Digital finance and green innovation efficiency: Empirical data from Chinese listed manufacturing companies

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Our discoveries suggest that digital finance improves manufacturing firms’ green innovation efficiency. After a few robustness tests, our results are still accurate. The effect is more pronounced in Zhejiang Province and central and western regions. According to a mechanism analysis, digital finance increases the effectiveness of green innovation by removing financing constraints. The findings offer policy suggestions for manufacturing companies to implement green innovation and improve its efficiency. Firstly, digital finance development should be accelerated. Secondly, keep an eye on the variations in digital finance. Finally, there has to be more regulation of digital finance. Digital finance Green innovation efficiency Financing constraints Manufacturing enterprises 1. Introduction In the past few decades, the manufacturing industry has made great contributions to the rapid growth of the Chinese economy. However, it brings a serious burden to the ecological environment at the same time. Thus, it is imperative to promote the manufacturing industry’s green innovation, which can not only effectively solve the problem of environmental pollution, but also promote green growth, thus providing a new way to the transformation of the Chinese economy (Liu et al., 2022 ). In a market economy, manufacturing enterprises not only generate economic activities but also cause environmental pollution. Manufacturing enterprises in the production process will inevitably cause pollution and injury to the environment, so it is very crucial to coordinate the economy and the environment (Feng et al., 2022 ). Manufacturing enterprises should have the courage to eliminate high energy-consuming and high-emission production processes, reduce the waste of production resources and ecological pollution, and force enterprises to realize green and low-carbon transformation. Meanwhile, manufacturing enterprises should increase the R&D of environmentally friendly technologies, processes, and equipment, accelerate the realization of green transformation, and improve resource utilization efficiency. Therefore, studying the factors that affect manufacturing companies’ adoption of innovation is therefore essential for realizing high-quality development (Rao et al., 2022 ; Liu et al., 2022 ). Green innovation activities are full of risks including high costs, extended cycles, irreversibility, and unstable return on investment (Hottenrott & Peters, 2012 ; Corradini et al., 2014 ), and thus firms need sufficient capital to ensure the success of green innovation. In the past, traditional finance has influenced firms’ innovation decisions (Hottenrott & Peters, 2012 ; Chong et al., 2013 ). When enterprises obtain a certain degree of credit funds from banks, the continuous increase of credit funds helps to advance corporate green innovation (Huang et al., 2019 ). Yu et al. ( 2021 ) find that green finance improves firms' R&D capital investment by alleviating their financing constraints, which in turn increases participation in green innovation activities. The emerging digital financial business has largely replaced the traditional financial business, and the change in the financial service model has changed the investment and financing environment of enterprises, which will inevitably have an impact on their operation and R&D decisions (Ozili, 2018 ; Rao et al., 2022 ). Unlike service-oriented firms, manufacturing firms need to acquire or lease the sites, equipment and materials needed for their production activities, and bear the risks associated with green innovation behaviors. This means that manufacturing firms will face severe financing constraints. So, research into how digital finance affects green innovation in manufacturing organizations is required. Theoretically, expanding the research field of digital finance and digging deeper into the effect of digital finance on micro subjects; practically, helping manufacturing enterprises to realize the importance of digital finance, so that they can make full use of digital finance to implement green innovation decisions and achieve sustainable development (Li et al., 2022 ). Our innovations are in the following three aspects. First, manufacturing enterprises will cause serious resource waste and environmental pollution problems in the production process, and green innovation can effectively solve the above problems. However, few kinds of literature explore green innovation behavior using manufacturing companies as a sample, which provides research ideas for this paper. Second, manufacturing enterprises engaging in green innovation activities face more severe financing constraints, while digital finance offers a more inclusive financing environment, being helpful to boost corporate green innovation. In this paper, we will reinterpret green innovation behaviors of manufacturing enterprises to broaden the research scope of digital finance. Third, existing studies mainly focus on corporate green innovation output (Liu et al., 2022 ; Feng et al., 2022 ), while few literature studies businesses’ green innovation efficiency from an input-output perspective. This research focuses on green innovation efficiency to make the conclusions of this study more precise. Other parts of this paper are structured as followed. Section 2 systematically compiles and summarizes relevant studies. Research hypotheses on relevant theories and literature are displayed in Section 3 . Models and variables are explained in Section 4 . Section 5 shows regression results. Finally, the conclusions. 2. Literature review 2.1 Traditional finance and corporate green innovation In recent decades, more countries have realized the importance of harmonizing links between social and environmental advancement. Enterprises, as a crucial part of the market economy, are also trying to explore circular, green and sustainable growth paths with a view to assuming the social responsibility of protecting the ecological environment. For this reason, a growing number of companies are engaging in green innovation activities. Firstly, green innovation activities increase resource utilization and reduce pollution emissions, which protects the ecological environment (Rennings, 2000 ; Barbieri et al., 2020 ). Secondly, green innovation activities have the attribute of "innovation", which improves production efficiency and creates intensive economic value through innovative production technologies and optimized factor combinations (Aghion & Howitt, 1990 ; Aghion & Howitt, 2006 ). However, green innovation activities are full of risks such as high cost, long cycle, irreversibility, and unstable return on investment (Hottenrott & Peters, 2012 ; Corradini et al., 2014 ), and thus require ample financial support as a guarantee for R&D expenditures. In the context of the traditional financial model, banks are the main suppliers of financial capital (Jin et al., 2021 ), and their expansionary and competitive behaviors can directly affect firms' innovation decisions (Chong et al., 2013 ). However, existing literature holds different views on the effect of traditional finance on firms' innovation decisions. Huang et al. ( 2019 ) point out that after banks provide a certain level of loans to firms, increasing loans fosters green innovation in business. Yu et al. ( 2021 ) argue that green financial policies adopted by banks can effectively ease financing limitations, provide abundant financial security for R&D activities, and then lead to the enhancement of green innovation. Some studies believe that although the financial supply of banks will increase green innovation quantity, it cannot improve green innovation quality (Wang et al., 2021 ), resulting in lower corporate green innovation efficiency, and even causing the waste and abuse of financial resources. We must recognize the role that traditional finance plays in advancing green technology, and the financial supply provided by banks does effectively alleviate corporate financing constraints, which finally improves green innovation (Huang et al., 2019 ; Yu et al., 2021 ). Meanwhile, we also need to dialectically view the drawbacks of traditional finance, such as information asymmetry caused by the waste and abuse of financial resources, which seriously affects the utilization efficiency of financing resources, and thus it’s important to consider alternative financial models. 2.2 The effect of digital finance on corporate green innovation Unlike traditional finance, digital finance has a number of advantages, including reducing financing service spending, broadening financial service channels, and expanding financial service coverage (Kshetri, 2016 ; Huang & Huang, 2018 ), which will undoubtedly become a potential opportunity for companies to make decisions on innovation. Moreover, the shortcomings of conventional financial information asymmetry will also be successfully addressed by digital finance (Rao et al., 2022 ). Specifically, digital technology can help financial institutions grasp the operation, management, and business financial status (Berger et al., 2005 ; Alessandrini et al., 2009 ; Hollander & Verriest, 2016 ), which improves green innovation. Recent studies have focused on the effect of digital finance on green innovation output (Rao et al., 2022 ; Lin and Ma, 2022 ; Liu et al., 2022 ; Feng et al., 2022 ), and the conclusions of these studies all indicate that digital finance may significantly improve green innovation. At this stage, most of the articles focus on the relationship between digital finance and enterprises’ innovation outputs, so there is a dearth of studies on the input-output efficiency perspective, which may lead to the abuse of financial resources (Wang et al., 2021 ). To fill this gap, this research investigates how digital finance affects the effectiveness of green innovation and takes efficiency into account when evaluating its impact. 3. Research hypotheses Financial services are successfully made more affordable, more accessible, and more comprehensive thanks to digital finance (Kshetri, 2016 ; Huang & Huang, 2018 ). Further, digital finance is having a huge impact on firms' green innovation decisions (Ozili, 2018 ; Rao et al., 2022 ). Firms' green innovation activities are full of risks such as high costs, long lead times, irreversibility, and unstable returns on investment (Hottenrott & Peters, 2012 ; Corradini et al., 2014 ), which implies that firms must overcome financing constraints to have the opportunity to implement green innovation activities. However, manufacturing companies are at a relative disadvantage in terms of financing capability, leading to their weak willingness to participate in green innovation efforts. Digital finance can create new advantages for manufacturing companies. Firstly, digital finance directly provides financial support for companies, alleviating their financing constraints, and improving corporate green innovation efficiency. Secondly, digital finance can help enterprises grasp industry and market information in a comprehensive and timely manner, tap new opportunities for green development, prevent ineffective or inefficient green innovation activities due to information asymmetry and rapid changes in the market, and reduce sunk costs and risk of failure of green innovations, thus improving enterprise innovation efficiency. In light of this, this paper presents the Hypothesis 1 : Hypothesis 1 Manufacturing companies are much more efficient in implementing green innovations thanks to digital finance. Unlike traditional finance, digital finance effectively reduces financial services costs and provides financial security for manufacturing enterprises, enhancing the effectiveness of green innovation. There are three primary areas where digital finance is advantageous. First, reducing transportation costs. Transportation costs of search, including time, that firms incur in finding the right financial product, effort, and expenses involved in communicating with financial institutions (Degryse & Ongena, 2005 ; Agarwal & Hauswald, 2010 ). Affected by the manufacturing industry production characteristics, most of the enterprises are in a relatively remote geographical location, which will undoubtedly increase the cost of transportation, and digital finance enables manufacturing enterprises to complete online communication with financial institutions, signing and other matters, effectively reducing the cost of transportation to achieve cooperation. Second, it reduces information costs. The application of digital technology can help financial institutions keep abreast of the operational, managerial, and financial status of manufacturing firms (Berger et al., 2005 ; Alessandrini et al., 2009 ; Hollander & Verriest, 2016 ), thus reducing the cost that financial institutions pay for obtaining information about firms. Third, reducing the cost of regulation. Digital finance can regulate corporate behavior in a timely manner with the help of digital means, avoiding the phenomenon of regulatory failure due to the geographical distance between the credit parties (Presbitero & Rabellotti, 2014 ). This significantly lowers the cost of regulatory compliance for financial institutions and advances the effectiveness of financial regulation. Green innovation will be stimulated whenever the credit capital of manufacturing enterprises reaches a certain scale and continues to grow (Huang et al., 2019 ). As a result, we get Hypothesis 2 : Hypothesis 2 By relieving the burden of financial restrictions, digital finance boosts the innovation efficiency of manufacturing enterprises. 4. Research design 4.1 Sample selection To research digital finance’s effect on the green innovation efficiency of manufacturing companies, this paper matches the digital finance index of prefecture-level cities with A-share manufacturing listed businesses, and the sample period is 2011–2021. This paper makes the following two notes on the selection of sample data: (1) Considering that the Digital Finance Index was initially measured in 2011 and the latest data period ends in 2021, the sample period of this paper is 2011–2021; (2) Most existing studies use provincial-level digital finance data (Liu et al., 2022 ; Feng et al., 2022 ), which may bias the conclusions, so we adopt the index of prefectural-level cities. Our data were processed in this paper as follows: (1) Companies with the trading status of ST, *ST and PT for the year were excluded; (2) Companies that have been delisted are excluded; (3) Samples in which the variables involved in the regression contained missing values were excluded. All of the variables involved in the regression were subjected to shrinking of the tails at the 1% level to eliminate the confounding influence of outliers. Our final sample includes 19943 observations. 4.2 Definition of variables 4.2.1 Dependent variable Existing literature usually uses patents amounts filed for innovations in green technology to measure innovation (Carrión-Flores & Innes, 2010 ; Amore & Bennedsen, 2016 ; Rao et al. 2022 ; Feng et al. 2022 ). They believe that enterprises have already demonstrated strong green innovation practice prowess in the process of applying for green technology invention patents, so the number of green technology invention patent applications is selected as a proxy for corporate green innovation. In fact, enterprises should consider their efforts to enhance green innovation efficiency activities from the input-output viewpoint, or else purely considering the output effect is prone to serious speculative behavior (Hu et al., 2021 ). And thus, we calculate the green innovation efficiency utilizing the ratio of green innovation outputs to inputs to assess efficiency (Greenlnnov). Considering the unavailability of data on green innovation investment, we use R&D expenditures as an approximate proxy. The green innovation output equals the natural logarithm of the output of green inventions, utility models, and design patents filed plus one. 4.2.2 Independent variable Following the existing literature, we measure digital finance with the help of the “Peking University Digital Finance Index” (Guo et al., 2020 ). The author matches the digital finance index at the prefecture-level with listed companies to empirically test its effect on the manufacturing industry’s innovation efficiency. To remove the impact of the quantitative outline on the empirical results, this paper will be treated as a digital financial index that has been “divided by 100”. 4.2.3 Mediating variables By examining the mediating role of financial restrictions, we investigate the mechanism of digital finance on the effectiveness of innovation in manufacturing companies. Regarding the setting of the financing constraint (FC) indicator, this paper chooses Whited & Wu’s ( 2006 ) WW index to measure it. 4.2.4 Control variables Following Tang et al. ( 2020 ), Ouyang et al. ( 2020 ), and Pan et al. ( 2021 ), we use company size (Size), leverage ratio (Lev), return on assets (ROA), fixed asset stock ratio (Fixed), operating income (Growth), board size (Board), listing period (ListAge), and audit unit type (Big4) as control variables. Growth), Board Size (Board), Listing Age (ListAge), and Audit Unit Type (Big4) as control variables. Variable definitions are reported in Table 1 . Table 1 Variable definitions Variable type Variable name Variable symbol Variable definition Dependent variable Green innovation efficiency Greenlnnov ln (Total patent applications for green technology inventions + 1)/R&D expenditures Independent variable Digital finance index DF Peking University Digital Finance Index (prefecture-level city level) Intermediary variable Financing constraints FC Reference to Whited & Wu ( 2006 ) Control variable Company size Size ln(Total assets + 1) Gearing Lev Total liabilities/Total assets Net profit margin on total assets ROA Net profit/Total assets Fixed asset ratio Fixed Net fixed assets/Total assets Revenue growth rate Growth Growth rate of current year's operating income relative to previous year's operating income Board size Board ln(Number of Board of Directors + 1) Number of years listed ListAge ln (Years of listing + 1) Type of audit unit Big4 1 if an international Big 4 accounting firm was hired in the year, 0 otherwise 4.3 Empirical models To investigate how digital finance affects the green innovation efficiency of manufacturing companies, we adopt model (1) for estimation: $$GreenInno{v_{it}}={\alpha _0}+{\alpha _1}D{F_{it}}+\sum {{\alpha _i}Contro{l_{it}}+} \sum {Industry+} \sum {Year+{\upvarepsilon _{it}}}$$ 1 where Greenlnnov is the green innovation efficiency of manufacturing companies, DF is Digital Finance Index, \(\sum Control\) represents the group of control variables, \(\sum Industry\) and \(\sum Year\) stands for industry and year dummy variables, respectively, which are used to eliminate the effects of industry differences and year differences on the empirical results. 5. Empirical findings 5.1 Descriptive statistics In Table 2 , we show descriptive data, including mean, standard deviation, maximum and minimum values. Table 2 Descriptive statistics of relevant variables Variable N Mean S. D Min Max Greenlnnov 19943 0.029 0.050 0.000 0.229 DF 19943 2.327 0.754 0.512 3.597 Size 19943 22.295 1.324 19.914 26.973 Lev 19943 0.421 0.199 0.041 0.923 ROA 19943 0.043 0.066 -0.301 0.252 FIXED 19943 0.210 0.149 0.002 0.706 Growth 19943 0.189 0.437 -0.559 3.909 Board 19943 2.127 0.196 1.609 2.708 ListAge 19943 1.996 0.922 0.000 3.367 Big4 19943 0.062 0.241 0.000 1.000 WW 19943 1.191 2.233 -7.558 6.448 5.2 Baseline results Table 3 shows our benchmark results. In column (1), it is clear that the coefficient of DF is considerably positive at the 1% level, demonstrating that the development of digital finance favorably influences the effectiveness of green innovation in manufacturing firms, and Hypothesis 1 is valid. When control variables are added in column (2), the coefficient of DF still shows a substantial positive value Digital finance provides manufacturing enterprises with more abundant financial support and effectively alleviates the pressure of financing constraints, allowing them to enhance R&D spending and continuously boost the effectiveness of green innovation. Table 3 Benchmark results Variable (1) (2) Greenlnnov Greenlnnov DF 0.00906*** 0.00751*** (5.50) (4.63) Size 0.01056*** (28.95) Lev 0.01387*** (6.18) ROA 0.02650*** (4.55) FIXED -0.01097*** (-4.23) Growth -0.00441*** (-5.73) Board 0.00773*** (4.47) ListAge -0.00530*** (-12.37) Big4 0.00876*** (6.03) Constant 0.00292 -0.23301*** (0.80) (-28.19) Industry FE Y Y Year FE Y Y R 2 0.096 0.173 N 19943 19943 Note: Levels of significance are indicated by ***(1%), **(5%), and *(10%). 5.3 Endogeneity test In the benchmark regression section, we attempt to account for time effects, industry effects, and factors affecting manufacturing businesses’ ability to innovate sustainably, but there are still potential endogeneity risks. Therefore, we use IV-2SLS regressions to address the risk of latent endogeneity in empirical studies. The following two conditions need to be met to select the appropriate instrumental variable: (1) Instrumental variables need to be correlated with the independent variables; (2) Instrumental variables need to be independent of the disturbance term. We refer to Bartik ( 2006 ) and construct the “Bartik instrument” (the first-order difference in time DDF of the financial inclusion index and the one-period lagged digital finance index LDF, denoted by LDF*DDF), and then conduct instrumental variable estimation. Table 4 presents the findings. The value of Cragg-Donald Wald F is 10373.5, larger than the empirical threshold (10% level) of 16.38, which indicates that there is no risk of “under-recognition” of the selected instrumental variables, according to the weak instrumental variables test. Table 4 Instrumental variable test results Variable First-stage Second-stage DF Greenlnnov LDF*DDF 1.33955*** (101.85) DF 0.00814*** (2.73) Constant 2.19740*** -0.27360*** (64.32) (-19.66) Controls Y Y Industry FE Y Y Year FE Y Y R 2 0.9435 0.1768 N 14797 14797 Cragg-Donald Wald F statistic 10373.5 Note: Levels of significance are indicated by ***(1%), **(5%), and *(10%). The coefficient of the IV (LDF*DDF) shows a substantial positive value, implying that the IV (LDF*DDF) and digital finance (DF) are positively corrected, as shown by the first-stage regression findings. According to second-stage regression, which is in accordance with the baseline findings, digital finance significantly enhances the green innovation efficiency (Greenlnnov) of manufacturing enterprises. 5.4 Robustness tests 5.4.1 Replacement of models According to Table 2 , it is not difficult to find that the explanatory variables (Greenlnnov) contain many zero values, and the existence of these special values may affect the accuracy of the benchmark regression, so we choose the Probit model to replace the OLS model. The results are displayed in Table 5 . After replacing the model, we find that the coefficient of DF remains positive, implying the baseline results are reliable. Table 5 Replacement model results Variable (1) (2) Greenlnnov Greenlnnov DF 0.15462*** 0.15701*** (3.29) (3.17) Constant -1.18729*** -6.47915*** (-10.50) (-24.97) Controls N Y Industry FE Y Y Year FE Y Y R 2 0.0757 0.1130 N 19943 19943 Note: Levels of significance are indicated by ***(1%), **(5%), and *(10%). 5.4.2 Deletion of samples (1) Deletion of the financial volatility sample Both firms’ green innovation behavior and digital finance are affected by financial shocks on a global scale, so ignoring the volatility of financial markets may bias the study’s conclusions to some extent. In our sample period, 2015–2017 is the stage of China’s stock market abnormal volatility, so we try to remove the sample in this stage. The outcomes are displayed in columns (1)-(2) of Table 6 . The coefficient on DF remains positive after the financial market volatility sample is taken out, supporting the robustness of the results. (2) Deletion of new crown outbreak samples In late 2019, novel coronaviruses began to spread globally, causing untold economic damage worldwide. Meanwhile, digital financial development and the daily business activities of enterprises have been seriously affected. For this reason, we delete the 2020–2021 sample, and the outcomes are disclosed in columns (3)-(4) of Table 6 . After deleting the New Crown epidemic sample, we find that the coefficient also remains significantly positive at the 1% level, suggesting that digital finance continues to positively impact manufacturing firms' capacity for green innovation even when the COVID-19 epidemic is taken into account. Table 6 Deletion of sample results Variable (1) (2) (3) (4) Greenlnnov Greenlnnov Greenlnnov Greenlnnov DF 0.00884*** 0.00737*** 0.01116*** 0.00890*** (4.89) (4.13) (5.28) (4.28) Constant 0.00347 -0.22916*** 0.00002 -0.24607*** (0.85) (-24.63) (0.00) (-24.41) Controls N Y N Y Industry FE Y Y Y Y Year FE Y Y Y Y R 2 0.100 0.176 0.089 0.168 N 14691 14691 14511 14511 Note: Levels of significance are indicated by ***(1%), **(5%), and *(10%). 5.4.3 Substitution of variables The Digital Finance Index is grounded on three dimensions of Breadth of Digital Finance Coverage (Breadth), Depth of Digital Finance Usage (Depth), and Degree of Digitization of Financial Inclusion (Digital). Considering that all these dimensions will affect the green innovation efficiency of manufacturing organizations to some extent, we decided to replace the independent variable DF with the dimension index. We next performed a robustness test, whose results can be seen in Table 7 . The coefficients of Breadth , Depth , and Digital are significantly positive, suggesting that all the dimensions of DF will positively affect the effectiveness of the green innovation in manufacturing companies, reaffirming that the findings are robust. Table 7 Results of substitution of variables Variable (1) (2) (3) (4) (5) (6) Greenlnnov Greenlnnov Greenlnnov Greenlnnov Greenlnnov Greenlnnov Breadth 0.00815*** 0.00694*** (6.16) (5.34) Depth 0.00360*** 0.00279** (2.66) (2.10) Digital 0.00264*** 0.00237** (2.66) (2.44) Constant 0.00290 -0.23322*** 0.00634* -0.22946*** 0.00690* -0.22941*** (0.80) (-28.28) (1.77) (-27.83) (1.94) (-27.90) Controls N Y N Y N Y Industry FE Y Y Y Y Y Y Year FE Y Y Y Y Y Y R 2 0.097 0.173 0.095 0.172 0.095 0.172 N 19943 19943 19943 19943 19943 19943 Note: Levels of significance are indicated by ***(1%), **(5%), and *(10%). 5.5 Mechanism testing To investigate how digital finance influences the green innovation efficiency of manufacturing businesses, this paper adopts the model (2) and (3) for estimation: $$F{C_{it}}={\beta _0}+{\beta _1}D{F_{it}}+\sum {{\beta _i}Contro{l_{it}}+\sum {Industry} +\sum {Year+{\delta _{it}}} }$$ 2 $$GreenInno{v_{it}}=φ +{φ _1}D{F_{it}}+{\gamma _2}F{C_{it}}+\sum {{φ _i}Contro{l_{it}}+\sum {Industry+\sum {Year+{\sigma _{it}}} } }$$ 3 where FC stands for Financing Constraints; DF stands for Digital Finance Index; \(Greenlnnov\) represents the level of green innovation in manufacturing companies; \(\sum Control\) represents control variables; \(\sum Industry\) and \(\sum Year\) imply industry and year dummy variables, respectively, which are used to eliminate the effects of industry differences and year differences on the empirical results. Table 8 Mechanism test results Variable (1) (2) WW Greenlnnov WW -0.03678*** (-3.20) DF -0.00464*** 0.00734*** (-4.63) (4.52) Constant 0.07562*** -0.23022*** (14.82) (-27.71) Controls Variables Y Y Industry FE Y Y Year FE Y Y R 2 19943 19943 N 0.854 0.174 Note: Levels of significance are indicated by ***(1%), **(5%), and *(10%). The fact that the coefficient of DF in column (1) is notably negative, suggests that digital finance relieves manufacturing firms’ financial burden. Digital finance not only opens financing channels but effectively reduces the cost of financing, which gives manufacturing companies the opportunity to obtain more abundant financial capital at a lower cost, effectively alleviating the pressure of financing restrictions encountered by manufacturing firms. In addition, column (2) of Table 8 displays a negative coefficient of financing constraints ( WW ), supporting Hypothesis 2 ’s validity. Digital finance can supply manufacturing organizations with abundant R&D funds by alleviating the pressure of financing constraints, stimulating companies to participate in green innovation activities, and gradually enhancing green innovation efficiency. 5.6 Heterogeneity test 5.6.1 Financial regional heterogeneity The Digital Finance Index was created together with the Hangzhou-based Ant Group Research Institute. Compared with manufacturing firms in other provinces, digital finance may preferentially benefit manufacturing firms in Zhejiang Province. So, we conduct a heterogeneity test based on financial region (whether the registration place of manufacturing firms is in Zhejiang Province or not) to determine whether there is a phenomenon of financial region heterogeneity in the impact of digital finance on the green innovation efficiency of manufacturing firms. Columns (1)-(2) of Table 9 report the impact of digital finance on the green innovation efficiency of manufacturing firms in Zhejiang Province; Columns (3)-(4) report the impact outside Zhejiang Province. The favorable role of digital finance in manufacturing firms’ ability to innovate sustainably is suggested by the fact that all of the DF coefficients are significantly positive. In addition, by comparing columns (1) with (3) and (2) with (4), it is not difficult to find that the coefficient of DF of the former is much larger than that of the latter, which suggests that there is a heterogeneous impact of the positive effect of digital finance on the effectiveness of green innovation in manufacturing firms. The reason is that Hangzhou’s relatively advanced digital finance development can prioritize the provision of high-quality and perfect financial services for manufacturing enterprises in the province, thus playing a greater role in enhancing the effectiveness of green innovation in manufacturing businesses. Table 9 Results of the financial regional heterogeneity test Variable (1) (2) (3) (4) Greenlnnov Greenlnnov Greenlnnov Greenlnnov DF 0.04804*** 0.04380*** 0.00971*** 0.00769*** (7.49) (7.01) (5.49) (4.40) Constant -0.03398 -0.25443*** 0.00270 -0.23387*** (-0.83) (-5.52) (0.72) (-26.78) Controls N Y N Y Industry FE Y Y Y Y Year FE Y Y Y Y R 2 0.151 0.216 0.097 0.176 N 2289 2289 17654 17654 Note: Levels of significance are indicated by ***(1%), **(5%), and *(10%). 5.6.2 Geographic heterogeneity Considering the diversity of China’s geography, there are obvious differences between different regions. For this reason, we conduct a heterogeneity test based on geographic region (whether the company is registered in the eastern region or not), which is used to determine whether there is a phenomenon of geographic region heterogeneity in the impact of digital finance on the green innovation efficiency of manufacturing firms. Columns (1)-(2) of Table 10 detail the impact of digital finance on manufacturing companies’ ability to innovate sustainably in the eastern region; Columns (3)-(4) report the effect in the Midwest. The favorable impact of digital finance on the manufacturing industry is likely defined by geographic regional heterogeneity, as indicated by the coefficient of DF being notably positive and being higher in the Midwest than the East. The pace of economic growth in the central and western regions is relatively slower than in the eastern regions, and there is a lack of a good financing environment to alleviate the pressure of financing constraints on manufacturing enterprises. However, digital finance can enhance the cross-regional flow of financial capital, and effectively compensate for the drawbacks of the traditional financial “capital barriers”, so that there is an opportunity to provide manufacturing enterprises with the much-needed financial capital for engaging in green innovation activities. Among these regions, the Midwest is where digital finance is most prominent. Table 10 Results of the geographic region heterogeneity test Variable East Midwest (1) (2) (3) (4) Greenlnnov Greenlnnov Greenlnnov Greenlnnov DF 0.00548** 0.00484** 0.01442*** 0.01270*** (2.39) (2.15) (4.14) (3.87) Constant 0.00051 -0.23163*** 0.00601 -0.23844*** (0.11) (-22.73) (1.02) (-16.57) Controls N Y N Y Industry FE Y Y Y Y Year FE Y Y Y Y R 2 0.109 0.174 0.074 0.191 N 14299 14299 5644 5644 Note: Levels of significance are indicated by ***(1%), **(5%), and *(10%). 6. Conclusions and recommendations 6.1 Conclusions It is imperative to realize green transformation as the Chinese economy has stepped up to high quality and avoid irreversible damage to the ecological environment as a result of the excessive pursuit of economic growth. Manufacturing enterprises are both creators of economic activities and polluters of the ecological environment, so they naturally must fulfill the responsibility of green production, and contribute to the transformation of the economy. Investigating the variables that affect the effectiveness of green innovation in manufacturing organizations is therefore important. This paper utilizes the sample of Chinese A-share manufacturing listed companies to study the effect of digital finance on the effectiveness of green innovation. The main findings are: (1) Digital finance effectively improves green innovation efficiency in manufacturing firms, and the conclusion persists after several robustness tests; (2) Mechanism analysis finds that digital finance can increase the effectiveness of green innovation in manufacturing enterprises by alleviating the burden of financing limitations; (3) The impact of digital finance on manufacturing firms is revealed by heterogeneity analysis to be characterized by financial regional and geographic regional heterogeneity, and the role is more pronounced in Zhejiang Province and the central and western regions. 6.2 Recommendations Grounded on these findings, this paper provides the following legislative suggestions in an effort to maximize the contribution of digital finance to increasing the effectiveness of green innovation in manufacturing firms. First, digital finance development should be accelerated. The voids left by traditional finance can be filled by digital finance, which can also broaden financing channels, and scope while significantly lowering financing costs. Therefore, digital financial institutions should actively explore and innovate in the scope of services, funding channels, financial products, etc., to optimize the spreading effects of digital finance, alleviate the pressure of financing restrictions faced by firms, and support enterprises in implementing green innovation activities and continuously improve their green innovation efficiency. Second, keep an eye on the variations in digital finance. The effect of digital finance is difference by financial region and geographic region heterogeneity, which indicates that there is variability in the service effect of digital finance. Therefore, regulators need to focus on the variations in how digital finance has developed and implement policies based on local conditions to continuously make digital financial services more efficient. Taking central and western regions as an example, the role of digital finance is more significant than it is in the eastern region, which means that the government can appropriately tilt its resources when formulating the macro digital finance development strategy to maximize its overall advantages and accelerate linkage effect of green innovation in different regions. Finally, there has to be more regulation of digital finance. Digital finance, as opposed to traditional finance, enables cross-regional movement of financial capital, optimizes the efficiency of financial capital allocation to a certain extent, and creates a brand-new possibility for the advancement of manufacturing enterprises. Moreover, digital finance implies gradual dilution of borders between regions and financial institutions, which may trigger systemic risks and cause immeasurable and serious consequences for the financial market, and therefore the regulators need to innovate the regulatory model, broaden the scope of supervision, and improve regulatory policies, with a view to coping with the potential risks of digital finance. 6.3 Limitations and research perspectives This work investigates the effect of digital finance on the green innovation efficiency of manufacturing firms from an input-output perspective, which is an instrumental addition to previous research, but it still has some limitations. First, limit to the availability of data, this paper only investigates how digital finance affects the efficiency of green innovation in listed manufacturing firms, and lacks observation of a sample of non-listed manufacturing companies. Second, the approximate substitution of R&D expenditures for green R&D expenditures to assess the effectiveness of green innovation in this paper may affect the robustness of the study’s conclusions, and it is therefore necessary to further enhance the measurement of green innovation efficiency. Finally, the paper tentatively investigates financing constraints as a mechanism of action, so future research can further expand mechanisms of action of digital finance in influencing the efficiency of innovation in manufacturing firms. Declarations Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Authors and Affiliations School of Economics, Tianjin University of Finance and Economics, Tianjin 300222, China Hongyu Lu School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China Zhao Cheng Author Contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Hongyu Lu and Zhao Cheng. The first draft of the manuscript was written by Hongyu Luand all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Corresponding Authors Correspondence to Hongyu Lu Ethical Approval Not applicable. Consent to Participate Not applicable. Consent for publication Not applicable. Availability of data and materials Available upon request by contacting the author. Conflict of interest The authors declare no competing interests. References Agarwal, S., Hauswald, R. (2010). Distance and private information in lending. Rev Financ Stud , 23(7), 2757-2788. https://doi.org/10.1093/rfs/hhq001 Alessandrini, P., Presbitero, A. F., Zazzaro, A. (2009). Banks, distances and firms' financing constraints. Int. Rev. Finance , 13(2), 261-307. https://doi.org/10.1093/rof/rfn010 Aghion, P., Howitt, P. (1990). 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How do the effects of local growth on employment rates vary with initial labor market conditions? https://dx.doi.org/10.2139/ssrn.1372814 Corradini, M., Costantini, V., Mancinelli, S., Mazzanti, M. (2014). Unveiling the dynamic relation between R&D and emission abatement: national and sectoral innovation perspectives from the EU. Ecol Econ , 102, 48-59. https://doi.org/10.1016/j.ecolecon.2014.03.017 Chong, T. T. L., Lu, L., Ongena, S. (2013). Does banking competition alleviate or worsen credit constraints faced by small-and medium-sized enterprises? Evidence from China. J Bank Financ , 37(9), 3412-3424. https://doi.org/10.1016/j.jbankfin.2013.05.006 Carrión-Flores, C. E., Innes, R. (2010). Environmental innovation and environmental performance. J Environ Econ Manage , 59(1), 27-42. https://doi.org/10.1016/j.jeem.2009.05.003 Chong, T. T. L., Lu, L., Ongena, S. (2013). Does banking competition alleviate or worsen credit constraints faced by small-and medium-sized enterprises? Evidence from China. J Bank Financ , 37(9), 3412-3424. https://doi.org/10.1016/j.jbankfin.2013.05.006 Degryse, H., Ongena, S. (2005). Distance, lending relationships, and competition. J Finance , 60(1), 231-266. https://doi.org/10.1111/j.1540-6261.2005.00729.x Feng, S., Zhang, R., Li, G. (2022). Environmental decentralization, digital finance and green technology innovation. Struct. Chang. Econ. Dyn. , 61, 70-83. https://doi.org/10.1016/j.strueco.2022.02.008 Guo, F., Wang, J., Wang, F., Kong, T., Zhang, X., Cheng, Z. (2020). Measuring China’s digital financial inclusion: Index compilation and spatial characteristics. China Economic Quarterly , 19(4), 1401-1418. (in Chinese) Gomber, P., Kauffman, R. J., Parker, C., Weber, B. W. (2018). On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. J Manag Inf Syst , 35(1), 220-265. https://doi.org/10.1080/07421222.2018.1440766 Hottenrott, H., Peters, B. (2012). Innovative capability and financing constraints for innovation: more money, more innovation? Rev Econ Stat , 94(4), 1126-1142. https://doi.org/10.1162/REST_a_00227 Huang, Y., Huang, Z. (2018). The development of digital finance in China: Present and future. China Economic Quarterly , 17(4), 205-218. (in Chinese) Huang, Z., Liao, G., Li, Z. (2019). Loaning scale and government subsidy for promoting green innovation. Technol Forecast Soc Change , 144, 148-156. https://doi.org/10.1016/j.techfore.2019.04.023 Hollander, S., Verriest, A. (2016). Bridging the gap: the design of bank loan contracts and distance. J financ econ , 119(2), 399-419. https://doi.org/10.1016/j.jfineco.2015.09.006 Hu, C., Mao, J., Tian, M., Wei, Y., Guo, L., Wang, Z. (2021). Distance matters: Investigating how geographic proximity to ENGOs triggers green innovation of heavy-polluting firms in China. J. Environ. Manage. , 279, 111542. https://doi.org/10.1016/j.jenvman.2020.111542 Jin, Y., Gao, X., Wang, M. (2021). The financing efficiency of listed energy conservation and environmental protection firms: evidence and implications for green finance in China. Energy Policy , 153, 112254. https://doi.org/10.1016/j.enpol.2021.112254 Kshetri, N. (2016). Big data’s role in expanding access to financial services in China. Int J Inf Manage , 36(3), 297-308. https://doi.org/10.1016/j.ijinfomgt.2015.11.014 Liu, J., Jiang, Y., Gan, S., He, L., Zhang, Q. (2022). Can digital finance promote corporate green innovation? ENVIRON SCI POLLUT R , 29(24), 35828-35840.https://doi.org/10.1007/s11356-022-18667-4 Li, G., Zhang, R., Feng, S., Wang, Y. (2022). Digital finance and sustainable development: Evidence from environmental inequality in China. Bus Strategy Environ , 31(7), 3574-3594. https://doi.org/10.1002/bse.3105 Lin, B., Ma, R. (2022). How does digital finance influence green technology innovation in China? Evidence from the financing constraints perspective. J. Environ. Manage. , 320, 115833.https://doi.org/10.1016/j.jenvman.2022.115833 Ozili, P. K. (2018). Impact of digital finance on financial inclusion and stability. Borsa Istanbul Rev. , 18(4), 329-340. https://doi.org/10.1016/j.bir.2017.12.003 Ouyang, X., Li, Q., Du, K. (2020). How does environmental regulation promote technological innovations in the industrial sector? Evidence from Chinese provincial panel data. Energy Policy , 139, 111310. https://doi.org/10.1016/j.enpol.2020.111310 Presbitero, A. F., Rabellotti, R. (2014). Geographical distance and moral hazard in microcredit: Evidence from Colombia. J Int Dev , 26(1), 91-108. https://doi.org/10.1002/jid.2901 Pan, Z., Liu, L., Bai, S., Ma, Q. (2021). Can the social trust promote corporate green innovation? Evidence from China. ENVIRON SCI POLLUT R , 28(37), 52157-52173. https://doi.org/10.1007/s11356-021-14293-8 Rennings, K. (2000). Redefining innovation—eco-innovation research and the contribution from ecological economics. ECOL ECON , 32(2), 319-332. https://doi.org/10.1016/S0921-8009(99)00112-3 Rao, S., Pan, Y., He, J., Shangguan, X. (2022). Digital finance and corporate green innovation: quantity or quality? ENVIRON SCI POLLUT R , 29(37), 56772-56791. https://doi.org/10.1007/s11356-022-19785-9 Tang, S., Wu, X., Zhu, J. (2020). Digital finance and enterprise technology innovation: Structural feature, mechanism identification and effect difference under financial supervision. Management World , 36(5), 52-66. (in Chinese) Whited, T. M., Wu, G. (2006). Financial constraints risk. Rev Financ Stud , 19(2), 531-559. https://doi.org/10.1093/rfs/hhj012 Wang, M., Li, X., Wang, S. (2021). Discovering research trends and opportunities of green finance and energy policy: A data-driven scientometric analysis. Energy Policy , 154, 112295. https://doi.org/10.1016/j.enpol.2021.112295 Yu, C. H., Wu, X., Zhang, D., Chen, S., Zhao, J. (2021). Demand for green finance: Resolving financing constraints on green innovation in China. Energy Policy , 153, 112255. https://doi.org/10.1016/j.enpol.2021.112255 Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2023 Read the published version in Environmental Science and Pollution Research → Version 1 posted Reviewers agreed at journal 07 Oct, 2023 Reviewers invited by journal 28 Aug, 2023 Editor invited by journal 28 Aug, 2023 Editor assigned by journal 18 Aug, 2023 First submitted to journal 15 Aug, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3258116","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":229422100,"identity":"4c0cf4ea-3dee-453c-8f4a-39a6643da0b3","order_by":0,"name":"Hongyu Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIie3QMQrCMBTG8RcEuwR0fCDUEwgJBV2kXsUg6KqbmwmFuPQAFQfP4OIcCejSA9TN4gW6CiLq6NSMgvnN359HAuB5P8lABUsMu4FSZeUSUDAkg3wY8dQmEbolQDKip0IWM92mLsmI5FxWTUvUptSAEIc9WXsl5yqjthF0hL7OYRL1TX0yuFG0TbIVa4ZgxMEh4cmDWQqXo0bqnMB4ilAQ18ScFio1Q8ZT8f5k5vCWILN7eX/iahecy7JaxmFtAvi1YHXzj5Z0WXme5/21F5DIRzkGapSKAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-6344-5356","institution":"Tianjin University of Finance and Economics","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Lu","suffix":""},{"id":229422101,"identity":"29d089e9-fa1b-4d6f-88c2-5ed9589ab09a","order_by":1,"name":"Zhao Cheng","email":"","orcid":"https://orcid.org/0009-0004-2882-3663","institution":"University of Science and Technology Beijing","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2023-08-12 12:10:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3258116/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3258116/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-023-31153-9","type":"published","date":"2023-11-28T15:01:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":47561251,"identity":"e017c763-c8bf-4dd9-ae54-6b9c67b2d79d","added_by":"auto","created_at":"2023-12-04 15:10:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":572902,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3258116/v1/d4b86c1c-5ac4-4962-87c7-f538cffe5380.pdf"}],"financialInterests":"","formattedTitle":"Digital finance and green innovation efficiency: Empirical data from Chinese listed manufacturing companies","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the past few decades, the manufacturing industry has made great contributions to the rapid growth of the Chinese economy. However, it brings a serious burden to the ecological environment at the same time. Thus, it is imperative to promote the manufacturing industry\u0026rsquo;s green innovation, which can not only effectively solve the problem of environmental pollution, but also promote green growth, thus providing a new way to the transformation of the Chinese economy (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn a market economy, manufacturing enterprises not only generate economic activities but also cause environmental pollution. Manufacturing enterprises in the production process will inevitably cause pollution and injury to the environment, so it is very crucial to coordinate the economy and the environment (Feng et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Manufacturing enterprises should have the courage to eliminate high energy-consuming and high-emission production processes, reduce the waste of production resources and ecological pollution, and force enterprises to realize green and low-carbon transformation. Meanwhile, manufacturing enterprises should increase the R\u0026amp;D of environmentally friendly technologies, processes, and equipment, accelerate the realization of green transformation, and improve resource utilization efficiency. Therefore, studying the factors that affect manufacturing companies\u0026rsquo; adoption of innovation is therefore essential for realizing high-quality development (Rao et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGreen innovation activities are full of risks including high costs, extended cycles, irreversibility, and unstable return on investment (Hottenrott \u0026amp; Peters, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Corradini et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and thus firms need sufficient capital to ensure the success of green innovation. In the past, traditional finance has influenced firms\u0026rsquo; innovation decisions (Hottenrott \u0026amp; Peters, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Chong et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). When enterprises obtain a certain degree of credit funds from banks, the continuous increase of credit funds helps to advance corporate green innovation (Huang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Yu et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) find that green finance improves firms' R\u0026amp;D capital investment by alleviating their financing constraints, which in turn increases participation in green innovation activities. The emerging digital financial business has largely replaced the traditional financial business, and the change in the financial service model has changed the investment and financing environment of enterprises, which will inevitably have an impact on their operation and R\u0026amp;D decisions (Ozili, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rao et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Unlike service-oriented firms, manufacturing firms need to acquire or lease the sites, equipment and materials needed for their production activities, and bear the risks associated with green innovation behaviors. This means that manufacturing firms will face severe financing constraints. So, research into how digital finance affects green innovation in manufacturing organizations is required. Theoretically, expanding the research field of digital finance and digging deeper into the effect of digital finance on micro subjects; practically, helping manufacturing enterprises to realize the importance of digital finance, so that they can make full use of digital finance to implement green innovation decisions and achieve sustainable development (Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur innovations are in the following three aspects. First, manufacturing enterprises will cause serious resource waste and environmental pollution problems in the production process, and green innovation can effectively solve the above problems. However, few kinds of literature explore green innovation behavior using manufacturing companies as a sample, which provides research ideas for this paper. Second, manufacturing enterprises engaging in green innovation activities face more severe financing constraints, while digital finance offers a more inclusive financing environment, being helpful to boost corporate green innovation. In this paper, we will reinterpret green innovation behaviors of manufacturing enterprises to broaden the research scope of digital finance. Third, existing studies mainly focus on corporate green innovation output (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Feng et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while few literature studies businesses\u0026rsquo; green innovation efficiency from an input-output perspective. This research focuses on green innovation efficiency to make the conclusions of this study more precise.\u003c/p\u003e \u003cp\u003eOther parts of this paper are structured as followed. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e systematically compiles and summarizes relevant studies. Research hypotheses on relevant theories and literature are displayed in Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Models and variables are explained in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows regression results. Finally, the conclusions.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Traditional finance and corporate green innovation\u003c/h2\u003e \u003cp\u003eIn recent decades, more countries have realized the importance of harmonizing links between social and environmental advancement. Enterprises, as a crucial part of the market economy, are also trying to explore circular, green and sustainable growth paths with a view to assuming the social responsibility of protecting the ecological environment. For this reason, a growing number of companies are engaging in green innovation activities. Firstly, green innovation activities increase resource utilization and reduce pollution emissions, which protects the ecological environment (Rennings, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Barbieri et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Secondly, green innovation activities have the attribute of \"innovation\", which improves production efficiency and creates intensive economic value through innovative production technologies and optimized factor combinations (Aghion \u0026amp; Howitt, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Aghion \u0026amp; Howitt, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, green innovation activities are full of risks such as high cost, long cycle, irreversibility, and unstable return on investment (Hottenrott \u0026amp; Peters, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Corradini et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and thus require ample financial support as a guarantee for R\u0026amp;D expenditures. In the context of the traditional financial model, banks are the main suppliers of financial capital (Jin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and their expansionary and competitive behaviors can directly affect firms' innovation decisions (Chong et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, existing literature holds different views on the effect of traditional finance on firms' innovation decisions. Huang et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) point out that after banks provide a certain level of loans to firms, increasing loans fosters green innovation in business. Yu et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) argue that green financial policies adopted by banks can effectively ease financing limitations, provide abundant financial security for R\u0026amp;D activities, and then lead to the enhancement of green innovation. Some studies believe that although the financial supply of banks will increase green innovation quantity, it cannot improve green innovation quality (Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), resulting in lower corporate green innovation efficiency, and even causing the waste and abuse of financial resources.\u003c/p\u003e \u003cp\u003eWe must recognize the role that traditional finance plays in advancing green technology, and the financial supply provided by banks does effectively alleviate corporate financing constraints, which finally improves green innovation (Huang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Meanwhile, we also need to dialectically view the drawbacks of traditional finance, such as information asymmetry caused by the waste and abuse of financial resources, which seriously affects the utilization efficiency of financing resources, and thus it\u0026rsquo;s important to consider alternative financial models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The effect of digital finance on corporate green innovation\u003c/h2\u003e \u003cp\u003eUnlike traditional finance, digital finance has a number of advantages, including reducing financing service spending, broadening financial service channels, and expanding financial service coverage (Kshetri, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Huang \u0026amp; Huang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which will undoubtedly become a potential opportunity for companies to make decisions on innovation. Moreover, the shortcomings of conventional financial information asymmetry will also be successfully addressed by digital finance (Rao et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Specifically, digital technology can help financial institutions grasp the operation, management, and business financial status (Berger et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Alessandrini et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hollander \u0026amp; Verriest, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which improves green innovation. Recent studies have focused on the effect of digital finance on green innovation output (Rao et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lin and Ma, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Feng et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and the conclusions of these studies all indicate that digital finance may significantly improve green innovation.\u003c/p\u003e \u003cp\u003eAt this stage, most of the articles focus on the relationship between digital finance and enterprises\u0026rsquo; innovation outputs, so there is a dearth of studies on the input-output efficiency perspective, which may lead to the abuse of financial resources (Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To fill this gap, this research investigates how digital finance affects the effectiveness of green innovation and takes efficiency into account when evaluating its impact.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research hypotheses","content":"\u003cp\u003eFinancial services are successfully made more affordable, more accessible, and more comprehensive thanks to digital finance (Kshetri, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Huang \u0026amp; Huang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Further, digital finance is having a huge impact on firms' green innovation decisions (Ozili, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rao et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Firms' green innovation activities are full of risks such as high costs, long lead times, irreversibility, and unstable returns on investment (Hottenrott \u0026amp; Peters, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Corradini et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which implies that firms must overcome financing constraints to have the opportunity to implement green innovation activities. However, manufacturing companies are at a relative disadvantage in terms of financing capability, leading to their weak willingness to participate in green innovation efforts. Digital finance can create new advantages for manufacturing companies. Firstly, digital finance directly provides financial support for companies, alleviating their financing constraints, and improving corporate green innovation efficiency. Secondly, digital finance can help enterprises grasp industry and market information in a comprehensive and timely manner, tap new opportunities for green development, prevent ineffective or inefficient green innovation activities due to information asymmetry and rapid changes in the market, and reduce sunk costs and risk of failure of green innovations, thus improving enterprise innovation efficiency. In light of this, this paper presents the Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003eManufacturing companies are much more efficient in implementing green innovations thanks to digital finance.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eUnlike traditional finance, digital finance effectively reduces financial services costs and provides financial security for manufacturing enterprises, enhancing the effectiveness of green innovation. There are three primary areas where digital finance is advantageous. First, reducing transportation costs. Transportation costs of search, including time, that firms incur in finding the right financial product, effort, and expenses involved in communicating with financial institutions (Degryse \u0026amp; Ongena, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Agarwal \u0026amp; Hauswald, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Affected by the manufacturing industry production characteristics, most of the enterprises are in a relatively remote geographical location, which will undoubtedly increase the cost of transportation, and digital finance enables manufacturing enterprises to complete online communication with financial institutions, signing and other matters, effectively reducing the cost of transportation to achieve cooperation. Second, it reduces information costs. The application of digital technology can help financial institutions keep abreast of the operational, managerial, and financial status of manufacturing firms (Berger et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Alessandrini et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hollander \u0026amp; Verriest, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), thus reducing the cost that financial institutions pay for obtaining information about firms. Third, reducing the cost of regulation. Digital finance can regulate corporate behavior in a timely manner with the help of digital means, avoiding the phenomenon of regulatory failure due to the geographical distance between the credit parties (Presbitero \u0026amp; Rabellotti, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This significantly lowers the cost of regulatory compliance for financial institutions and advances the effectiveness of financial regulation. Green innovation will be stimulated whenever the credit capital of manufacturing enterprises reaches a certain scale and continues to grow (Huang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As a result, we get Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003eBy relieving the burden of financial restrictions, digital finance boosts the innovation efficiency of manufacturing enterprises.\u003c/p\u003e \u003c/p\u003e"},{"header":"4. Research design","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Sample selection\u003c/h2\u003e \u003cp\u003eTo research digital finance\u0026rsquo;s effect on the green innovation efficiency of manufacturing companies, this paper matches the digital finance index of prefecture-level cities with A-share manufacturing listed businesses, and the sample period is 2011\u0026ndash;2021. This paper makes the following two notes on the selection of sample data: (1) Considering that the Digital Finance Index was initially measured in 2011 and the latest data period ends in 2021, the sample period of this paper is 2011\u0026ndash;2021; (2) Most existing studies use provincial-level digital finance data (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Feng et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which may bias the conclusions, so we adopt the index of prefectural-level cities.\u003c/p\u003e \u003cp\u003eOur data were processed in this paper as follows: (1) Companies with the trading status of ST, *ST and PT for the year were excluded; (2) Companies that have been delisted are excluded; (3) Samples in which the variables involved in the regression contained missing values were excluded. All of the variables involved in the regression were subjected to shrinking of the tails at the 1% level to eliminate the confounding influence of outliers. Our final sample includes 19943 observations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Definition of variables\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Dependent variable\u003c/h2\u003e \u003cp\u003eExisting literature usually uses patents amounts filed for innovations in green technology to measure innovation (Carri\u0026oacute;n-Flores \u0026amp; Innes, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Amore \u0026amp; Bennedsen, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rao et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Feng et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). They believe that enterprises have already demonstrated strong green innovation practice prowess in the process of applying for green technology invention patents, so the number of green technology invention patent applications is selected as a proxy for corporate green innovation. In fact, enterprises should consider their efforts to enhance green innovation efficiency activities from the input-output viewpoint, or else purely considering the output effect is prone to serious speculative behavior (Hu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). And thus, we calculate the green innovation efficiency utilizing the ratio of green innovation outputs to inputs to assess efficiency (Greenlnnov). Considering the unavailability of data on green innovation investment, we use R\u0026amp;D expenditures as an approximate proxy. The green innovation output equals the natural logarithm of the output of green inventions, utility models, and design patents filed plus one.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Independent variable\u003c/h2\u003e \u003cp\u003eFollowing the existing literature, we measure digital finance with the help of the \u0026ldquo;Peking University Digital Finance Index\u0026rdquo; (Guo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The author matches the digital finance index at the prefecture-level with listed companies to empirically test its effect on the manufacturing industry\u0026rsquo;s innovation efficiency. To remove the impact of the quantitative outline on the empirical results, this paper will be treated as a digital financial index that has been \u0026ldquo;divided by 100\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Mediating variables\u003c/h2\u003e \u003cp\u003eBy examining the mediating role of financial restrictions, we investigate the mechanism of digital finance on the effectiveness of innovation in manufacturing companies. Regarding the setting of the financing constraint (FC) indicator, this paper chooses Whited \u0026amp; Wu\u0026rsquo;s (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) WW index to measure it.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e4.2.4 Control variables\u003c/h2\u003e \u003cp\u003eFollowing Tang et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Ouyang et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and Pan et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we use company size (Size), leverage ratio (Lev), return on assets (ROA), fixed asset stock ratio (Fixed), operating income (Growth), board size (Board), listing period (ListAge), and audit unit type (Big4) as control variables. Growth), Board Size (Board), Listing Age (ListAge), and Audit Unit Type (Big4) as control variables. Variable definitions are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable definitions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariable definition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen innovation efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eln (Total patent applications for green technology inventions\u0026thinsp;+\u0026thinsp;1)/R\u0026amp;D expenditures\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital finance index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeking University Digital Finance Index (prefecture-level city level)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediary variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinancing constraints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference to Whited \u0026amp; Wu (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eControl variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompany size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eln(Total assets\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGearing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal liabilities/Total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNet profit margin on total assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNet profit/Total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed asset ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNet fixed assets/Total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRevenue growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrowth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrowth rate of current year's operating income relative to previous year's operating income\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoard size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eln(Number of Board of Directors\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of years listed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eListAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eln (Years of listing\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of audit unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBig4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 if an international Big 4 accounting firm was hired in the year, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Empirical models\u003c/h2\u003e \u003cp\u003eTo investigate how digital finance affects the green innovation efficiency of manufacturing companies, we adopt model (1) for estimation:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$GreenInno{v_{it}}={\\alpha _0}+{\\alpha _1}D{F_{it}}+\\sum {{\\alpha _i}Contro{l_{it}}+} \\sum {Industry+} \\sum {Year+{\\upvarepsilon _{it}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eGreenlnnov\u003c/em\u003e is the green innovation efficiency of manufacturing companies, \u003cem\u003eDF\u003c/em\u003e is Digital Finance Index, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum Control\\)\u003c/span\u003e\u003c/span\u003e represents the group of control variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum Industry\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum Year\\)\u003c/span\u003e\u003c/span\u003e stands for industry and year dummy variables, respectively, which are used to eliminate the effects of industry differences and year differences on the empirical results.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Empirical findings","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we show descriptive data, including mean, standard deviation, maximum and minimum values.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of relevant variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS. D\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIXED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eListAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBig4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-7.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Baseline results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows our benchmark results. In column (1), it is clear that the coefficient of \u003cem\u003eDF\u003c/em\u003e is considerably positive at the 1% level, demonstrating that the development of digital finance favorably influences the effectiveness of green innovation in manufacturing firms, and Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is valid. When control variables are added in column (2), the coefficient of \u003cem\u003eDF\u003c/em\u003e still shows a substantial positive value Digital finance provides manufacturing enterprises with more abundant financial support and effectively alleviates the pressure of financing constraints, allowing them to enhance R\u0026amp;D spending and continuously boost the effectiveness of green innovation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBenchmark results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00906***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00751***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01056***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(28.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01387***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02650***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFIXED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01097***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-4.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGrowth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00441***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-5.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBoard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00773***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eListAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00530***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-12.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBig4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00876***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.23301***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-28.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Levels of significance are indicated by ***(1%), **(5%), and *(10%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Endogeneity test\u003c/h2\u003e \u003cp\u003eIn the benchmark regression section, we attempt to account for time effects, industry effects, and factors affecting manufacturing businesses\u0026rsquo; ability to innovate sustainably, but there are still potential endogeneity risks. Therefore, we use IV-2SLS regressions to address the risk of latent endogeneity in empirical studies. The following two conditions need to be met to select the appropriate instrumental variable: (1) Instrumental variables need to be correlated with the independent variables; (2) Instrumental variables need to be independent of the disturbance term. We refer to Bartik (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and construct the \u0026ldquo;Bartik instrument\u0026rdquo; (the first-order difference in time DDF of the financial inclusion index and the one-period lagged digital finance index LDF, denoted by LDF*DDF), and then conduct instrumental variable estimation. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the findings. The value of Cragg-Donald Wald F is 10373.5, larger than the empirical threshold (10% level) of 16.38, which indicates that there is no risk of \u0026ldquo;under-recognition\u0026rdquo; of the selected instrumental variables, according to the weak instrumental variables test.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInstrumental variable test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst-stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecond-stage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLDF*DDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33955***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(101.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00814***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.19740***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.27360***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(64.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-19.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCragg-Donald Wald F statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10373.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Levels of significance are indicated by ***(1%), **(5%), and *(10%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe coefficient of the \u003cem\u003eIV (LDF*DDF)\u003c/em\u003e shows a substantial positive value, implying that the \u003cem\u003eIV (LDF*DDF)\u003c/em\u003e and digital finance (DF) are positively corrected, as shown by the first-stage regression findings. According to second-stage regression, which is in accordance with the baseline findings, digital finance significantly enhances the green innovation efficiency (Greenlnnov) of manufacturing enterprises.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Robustness tests\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e5.4.1 Replacement of models\u003c/h2\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it is not difficult to find that the explanatory variables (Greenlnnov) contain many zero values, and the existence of these special values may affect the accuracy of the benchmark regression, so we choose the Probit model to replace the OLS model. The results are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. After replacing the model, we find that the coefficient of \u003cem\u003eDF\u003c/em\u003e remains positive, implying the baseline results are reliable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReplacement model results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15462***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15701***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.18729***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.47915***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-10.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-24.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Levels of significance are indicated by ***(1%), **(5%), and *(10%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e5.4.2 Deletion of samples\u003c/h2\u003e \u003cp\u003e(1) Deletion of the financial volatility sample\u003c/p\u003e \u003cp\u003eBoth firms\u0026rsquo; green innovation behavior and digital finance are affected by financial shocks on a global scale, so ignoring the volatility of financial markets may bias the study\u0026rsquo;s conclusions to some extent. In our sample period, 2015\u0026ndash;2017 is the stage of China\u0026rsquo;s stock market abnormal volatility, so we try to remove the sample in this stage. The outcomes are displayed in columns (1)-(2) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The coefficient on \u003cem\u003eDF\u003c/em\u003e remains positive after the financial market volatility sample is taken out, supporting the robustness of the results.\u003c/p\u003e \u003cp\u003e(2) Deletion of new crown outbreak samples\u003c/p\u003e \u003cp\u003eIn late 2019, novel coronaviruses began to spread globally, causing untold economic damage worldwide. Meanwhile, digital financial development and the daily business activities of enterprises have been seriously affected. For this reason, we delete the 2020\u0026ndash;2021 sample, and the outcomes are disclosed in columns (3)-(4) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. After deleting the New Crown epidemic sample, we find that the coefficient also remains significantly positive at the 1% level, suggesting that digital finance continues to positively impact manufacturing firms' capacity for green innovation even when the COVID-19 epidemic is taken into account.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeletion of sample results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00884***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00737***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01116***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00890***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.22916***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.24607***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-24.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-24.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Levels of significance are indicated by ***(1%), **(5%), and *(10%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e5.4.3 Substitution of variables\u003c/h2\u003e \u003cp\u003eThe Digital Finance Index is grounded on three dimensions of Breadth of Digital Finance Coverage (Breadth), Depth of Digital Finance Usage (Depth), and Degree of Digitization of Financial Inclusion (Digital). Considering that all these dimensions will affect the green innovation efficiency of manufacturing organizations to some extent, we decided to replace the independent variable \u003cem\u003eDF\u003c/em\u003e with the dimension index. We next performed a robustness test, whose results can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The coefficients of \u003cem\u003eBreadth\u003c/em\u003e, \u003cem\u003eDepth\u003c/em\u003e, and \u003cem\u003eDigital\u003c/em\u003e are significantly positive, suggesting that all the dimensions of \u003cem\u003eDF\u003c/em\u003e will positively affect the effectiveness of the green innovation in manufacturing companies, reaffirming that the findings are robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of substitution of variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBreadth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00815***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00694***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(6.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00360***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00279**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00264***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00237**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.23322***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00634*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.22946***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00690*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.22941***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-28.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-27.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-27.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Levels of significance are indicated by ***(1%), **(5%), and *(10%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Mechanism testing\u003c/h2\u003e \u003cp\u003eTo investigate how digital finance influences the green innovation efficiency of manufacturing businesses, this paper adopts the model (2) and (3) for estimation:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$F{C_{it}}={\\beta _0}+{\\beta _1}D{F_{it}}+\\sum {{\\beta _i}Contro{l_{it}}+\\sum {Industry} +\\sum {Year+{\\delta _{it}}} }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$GreenInno{v_{it}}=φ +{φ _1}D{F_{it}}+{\\gamma _2}F{C_{it}}+\\sum {{φ _i}Contro{l_{it}}+\\sum {Industry+\\sum {Year+{\\sigma _{it}}} } }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eFC\u003c/em\u003e stands for Financing Constraints; \u003cem\u003eDF\u003c/em\u003e stands for Digital Finance Index; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Greenlnnov\\)\u003c/span\u003e\u003c/span\u003e represents the level of green innovation in manufacturing companies; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum Control\\)\u003c/span\u003e\u003c/span\u003e represents control variables; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum Industry\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum Year\\)\u003c/span\u003e\u003c/span\u003e imply industry and year dummy variables, respectively, which are used to eliminate the effects of industry differences and year differences on the empirical results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03678***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00464***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00734***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07562***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.23022***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(14.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-27.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Levels of significance are indicated by ***(1%), **(5%), and *(10%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe fact that the coefficient of \u003cem\u003eDF\u003c/em\u003e in column (1) is notably negative, suggests that digital finance relieves manufacturing firms\u0026rsquo; financial burden. Digital finance not only opens financing channels but effectively reduces the cost of financing, which gives manufacturing companies the opportunity to obtain more abundant financial capital at a lower cost, effectively alleviating the pressure of financing restrictions encountered by manufacturing firms. In addition, column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e displays a negative coefficient of financing constraints (\u003cem\u003eWW\u003c/em\u003e), supporting Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026rsquo;s validity. Digital finance can supply manufacturing organizations with abundant R\u0026amp;D funds by alleviating the pressure of financing constraints, stimulating companies to participate in green innovation activities, and gradually enhancing green innovation efficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Heterogeneity test\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e5.6.1 Financial regional heterogeneity\u003c/h2\u003e \u003cp\u003eThe Digital Finance Index was created together with the Hangzhou-based Ant Group Research Institute. Compared with manufacturing firms in other provinces, digital finance may preferentially benefit manufacturing firms in Zhejiang Province. So, we conduct a heterogeneity test based on financial region (whether the registration place of manufacturing firms is in Zhejiang Province or not) to determine whether there is a phenomenon of financial region heterogeneity in the impact of digital finance on the green innovation efficiency of manufacturing firms. Columns (1)-(2) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e report the impact of digital finance on the green innovation efficiency of manufacturing firms in Zhejiang Province; Columns (3)-(4) report the impact outside Zhejiang Province. The favorable role of digital finance in manufacturing firms\u0026rsquo; ability to innovate sustainably is suggested by the fact that all of the \u003cem\u003eDF\u003c/em\u003e coefficients are significantly positive. In addition, by comparing columns (1) with (3) and (2) with (4), it is not difficult to find that the coefficient of \u003cem\u003eDF\u003c/em\u003e of the former is much larger than that of the latter, which suggests that there is a heterogeneous impact of the positive effect of digital finance on the effectiveness of green innovation in manufacturing firms. The reason is that Hangzhou\u0026rsquo;s relatively advanced digital finance development can prioritize the provision of high-quality and perfect financial services for manufacturing enterprises in the province, thus playing a greater role in enhancing the effectiveness of green innovation in manufacturing businesses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the financial regional heterogeneity test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04804***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04380***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00971***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00769***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(7.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(7.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.25443***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.23387***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-5.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-26.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Levels of significance are indicated by ***(1%), **(5%), and *(10%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e5.6.2 Geographic heterogeneity\u003c/h2\u003e \u003cp\u003eConsidering the diversity of China\u0026rsquo;s geography, there are obvious differences between different regions. For this reason, we conduct a heterogeneity test based on geographic region (whether the company is registered in the eastern region or not), which is used to determine whether there is a phenomenon of geographic region heterogeneity in the impact of digital finance on the green innovation efficiency of manufacturing firms. Columns (1)-(2) of Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e detail the impact of digital finance on manufacturing companies\u0026rsquo; ability to innovate sustainably in the eastern region; Columns (3)-(4) report the effect in the Midwest. The favorable impact of digital finance on the manufacturing industry is likely defined by geographic regional heterogeneity, as indicated by the coefficient of \u003cem\u003eDF\u003c/em\u003e being notably positive and being higher in the Midwest than the East. The pace of economic growth in the central and western regions is relatively slower than in the eastern regions, and there is a lack of a good financing environment to alleviate the pressure of financing constraints on manufacturing enterprises. However, digital finance can enhance the cross-regional flow of financial capital, and effectively compensate for the drawbacks of the traditional financial \u0026ldquo;capital barriers\u0026rdquo;, so that there is an opportunity to provide manufacturing enterprises with the much-needed financial capital for engaging in green innovation activities. Among these regions, the Midwest is where digital finance is most prominent.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the geographic region heterogeneity test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMidwest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGreenlnnov\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00548**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00484**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01442***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01270***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.23163***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.23844***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-22.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-16.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Levels of significance are indicated by ***(1%), **(5%), and *(10%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6. Conclusions and recommendations","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Conclusions\u003c/h2\u003e \u003cp\u003eIt is imperative to realize green transformation as the Chinese economy has stepped up to high quality and avoid irreversible damage to the ecological environment as a result of the excessive pursuit of economic growth. Manufacturing enterprises are both creators of economic activities and polluters of the ecological environment, so they naturally must fulfill the responsibility of green production, and contribute to the transformation of the economy. Investigating the variables that affect the effectiveness of green innovation in manufacturing organizations is therefore important. This paper utilizes the sample of Chinese A-share manufacturing listed companies to study the effect of digital finance on the effectiveness of green innovation. The main findings are: (1) Digital finance effectively improves green innovation efficiency in manufacturing firms, and the conclusion persists after several robustness tests; (2) Mechanism analysis finds that digital finance can increase the effectiveness of green innovation in manufacturing enterprises by alleviating the burden of financing limitations; (3) The impact of digital finance on manufacturing firms is revealed by heterogeneity analysis to be characterized by financial regional and geographic regional heterogeneity, and the role is more pronounced in Zhejiang Province and the central and western regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Recommendations\u003c/h2\u003e \u003cp\u003eGrounded on these findings, this paper provides the following legislative suggestions in an effort to maximize the contribution of digital finance to increasing the effectiveness of green innovation in manufacturing firms. First, digital finance development should be accelerated. The voids left by traditional finance can be filled by digital finance, which can also broaden financing channels, and scope while significantly lowering financing costs. Therefore, digital financial institutions should actively explore and innovate in the scope of services, funding channels, financial products, etc., to optimize the spreading effects of digital finance, alleviate the pressure of financing restrictions faced by firms, and support enterprises in implementing green innovation activities and continuously improve their green innovation efficiency. Second, keep an eye on the variations in digital finance. The effect of digital finance is difference by financial region and geographic region heterogeneity, which indicates that there is variability in the service effect of digital finance. Therefore, regulators need to focus on the variations in how digital finance has developed and implement policies based on local conditions to continuously make digital financial services more efficient. Taking central and western regions as an example, the role of digital finance is more significant than it is in the eastern region, which means that the government can appropriately tilt its resources when formulating the macro digital finance development strategy to maximize its overall advantages and accelerate linkage effect of green innovation in different regions. Finally, there has to be more regulation of digital finance. Digital finance, as opposed to traditional finance, enables cross-regional movement of financial capital, optimizes the efficiency of financial capital allocation to a certain extent, and creates a brand-new possibility for the advancement of manufacturing enterprises. Moreover, digital finance implies gradual dilution of borders between regions and financial institutions, which may trigger systemic risks and cause immeasurable and serious consequences for the financial market, and therefore the regulators need to innovate the regulatory model, broaden the scope of supervision, and improve regulatory policies, with a view to coping with the potential risks of digital finance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Limitations and research perspectives\u003c/h2\u003e \u003cp\u003eThis work investigates the effect of digital finance on the green innovation efficiency of manufacturing firms from an input-output perspective, which is an instrumental addition to previous research, but it still has some limitations. First, limit to the availability of data, this paper only investigates how digital finance affects the efficiency of green innovation in listed manufacturing firms, and lacks observation of a sample of non-listed manufacturing companies. Second, the approximate substitution of R\u0026amp;D expenditures for green R\u0026amp;D expenditures to assess the effectiveness of green innovation in this paper may affect the robustness of the study\u0026rsquo;s conclusions, and it is therefore necessary to further enhance the measurement of green innovation efficiency. Finally, the paper tentatively investigates financing constraints as a mechanism of action, so future research can further expand mechanisms of action of digital finance in influencing the efficiency of innovation in manufacturing firms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSchool of Economics, Tianjin University of Finance and Economics, Tianjin 300222, China\u003c/p\u003e\n\u003cp\u003eHongyu Lu\u003c/p\u003e\n\u003cp\u003eSchool of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China\u003c/p\u003e\n\u003cp\u003eZhao Cheng\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Hongyu Lu and Zhao Cheng. The first draft of the manuscript was written by Hongyu Luand all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to\u0026nbsp;Hongyu Lu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAvailable upon request by contacting the author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgarwal, S., Hauswald, R. 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H., Wu, X., Zhang, D., Chen, S., Zhao, J. (2021). Demand for green finance: Resolving financing constraints on green innovation in China. \u003cem\u003eEnergy Policy\u003c/em\u003e, 153, 112255. https://doi.org/10.1016/j.enpol.2021.112255\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Digital finance, Green innovation efficiency, Financing constraints, Manufacturing enterprises","lastPublishedDoi":"10.21203/rs.3.rs-3258116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3258116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEmploying Chinese A-share listed manufacturing businesses between 2011 and 2021, this research conducts an empirical analysis to study the effect of digital finance on corporate green innovation efficiency. Our discoveries suggest that digital finance improves manufacturing firms\u0026rsquo; green innovation efficiency. After a few robustness tests, our results are still accurate. The effect is more pronounced in Zhejiang Province and central and western regions. According to a mechanism analysis, digital finance increases the effectiveness of green innovation by removing financing constraints. The findings offer policy suggestions for manufacturing companies to implement green innovation and improve its efficiency. Firstly, digital finance development should be accelerated. Secondly, keep an eye on the variations in digital finance. Finally, there has to be more regulation of digital finance.\u003c/p\u003e","manuscriptTitle":"Digital finance and green innovation efficiency: Empirical data from Chinese listed manufacturing companies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-31 12:11:43","doi":"10.21203/rs.3.rs-3258116/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2023-10-07T06:34:48+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-08-28T16:54:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2023-08-28T12:43:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-08-18T05:12:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2023-08-15T08:44:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"28f5c8ea-c9c6-4a8e-aac7-18cfccb9d1a9","owner":[],"postedDate":"August 31st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-12-04T15:05:48+00:00","versionOfRecord":{"articleIdentity":"rs-3258116","link":"https://doi.org/10.1007/s11356-023-31153-9","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2023-11-28 15:01:08","publishedOnDateReadable":"November 28th, 2023"},"versionCreatedAt":"2023-08-31 12:11:43","video":"","vorDoi":"10.1007/s11356-023-31153-9","vorDoiUrl":"https://doi.org/10.1007/s11356-023-31153-9","workflowStages":[]},"version":"v1","identity":"rs-3258116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3258116","identity":"rs-3258116","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}