scholars have conducted research on environmental regulation of water and air pollution. Langpap et al. found that informal environmental regulation can effectively improve the effect of water pollution control by studying the public's lawsuits against environmental pollution [13]. Shapiro et al. studied air pollution emissions from manufacturing in the United States and found that the strengthening of environmental regulation is one of the important factors for reducing air pollution emissions [14]. Dasgupta et al. used the GMM estimation method to analyze the data of Zhenjiang polluting enterprises from 1993 to 1997 and found that environmental regulation had promoted the reduction of air pollutants significantly [15]. Further research has also found evidence that government energy policies and regulation plans could improve city air quality [16,17]. However, some scholars have a contrary view. For instance, Sinn postulated the "green paradox", and believed that environmental regulation would reduce the operating efficiency of enterprises, which is not conducive to the reduction of environmental pollutants [18]. Subsequent studies by Hoel and Eichner et al. further verified that the "green paradox" may occur due to strict environmental regulations [19,20]. Saltari et al. and Leiter et al. illustrated similar conclusions [21,22]. Greenstone tested the effectiveness of the Clean Air Act in reducing the concentration of sulfur dioxide, concluding that it played a minor role from the 1970s to the 1990s in the US [23]. Blackman et al. found no evidence that stricter regulation would increase the adoption of cleaner technologies when studying plant-level data [24]. The research of Smulders et al. found that the promulgation of the carbon emission tax policy had a reverse effect on carbon emissions [25]. In addition, the incentive structure of the central government caused deviation that appeared in the process of policy implementation among local governments and the high costs of environmental regulation that enterprises spent, which could not eventually produce good results [26,27]. However, some scholars believe that the relationship between environmental regulation and environmental pollution is uncertain, and may be closely related to the intensity of environmental regulation [28]. Although environmental regulation is beneficial to restraining the emissions of environmental pollution, it will increase the operating cost of enterprises and may affect environmental regulation [29]. Wang et al. used a data envelopment analysis model to find an inverted U-shaped relationship between environmental regulation and environmental productivity in China [30]. Xie et al. found that both executive-order-type and market-type environmental regulations have a nonlinear shape-curve relationship with green productivity [31]. Guo et al. found that there is a significant inverted U-shaped curve relationship between environmental regulation and carbon emissions and carbon emission intensity [32]. Research by Ouyang et al. showed that with the increase in the severity of environmental policies, PM2.5 emissions first increased and then decreased [33].
The second category encompasses research on the relationship between finance and the environment. There are three main views. First, financial development can help reduce transaction costs and promote the investment and financing of green and low-carbon projects, which can effectively improve the ecological environment. For example, Tamazian et al. used the ratio of total bank deposits and loans to GDP as a financial development index, and earlier confirmed that financial development helps reduce pollution emissions [34]. Shahbaz et al. came to a similar conclusion [35]. Shahbaz et al. proposed that the improvement of carbon emission efficiency depends on economic growth, energy structure improvement, and government intervention, and requires the development of financial markets [36]. Second, financial development can help ease consumer budget constraints and stimulate the consumption of commodities such as automobiles and refrigerators, which will increase energy consumption and pollution emissions. For example, Zhang used the ratio of total loans of financial institutions to GDP as a financial development index, confirming that financial development increases carbon emissions [37]. Zhao et al. came to a similar conclusion [38]. The third is that financial development has a more complex nonlinear impact on the environment. For example, Charfeddine et al. found an inverted U-shaped relationship between financial development and carbon emissions [39]. Hu et al. confirmed that the relationship between financial development and environmental pollution is an inverted U-shaped curve [40]. In recent years, with the development of financial inclusion, some scholars have explored how financial inclusion affects the environment. For example, Xu et al. confirmed that the development of inclusive finance does not directly affect the environment, but there are economic growth mechanisms and technological innovation mechanisms that affect the environment [41]. Zaidi et al. confirmed that the development of inclusive finance will increase carbon emissions [42]. Furthermore, the research on the environment of digital financial inclusion is also in the preliminary research stage. For example, Xu et al. confirmed that digital finance reduces pollution by entrepreneurial innovation and industrial upgrading [43]. Wan et al. confirmed a significant negative correlation between digital finance and pollutant emissions using a dynamic spatial econometric model [44]. Zheng et al. have empirically investigated the effects of governance efficacy, heterogeneous characteristics, and conduction paths of digital finance development on environmental pollution [45]. Liu proposed that digital finance can promote the green transformation of the economy and reduce the adverse impact on the environment, which can help the coordinated development of the economy and the environment [46]. Research by Li et al. shows that there is a significant positive U-shaped nonlinear relationship between digital financial inclusion and green development [47]. Yang et al. empirically tested the effect of digital financial inclusion on PM2.5 concentration, confirming the governance function of digital financial inclusion [48]. Zhao et al. verified that digital financial inclusion has a significant inhibitory effect on carbon emissions [49]. Furthermore, some scholars have confirmed that digital financial inclusion can significantly reduce carbon emission intensity, improve carbon emission efficiency, and improve total factor productivity [50][51][52].
Some scholars have confirmed the interaction between environmental regulation and finance. For example, Ni et al. confirmed that single financial development and environmental regulation have a certain role in promoting green total factor productivity. At this stage, the combination of financial development and environmental regulation inhibits green total factor productivity [53]. Li et al. studied the impact of environmental regulation on the efficiency of regional green development and the mechanism of action, and concluded that the strengthening of environmental regulation will reduce the efficiency of green development by promoting the allocation of financial resources to the secondary industry [54]. The research of Li et al. showed that the introduction of the intersection of environmental regulation and financial development can effectively promote the upgrading of the industrial structure [55]. However, Wang et al. came to the opposite conclusion [56]. With the development of digital financial inclusion, scholars have also conducted research on the interaction between environmental regulation and digital financial inclusion. It mainly focuses on green total factor productivity, industrial structure upgrading, energy-environmental performance, and regional economic growth. For example, Li et al. discussed the adjustment threshold effects of inclusive digital finance on environmental regulation, affecting industrial upgrading [57]. Studies by Shangguan et al. have revealed that the interaction between digital finance and environmental regulation could positively promote high-quality economic development [58]. Cao et al. pointed out that financial supervision and environmental regulation from the Chinese government can reinforce the role of digital finance in promoting energy-environmental performance [59]. Research by Ding et al. shows that the combined effect of environmental regulation and digital financial inclusion can significantly promote regional economic growth [60].
Based on the literature review, we can find some characteristics of previous studies. First, most literature focuses on the relationship between the impact of environmental regulation on environmental pollution and the impact of digital financial inclusion on environmental pollution. Moreover, the environmental governance function of digital financial inclusion has been initially confirmed, but further exploration is needed. Second, scholars have conducted preliminary research on the joint role of digital financial inclusion and environmental regulation. The research mainly focuses on green total factor productivity, industrial structure upgrading, energy-environmental performance, and regional economic growth, which provide theoretical and empirical support for this paper. Therefore, based on the existing research, we have further enriched the theoretical and empirical research on digital financial inclusion, environmental regulation, and environmental pollution. It is mainly reflected in the following aspects. First, the interaction mechanism between digital financial inclusion, environmental regulation, and environmental pollution is analyzed. Second, the spatial Durbin model is used to test the independent and synergistic effects of digital financial inclusion and environmental regulation on environmental pollution, and two spatial matrices are used to verify the stability of the model. Third, the panel threshold model is used to verify whether there is a threshold effect between environmental regulation, digital financial inclusion, and environmental pollution.
The main contributions of this paper are as follows. First, this paper incorporates environmental regulation, digital financial inclusion, and environmental pollution into the same research framework and analyzes their mechanisms of action, which enriches the literature on environmental pollution control. Second, according to the test results of the spatial econometric model, a time-fixed and individual-fixed spatial Durbin model is constructed. The spatial spillover effect between environmental regulation, digital financial inclusion, and environmental pollution is analyzed. Two spatial matrices, the adjacency distance matrix and the economic distance matrix, are constructed to verify the stability of the model. At the same time, the cross term of environmental regulation and digital financial inclusion is introduced to verify the synergistic effect between the two. It can further enrich the empirical research on environmental pollution problems. Third, the panel threshold model verifies that environmental regulation is affected by digital financial inclusion in mitigating environmental pollution. The environmental governance function of digital financial inclusion is also affected by the intensity of environmental regulation. It further illustrates the relationship between the three and provides a basis for the government to formulate environmental regulation policies and digital financial inclusion policies in the process of environmental pollution control.

Research Hypothesis
The pollution shelter hypothesis contends that environmental regulation increases the production costs of enterprises by internalizing the externality of pollution; this effect has been called the compliance cost effect [61]. As the price of using environmental resources and natural factors increases, the cost of enterprise pollutant discharge will increase accordingly [62]. Meanwhile, the Porter hypothesis holds that reasonable environmental regulations can promote technological innovation and reduce production costs, thereby inducing the so-called innovation compensation effect [63]. An increased environmental regulation intensity indicates that the government has imposed additional stringent environmental constraints on enterprises [64]. To meet high environmental standards, enterprises tend to develop and apply clean technologies to control their pollutant emissions [14]. Based on the above analysis, the following hypotheses are proposed: Hypothesis 1. Environmental regulation can alleviate the problem of environmental pollution.
Digital financial inclusion can lay the capital foundation for economic development and promote industrial structure development and technological innovation of enterprises to alleviate environmental pollution problems. According to the in-depth research of scholars, China's economic growth does not necessarily lead to environmental degradation [65][66][67][68][69]. Digital finance can convert idle funds into small deposit investments and wealth management products, increasing social capital in circulation and laying a capital foundation for economic development. Additionally, digital finance lowers the threshold for financial services, improves the ability to allocate financial resources, and stabilizes the "blood transfusion" for economic scale expansion [70]. Digital financial platforms continue to launch green credit products. The precise placement of these green credit products in the high-tech product industry is conducive to the green and advanced transformation of the industry, which leads to the development of emerging business formats and fundamentally solves the dilemma of "structural pollution" in social production [45]. The inclusive and fair nature of digital finance is conducive to improving the matching degree of financial capital and real industrial capital, weakening adverse selection and moral hazard caused by information asymmetry, meeting the green credit needs of enterprises, and improving the efficiency of financial resources utilization. Ultimately, it provides support for the technological innovation of enterprises [71] and improves the enthusiasm of enterprises for green activities [72,73]. In summary, the following assumptions are proposed in this paper: Hypothesis 2. The development of digital financial inclusion can reflect the function of environmental governance.
In the context of tightening environmental regulations, various types of enterprises are facing constraints on their production funds. However, digital financial inclusion can use its own information and technological advantages to broaden corporate financing channels, reduce rent-seeking space, and alleviate financial discrimination [74]. Digital financial inclusion can reach more long-tail groups who face financial constraints due to tightening environmental regulations. By optimizing the structure of resource allocation, alleviating the problem of financial mismatch, and promoting the continuous improvement of the financial system, it can better provide potential support for technological innovation of enterprises and promote the green transformation of enterprises. In addition, when companies face loose capital constraints, companies face less cash flow pressure. Then, compared with production reduction, the benefits of corporate pollution control may be greater, and companies will adopt environmental investment and pollution control methods to reduce emissions [75]. Moreover, as the intensity of environmental regulation increases, green credit products will be guided to be accurately placed in the high-tech product industry, and the green transformation of the industry will be promoted. Therefore, enterprises will be constrained by environmental regulations and funds in the process of pollution control, which will affect the results of pollution control. The framework shown in Figure 1 can be obtained by considering the above studies on the impact of environmental regulation, digital financial inclusion, and environmental pollution. Based on this fact, the following assumptions are proposed in this paper:

Explained Variable
Environmental pollution: At present, there is no unified opinion on the measurement and calculation of pollutants. There are many indicators of environmental pollution, such as carbon dioxide [76], PM2.5 [77], and sulfur dioxide [78,79]. However, industrial sulfur dioxide is the main source of environmental pollution and the most typical discharge of industrial pollution, and it will affect human health. Therefore, this paper uses the sulfur dioxide emission per unit output to measure the environmental pollution according to the research of Shi [80]. Hypothesis 3. Environmental regulation and digital financial inclusion can work together to tackle environmental pollution.

Hypothesis 4.
Environmental regulation may be a threshold variable for digital financial inclusion to reduce environmental pollution.

Hypothesis 5.
Digital financial inclusion may be a threshold variable for environmental regulation to reduce environmental pollution.

Explained Variable
Environmental pollution: At present, there is no unified opinion on the measurement and calculation of pollutants. There are many indicators of environmental pollution, such as carbon dioxide [76], PM2.5 [77], and sulfur dioxide [78,79]. However, industrial sulfur dioxide is the main source of environmental pollution and the most typical discharge of industrial pollution, and it will affect human health. Therefore, this paper uses the sulfur dioxide emission per unit output to measure the environmental pollution according to the research of Shi [80].

Core Explanatory Variables
Environmental regulation: Environmental regulation is mainly measured using a single index [81,82], policy shock [83,84], and comprehensive index [85,86]. Commonly used single-index environmental control measures mainly include pollution discharge fee income, pollution control implementation and operation costs, number of environmental regulation laws and policies, and local government environmental protection expenditures. Referring to the research of Li et al. and Li et al., this paper selects the proportion of local government's environmental protection fiscal expenditure to total fiscal expenditure to measure the level of environmental regulation [87,88]. It should be noted that the study of environmental regulation in this paper focuses on economical environmental regulation, not including environmental laws and regulations.
Digital financial inclusion: The digital financial inclusion index (divided by 100) released by Peking University is used to measure the level of digital financial inclusion based on the common practice of existing research [89].

Control Variables
Regional economy: regional economy is expressed in terms of GDP per capita [90]. Different provinces in China are quite different in terms of geographic location, population, resource storage, and economic development, which may lead to large differences in environmental regulation in different provinces. Therefore, regional economic growth is selected as one of the control variables.
Industrial structure: Industrial structure has a great influence on environmental pollution, and environmental pollutants are mainly produced by the secondary industry [91]. Thus, industrial structure is usually believed to affect environmental pollution [92]. Here, the industrial structure is represented by the ratio of the added value of the tertiary industry to the added value of the secondary industry [93].
Technological innovation: According to the Porter hypothesis, stimulating the "innovation compensation" effect of enterprises through technological innovation is the key means to achieve pollution reduction and enhance the competitiveness of enterprises. There has been a consensus in the academic community that technological innovation can improve environmental pollution. Therefore, Technological innovation is represented by the number of domestic patent authorizations [94].
Population size: Many studies attribute environmental pollution to over-industrialization and urbanization, which are based on the emission of various pollutants generated by the large amount of energy consumed by the economic activities of the urbanized population [95]. Moreover, the contradiction between population and environment has existed since the development of human society, and the expansion of population scale has continuously increased the pressure on the environment. Therefore, the population size is measured by the resident population at the end of the year [96].
Educational level: Studies have shown that the improvement of population quality helps to increase the demand for a high-quality environment [97]. Here, the educational level is represented by the number of students in regular institutions of higher learning [98].
Urbanization level: There are many specific pollutants during the process of urbanization, and thus urbanization should be taken into consideration as a factor that affects environmental pollution. Liang et al. put forward that urbanization is positively related to environmental pollution. The ratio of urban population to total population is employed to indicate the urbanization [99]. Therefore, the urbanization level is expressed by the proportion of urban population in the total population [100].
Foreign direct investment: Studies have shown that FDI may have both positive and negative effects on environmental quality [101][102][103][104]. Herein, foreign direct investment is measured by the ratio of the actual use of foreign direct investment to the regional GDP. In the calculation process, the actual utilization of foreign direct investment is converted according to the annual average exchange rate of RMB against the US dollar [105].
Fixed asset investment: Studies by scholars have shown that investment in fixed assets can promote pollutant emissions. For example, Palmer et al. found that the increase in the actual investment in the power industry will reduce the price of electricity by studying the power industry in Maryland, USA. However, falling electricity prices can lead to higher electricity consumption, which increases sulfur dioxide emissions [106]. Mondschein found that there is a positive correlation between investment and pollutant emissions [107]. Kingston analyzed the relationship between mineral investment and environmental pollution and found that there was a long-term one-way positive correlation between the two [108]. Herein, investment in fixed assets is measured by the proportion of the total investment in fixed assets in the whole society to the regional GDP.
Level of government intervention: Scholars generally believe that fiscal expenditure has a certain impact on environmental quality. For example, Bernauer et al. argued that the increase in fiscal expenditure will curb environmental pollution, but the premise is to ensure administrative efficiency and good control of corruption [109]. Halkos et al. conducted an empirical study on the direct and indirect effects of fiscal expenditure on environmental pollution. It is believed that the indirect impact is mainly due to the increase of the per capita income level by fiscal expenditure, and the increase of the per capita level will restrain the emission of sulfur dioxide [110]. Lopez et al. believed that increasing the proportion of fiscal expenditure in the GDP and increasing the proportion of public goods expenditure can help reduce environmental pollution [111]. The article uses the proportion of government fiscal expenditure to regional GDP to measure the level of government intervention.

Data Description
To ensure the integrity of the data, this paper uses panel data from 30 provinces and autonomous regions in China (excluding Tibet, Hong Kong, Taiwan, and Macao) from 2011 to 2019. Furthermore, environmental pollution data come from the "China Environment Yearbook". Environmental regulation data come from the "China Statistical Yearbook". The data of digital financial inclusion come from the Peking University Digital Financial Inclusion Index (2011-2020). Some variables come from the "China Statistical Yearbook", such as regional economics, industrial structure, technological innovation, population size, education level, urbanization level, and the level of government intervention. Actual use of foreign capital and fixed asset investment are selected from the statistical yearbooks of all provinces and cities in China (2012-2020). The descriptive statistics for the variables used in this study are presented in Table 1.

Spatial Correlation Test
Before the introduction of the spatial econometric model, it is necessary to measure the spatial dependence of environmental pollution in China. This paper conducts analyses from two perspectives of global space autocorrelation and local space autocorrelation. The global space autocorrelation is tested by Moran's index and the calculation formula is as follows.
x i n , X i and X j are observations in regions, i and j are the total number of provinces, W ij is the space matrix, and Moran's I is generally in the range of [−1, 1]. When this latter value is less than 0, it means that the space is negatively correlated. When it is equal to 0, it means that the space is not correlated. When it is greater than 0, it means that the space is positively correlated. The local spatial autocorrelation is represented by the Moran index scatterplot.

Weight Matrix Settings
In this paper, two spatial weight matrices are constructed according to the spatial characteristics and regional economic development. They are the adjacency distance weight matrix and the economic distance space weight matrix, respectively. The adjacency distance matrix (W 1 ) is defined as follows: The economic distance matrix (W 2 ) constructed in this paper is as follows: PGDP represents the per capita GDP of the region and n is the year. Furthermore, in order to measure the average impact of regional spatial spillovers in the empirical study, this paper normalizes the adjacency distance matrix and the economic distance matrix. The estimation results under the two matrices are given as robust reference.

Spatial Econometric Model
The results of the LR test and Wald test showed that the null hypothesis was rejected at the level of 1%, indicating that the spatial Durbin model (SDM) should be used. The spatial Durbin model (SDM) comprehensively considers the spatial lag factors of explanatory variables and explained variables. The SDM in this study is set as follows: In order to further study the impact of the interaction between environmental regulation and digital financial inclusion on environmental pollution, this study added the intersection of environmental regulation and digital financial inclusion on the basis of model (4). The constructed model is as follows: In the formula, Inpoll, reg it , and dfi it represent environmental pollution, environmental regulation, and digital financial inclusion, respectively, in region i in year t. reg it × dfi it represents the intersection of environmental regulation and digital financial inclusion. X it represents the control variable, W represents the spatial weight matrix, and ε it represents a random disturbance term.

Panel Threshold Model
In this paper, the panel threshold model proposed by Hansen was selected to explore whether the explanatory variables are disturbed by the threshold effect, and the threshold model was developed with the environmental regulation and digital financial inclusion as the threshold variables. The specific form of these is shown in Equations (6) and (7): In these formulas, C represents individual effect, α is for the parameter of the thresholddependent variable to be estimated, I(·) represents an indicator function with a value of 0 or 1, X it stands for the control variable, β represents the parameter to be estimated by the control variable, ε it is the error term, and δ stands for the threshold value. The above formula is a single-threshold variable model, and the double-threshold model can be extended.

Spatial Correlation Test
This paper uses exploratory spatial analysis to test the global autocorrelation and local spatial autocorrelation measures of environmental pollution. The Moran's I value is usually used for spatial autocorrelation tests, and the results are shown in Table 2.
The results in Table 2 show that whether it is the adjacency distance matrix or the economic distance matrix, the Moran's I index of environmental pollution passes the significance test. Therefore, it can be shown that environmental pollution has significant spatial agglomeration effects and spatial spillover effects in the spatial distribution. In order to further analyze the local autocorrelation, we created the Moran scatter plot of environmental pollution in 2011 and 2019 under the adjacency distance matrix (Figure 2) and the Moran scatter plot of environmental pollution in 2011 and 2019 under the economic distance matrix (Figure 3).         As illustrated in Figure 4, the degree of environmental pollution showed an overall downward trend from 2011 to 2019. Specifically, Ningxia has always been at the highest As illustrated in Figure 4, the degree of environmental pollution showed an overall downward trend from 2011 to 2019. Specifically, Ningxia has always been at the highest level of pollution emissions, and Beijing, Tianjin, Shanghai, Zhejiang, Guangdong, and Hainan have been at the lowest level. In Figure 5, compared to 2011, the intensity of environmental regulation across regions has mostly increased in 2019. The intensity of environmental regulation in Liaoning Province has always been at the lowest level, the intensity of environmental regulation in Hebei and Tianjin has gradually increased, and the environmental intensity in Qinghai, Gansu, and Ningxia has declined. In Figure 6  As illustrated in Figure 4, the degree of environmental pollution showed an overall downward trend from 2011 to 2019. Specifically, Ningxia has always been at the highest level of pollution emissions, and Beijing, Tianjin, Shanghai, Zhejiang, Guangdong, and Hainan have been at the lowest level. In Figure 5, compared to 2011, the intensity of environmental regulation across regions has mostly increased in 2019. The intensity of environmental regulation in Liaoning Province has always been at the lowest level, the intensity of environmental regulation in Hebei and Tianjin has gradually increased, and the environmental intensity in Qinghai, Gansu, and Ningxia has declined. In Figure 6, the development of digital financial inclusion in various regions in China was relatively slow in 2011, while the development level of digital financial inclusion in various regions in 2019 has been significantly improved. Digital financial inclusion in the eastern region has been developing rapidly. Based on the above analysis, with the gradual improvement of the intensity of environmental regulation and the development level of digital finance, the degree of environmental pollution in various regions is gradually reduced.

Analysis of Spatial Econometric Models
In Table 3, the Hausman test passed the significance test at the 1% level. Therefore, a fixed-effects model should be selected. Furthermore, the likelihood ratio (LR) test in Table  3 found that the spatial and temporal fixed effects were jointly significant. The Wald test and LR test of the SAR and SEM models in Table 4 passed the significance test at the 1% level, rejecting the null hypothesis that the SDM model can be simplified to the SAR and

Analysis of Spatial Econometric Models
In Table 3, the Hausman test passed the significance test at the 1% level. Therefore, a fixed-effects model should be selected. Furthermore, the likelihood ratio (LR) test in Table 3 found that the spatial and temporal fixed effects were jointly significant. The Wald test and LR test of the SAR and SEM models in Table 4 passed the significance test at the 1% level, rejecting the null hypothesis that the SDM model can be simplified to the SAR and SEM models. Therefore, a time-fixed and space-fixed Durbin model (SDM) should be established to analyze the impact of environmental regulation and digital financial inclusion on environmental pollution, which is more in line with objective reality.   Table 5 shows the regression results of the time-fixed and space-fixed Durbin model (SDM) of environmental regulation and digital financial inclusion on environmental pollution. The results of Model 1 verify the independent effects of environmental regulation and digital financial inclusion on environmental pollution. Model II adds the intersection of environmental regulation and digital financial inclusion to examine their synergies.  Models I and III represent the independent effects of environmental regulation and digital financial inclusion on environmental pollution under the adjacency distance matrix and the economic distance matrix. Models II and IV represent the synergistic effects of environmental regulation and digital financial inclusion on environmental pollution under the adjacency distance matrix and the economic distance matrix, respectively. The regression results show that the spatial autocorrelation coefficients of all models have passed the significance test, and the sign and significance of the core explanatory variables have not changed, indicating that the constructed models are stable. The following analysis mainly focuses on the time-fixed and space-fixed spatial Durbin model constructed under the adjacency distance matrix.
In the independent effects test, the spatial autocorrelation coefficient in model (1) is −0.343, which is significant at the 1% significance level. It shows that local environmental pollution has a negative spatial spillover effect on the environmental pollution of adjacent cities, and the aggravation of local environmental pollution is conducive to improving the environmental quality of adjacent cities. From the perspective of agglomeration economics, cities in the same region generally have different urban scales, and there are often one or several large-scale central cities. A larger city scale means that there will be more population and a larger market, which will bring more consumption of life and production and form a spatial agglomeration of environmental pollution. In this way, the environmental pollution of the central city in a certain area will be aggravated, but the environmental pollution of the surrounding cities will be relatively alleviated. From the perspective of core variables, the regression coefficient of environmental regulation on environmental pollution is −8.122, which is significantly negative. It shows that the intensity of envi-ronmental regulation in this region increases, and the environmental pollution situation can be significantly improved, which confirms Hypothesis 1. The possible reasons are as follows. As the intensity of environmental regulation increases, the low-end and middleend manufacturing industries gradually withdraw from the market due to the increase of environmental protection costs and the decline of competitiveness. However, environmental regulation can force enterprises to carry out technological innovation, promote the green transformation of enterprises and industries, and reduce resource consumption and pollutant emissions. The regression coefficient of digital finance on environmental pollution is −0.598, which is negative at the 5% significant level. It shows that the development of digital financial inclusion can restrain environmental pollution in the region and reflects the function of environmental governance. A possible reason for this is that the development of digital financial inclusion can reduce the threshold of financial services and corporate financing costs and promote technological innovation of enterprises, which can upgrade the industrial structure and achieve the purpose of energy conservation and emission reduction. Therefore, Hypothesis 2 is confirmed. In model II, the cross-term coefficient of environmental regulation and digital financial inclusion is negative and significant at the 5% level, which indicates that the synergistic effect of the two can effectively alleviate the problem of environmental pollution. A possible reason for this is that the tightening of environmental regulation policies will require enterprises to invest more environmental protection funds, while digital inclusive finance can provide inclusive financial support, ease the financial constraints of enterprises, and promote technological innovation. Under the guidance of environmental regulations, digital inclusive finance can put funds into green industries in a targeted manner to promote the transformation of enterprises. This is conducive to reducing pollutant emissions and effectively solving the problem of environmental pollution. Therefore, Hypothesis 3 is confirmed.
For the control variables, the regression coefficients of economic development level, education level, and government intervention level in models I and II are significantly negative. This shows that the level of economic development can alleviate environmental pollution to a certain extent. A possible reason for this is that with the improvement of economic level, the ability to treat pollutants is also stronger, so the problem of environmental pollution will be alleviated. The improvement of education level represents the improvement of population quality and the gradual enhancement of people's awareness of environmental protection, so it can alleviate the problem of environmental pollution to a certain extent. The regression coefficient of government intervention level is significantly negative, indicating that the stronger the government's control, the greater the regulatory effect on environmental pollution. However, the regression coefficient of fixed asset investment is significantly positive, indicating that fixed asset investment aggravates environmental pollution in the region. It shows that the economic development mode with fixed asset investment as the main driving force is not conducive to the improvement of environmental quality.

Spatial Effect Decomposition
To further analyze the spatial impact of environmental regulation and digital financial inclusion on environmental pollution, this paper breaks down the spatial effect with the help of a partial differential equation. Table 6 presents the independent models for environmental regulation and digital financial inclusion, along with the direct effect, indirect effect, and total effect of each variable on environmental pollution in a collaborative model between the two variables.  In model I, the direct effect of environmental regulation is significantly negative, the indirect effect is significantly positive, and the total effect is not significant, indicating that the local environmental regulation can effectively alleviate the environmental pollution problem in the local area, but it will aggravate the environmental pollution in the surrounding areas. The possible reasons are as follows. When the degree of environmental regulation in a region increases, enterprises need to reduce pollution in order to meet the requirements of environmental regulation. However, for pollution-intensive industries, pollution control requires higher investment costs. Driven by profit maximization, pollution-intensive enterprises tend to move to areas with low social and environmental awareness in surrounding areas. Therefore, the improvement of the degree of environmental regulation in the region will lead to the reduction of pollution emissions in the region, but it will also lead to the transfer of pollution-intensive industries to neighboring regions and increase the environmental pollution emissions of neighboring regions. The direct effect of digital financial inclusion is significant, but the indirect effect and total effect are not significant. This shows that the development of digital inclusive finance in this region can alleviate the environmental pollution problem at this stage, but it cannot have a significant impact on the environmental pollution problem in the adjacent areas. It can be seen from model II that after adding the cross term of environmental regulation and digital financial inclusion, the direct effect of environmental regulation is still significantly negative, and the indirect effect is significantly positive. The direct effect of digital financial inclusion is still significantly negative. The direct effect of the cross term was significantly negative, the indirect effect was significantly positive, and the total effect was not significant. However, the negative impact of the intersection of environmental regulation and digital financial inclusion on surrounding areas is smaller than the impact of environmental regulation on surrounding areas. A possible reason for this is that the addition of digital inclusive finance can effectively ease the financial constraints of enterprises, make up for the environmental protection costs of some pollution-intensive enterprises, and reduce the number of transfers to surrounding areas. Therefore, the negative impact on the surrounding area will be reduced.

Research on Regional Space
Our study analyzed regional differences across the eastern, central, and western regions. A spatial Durbin model was then used to empirically analyze the spillover effect of each variable. The results are listed in Table 7. Note: z statistic in parentheses. ***, **, and * indicate statistical significance at 1%, 5%, and 10%, respectively.
The results in Table 7 show that the spatial lag coefficients in the eastern, central and western regions all passed the significance test. The spatial lag coefficients in the eastern and central regions are significantly negative, and the spatial lag coefficients in the western region are significantly positive. This shows that the environmental pollution in the eastern and central regions has a significant negative spatial spillover effect while the environmental pollution in the western region has a significant positive spatial spillover effect. In the independent effect test of models V, VII, and IX, the environmental regulation coefficient of the eastern region is −9.453, which is significantly negative at the 5% significance level. This shows that environmental regulation in the eastern region has a significant inhibitory effect on environmental pollution. A possible reason for this is that the economic development level of the cities in the eastern region is relatively high, which can effectively compensate for the cost effect brought by environmental regulation to enterprises, force enterprises to innovate in technology, and reduce environmental pollution. However, the coefficient of environmental regulation in the central and western regions is not significant. A possible reason for this is that there are many underdeveloped cities in the central and western regions, and economic development may not be able to make up for the cost of technological innovation of enterprises caused by environmental regulation. Moreover, the central and western regions also play the role of "pollution transfer paradise" in the process of undertaking industrial transfer from developed cities, so the effect of environmental regulation intensity in the central and western regions on environmental pollution control is not significant. For digital financial inclusion, the regression coefficient of digital financial development in the central region is −0.931, which is significantly negative at the 5% significance level, indicating that digital financial inclusion in the central region has a strong environmental governance function. A possible reason for this is that there are many economic development models dominated by heavy industry in the central region, the overall ecological environment is relatively fragile, and the marginal emission reduction validity of digital finance development is more significant. However, digital financial inclusion in the eastern and western regions has not had a significant effect on environmental pollution. A possible reason for this is that the industrial sulfur dioxide emissions per unit of output value in the eastern and western regions are relatively large, especially in Hebei and Liaoning in the eastern region and Inner Mongolia, Guizhou, Yunnan, Gansu, Qinghai, Ningxia, and Xinjiang in the western region. It is difficult for finance to play the role of environmental governance. In models VI, VIII, and X, only the cross-term coefficient of eastern environmental regulation and digital financial inclusion is −10.693, which is significantly negative at the 1% significance level. This shows that environmental regulation and digital inclusive finance in the eastern region can coordinately govern environmental issues. Moreover, the synergistic effect of environmental regulation and digital financial inclusion is greater than the independent effect of the two. However, after adding the cross term, the significance of the effect of environmental regulation and digital financial inclusion is reduced.

Threshold Effect Test
To further develop the mechanism of synergy between environmental regulation and digital financial inclusion, the panel threshold mode is adopted. This establishes whether environmental regulation and digital financial inclusion have a threshold effect on environmental pollution. The threshold effect was tested with environmental regulation and digital financial inclusion as the threshold variables. The results are given in Table 8. The results in Table 8 show that the single threshold of environmental regulation is significant at the 5% significance level but fails the double threshold test. This shows that when environmental regulation is used as the threshold variable, the impact of digital inclusive finance on environmental pollution has a significant single-threshold effect. Therefore, Hypothesis 4 is proved. Similarly, the single-threshold test of digital financial inclusion is significant at the 1% significance level but fails the double-threshold test. This shows that when digital financial inclusion is used as the threshold variable, the impact of environmental regulation on environmental pollution has a significant single-threshold effect. Therefore, Hypothesis 5 is proved. Figure 7a,b shows the likelihood ratio function graphs with environmental regulation and digital financial inclusion as threshold variables, respectively. For the environmental regulation threshold, the estimated threshold is 0.0529, with a 95% confidence interval of (0.0506, 0.0532). When the threshold value is within the corresponding confidence interval, its LR value is less than the critical value at the 5% significance level (dashed line in Figure 7). Similarly, the estimated threshold for digital financial inclusion is 2.5678, with a 95% confidence interval of (2.5447, 2.5817).

Analysis of Threshold Regression Results
According to the results of the threshold effect test in Table 8, the regression analysis is carried out with digital financial inclusion and environmental regulation as the threshold variables, respectively. The regression results are shown in Table 9. The results in Table 9 show that when the intensity of environmental regulation is less than 0.0529, the impact of digital financial inclusion on environmental pollution is negative, but the effect is not significant. When the intensity of environmental regulation is greater than 0.0529, the impact of digital financial inclusion on environmental pollution is negative and significant at the 1% significance level. A possible reason for this is that

Analysis of Threshold Regression Results
According to the results of the threshold effect test in Table 8, the regression analysis is carried out with digital financial inclusion and environmental regulation as the threshold variables, respectively. The regression results are shown in Table 9. The results in Table 9 show that when the intensity of environmental regulation is less than 0.0529, the impact of digital financial inclusion on environmental pollution is negative, but the effect is not significant. When the intensity of environmental regulation is greater than 0.0529, the impact of digital financial inclusion on environmental pollution is negative and significant at the 1% significance level. A possible reason for this is that when the intensity of environmental regulation is low, the cost of enterprises investing in pollution control is relatively small, and the company's own funds can fully meet the needs of pollution control and technology development costs. Therefore, the impact of digital financial inclusion on environmental pollution is not significant. However, with the increase in the intensity of environmental regulation, enterprises are required to make more investments in environmental protection. Under the circumstance of limited funds, investment in pollution control may crowd out part of the funds originally used to upgrade the technical level and adjust the product structure. At this time, digital inclusive finance can effectively supplement the financial system of enterprises and promote the technological innovation activities of enterprises. Therefore, when the intensity of environmental regulation crosses the threshold, digital inclusive finance will play an environmental governance function.
The regression results using digital financial inclusion as the threshold show that when the development level of digital financial inclusion is below 2.5678, the impact of environmental regulation on environmental pollution is negative, but the effect is not significant. When the digital financial inclusion level is greater than 2.5678, the impact of environmental regulation on environmental pollution is negative, and it passes the 1% significance test. This shows that when the level of digital financial inclusion is not fully developed due to the lack of corresponding support conditions, the impact of environmental regulation on environmental pollution is not obvious. A reason for this may be that the increase in the intensity of environmental regulation has put pressure on the supporting funds of enterprises. If there is no corresponding financial support or the level of financial development is low, the innovative compensation effect of environmental regulation cannot be brought into play. With the development and improvement of digital inclusive finance, inclusive funds can be supplied to enterprises in the process of strengthening environmental regulations. This enables enterprises to have sufficient funds to carry out green production innovations, upgrade industrial structures, and alleviate environmental pollution. Therefore, when digital financial inclusion crosses the threshold, environmental regulation can effectively alleviate the problem of environmental pollution.

Discussion of Empirical Results
First, models I, II, III, and IV all passed the significance test, indicating that the spatial econometric model constructed in this paper is robust. The results are mainly analyzed via the Durbin model based on the proximity distance, and the relationship between environmental regulation, digital financial inclusion, and environmental pollution is explained. Secondly, the spatial autocorrelation coefficients in Model I and Model II are negative, indicating that environmental pollution has a significant negative spillover effect. This is consistent with the research conclusions of Sun et al., Ping et al.,, who used static and dynamic spatial Durbin models to explore the impact of formal environmental regulation and informal environmental regulation on environmental pollution, respectively. Studies have confirmed that local environmental pollution has a negative spatial spillover effect on the environmental pollution of adjacent cities; the aggravation of local environmental pollution is conducive to improving the environmental quality of adjacent cities. Then, the regression coefficient of environmental regulation on environmental pollution is significantly negative, indicating that the increase in the intensity of environmental regulation in the region can effectively improve the environment in the region. The research in this paper is consistent with the conclusion that environmental regulation can effectively alleviate the environmental pollution in the region, obtained by He et al. [115]. In the research on the relationship between digital financial inclusion and environmental pollution, this paper finds that the development of digital financial inclusion can restrain environmental pollution in the region. This is consistent with the conclusion of Zheng et al. in their research on whether the development of digital finance is conducive to environmental pollution control [45]. Finally, the research of the article shows that the intersection of environmental regulation and digital finance can effectively alleviate the problem of environmental pollution, indicating that the two have a synergistic effect in the process of environmental pollution control. This is similar to the conclusion that environmental regulation and digital inclusive finance have synergistic effects on high-quality economic development, urban industrial upgrading, and regional economic growth, which was drawn in studies by Li et al., Shangguan et al., and Ding et al. [57,58,60].
In addition, this paper also explores whether digital financial inclusion and environmental regulation have threshold effects in the process of environmental pollution. The research results of the article show that when digital financial inclusion is used as the threshold variable, the impact of environmental regulation on environmental pollution has a significant single threshold effect. Similarly, when environmental regulation is used as the threshold variable, the impact of digital financial inclusion on environmental pollution has a significant single-threshold effect. This shows that there is a mutual influence between environmental regulation and digital inclusive finance. With the improvement of the level of digital inclusive finance, environmental regulation can more effectively alleviate the problem of environmental pollution. At the same time, as the intensity of environmental regulation increases, digital inclusive finance also reflects a strong environmental governance function. This shows that the coordinated development of the two can effectively alleviate the problem of environmental pollution.

Conclusions
Based on the theory and literature analysis of the impact of environmental regulation and digital financial inclusion on environmental pollution, in this study, we used provincial panel data from 2011 to 2019 to conduct an empirical analysis. Firstly, the spatial Durbin model (SDM) was used to study the direct effects and spatial spillover effects of environmental regulation and digital inclusive finance on environmental pollution. Secondly, the panel threshold model was used to verify whether there is a threshold effect in the process of environmental regulation and digital inclusive finance affecting environmental pollution. Furthermore, we divided the research samples into three regions (eastern, central, and western regions) to study the regional heterogeneity impact of environmental regulation and digital inclusive finance on environmental pollution. The empirical results show the following: In the independent effect test, both environmental regulation and the development of digital inclusive finance can significantly alleviate the problem of environmental pollution. Moreover, the increase in the intensity of environmental regulation in this region will aggravate the environmental pollution in the surrounding areas, and the development of digital inclusive finance at this stage has not yet had an effect on the environmental pollution in the surrounding areas. In the joint effect test, it was found that the intersection of environmental regulation and digital financial inclusion can significantly alleviate the problem of environmental pollution. This shows that environmental regulation and digital inclusive finance have a synergistic governance function for environmental pollution. In the threshold effect test, when environmental regulation is used as the threshold variable, the impact of digital inclusive finance on environmental pollution has a significant single threshold effect. As the intensity of environmental regulation increases, digital financial inclusion will gradually reflect the function of environmental governance. Similarly, when digital financial inclusion is used as the threshold variable, there is a significant single threshold effect of environmental regulation on environmental pollution. With the improvement of the development level of digital inclusive finance, environmental regulation can effectively alleviate the problem of environmental pollution.
The results of the heterogeneity test showed that the spatial lag coefficients in the eastern, central, and western regions all passed the significance test. The spatial lag coefficient in the eastern and central regions is significantly negative, and the spatial lag coefficient in the western region is significantly positive. This shows that the environmental pollution in the eastern and central regions has a significant negative spatial spillover effect, while the environmental pollution in the western region has a significant positive spatial spillover effect. In the independent effect test, environmental regulation in the eastern region can significantly suppress pollution emissions, while the coefficients of environmental regulation in the central and western regions are not significant. The development of digital finance in the central region has a significant effect on reducing environmental pollution, while the digital financial inclusion in the eastern and western regions has no significant effect on environmental pollution. The combined effect regression results showed that only the cross-term coefficient in the east was significantly negative. This shows that environmental regulation and digital inclusive finance in the eastern region can coordinately govern environmental issues. Moreover, the synergistic effect of environmental regulation and digital financial inclusion is greater than the independent effect of the two. However, after adding the cross term, the significance of the effect of environmental regulation and digital financial inclusion is reduced.
According to the above research conclusions, the main research purpose of this article has been confirmed. First, environmental regulation can effectively alleviate the problem of environmental pollution. Second, digital inclusive finance has certain environmental governance functions in the process of environmental pollution governance. Third, environmental regulation and digital inclusive finance have a synergistic effect in the process of environmental pollution control. With the improvement of the development level of digital inclusive finance, the inhibitory effect of environmental regulation on environmental pollution has increased. Similarly, as the intensity of environmental regulation increases, digital financial inclusion will show stronger environmental governance functions. Based on this, this paper confirms that environmental regulation and digital inclusive finance have certain synergistic effects in the process of environmental governance and achieves the research purpose.

Policy Recommendations
Environmental regulation can effectively alleviate the problem of environmental pollution. Therefore, diversified environmental regulation methods should be established to jointly restrain corporate pollution behaviors from the perspectives of the government, the market, and the public. By enhancing environmental regulation, enterprises will be forced to innovate in technology and reduce pollution emissions. Secondly, local governments should formulate more targeted assessment targets for environmental governance in various regions based on local objective conditions and the suggestions of local residents. In this way, local governments can reach a consensus on coordinated regulation on the goals of environmental governance and achieve coordinated governance between regions. Furthermore, local governments should choose and set the intensity and means of environmental regulation according to local conditions. At the same time, the government should encourage the central and western provinces to learn from the experience of environmental governance and appropriately increase investment in environmental protection. Finally, relevant environmental protection departments should strengthen local environmental protection publicity and education, which can enhance the environmental protection awareness of local residents, make local residents aware of the necessity of pollution reduction, and encourage local residents to actively participate in environmental protection. At the same time, the incentive mechanism for local residents to participate needs to be improved. This can improve the enthusiasm and initiative of local residents to participate in environmental protection, fully mobilize the supervision force of local residents, and jointly promote the sustainable development of the environment.
In terms of digital inclusive finance, first, empirical results show that digital financial inclusion has the function of environmental governance. Therefore, the development of digital financial inclusion can be strongly supported. Specific measures include further improving digital infrastructure, promoting the integration of financial services with modern digital technologies, and improving the availability of various financial products, which provides a diversified portfolio of financial products for green transformation activities. This can promote the green technology innovation of enterprises and reduce environmental pollution from the source. Secondly, it is necessary to enhance the quality of digital financial services and build a linkage mechanism between the financial market and the environmental protection industry. At the same time, building a more diverse digital financial environmental protection platform can improve the efficiency of resource utilization.
However, it is necessary to give full play to the inclusiveness, accessibility, and long tail of digital financial services, and reduce the threshold of financial services and corporate financing costs. The continuous power output of digital finance development and emission reduction will be realized. Finally, in the process of the regional layout of digital finance, it is worth noting the heterogeneous characteristics of digital finance-driven environmental governance, strengthening inter-regional cooperation the "flexible" supervision of digital finance. At the same time, it is possible to build a regulatory model that includes multiple subjects of the government, the market, and society, and establish a digital financial governance system that includes consultation, co-governance, and sharing, which can fully prevent digital financial risks.
In the collaborative governance of environmental regulation and digital financial inclusion, the role of environmental regulation and the development of digital financial inclusion are complementary. In regions with a high level of development of digital inclusive finance, technological innovation of enterprises can obtain financial support from the digital inclusive financial system. Therefore, when enterprises in these regions face the increasing intensity of environmental regulation, they can obtain sufficient funds to engage in technological innovation and promote the compensation effect of environmental regulation innovation. Moreover, the increase in the intensity of environmental regulation can guide digital inclusive finance to support the promotion of new emission reduction technologies and green new products, which can promote technological innovation of enterprises, realize green transformation of enterprises, and reduce pollution emissions. In conclusion, all regions should strengthen regional cooperation. It is necessary that regional cooperation and coordination mechanisms for environmental regulation and digital financial inclusion should be jointly explored. At the same time, cross-regional environmental protection law enforcement and financial instrument implementation cooperation should be carried out. However, it is necessary to be cautious of the "race to the bottom" phenomenon in environmental regulation in order to undertake industrial transfer between regions. Only in this way can we give full play to the collaborative governance function of environmental regulation and digital inclusive finance on environmental pollution.

Research Limitations and Future Research
This research was subject to some limitations, which should be considered in further research. First, it can be seen from theoretical research that the current environmental regulation and digital inclusive finance will alleviate the problem of environmental pollution through industrial structure upgrading, technological innovation, and other variables. The next step will use the mediation model to further explore the mechanism of action. Second, from the empirical results, we can see that overall environmental regulation will be affected by digital financial inclusion when it affects environmental pollution. Similarly, the environmental governance function of digital financial inclusion will be affected by environmental regulation. At present, The article only conducted a threshold study on the overall sample of 30 provinces in China. In the next step, the sample can be divided into eastern, central, and western regions for research to explore whether the same threshold effect exists. Thirdly, in this paper, environmental regulation focuses on the study of economical environmental regulation, and the next step can be to further study the impact of legal environmental regulation and digital inclusive finance on environmental regulation. In addition, the impact of negative information in the development of digital financial inclusion on environmental pollution needs further research. At the same time, the selection of indicators for environmental regulation is relatively simple. In the next step, comprehensive environmental regulation indicators or non-governmental environmental regulation can be considered. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement:
The data and estimation commands that support the findings of this paper are available upon request from the first and corresponding authors.

Conflicts of Interest:
The authors declare no conflict of interest.