Carrot or Stick? Green Finance, Regulatory Distance and Green Total Factor Productivity of Manufacturing Enterprises

Carrot or Stick? 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Green Finance, Regulatory Distance and Green Total Factor Productivity of Manufacturing Enterprises Liang Zhang, Yong Qiu, Kunlin Guo, Guiqing Wu, Chao Xie, Yufeng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6963369/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract Green finance represents a transformative policy innovation designed to incentivize corporate environmental stewardship and accelerate sustainable economic development. However, the effectiveness of green finance in enhancing firm-level environmental performance remains underexplored, particularly considering the role of regulatory oversight. This study investigates how green finance policies impact Enterprise Green Total Factor Productivity (EGTFP), incorporating the moderating effect of regulatory distance. Using a Difference-in-Differences (DID) approach and the 2017 Green Finance Reform and Innovation Pilot Zones in China as a quasi-natural experiment, we analyze A-share listed manufacturing firms from 2010 to 2022. Our findings reveal that green finance initiatives significantly boost EGTFP, especially among firms with environmentally-experienced executives, state-owned ownership, heavy pollution profiles, and those operating in more economically developed, market-oriented, and digitally inclusive regions. Mechanism analyses indicate that green finance policies foster green technological innovation, mitigate financing constraints, and promote environmental investments, thereby internalizing environmental costs. Furthermore, greater regulatory distance weakens the positive effects of green finance on firms' green productivity. These results highlight the necessity of bridging financial instruments and environmental regulatory frameworks to maximize the effectiveness of green finance. Strengthening coordination between financial institutions and regulatory authorities is crucial to advancing the green transformation of the manufacturing sector. Green Finance Environmental Regulation Enterprise Green Total Factor Productivity Regulatory Distance Green Technological Innovation Manufacturing Firms Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Since the late 20th century, rapid industrialization and urbanization have led to severe problems such as over-exploitation of resources and environmental pollution. In the context of global climate change and ecological crises, green development has increasingly become a central issue for governments and enterprises worldwide [ 1 , 2 ]. As primary participants in economic activities, enterprises are responsible for improving energy efficiency and reducing resource consumption and pollution through the adoption of green technologies and management practices [ 3 , 4 ]. Green development seeks to enhance enterprise performance while ensuring environmental protection [ 3 , 5 ]. Existing research suggests that Enterprise Green Total Factor Productivity (EGTFP) is a key metric for assessing the dual benefits of economic and environmental outcomes [ 6 , 7 ]. EGTFP not only focuses on economic output but also incorporates resource consumption and environmental impact, reflecting an enterprise's ability to reduce pollution and resource use while pursuing economic growth [ 8 , 9 ]. Enhancing EGTFP is a crucial pathway to achieving a green economic transformation [ 10 , 11 ]. Among the various policy tools designed to promote enterprise green transformation, green finance has emerged as an effective approach. Green finance involves channeling capital into green, environmentally friendly, and low-carbon projects and enterprises through financial markets. This process fosters the innovation and application of green technologies while promoting the economic transition to a green economy. By utilizing green finance, governments can direct more social capital into the environmental sector, supporting enterprises with green investments and technological R&D, thus enhancing environmental performance [ 12 , 13 ]. This is particularly important in developing economies, where resource and environmental constraints are more pronounced. These countries often face the dual challenges of environmental pollution and resource depletion as they pursue economic growth [ 14 , 15 ]. Green finance policies can provide financial support for environmental governance, helping enterprises address the shortage of environmental funds [ 16 , 17 ]. Furthermore, the green finance policies can incentivize enterprises to adjust their production processes and operations, reducing environmental burdens and driving the development of a green, low-carbon economy[ 18 , 19 ]. Despite the important role of green finance policies in promoting enterprise green transformation and improving EGTFP [ 20 , 21 ], existing research primarily focuses on the macroeconomic aspect of green finance policies [ 22 , 23 ], with limited systematic investigation at the micro-enterprise level regarding how these policies specifically influence EGTFP. The effectiveness of the green finance policies can be constrained and influenced by various factors, including industry characteristics, enterprise size, market environment, and regional regulatory intensity [ 24 , 25 ]. Censequently, there is considerable heterogeneity in how different enterprises respond to green finance policies, a topic that has not been sufficiently addressed in the existing literature. To fill this gap, this study selects Chinese A-share-listed manufacturing enterprises from 2010 to 2022 as the research subjects and uses a Difference-in-Differences (DID) model to analyze the effectiveness of green finance policies. By leveraging the Green Finance Reform and Innovation Pilot Zone policy introduced in 2017, the DID model compares the performance of enterprises impacted by the policy with those unaffected, effectively controlling for time trends and other potential confounding factors to isolate the net effect of the policy. The study evaluates the impact of green finance policies on EGTFP and further examines the policy effects in different contexts through heterogeneity analysis. In addition to analyzing the direct impact of green finance policies on EGTFP, this research also investigates the mechanisms through which intermediary variables such as enterprise green technological innovation capabilities, financing constraints, and environmental regulation play between green finance policies and EGTFP. Green technological innovation capabilities are essential drivers for improving EGTFP; through technological R&D and innovation, enterprises can enhance resource utilization efficiency and reduce pollution emissions, thereby improving their environmental performance. Financing constraints present significant obstacles to implementing green technological innovations, and green finance policies can alleviate these constraints by providing financial support, thereby fostering the development and application of green technologies. Environmental regulation, as an external governance mechanism, also plays a key role in the green transformation process by constraining and supervising enterprise environmental behaviors. By examining the impacts of these intermediary variables, this study aims to reveal the pathways through which green finance policies infulence EGTFP. In comparison to prior research, this study makes substantial contributions in three key areas: (1) The study uses a Difference-in-Differences (DID) model to analyze the effectiveness of green finance policies among Chinese A-share listed manufacturing enterprises from 2010 to 2022. The DID model is widely used to examine the impact of macro policies on firms. However, this study applies it to green finance policies. It provides a thorough empirical analysis of their effects on both economic and environmental outcomes. This approach offers a solid quantitative framework for evaluating the impacts of these policies. It complements existing studies and deepens our understanding of their effectiveness. (2) The study integrates green finance policies and environmental green total factor productivity into a single analytical framework. It conducts a heterogeneity analysis at the enterprise, industry, and provincial levels. The analysis reveals how the effects of green finance policies differ depending on the context. This provides multidimensional empirical evidence that can help guide the implementation of green finance strategies. By addressing these contextual differences, the study fills a gap in the literature regarding the varied impacts of green finance across sectors and regions. (3) The study investigates the micro-mechanisms through which green finance policies enhance green total factor productivity. It identifies key drivers such as: (i) improving green technological innovation in enterprises, (ii) increasing financing constraints, and (iii) strengthening environmental regulations. The research clarifies the operational logic of green finance policies at the micro level. It offers valuable theoretical insights and provides evidence that supports the refinement of green finance policies to foster high-quality green economic transformation. 2. Institutional Background and Hypothesis Development 2.1 Institutional Background The Green Finance Reform and Innovation Pilot Zones in China represent a significant initiative to explore the use of financial tools for environmental governance. The main objective is to support ecological improvement, resource conservation, and efficient utilization, thereby promoting the green transformation of the economy [ 26 , 27 ]. In August 2016, the People's Bank of China, the Ministry of Finance, and five other ministries issued the "Guidelines on Establishing a Green Financial System," providing strategic guidance for the development of green finance. Then, in June 2017, the State Council approved the establishment of the first group of Green Finance Reform and Innovation Pilot Zones in selected regions, including Zhejiang, Guangdong, Guizhou, Jiangxi, and Xinjiang. Gansu joined in 2019, followed by Chongqing in 2022. The primary goal of these pilot zones is to enchance financial support for ecological improvement, resource conservation, and efficient utilization. They aim to offer replicable and scalable experiences to assist China’s green economic transition [ 26 , 28 ]. The policy features include: (1) Pilot regions selected by the government, characterized by differentiation and representativeness. These regions, located in the eastern, central, and western parts of China, are chosen to reflect different stages of economic development, spatial layouts, and resource endowments. This ensures that the pilot zones are representative in terms of regional distribution and industrial characteristics. (2) Encouraging financial institutions to establish departments and green branches within the pilot zones. This involves setting standardized business processes, which enhance the professionalism and standardization of green financial services. It also strengthens the ability of financial institutions to support green projects. (3) Increasing the proportion of green finance tools in total loan volumes. Financial institutions are encouraged to increase green credit disbursement, support certified green enterprises through green bond financing, and develop alternative green financial tools, such as carbon futures and carbon options. This helps expand financing channels for green enterprises. (4) Establishing a public environmental rights trading platform. This includes creating market platforms for trading water rights, pollution rights, energy usage rights, and carbon emission rights. Additionally, an information-sharing mechanism is to be established to address information asymmetry and facilitate the open and fair trading of environmental rights. These policy arrangements create favorable conditions for treating the Green Finance Reform and Innovation Pilot Zones as a quasi-natural experiment for the development of green finance. Firstly, the pilot zones are located in the eastern, central, and western regions, each with distinct levels of economic development and industrial structures. This ensures both differentiation and representativeness. Secondly, while the zones are uniformly planned by the government, each region has its own specific focus in implementation. This blend of uniformity and diversity in policy design creates a natural experiment-like environment, which helps identify the causal effects of the policy. Thirdly, the gradual rollout of the policy and the staggered approval process across regions provide temporal and spatial variations. This variation facilitates the use of quasi-experimental methods for evaluating the policy's impact. 2.2 Hypothesis Development This paper examines the indirect pathways through which green finance enhances the green total factor productivity of manufacturing enterprises from five perspectives: green technological innovation, financing constraints, the scale of environmental investments, financial mismatch mechanisms, and regulatory effects. 2.2.1 Green Technological Innovation Green technological innovation plays a key role in enhancing Enterprise Green Total Factor Productivity (EGTFP) [ 29 ]. Green finance policies influence EGTFP through three main pathways: Firstly, these policies provide targeted financial support, such as green credit, green bonds, and green funds, which reduce the financial barriers for enterprises to invest in green technological innovation. Access to such funding allows enterprises to undertake high-risk, high-cost R&D in green technologies, facilitating the development and application of new environmental technologies [ 30 , 31 ]. Secondly, green finance policies send market signals that encourage investors and consumers to consider an enterprise's environmental performance. This increases reputational pressure on firms, motivating them to invest more in green technology R&D and promoting the diffusion and industrialization of green technologies [ 32 , 33 ]. Additionally, these signals enhance firms' ability to attract top talent and foster research collaborations, accelerating green technological innovation [ 34 , 35 ]. Lastly, green finance policies encourage enterprises to join green technology alliances and collaborate on R&D, promoting knowledge sharing and technology transfer. Such cooperation helps reduce R&D costs and risks, improving both innovation efficiency and the quality of outcomes. This, in turn, enhances the green technological innovation capabilities of enterprises. Numerous studies confirm that technological innovation can significantly boost total factor productivity [ 36 , 37 ]. Therefore, the development of green finance can improve EGTFP by fostering green technological innovation. 2.2.2 Financing Constraints Green finance policies impose stricter financing conditions on high-pollution, high-emission manufacturing enterprises, increasing their financing difficulties and costs [ 38 , 39 ]. Specifically, green finance pilot policies require financial institutions to enforce strict credit thresholds, making environmental protection and governance performance a key criterion for loan approval [ 40 , 41 ]. Manufacturing enterprises, due to their high energy consumption and emissions, are often viewed as heavy polluters[ 42 , 43 ]. As a result, these enterprises face higher financing thresholds and costs, which exacerbate their financing constraints. With limited access to financing, heavily polluting enterprises are forced to improve their environmental performance in order to alleviate these constraints. This pressure encourages them to increase investments in green technologies [ 44 ], which enhances their green innovation capabilities, enables them to meet the requirements of green finance policies, and helps reduce financing costs, thereby securing financial support [ 45 ]. Thus, by intensifying external financing constraints, green finance policies indirectly compel heavily polluting enterprises to undergo green transformation and technological upgrades, ultimately enhancing their EGTFP. 2.2.3 Financial Misalignment Mechanism The misallocation of financial resources occurs when environmental risks are not effectively identified and managed during capital allocation. This leads to more capital flowing toward high-pollution, high-energy industries instead of environmentally friendly ones. This misallocation not only worsens the inefficient use of resources but also limits financial support for innovation and sustainable development, ultimately reducing the green total factor productivity (EGTFP) of the economy. Financial misalignment weakens the investment and dissemination of green technologies, causing green industries to face financing difficulties and high costs, thereby hindering the development and transformation of the green economy [ 46 , 47 ]. Green finance policies enhance EGTFP by optimizing resource allocation and directing funds toward environmentally sustainable industries [ 48 , 49 ]. Specifically, financial instruments such as green credit, green bonds, and green funds help correct the market’s overinvestment in high-pollution, high-energy industries, reducing resource misallocation and promoting the green transformation of the industrial structure [ 50 , 51 ]. Furthermore, green finance policies require financial institutions to incorporate environmental, social, and governance (ESG) factors into their risk assessments, ensuring environmental risks are fully considered in credit decisions [ 52 , 53 ]. By adjusting loan conditions and investment criteria, these policies reduce financial support for industries with high environmental risks, addressing the financial misalignment caused by under-assessed environmental risks in the market. In addition, green finance policies offer incentives, such as tax benefits, interest subsidies for green projects, and fiscal subsidies, to lower the financing costs of green initiatives and increase the attractiveness of green investments. These measures correct market failures and boost market participation in green investments, optimizing the allocation of financial resources [ 54 , 55 ]. Green finance policies also promote transparency by requiring enterprises and financial institutions to disclose environmental performance and carbon emission data. This helps investors more accurately assess the environmental performance of enterprises, reducing capital misallocation caused by information asymmetry [ 56 , 57 ]. Through financial instruments, risk management measures, and incentive mechanisms, green finance policies effectively correct the misalignment in traditional financial markets. These policies support the efficient use of resources and the dissemination of green technologies, providing financial backing for enterprise innovation and sustainable development. Ultimately, green finance policies enhance EGTFP by comprehensively optimizing financial resource allocation. 2.2.4 Scale of Environmental Investments Firstly, green finance policies help alleviate the financial barriers to environmental investments by offering targeted financial instruments, such as green credit, green bonds, and green funds. These tools provide substantial capital for companies to fund their environmental projects [ 30 , 58 ]. The availability of such funds allows businesses to invest in green technologies and renewable resources, which in turn reduces energy consumption, improves resource efficiency, and decreases pollutant emissions, thus enhancing environmental quality [ 59 , 60 ]. Secondly, green finance-driven environmental investments improve enterprises' competitive advantage and economic returns. According to Porter's hypothesis, effective policy regulation can encourage businesses to adopt environmentally friendly practices, thereby increasing resource efficiency and competitiveness [ 24 , 61 ]. These investments not only help companies comply with environmental regulations and avoid penalties or reputational damage due to non-compliance but also improve production efficiency, reduce energy and resource consumption, and lower production costs through the adoption of advanced environmental technologies and process innovations [ 62 , 63 ]. Finally, proactive environmental investment behaviors signal an enterprise's commitment to corporate social responsibility and sustainable development. This strengthens the company’s brand image and market reputation, attracting more consumers and investors [ 64 , 65 ]. In summary, supported by green finance policies, enterprises have scaled up their environmental investments, improved their environmental performance and resource efficiency, and fostered green technological innovation, ultimately enhancing their EGTFP. 2.2.5 Moderating Effect Analysis The successful implementation of green finance policies depends not only on the active participation of financial institutions but also on the cooperation and support of environmental regulatory agencies, such as the Environmental Protection Bureau. Studies indicate that the involvement of the Environmental Protection Bureau can significantly improve the effectiveness of green finance policies by enhancing policy promotion, supervision, and encouraging businesses to invest in green technologies and innovations. Research also shows that close collaboration between the Environmental Protection Bureau and financial institutions helps reduce information asymmetry, increases the transparency and credibility of policy implementation, and fosters greater trust in green financial instruments, ultimately boosting environmental performance. According to spatial economics theory, geographical distance plays a key role in determining the efficiency of policy transmission and regulatory effectiveness [ 66 ]. The farther enterprises are from the Environmental Protection Bureau, the more difficult it becomes for regulatory agencies to monitor environmental behavior, raising supervision costs and diminishing regulatory impact [ 67 ]. In such scenarios, companies may be less inclined to comply with environmental regulations and may reduce investments in green technologies and environmental initiatives, thus slowing the improvement of EGTFP [ 68 , 69 ]. In conclusion, the greater the distance between enterprises and the Environmental Protection Bureau, the weaker the positive effect of green finance policies on EGTFP. 2.2.6 Hypotheses Propose d Based on the above theoretical analysis, we propose the following hypotheses: Hypothesis 1 Green finance policies increase EGTFP. Hypothesis 2 Green finance policies enhance EGTFP by promoting green technological innovation. Hypothesis 3 Green finance policies enhance EGTFP by strengthening financing constraints. Hypothesis 4 Green finance policies enhance EGTFP by alleviating financial mismatch. Hypothesis 5 Green finance policies enhance EGTFP by expanding environmental investment. Hypothesis 6 The distance between enterprises and the Environmental Protection Bureau negatively moderates the positive impact of green finance policies on EGTFP. Figure 1. demonstrates the influence mechanisms of green finance policies on EGTFP. 3. Data and Methods 3.1 Sample Selection Exclusion of Financial Enterprises: Financial enterprises were excluded due to their distinct financial structures and operational characteristics, which differ significantly from those of manufacturing firms, ensuring consistency within the sample. Exclusion of Financially Distressed or Irregular enterprises: Enterprises classified as ST (Special Treatment), *ST (Delisting Warning), and PT (Particular Transfer) were excluded to avoid potential biases in the study's findings, which could arise from financial distress or significant regulatory non-compliance. Exclusion of Samples with Missing Key Variables: To ensure the reliability of the analysis, samples with substantial missing data on key variables were excluded.. Winsorization of Continuous Variables: To address potential distortions caused by extreme values, all continuous variables were winsorized at the 1% level on both ends. Ultimately, a sample comprising 968 manufacturing enterprises with a total of 12,584 annual observations was constructed. Unless otherwise specified, all variable data used in this study were sourced from the CSMAR (China Securities Market and Accounting Research) database. 3.2 Variable Definitions 3.2.1 Enterprise Green Total Factor Productivity (EGTFP) EGTFP was measured using MaxDEA software, employing the Slack-Based Measure (SBM) model, which incorporates both radial and non-radial distance functions, as outlined by Wang et al. (2023)[ 70 ]. The EGTFP measurement considered three input indicators and two types of outputs, as detailed below: 1. Input Indicators: Capital Input: Represented by the net value of fixed assets at the end of the year. To account for price inflation, the fixed asset investment price index was used for deflation. Labor Input: Represented by the number of employees. Energy Input: Represented by the consumption of standard coal. Due to the lack of enterprise-level data on standard coal consumption, this study followed the approach of Wang et al. (2023) to estimate enterprise-level coal consumption from regional data[ 70 ]. First, regional standard coal consumption data were sourced from the China Energy Statistical Yearbook. An adjustment coefficient for regional standard coal consumption was then calculated to derive a weighted adjusted figure. Finally, the enterprise's standard coal consumption was estimated based on its share of regional industrial output. 2. Expected Output: Represented by operating income. 3. Non-Expected Output: Measured by the emissions of industrial waste, specifically industrial sulfur dioxide, industrial wastewater, and industrial smoke and dust emissions. Similar to the energy input estimation method, pollution emissions were calculated based on the enterprise's share of total energy input. Given that the Global Malmquist-Luenberger (GML) index can effectively measure the growth rate of EGTFP, it was employed as a robustness check proxy variable. The study also generated trend graphs of EGTFP for the years 2010, 2014, 2018, and 2022, as shown in Fig. 2 . The trend analysis revealed a consistent annual increase in EGTFP over the study period, indicating steady improvements in the green productivity of the analyzed enterprises. 3.2.2 Green Finance The treatment variable for green finance reform policy ( \(\:did\) ) is measured by the interaction of the treatment group dummy variable ( \(\:treat\) ) and the policy implementation time dummy variable ( \(\:post\) ), expressed as \(\:did=treat\times\:post\) . If an enterprise's city is designated as a Green Finance Reform and Innovation Pilot Zone during the sample period, \(\:treat\) is set to 1; otherwise, it is 0. The dummy variable \(\:post\) is set to 1 in the year (2017) the green finance policy was implemented and in subsequent years; otherwise, it is 0. When both dummy variables are 1, \(\:did\) equals 1, indicating that the enterprise is in a pilot area of the green finance policy. 3.2.3 Mechanism Variables Green Technological Innovation (GTI): Given the time lag between patent application and grant, which typically spans one to two years, patent applications are used rather than grants, as they provide a more timely reflection of an enterprise's GTI activities. The level of GTI is measured by the total number of green invention and utility model patent applications filed by the enterprise. Financing Constraints (FC): Following Kaplan and Zingales (1997)[ 71 ], the KZ index is used to measure the level of financing constraints faced by the enterprise. Financial Misalignment (FM): According to Wang and Ma (2023)[ 72 ], the degree to which an enterprise's capital cost deviates from the industry average is used as a proxy for financial misalignment. This is measured by the ratio of interest expenses in financial costs to total liabilities, excluding accounts payable. Environmental Investment (INV): Following Zhang et al. (2019)[ 73 ], environmental investment is calculated by summing expenditures directly related to environmental protection, such as desulfurization, denitration, wastewater treatment, waste gas treatment, dust removal, and energy-saving projects, as reported in the construction in progress section of an enterprise’s annual report. The ratio of the annual increase in environmental investment to the enterprise’s size is used to measure environmental investment. To improve the readability of subsequent regression coefficients, this variable is multiplied by 100. 3.2.4 Regulatory Distance Following Liao and Zhang (2024)[ 95 ], the core variable for geographical distance from the regional Environmental Protection Bureau (EPB) to the enterprise is constructed. This requires detailed location data for both China's county-level EPBs and the enterprises. The location data for the enterprise offices are obtained from the CSMAR database. For the geographical information of EPBs, we manually collected the location data of county-level EPBs across China using Baidu Maps and extracted their latitude and longitude coordinates. Subsequently, we calculated the straight-line distance from each enterprise to the nearest EPB in its region, using this distance as a proxy for regulatory distance. A larger regulatory distance indicates weaker environmental regulatory intensity. 3.2.5 Control Variables To control for other factors influencing Enterprise Green Total Factor Productivity (EGTFP), the following firm characteristics were included in the analysis: Enterprise Age (age): Measured by the number of years since the enterprise's establishment. Leverage Ratio (lev): Calculated as the total liabilities at the end of the year divided by total assets at the end of the year. Return on Assets (roa): Measured by dividing net profit by the average balance of total assets. Net Profit Growth Rate (profit): Measured by the difference between current and previous period net profits divided by the net profit of the previous period. Cash Flow Ratio (cash): Represented by the cash flow from operating activities divided by total assets. Shareholder Concentration (top 1): Measured by the shareholding percentage of the largest shareholder. Revenue Growth Rate (growth): Calculated as the ratio of the current year's operating revenue to the previous year's operating revenue minus one. Relative Firm Value (tobinq): Depicted using Tobin's Q ratio. To examine the relationship between green finance and EGTFP, we constructed a multidimensional fixed-effects model covering firm, time, industry, and province dimensions. The specific model configuration is as follows: $$\:\begin{array}{c}{\text{E}\text{G}\text{T}\text{F}\text{P}}_{\text{i}\text{t}}={{\alpha\:}}_{0}+{{\alpha\:}}_{1}{\text{d}\text{i}\text{d}}_{\text{i}\text{t}}+\sum\:{\alpha\:}{\text{X}}_{\text{i}\text{t}}+{{\lambda\:}}_{\text{i}}+{{\lambda\:}}_{\text{t}}+{{\lambda\:}}_{\text{i}\text{n}\text{d}}+{{\lambda\:}}_{\text{p}}+{{\epsilon\:}}_{\text{i}\text{t}}\#\end{array}\left(1\right)$$ \(\:{\text{E}\text{G}\text{T}\text{F}\text{P}}_{\text{i}\text{t}}\) represents the green total factor productivity of firm i in year t. \(\:{\text{d}\text{i}\text{d}}_{\text{i}\text{t}}\) is the treatment variable for the green finance policy. \(\:{{\alpha\:}}_{1}\) is the coefficient measuring the impact of green finance on EGTFP. \(\:{{\lambda\:}}_{\text{i}}\) 、 \(\:{{\lambda\:}}_{\text{t}}\) 、 \(\:{{\lambda\:}}_{\text{i}\text{n}\text{d}}\) 和 \(\:{{\lambda\:}}_{\text{p}}\) represent the fixed effects controlled for firms, years, industries, and provinces respectively. \(\:{{\epsilon\:}}_{\text{i}\text{t}}\) is the random error term. Considering the theoretical mechanisms through which green finance affects EGTFP, we reference Chen et al. (2020) to construct the following mechanism test model[ 74 ]: $$\:\begin{array}{c}{\text{M}}_{\text{i}\text{t}}={{\beta\:}}_{0}+{{\beta\:}}_{1}{\text{d}\text{i}\text{d}}_{\text{i}\text{t}}+\sum\:{\beta\:}{\text{X}}_{\text{i}\text{t}}+{{\lambda\:}}_{\text{i}}+{{\lambda\:}}_{\text{t}}+{{\lambda\:}}_{\text{i}\text{n}\text{d}}+{{\lambda\:}}_{\text{p}}+{{\epsilon\:}}_{\text{i}\text{t}}\#\end{array}\left(2\right)$$ \(\:{\text{M}}_{\text{i}\text{t}}\) represents the four mechanism variables: GTI, Financing Constraints (FC), Financial Misalignment (FM), and Environmental Investment (INV). Further, we constructed the following model to discuss the moderating effect of regulatory distance on the relationship between green finance and EGTFP: $$\:\begin{array}{c}{\text{E}\text{G}\text{T}\text{F}\text{P}}_{\text{i}\text{t}}={{\upvartheta\:}}_{0}+{{\upvartheta\:}}_{1}{\text{d}\text{i}\text{d}}_{\text{i}\text{t}}+{{\upvartheta\:}}_{2}{\text{d}\text{i}\text{d}}_{\text{i}\text{t}}\times\:{\text{D}\text{I}\text{S}}_{\text{i}\text{t}}+\sum\:{\upvartheta\:}{\text{X}}_{\text{i}\text{t}}+{{\lambda\:}}_{\text{i}}+{{\lambda\:}}_{\text{i}\text{n}\text{d}}+{{\lambda\:}}_{\text{p}}+{{\epsilon\:}}_{\text{i}\text{t}}\#\end{array}\left(3\right)$$ \(\:{\text{D}\text{I}\text{S}}_{\text{i}\text{t}}\) is the proxy variable for regulatory distance, specifically the geographical distance from the firm to the nearest Environmental Protection Bureau in its region. As regulatory distance data is cross-sectional, we only include firm, industry, and province fixed effects in Eq. (3). 4. Results and Discussion 4.1 Baseline Results The regression in Eq. (1) tests the impact of green finance on EGTFP, with the results shown in Table 2 . Specifically: Table 1 Statistical Characteristics of Core Variables Variables N mean sd min max EGTFP 12,584 0.983 0.104 0.792 1.284 GTI 12,584 0.7436 1.1881 0 7.6582 FM 12,241 0.0591 0.871 -4.747 17.78 FC 12,004 1.237 2.133 -5.724 6.837 INV 11,761 0.0835 0.111 0 1.264 DIS 12,584 7.394 7.861 0.0140 101.9 age 12,584 2.855 0.380 0.693 4.025 lev 12,584 0.403 0.197 0.0483 0.912 roa 12,584 0.0428 0.0638 -0.210 0.235 profit 12,584 -1.249 6.074 -42.77 8.274 cash 12,584 0.0506 0.0649 -0.134 0.242 top1 12,584 33.02 14.15 8.448 71.56 growth 12,584 0.153 0.287 -0.441 1.475 tobinq 12,584 2.074 1.300 0.861 8.464 Table 2 Baseline Regression Results of Green Finance Policy on EGTFP Variables OLS RE FE (1) (2) (3) (4) did 0.0718 *** 0.0064 *** 0.0082 *** 0.0082 *** (0.0049) (0.0012) (0.0014) (0.0014) Constant 0.6036 *** 0.8366 *** 0.8316 *** 0.9917 *** (0.0065) (0.0022) (0.0051) (0.0059) Control Yes Yes Yes Yes firm No Yes Yes Yes year No Yes Yes Yes industry No No No Yes province No No No Yes N 12584 12584 12584 12584 adj. R 2 0.3625 0.9731 0.9733 Note: Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. Column (1) presents the Ordinary Least Squares (OLS) estimation results. Columns (2) and (3) show the results with random effects and fixed effects, incorporating firm and time dimensions. Column (4) includes all fixed effects and control variables. In Columns (1) to (4), the estimated coefficients of did are significantly positive at the 1% level, indicating that the implementation of green finance policies enhances EGTFP, thereby supporting Hypothesis 1 . While previous studies have explored the impact of green finance on EGTFP, their conclusions are primarily based on macro-regional data and often rely on the problematic Green Finance Development Index, which suffers from significant endogeneity. Examples include studies by Lee and Lee (2022), Tong et al. (2022), Yue et al. (2024), and Feng et al. (2024)[ 75 , 76 , 77 , 78 ]. Some researchers have examined the effects of green finance on firm-level total factor productivity but without integrating environmental factors, as seen in studies by Zhao et al. (2023)[ 79 ] and Zhou et al. (2023)[ 80 ]. Our study, based on a specific green finance policy implemented by the Chinese government, effectively mitigates the endogeneity concerns associated with indicator selection. Furthermore, by considering both firm-level energy inputs and pollution outputs in the measurement of EGTFP, our research provides a more accurate reflection of firms' green development. As such, our study addresses the limitations of previous research and offers more concrete policy implications. Our findings reveal how green finance enhances enterprise operational performance and strengthens pollution control, thus improving EGTFP. First, green finance, with its dual focus on finance and sustainability, not only provides essential financial support for firms' green transformation and industrial upgrading but also optimizes resource allocation. This enables investments in green innovations that offset the short-term operational pressures of environmental expenditures, thereby supporting sustained growth in operational performance. Second, by internalizing environmental costs into investment decisions, green finance incentivizes firms to actively engage in green and low-carbon projects. This shift not only enhances enterprise social responsibility but also redirects financial resources from high-pollution sectors to environmentally friendly enterprises, creating a positive financial incentive for pollution control. As green finance increasingly supports low-carbon initiatives, it promotes the rational allocation of financial resources, encouraging polluting enterprises to actively participate in environmental governance. Consequently, green finance facilitates the transition of firms to more sustainable, efficient, and green development models, significantly boosting EGTFP. 4.2 Robustness Tests 4.2.1 Parallel Trends Test Building on the research by Beck et al. (2010)[ 81 ], a parallel trend test was performed, which confirmed the absence of pre-existing trends in EGTFP prior to the implementation of green finance policies. The dynamic effects of these policies on EGTFP are depicted in Fig. 3 . Prior to the policy implementation, there were no significant differences in EGTFP between the pilot and non-pilot regions. However, after the policy rollout, EGTFP in the pilot regions experienced a notable increase, thereby supporting the validity of the parallel trend assumption. 4.2.2. Double Machine Learning (DDML) To evaluate the robustness of the effect of green finance on EGTFP, we utilize the Double Machine Learning (DDML) method introduced by Chernozhukov et al. (2018)[ 82 ]. Unlike traditional causal inference techniques, DDML integrates machine learning models into the auxiliary equations, mitigating regularization bias. This approach not only mitigates the dimensionality problem caused by an excessive number of control variables but also improves the accuracy of nonlinear estimates through non-parametric feature selection [ 83 ]. In our analysis, we employed four methods—random forest, Lasso regression, gradient boosting, and neural networks—to predict the results of both the primary and auxiliary regressions. The sample split ratio was set at 1:4, and squared terms of the control variables were included. As presented in columns (1) to (4) of Table 3 , the coefficient estimates for the "did" variable remain significantly positive, confirming the robustness of the green finance effect on EGTFP. Table 3 Results of the DDML Analysis Variables rf lassocv gradboost nnet (1) (2) (3) (4) did 0.0111 *** 0.0109 *** 0.0107 *** 0.0111 *** (0.0028) (0.0028) (0.0028) (0.0029) Constant -0.0004 *** -0.0004 ** -0.0005 *** -0.0004 ** (0.0001) (0.0002) (0.0001) (0.0002) Control Yes Yes Yes Yes firm Yes Yes Yes Yes year Yes Yes Yes Yes industry Yes Yes Yes Yes province Yes Yes Yes Yes N 12584 12584 12584 12584 Note: Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. 4.2.3. Propensity Score Matching (PSM) Considering the significant differences between enterprises supported by green finance policies in pilot regions and those outside the scope of these policies, we apply kernel matching to select the PSM samples and re-assess the research model using DID regression. The results in column (1) of Table 4 further support the baseline findings. Table 4 Results of Robustness Checks Variables PSM-DID GML Interactive Fixed Effects Exclusion of Interference Key Polluting Industries (1) (2) (3) (4) (5) (6) did 0.0082 *** 0.0063 *** 0.0086 *** 0.0070 *** 0.0189 *** 0.0126 ** (0.0014) (0.0022) (0.0014) (0.0016) (0.0027) (0.0059) feigaishui 0.0699 *** (0.0010) Constant 0.9919 *** 0.5668 *** 0.9906 *** 0.9329 *** 0.1357 *** 0.0214 ** (0.0059) (0.0097) (0.0060) (0.0066) (0.0062) (0.0103) Control Yes Yes Yes Yes Yes Yes firm Yes Yes Yes Yes Yes Yes year Yes Yes Yes Yes Yes Yes industry Yes Yes Yes Yes Yes Yes province Yes Yes Yes Yes Yes Yes N 12530 12584 12584 9680 12584 3588 adj. R 2 0.9733 0.9598 0.9732 0.9603 0.8956 0.8579 Note: Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. 4.2.4. GML Index As described in Section 3.1.1, we use the GML index as a proxy variable for EGTFP to investigate the impact of the did variable on the dynamic rate of change in EGTFP. The estimated value of did in column (2) of Table 4 is 0.0063 and is significant at the 1% level. This not only strengthens the baseline finding regarding green finance's effect on EGTFP but also reflects its dynamic enhancement effect on the high-quality development of enterprises. 4.2.5. Changes in Interaction Fixed Effects To account for the heterogeneity of time trends across industries, we introduce interaction fixed effects for time and industry as a robustness check, controlling for industry characteristics that may evolve over time. The estimated DID coefficient in column (3) of Table 4 is 0.0086 (p < 0.01), further confirming the positive impact of green finance on EGTFP. 4.2.6. Excluding External Environmental Disturbances We address the potential influence of external environmental factors on the baseline results from two perspectives. First, to account for the impact of COVID-19 on enterprise performance, production decisions, and governance responsibilities, we limit the sample period to 2011–2019. Second, the formal implementation of the Environmental Protection Tax Law in 2018 may distort the effect of green finance pilot policies on EGTFP, potentially confounding our regression results. To control for this possible interference, we include a dummy variable for the environmental tax reform in the regression model. The results from these two approaches are reported in columns (4) and (5) of Table 4 . The coefficient of the core explanatory variable remains statistically significant and retains its original sign, suggesting that the core conclusion holds robust even after accounting for potential external disturbances. 4.2.7. Key Polluting Enterprises There may be some degree of correlation between green finance policies and the classification of enterprises as "key polluting enterprises." These policies are likely more inclined to support non-key polluting enterprises in promoting their transition to greener practices, while key polluting enterprises may face stricter financing restrictions. To address this, we conducted an analysis excluding key polluting enterprises. The results presented in column (6) of Table 4 show that, after removing the influence of key polluting enterprises on the policy effects, the positive impact of green finance policies on EGTFP remains statistically significant. 4.2.8. Placebo Test To conduct a placebo test, we randomly assigned treatment and control groups while maintaining the original policy timing. A number of enterprises equal to those covered by the green finance policy were randomly selected from the sample to form a random treatment group. The virtual policy effects of this random grouping were estimated based on Eq. (1), with 1,000 simulations performed. Figure 4 illustrates that the green finance policy had no effect on EGTFP in this random grouping. Regarding the significance of the random results, the majority of p-values exceeded 0.1, indicating non-significance at the 10% level, thereby passing the placebo test. 5. Further Analysis 5.1. Heterogeneity Analysis 5.1.1. Enterprise-Level Heterogeneity (1) Background of Enterprise Executives in Environmental Protection Differences in the backgrounds, experiences, and values of executive teams across various enterprises may result in varying responses and effects when confronted with green finance policies [ 84 ]. By examining the heterogeneity associated with whether enterprise executives have an environmental background, we can gain deeper insights into the implementation effects and mechanisms of green finance policies across different types of enterprises. This understanding will help policymakers better formulate and adjust policies to facilitate the green transformation and sustainable development of enterprises. To capture this heterogeneity, we introduce a dummy variable, "X1." If "X1" equals 1, it indicates that the enterprise executives have an environmental background; otherwise, "X1" equals 0. The coefficient of "did×X1" in column (1) of Table 5 is 0.0059 (p < 0.05), suggesting that when enterprise executives have an environmental background, the impact of green finance policies on EGTFP is more pronounced. A plausible explanation is that executives with an environmental background are more likely to integrate environmental protection and sustainable development into the enterprise’s long-term strategic planning, thereby fostering innovations in green technologies, products, and services [ 85 ]. This strategic shift directly contributes to improvements in EGTFP. Table 5 Results of Heterogeneity Analysis Variables Enterprise Level Regional Level Environmental Protection State-Owned Enterprise Heavy Pollution Eastern Region Marketization DIF (1) (2) (3) (4) (5) (6) did×X1 0.0059 ** (0.0028) did×X2 0.0088 *** (0.0020) did×X3 0.0098 *** (0.0030) did×X4 0.0050 *** (0.0018) did×X5 0.0060 *** (0.0017) did×X6 0.0078 *** (0.0014) Constant 0.9828 *** 0.9912 *** 0.9917 *** 0.9923 *** 0.9923 *** 0.9918 *** (0.0064) (0.0059) (0.0059) (0.0059) (0.0059) (0.0059) Control Yes Yes Yes Yes Yes Yes firm Yes Yes Yes Yes Yes Yes year Yes Yes Yes Yes Yes Yes industry Yes Yes Yes Yes Yes Yes province Yes Yes Yes Yes Yes Yes N 11977 12584 12584 12584 12584 12584 adj. R 2 0.9712 0.9732 0.9732 0.9732 0.9732 0.9733 Note: Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. (2) Nature of Ownership State-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs) exhibit significant differences in terms of their nature, ownership structure, and management practices. As a critical economic pillar of the state, SOEs often receive guidance and support from national policies, particularly with respect to the implementation of green finance policies, which may enhance their access to policy incentives and financial support. In contrast, non-SOEs may face greater challenges in securing policy benefits and funding. We introduce a dummy variable, "X2," where a value of 1 indicates that the enterprise is an SOE, and a value of 0 indicates a non-SOE. The positive and statistically significant coefficient of "did×X2" in column (2) of Table 5 indicates that green finance has a stronger promoting effect on the EGTFP of SOEs. Specifically, SOEs are more likely to achieve greener transformations and improve green total factor productivity more rapidly due to their advantages in policy support and resource acquisition. In contrast, non-SOEs must strengthen internal management, enhance technological innovation capabilities, and explore diversified financing channels to overcome the challenges posed by green finance policies. (3) Heavily Polluting Enterprises Green finance policies are designed to guide enterprises toward greener, low-carbon, and environmentally friendly development through financial mechanisms. For heavily polluting enterprises, these policies may impose stricter loan conditions and higher financing costs to limit high-pollution, high-energy-consuming production activities and encourage technological upgrades and green transitions [ 86 ]. We introduce a dummy variable, "X3," to reflect the pollution type of the enterprise. If the enterprise is heavily polluting, "X3" is set to 1; otherwise, it is set to 0. The coefficient of "did×X3" in column (3) of Table 5 is 0.0098 (p < 0.01), indicating that green finance policies have a more significant improvement effect on EGTFP for heavily polluting enterprises. This effect may stem from the requirement that financial institutions conduct rigorous evaluations of enterprises' environmental performance during the loan approval process, which involves implementing stricter loan conditions or denying loans to those failing to meet environmental standards. This policy constraint mechanism effectively incentivizes heavily polluting enterprises to increase their investments in environmental protection and technological innovation, thereby enhancing their EGTFP. 5.1.2. Regional-Level Heterogeneity (1) Economic Development Level Local governments in economically developed regions often demonstrate stronger execution and higher efficiency when implementing green finance policies. These regions are better positioned to swiftly establish and refine green finance policy frameworks, facilitating effective policy implementation. Additionally, financial institutions in economically advanced areas tend to have a denser presence and more abundant resources, which better supports the implementation of green finance policies. As a result, the policy effects of green finance may vary depending on the development level of the region in which the enterprise operates. The significant economic disparities between the eastern and central/western regions of China provide a valuable context for exploring the implications of these differences on policy effectiveness [ 87 ]. To capture the economic development level of the region where the enterprise is located, we introduce a dummy variable, "X4." If the enterprise is situated in eastern China, "X4" is equal to 1, indicating a more favorable economic environment; if located in central or western China, "X4" is equal to 0. The coefficient of "did×X4" in column (4) of Table 5 is 0.0050 (p < 0.01), suggesting that green finance policies have a more significant impact on EGTFP in eastern regions. The industrial structure in eastern regions is relatively optimized, with a higher proportion of high-tech and green industries. These sectors have a more pressing demand for green finance policies and can derive greater benefits from them. In contrast, the industrial structure in central and western regions may still be more reliant on traditional, energy-intensive, and polluting industries, making the transition to greener practices more challenging and potentially resulting in a weaker response to green finance policies. (2) Marketization Level The degree of marketization influences enterprise behavior [ 88 ]and subsequently affects the promotional impact of green finance on EGTFP. Drawing on a series of studies, including Fan et al. (2003)[ 89 ], we utilize marketization indices across various provinces to represent the marketization level of the regions where enterprises operate. We create a dummy variable, "X5," using the median of the marketization index as a cutoff. If the marketization index of the enterprise's region exceeds the median, "X5" is set to 1, indicating a high degree of marketization; otherwise, "X5" is set to 0. The coefficient of "did×X5" in column (5) of Table 5 is 0.0060 (p < 0.01), suggesting that green finance is more effective in promoting EGTFP growth in regions with higher marketization levels. On the one hand, enterprises in highly marketized areas face greater market competition, which drives them to adopt new technologies and processes to enhance production efficiency and resource utilization. On the other hand, the regulatory framework in these regions is generally more comprehensive, with stricter oversight of environmental pollution and resource waste. The combination of green finance policies and robust environmental regulations effectively constrains pollution behaviors, incentivizing enterprises to increase environmental investments and improve EGTFP. (3) Digital Inclusive Finance Level Digital inclusive finance, through its extensive coverage and efficient financial services, can provide enterprises facing financing difficulties with more accessible and cost-effective funding channels [ 90 ]. This characteristic aligns closely with the goals of green finance, facilitating the green transitions of enterprises and enhancing EGTFP. We measure the level of digital inclusive finance in the region where the enterprise operates using an index compiled by Peking University's Digital Finance Research Center. Similarly, we introduce a dummy variable, "X6," using the median of the digital inclusive finance index as a benchmark. If the index exceeds the median, "X6" is set to 1, indicating a high level of digital inclusive finance; otherwise, "X6" is set to 0. The coefficient of "did×X6" in column (6) of Table 5 is 0.0078 (p < 0.01). The results suggest that in regions with high levels of digital inclusive finance, green finance significantly enhances EGTFP. This may be due to enterprises in areas with well-developed digital inclusive finance finding it easier to access support from green finance, making the policy effects more pronounced. In contrast, enterprises in regions with lower levels of digital inclusive finance may face challenges such as higher financing costs, which limit the effectiveness of green finance policies and the improvement of EGTFP. 5.2 Impact Mechanism Analysis This section further investigates the mechanisms through which green finance influences the enhancement of green total factor productivity (EGTFP) from four perspectives: Green Technology Innovation (GTI), financing constraints, financial mismatch, and environmental investment. Table 6 presents the regression results from Eq. (2). Table 6 Results of the Impact Mechanism Analysis Variables GTI Financing Constraints Financial Mismatch Environmental Investment (1) (2) (3) (4) GTI FC FM INV did 1.8982 *** 0.9832 *** -0.1398 *** 0.0214 *** (0.0559) (0.0725) (0.0528) (0.0073) Constant 0.8139 *** -4.8319 *** -0.3867 0.0366 (0.2422) (0.3320) (0.2420) (0.0350) Control Yes Yes Yes Yes firm Yes Yes Yes Yes year Yes Yes Yes Yes industry Yes Yes Yes Yes province Yes Yes Yes Yes N 12584 12004 12241 11761 adj. R 2 0.6532 0.8286 0.4405 0.3516 Note: Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. Column (1) illustrates the transmission mechanism of GTI, where the coefficient of the DID variable is 1.8982 (p < 0.01), indicating a significant positive impact of green finance on EGTFP. This result suggests that green finance enhances enterprises' green innovation capabilities, thereby improving EGTFP. Column (2) reports the results related to financing constraints, where the coefficient is 0.9832 (p < 0.01). This finding implies that green finance alleviates the financing constraints faced by manufacturing enterprises, motivating them to transition towards green and low-carbon practices, which, in turn, improves EGTFP. Column (3) demonstrates the results indicating that green finance mitigates financial mismatch and influences EGTFP, with a coefficient of -0.1398 (p < 0.01). This suggests that green finance reduces the misallocation of financial resources among manufacturing firms, enhancing the efficiency of financial resource allocation and driving sustained growth in EGTFP. In Column (4), the coefficient for DID is 0.0214 (p < 0.01), indicating that green finance drives EGTFP growth by increasing environmental investment by enterprises. Overall, this analysis supports Hypothesis 2 , highlighting the mechanisms through which green finance enhances EGTFP. From the perspective of the Green Technology Innovation (GTI) mechanism, Schumpeter's theory of innovation suggests that technological innovation is a key driver of economic growth. Under the influence of green finance policies, enterprises invest in research and development of environmentally friendly technologies, improvement of production processes, and development of green products [ 91 ]. These efforts not only reduce resource consumption and environmental pollution but also enhance production efficiency and product quality, thereby boosting EGTFP. With regard to the financing constraint mechanism, green finance policies compel manufacturing enterprises, particularly those in heavily polluting industries, to improve their production methods to reduce energy and resource consumption[ 92 ]. Under these policies, banks and investment institutions are more likely to provide loans to enterprises that meet green standards, thus limiting financial support for polluting firms. This shift constrains production decisions, facilitates the exit of polluting projects, and promotes the entry of green projects, ultimately improving EGTFP. In terms of financial mismatch, green finance policies direct capital towards green, low-carbon, and environmentally friendly sectors, thereby reducing excessive investment in traditional high-pollution and high-energy-consuming industries and decreasing financial misallocation [ 93 ]. This mechanism aligns with the theory of industrial structural upgrading, which emphasizes the importance of transitioning to a more rational and efficient industrial structure for sustained economic growth. By optimizing the allocation of financial resources, green finance policies facilitate the shift from polluting industries to clean industries, contributing to the growth of EGTFP. Finally, environmental investment plays a crucial role in enabling enterprises to achieve green development and enhance EGTFP. By increasing environmental investments, enterprises can internalize the negative externalities associated with environmental pollution as part of their operational costs, thereby incentivizing the adoption of more environmentally friendly production methods [ 75 ]. Green finance policies, through incentives such as policy benefits and tax breaks, encourage enterprises to increase their environmental investments. This enables them to meet their environmental and social responsibilities while simultaneously improving both performance and efficiency. 5.3 Moderating Effect Analysis Table 7 presents the regression results for Eq. (3) under two different fixed effects specifications. The coefficients for "did×DIS" in both Column (1) and Column (2) are − 0.0081 (p < 0.01), indicating that regulatory distance (DIS) negatively moderates the impact of green finance on EGTFP, thus supporting Hypothesis 3 . As previously mentioned, we measure regulatory distance using the proximity of enterprises to their respective regional environmental protection bureaus. An increase in regulatory distance significantly weakens the effect of green finance on EGTFP. This study incorporates regulatory distance into the research framework on green finance and enterprise green transformation, extending the work of Hu et al. (2021) [ 94 ]and Liao and Zhang (2024)[ 95 ], which primarily focus on the influence of regulatory distance on enterprise innovation activities. Table 7 Results of the Moderating Effect of Regulatory Distance Variables (1) (2) did 0.0447 *** 0.0449 *** (0.0046) (0.0046) did×DIS -0.0081 *** -0.0081 *** (0.0026) (0.0026) Constant -0.1266 *** -0.1263 *** (0.0060) (0.0060) Control Yes Yes firm Yes Yes industry No Yes province No Yes N 12584 12584 adj. R 2 0.8502 0.8515 Note: Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. The moderating effects revealed in this analysis illustrate the complex role of regulatory distance in the implementation of green finance policies and their impact on EGTFP. We offer the following explanations: From the perspective of information asymmetry and regulatory costs, increased regulatory distance may exacerbate the information asymmetry between enterprises and environmental protection agencies. A greater distance complicates the agencies' ability to access real-time and accurate information regarding enterprise environmental behaviors, thus increasing regulatory costs and challenges [ 96 ]. This information asymmetry diminishes the effectiveness of green finance policy implementation, hindering its potential to promote enterprise green transformation. Regarding the externality effects of increased geographical distance, enterprises may reduce their investments in green technologies and management due to a perceived decrease in regulatory pressure as the regulatory distance widens, thereby weakening the effectiveness of green finance policies on EGTFP enhancement. Additionally, in regions with more lenient environmental regulations, enterprises may find it easier to evade regulatory responsibilities and obligations for green transformation [ 97 ]. This results in a distance decay effect on the efficacy of green finance policy implementation, ultimately limiting its ability to effectively promote EGTFP. 6. Conclusion and Policy Implications 6.1 Conclusion This study investigates the impact of green finance on green total factor productivity (EGTFP) from the micro perspective of manufacturing enterprises, using the 2017 implementation of China's green finance reform and innovation pilot zones as an exogenous event. The empirical results indicate that green finance significantly enhances EGTFP, highlighting the positive incentive effects of green finance policies on both enterprise performance and environmental protection. The heterogeneity analysis at the enterprise level reveals that the effectiveness of green finance policies in enhancing EGTFP is particularly pronounced in enterprises with environmentally conscious executives, state-owned enterprises, and heavily polluting industries. From a regional perspective, the positive effects are more substantial in economically developed areas, regions with higher levels of marketization, and areas with advanced digital inclusive finance. The mechanism analysis shows that green finance improves enterprises' levels of green technology innovation (GTI), increases financing constraints for manufacturing enterprises, reduces financial resource misallocation, and enhances environmental investment intensity, all of which contribute to the improvement of EGTFP. Additionally, the study identifies a negative moderating effect of regulatory distance on the relationship between green finance and EGTFP, suggesting that an increase in regulatory distance weakens the policy effectiveness of green finance. 6.2 Policy Implications First and foremost, it is crucial to actively promote green finance, with a primary focus on enhancing green total factor productivity (EGTFP). Green finance provides the necessary capital for enterprises to transition to greener practices. It also holds polluting enterprises accountable, encouraging them to incorporate environmental requirements into their development strategies. This drives them to quickly adjust their production methods. Therefore, the government should increase financial support, broaden the scope of green finance, and promote the use of financial instruments such as green loans and green bonds. Green finance should be aligned with clean production, energy conservation, and carbon reduction projects. By leveraging advanced technologies, these efforts can help promote energy savings and emissions reductions, ultimately guiding enterprises toward a more sustainable development path. Second, considering that green finance influences EGTFP through four mechanisms—green technology innovation (GTI), financing constraints, financial mismatch, and environmental investment—we propose four targeted recommendations: Green Finance Policy + Enterprise Green Technology Innovation: The government should introduce more specific incentive policies, such as tax reductions and research subsidies, to encourage enterprises to invest more in GTI. Additionally, establishing a dedicated GTI fund could prioritize support for innovative and market-potential green technology projects. The government can also create information exchange platforms to foster collaboration between financial institutions and enterprises. Green Finance Policy + Financing Constraints: A robust credit rating and risk assessment system for green projects should be established to improve their access to financing. Financial institutions need to improve their risk management for green projects to ensure safe and effective use of funds. Moreover, optimizing the credit structure to allocate more resources to environmental sectors will help alleviate financing constraints for green projects. Green Finance Policy + Financial Mismatch: Regulatory oversight of financial institutions’ green finance activities should be strengthened to prevent misallocation of resources. A more scientific approach to allocating green finance resources should be developed to ensure funds are directed toward clean sectors. Financial institutions should set appropriate financing scales and interest rates based on the unique risks and needs of green projects. Green Finance Policy + Enterprise Environmental Investment: The government should support environmental enterprises through mechanisms such as issuing green bonds to raise funds for environmental projects. Policies should also encourage private sector participation in the construction and management of environmental investment projects, helping to ease the financial burden on enterprises. Third, since regulatory distance reduces the effectiveness of green finance policies, it is essential to strengthen the role of environmental protection agencies in policy enforcement. Regulatory bodies should focus on manufacturing enterprises, especially publicly listed companies, that are geographically distant from regulatory oversight. Increased on-site inspections, along with the innovative use of technologies such as satellite remote sensing, should be employed to enhance regulatory effectiveness. This will ensure stricter compliance with environmental regulations. Additionally, the government should develop a digital regulatory system that leverages big data, cloud computing, and the Internet of Things. This system will comprehensively track environmental data from enterprises and enable real-time monitoring of key indicators such as the operation of environmental facilities and pollutant emissions. 6.3 Research Limitations This study is subject to limitations due to the insufficient disclosure of enterprise environmental information, which restricts the direct acquisition of detailed environmental indicators at the enterprise level. As a result, we used industry-level data on energy consumption and pollutant emissions as proxies, constructing an indirect framework for environmental data at the enterprise level through reasonable estimation methods. However, such indirect estimation methods inevitably introduce potential biases, which may affect the accuracy of EGTFP calculations. To address this limitation, future research could focus on improving methods for obtaining energy consumption and environmental pollution data at the enterprise level. Advanced programming languages, such as Python, with their powerful data processing and analysis capabilities, could be employed to develop efficient data scraping and cleaning tools. These tools could facilitate the direct collection of original data on enterprise environmental performance from various channels and sources, thereby improving the accuracy and reliability of EGTFP calculations. Declarations Consent to Publish Declaration Consent to Publish declaration: not applicable. Consent to Participate Declaration Consent to Participate declaration: not applicable. Ethics Statement Ethics declaration: not applicable. Clinical Trial Declaration Our study does not fall under the category of a clinical trial, so it is not applicable. Clinical Trial Number Clinical trial number: not applicable. Data Availability Statement The datasets analysed during the current study are available from the corresponding author on reasonable request. Funding Statement This study is funded by the National Social Science Foundation of China. The project is titled "Research on the Financing Mechanism and Risk Governance of the Pig Industry Chain" and has the project number 21XGL007. Acknowledgements We gratefully acknowledge the support from the National Social Science Foundation of China for the project entitled "Research on Financing Mechanisms and Risk Governance in the Hog Industry Chain" (Grant No. 21XGL007), and we would like to thank the College of Management, Sichuan Agricultural University for the use of data analysis software provided by the Management Experiment Center of the College. Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution L.Z. and Y.Q. contributed to the conceptualization, methodology design, and empirical strategy. K.G. conducted the formal analysis, data processing, and robustness testing. G.W. collected and curated the data, and participated in the interpretation of empirical results. C.X. contributed to the literature review and assisted in drafting and editing the manuscript. 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EGTFP\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6963369/v1/015a8638a9e8843d0ded1fb4.jpg"},{"id":86659072,"identity":"dd77be4f-907a-4aaa-be26-ef2e445e68c2","added_by":"auto","created_at":"2025-07-14 10:28:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45090,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the Parallel Trend Test\u003c/p\u003e","description":"","filename":"Fig3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6963369/v1/23510958193171dd743b1d2b.jpg"},{"id":86660751,"identity":"c3adbc5a-92d3-4329-808a-afb1cdd1b163","added_by":"auto","created_at":"2025-07-14 10:36:28","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29568,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the Placebo Test\u003c/p\u003e","description":"","filename":"Fig4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6963369/v1/55198d82a11d18f52a8040ef.jpg"},{"id":86662599,"identity":"095dfa86-f8df-4204-a2fc-e0655acbdef9","added_by":"auto","created_at":"2025-07-14 10:44:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2337083,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6963369/v1/932d94dd-6570-4b76-85eb-b60484fde874.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Carrot or Stick? Green Finance, Regulatory Distance and Green Total Factor Productivity of Manufacturing Enterprises","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSince the late 20th century, rapid industrialization and urbanization have led to severe problems such as over-exploitation of resources and environmental pollution. In the context of global climate change and ecological crises, green development has increasingly become a central issue for governments and enterprises worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As primary participants in economic activities, enterprises are responsible for improving energy efficiency and reducing resource consumption and pollution through the adoption of green technologies and management practices [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Green development seeks to enhance enterprise performance while ensuring environmental protection [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Existing research suggests that Enterprise Green Total Factor Productivity (EGTFP) is a key metric for assessing the dual benefits of economic and environmental outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. EGTFP not only focuses on economic output but also incorporates resource consumption and environmental impact, reflecting an enterprise's ability to reduce pollution and resource use while pursuing economic growth [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Enhancing EGTFP is a crucial pathway to achieving a green economic transformation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAmong the various policy tools designed to promote enterprise green transformation, green finance has emerged as an effective approach. Green finance involves channeling capital into green, environmentally friendly, and low-carbon projects and enterprises through financial markets. This process fosters the innovation and application of green technologies while promoting the economic transition to a green economy. By utilizing green finance, governments can direct more social capital into the environmental sector, supporting enterprises with green investments and technological R\u0026amp;D, thus enhancing environmental performance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This is particularly important in developing economies, where resource and environmental constraints are more pronounced. These countries often face the dual challenges of environmental pollution and resource depletion as they pursue economic growth [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Green finance policies can provide financial support for environmental governance, helping enterprises address the shortage of environmental funds [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, the green finance policies can incentivize enterprises to adjust their production processes and operations, reducing environmental burdens and driving the development of a green, low-carbon economy[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the important role of green finance policies in promoting enterprise green transformation and improving EGTFP [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], existing research primarily focuses on the macroeconomic aspect of green finance policies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], with limited systematic investigation at the micro-enterprise level regarding how these policies specifically influence EGTFP. The effectiveness of the green finance policies can be constrained and influenced by various factors, including industry characteristics, enterprise size, market environment, and regional regulatory intensity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Censequently, there is considerable heterogeneity in how different enterprises respond to green finance policies, a topic that has not been sufficiently addressed in the existing literature. To fill this gap, this study selects Chinese A-share-listed manufacturing enterprises from 2010 to 2022 as the research subjects and uses a Difference-in-Differences (DID) model to analyze the effectiveness of green finance policies. By leveraging the Green Finance Reform and Innovation Pilot Zone policy introduced in 2017, the DID model compares the performance of enterprises impacted by the policy with those unaffected, effectively controlling for time trends and other potential confounding factors to isolate the net effect of the policy. The study evaluates the impact of green finance policies on EGTFP and further examines the policy effects in different contexts through heterogeneity analysis.\u003c/p\u003e\u003cp\u003eIn addition to analyzing the direct impact of green finance policies on EGTFP, this research also investigates the mechanisms through which intermediary variables such as enterprise green technological innovation capabilities, financing constraints, and environmental regulation play between green finance policies and EGTFP. Green technological innovation capabilities are essential drivers for improving EGTFP; through technological R\u0026amp;D and innovation, enterprises can enhance resource utilization efficiency and reduce pollution emissions, thereby improving their environmental performance. Financing constraints present significant obstacles to implementing green technological innovations, and green finance policies can alleviate these constraints by providing financial support, thereby fostering the development and application of green technologies. Environmental regulation, as an external governance mechanism, also plays a key role in the green transformation process by constraining and supervising enterprise environmental behaviors. By examining the impacts of these intermediary variables, this study aims to reveal the pathways through which green finance policies infulence EGTFP.\u003c/p\u003e\u003cp\u003eIn comparison to prior research, this study makes substantial contributions in three key areas: (1) The study uses a Difference-in-Differences (DID) model to analyze the effectiveness of green finance policies among Chinese A-share listed manufacturing enterprises from 2010 to 2022. The DID model is widely used to examine the impact of macro policies on firms. However, this study applies it to green finance policies. It provides a thorough empirical analysis of their effects on both economic and environmental outcomes. This approach offers a solid quantitative framework for evaluating the impacts of these policies. It complements existing studies and deepens our understanding of their effectiveness. (2) The study integrates green finance policies and environmental green total factor productivity into a single analytical framework. It conducts a heterogeneity analysis at the enterprise, industry, and provincial levels. The analysis reveals how the effects of green finance policies differ depending on the context. This provides multidimensional empirical evidence that can help guide the implementation of green finance strategies. By addressing these contextual differences, the study fills a gap in the literature regarding the varied impacts of green finance across sectors and regions. (3) The study investigates the micro-mechanisms through which green finance policies enhance green total factor productivity. It identifies key drivers such as: (i) improving green technological innovation in enterprises, (ii) increasing financing constraints, and (iii) strengthening environmental regulations. The research clarifies the operational logic of green finance policies at the micro level. It offers valuable theoretical insights and provides evidence that supports the refinement of green finance policies to foster high-quality green economic transformation.\u003c/p\u003e"},{"header":"2. Institutional Background and Hypothesis Development","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Institutional Background\u003c/h2\u003e\u003cp\u003eThe Green Finance Reform and Innovation Pilot Zones in China represent a significant initiative to explore the use of financial tools for environmental governance. The main objective is to support ecological improvement, resource conservation, and efficient utilization, thereby promoting the green transformation of the economy [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In August 2016, the People's Bank of China, the Ministry of Finance, and five other ministries issued the \"Guidelines on Establishing a Green Financial System,\" providing strategic guidance for the development of green finance. Then, in June 2017, the State Council approved the establishment of the first group of Green Finance Reform and Innovation Pilot Zones in selected regions, including Zhejiang, Guangdong, Guizhou, Jiangxi, and Xinjiang. Gansu joined in 2019, followed by Chongqing in 2022. The primary goal of these pilot zones is to enchance financial support for ecological improvement, resource conservation, and efficient utilization. They aim to offer replicable and scalable experiences to assist China\u0026rsquo;s green economic transition [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe policy features include: (1) Pilot regions selected by the government, characterized by differentiation and representativeness. These regions, located in the eastern, central, and western parts of China, are chosen to reflect different stages of economic development, spatial layouts, and resource endowments. This ensures that the pilot zones are representative in terms of regional distribution and industrial characteristics. (2) Encouraging financial institutions to establish departments and green branches within the pilot zones. This involves setting standardized business processes, which enhance the professionalism and standardization of green financial services. It also strengthens the ability of financial institutions to support green projects. (3) Increasing the proportion of green finance tools in total loan volumes. Financial institutions are encouraged to increase green credit disbursement, support certified green enterprises through green bond financing, and develop alternative green financial tools, such as carbon futures and carbon options. This helps expand financing channels for green enterprises. (4) Establishing a public environmental rights trading platform. This includes creating market platforms for trading water rights, pollution rights, energy usage rights, and carbon emission rights. Additionally, an information-sharing mechanism is to be established to address information asymmetry and facilitate the open and fair trading of environmental rights.\u003c/p\u003e\u003cp\u003eThese policy arrangements create favorable conditions for treating the Green Finance Reform and Innovation Pilot Zones as a quasi-natural experiment for the development of green finance. Firstly, the pilot zones are located in the eastern, central, and western regions, each with distinct levels of economic development and industrial structures. This ensures both differentiation and representativeness. Secondly, while the zones are uniformly planned by the government, each region has its own specific focus in implementation. This blend of uniformity and diversity in policy design creates a natural experiment-like environment, which helps identify the causal effects of the policy. Thirdly, the gradual rollout of the policy and the staggered approval process across regions provide temporal and spatial variations. This variation facilitates the use of quasi-experimental methods for evaluating the policy's impact.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Hypothesis Development\u003c/h2\u003e\u003cp\u003eThis paper examines the indirect pathways through which green finance enhances the green total factor productivity of manufacturing enterprises from five perspectives: green technological innovation, financing constraints, the scale of environmental investments, financial mismatch mechanisms, and regulatory effects.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Green Technological Innovation\u003c/h2\u003e\u003cp\u003eGreen technological innovation plays a key role in enhancing Enterprise Green Total Factor Productivity (EGTFP) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Green finance policies influence EGTFP through three main pathways:\u003c/p\u003e\u003cp\u003eFirstly, these policies provide targeted financial support, such as green credit, green bonds, and green funds, which reduce the financial barriers for enterprises to invest in green technological innovation. Access to such funding allows enterprises to undertake high-risk, high-cost R\u0026amp;D in green technologies, facilitating the development and application of new environmental technologies [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecondly, green finance policies send market signals that encourage investors and consumers to consider an enterprise's environmental performance. This increases reputational pressure on firms, motivating them to invest more in green technology R\u0026amp;D and promoting the diffusion and industrialization of green technologies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, these signals enhance firms' ability to attract top talent and foster research collaborations, accelerating green technological innovation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLastly, green finance policies encourage enterprises to join green technology alliances and collaborate on R\u0026amp;D, promoting knowledge sharing and technology transfer. Such cooperation helps reduce R\u0026amp;D costs and risks, improving both innovation efficiency and the quality of outcomes. This, in turn, enhances the green technological innovation capabilities of enterprises. Numerous studies confirm that technological innovation can significantly boost total factor productivity [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, the development of green finance can improve EGTFP by fostering green technological innovation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Financing Constraints\u003c/h2\u003e\u003cp\u003eGreen finance policies impose stricter financing conditions on high-pollution, high-emission manufacturing enterprises, increasing their financing difficulties and costs [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Specifically, green finance pilot policies require financial institutions to enforce strict credit thresholds, making environmental protection and governance performance a key criterion for loan approval [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Manufacturing enterprises, due to their high energy consumption and emissions, are often viewed as heavy polluters[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. As a result, these enterprises face higher financing thresholds and costs, which exacerbate their financing constraints. With limited access to financing, heavily polluting enterprises are forced to improve their environmental performance in order to alleviate these constraints. This pressure encourages them to increase investments in green technologies [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], which enhances their green innovation capabilities, enables them to meet the requirements of green finance policies, and helps reduce financing costs, thereby securing financial support [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Thus, by intensifying external financing constraints, green finance policies indirectly compel heavily polluting enterprises to undergo green transformation and technological upgrades, ultimately enhancing their EGTFP.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Financial Misalignment Mechanism\u003c/h2\u003e\u003cp\u003eThe misallocation of financial resources occurs when environmental risks are not effectively identified and managed during capital allocation. This leads to more capital flowing toward high-pollution, high-energy industries instead of environmentally friendly ones. This misallocation not only worsens the inefficient use of resources but also limits financial support for innovation and sustainable development, ultimately reducing the green total factor productivity (EGTFP) of the economy. Financial misalignment weakens the investment and dissemination of green technologies, causing green industries to face financing difficulties and high costs, thereby hindering the development and transformation of the green economy [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGreen finance policies enhance EGTFP by optimizing resource allocation and directing funds toward environmentally sustainable industries [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Specifically, financial instruments such as green credit, green bonds, and green funds help correct the market\u0026rsquo;s overinvestment in high-pollution, high-energy industries, reducing resource misallocation and promoting the green transformation of the industrial structure [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, green finance policies require financial institutions to incorporate environmental, social, and governance (ESG) factors into their risk assessments, ensuring environmental risks are fully considered in credit decisions [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. By adjusting loan conditions and investment criteria, these policies reduce financial support for industries with high environmental risks, addressing the financial misalignment caused by under-assessed environmental risks in the market.\u003c/p\u003e\u003cp\u003eIn addition, green finance policies offer incentives, such as tax benefits, interest subsidies for green projects, and fiscal subsidies, to lower the financing costs of green initiatives and increase the attractiveness of green investments. These measures correct market failures and boost market participation in green investments, optimizing the allocation of financial resources [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGreen finance policies also promote transparency by requiring enterprises and financial institutions to disclose environmental performance and carbon emission data. This helps investors more accurately assess the environmental performance of enterprises, reducing capital misallocation caused by information asymmetry [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThrough financial instruments, risk management measures, and incentive mechanisms, green finance policies effectively correct the misalignment in traditional financial markets. These policies support the efficient use of resources and the dissemination of green technologies, providing financial backing for enterprise innovation and sustainable development. Ultimately, green finance policies enhance EGTFP by comprehensively optimizing financial resource allocation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4 Scale of Environmental Investments\u003c/h2\u003e\u003cp\u003eFirstly, green finance policies help alleviate the financial barriers to environmental investments by offering targeted financial instruments, such as green credit, green bonds, and green funds. These tools provide substantial capital for companies to fund their environmental projects [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The availability of such funds allows businesses to invest in green technologies and renewable resources, which in turn reduces energy consumption, improves resource efficiency, and decreases pollutant emissions, thus enhancing environmental quality [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecondly, green finance-driven environmental investments improve enterprises' competitive advantage and economic returns. According to Porter's hypothesis, effective policy regulation can encourage businesses to adopt environmentally friendly practices, thereby increasing resource efficiency and competitiveness [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. These investments not only help companies comply with environmental regulations and avoid penalties or reputational damage due to non-compliance but also improve production efficiency, reduce energy and resource consumption, and lower production costs through the adoption of advanced environmental technologies and process innovations [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFinally, proactive environmental investment behaviors signal an enterprise's commitment to corporate social responsibility and sustainable development. This strengthens the company\u0026rsquo;s brand image and market reputation, attracting more consumers and investors [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, supported by green finance policies, enterprises have scaled up their environmental investments, improved their environmental performance and resource efficiency, and fostered green technological innovation, ultimately enhancing their EGTFP.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.5 Moderating Effect Analysis\u003c/h2\u003e\u003cp\u003eThe successful implementation of green finance policies depends not only on the active participation of financial institutions but also on the cooperation and support of environmental regulatory agencies, such as the Environmental Protection Bureau. Studies indicate that the involvement of the Environmental Protection Bureau can significantly improve the effectiveness of green finance policies by enhancing policy promotion, supervision, and encouraging businesses to invest in green technologies and innovations. Research also shows that close collaboration between the Environmental Protection Bureau and financial institutions helps reduce information asymmetry, increases the transparency and credibility of policy implementation, and fosters greater trust in green financial instruments, ultimately boosting environmental performance.\u003c/p\u003e\u003cp\u003eAccording to spatial economics theory, geographical distance plays a key role in determining the efficiency of policy transmission and regulatory effectiveness [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. The farther enterprises are from the Environmental Protection Bureau, the more difficult it becomes for regulatory agencies to monitor environmental behavior, raising supervision costs and diminishing regulatory impact [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. In such scenarios, companies may be less inclined to comply with environmental regulations and may reduce investments in green technologies and environmental initiatives, thus slowing the improvement of EGTFP [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn conclusion, the greater the distance between enterprises and the Environmental Protection Bureau, the weaker the positive effect of green finance policies on EGTFP.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e2.2.6 Hypotheses Propose\u003c/b\u003ed\u003c/h2\u003e\u003cp\u003eBased on the above theoretical analysis, we propose the following hypotheses:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e\u003cp\u003eGreen finance policies increase EGTFP.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cp\u003eGreen finance policies enhance EGTFP by promoting green technological innovation.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003cp\u003eGreen finance policies enhance EGTFP by strengthening financing constraints.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 4\u003c/strong\u003e\u003cp\u003eGreen finance policies enhance EGTFP by alleviating financial mismatch.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 5\u003c/strong\u003e\u003cp\u003eGreen finance policies enhance EGTFP by expanding environmental investment.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 6\u003c/strong\u003e\u003cp\u003eThe distance between enterprises and the Environmental Protection Bureau negatively moderates the positive impact of green finance policies on EGTFP.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cp\u003eFigure 1. demonstrates the influence mechanisms of green finance policies on EGTFP.\u003c/p\u003e"},{"header":"3. Data and Methods","content":"\u003cp\u003e\u003cb\u003e3.1 Sample Selection\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eExclusion of Financial Enterprises: Financial enterprises were excluded due to their distinct financial structures and operational characteristics, which differ significantly from those of manufacturing firms, ensuring consistency within the sample.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eExclusion of Financially Distressed or Irregular enterprises: Enterprises classified as ST (Special Treatment), *ST (Delisting Warning), and PT (Particular Transfer) were excluded to avoid potential biases in the study's findings, which could arise from financial distress or significant regulatory non-compliance.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eExclusion of Samples with Missing Key Variables: To ensure the reliability of the analysis, samples with substantial missing data on key variables were excluded..\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWinsorization of Continuous Variables: To address potential distortions caused by extreme values, all continuous variables were winsorized at the 1% level on both ends.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eUltimately, a sample comprising 968 manufacturing enterprises with a total of 12,584 annual observations was constructed. Unless otherwise specified, all variable data used in this study were sourced from the CSMAR (China Securities Market and Accounting Research) database.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Variable Definitions\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Enterprise Green Total Factor Productivity (EGTFP)\u003c/h2\u003e\u003cp\u003eEGTFP was measured using MaxDEA software, employing the Slack-Based Measure (SBM) model, which incorporates both radial and non-radial distance functions, as outlined by Wang et al. (2023)[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. The EGTFP measurement considered three input indicators and two types of outputs, as detailed below:\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003e1. Input Indicators:\u003c/h3\u003e\n\u003cp\u003eCapital Input: Represented by the net value of fixed assets at the end of the year. To account for price inflation, the fixed asset investment price index was used for deflation.\u003c/p\u003e\u003cp\u003eLabor Input: Represented by the number of employees.\u003c/p\u003e\u003cp\u003eEnergy Input: Represented by the consumption of standard coal. Due to the lack of enterprise-level data on standard coal consumption, this study followed the approach of Wang et al. (2023) to estimate enterprise-level coal consumption from regional data[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. First, regional standard coal consumption data were sourced from the China Energy Statistical Yearbook. An adjustment coefficient for regional standard coal consumption was then calculated to derive a weighted adjusted figure. Finally, the enterprise's standard coal consumption was estimated based on its share of regional industrial output.\u003c/p\u003e\u003cp\u003e2. Expected Output: Represented by operating income.\u003c/p\u003e\u003cp\u003e3. Non-Expected Output: Measured by the emissions of industrial waste, specifically industrial sulfur dioxide, industrial wastewater, and industrial smoke and dust emissions. Similar to the energy input estimation method, pollution emissions were calculated based on the enterprise's share of total energy input.\u003c/p\u003e\u003cp\u003eGiven that the Global Malmquist-Luenberger (GML) index can effectively measure the growth rate of EGTFP, it was employed as a robustness check proxy variable.\u003c/p\u003e\u003cp\u003eThe study also generated trend graphs of EGTFP for the years 2010, 2014, 2018, and 2022, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The trend analysis revealed a consistent annual increase in EGTFP over the study period, indicating steady improvements in the green productivity of the analyzed enterprises.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e3.2.2 Green Finance\u003c/div\u003e\u003cp\u003eThe treatment variable for green finance reform policy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:did\\)\u003c/span\u003e\u003c/span\u003e) is measured by the interaction of the treatment group dummy variable (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:treat\\)\u003c/span\u003e\u003c/span\u003e) and the policy implementation time dummy variable (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:post\\)\u003c/span\u003e\u003c/span\u003e), expressed as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:did=treat\\times\\:post\\)\u003c/span\u003e\u003c/span\u003e. If an enterprise's city is designated as a Green Finance Reform and Innovation Pilot Zone during the sample period, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:treat\\)\u003c/span\u003e\u003c/span\u003e is set to 1; otherwise, it is 0. The dummy variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:post\\)\u003c/span\u003e\u003c/span\u003e is set to 1 in the year (2017) the green finance policy was implemented and in subsequent years; otherwise, it is 0. When both dummy variables are 1, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:did\\)\u003c/span\u003e\u003c/span\u003e equals 1, indicating that the enterprise is in a pilot area of the green finance policy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e3.2.3 Mechanism Variables\u003c/div\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eGreen Technological Innovation (GTI): Given the time lag between patent application and grant, which typically spans one to two years, patent applications are used rather than grants, as they provide a more timely reflection of an enterprise's GTI activities. The level of GTI is measured by the total number of green invention and utility model patent applications filed by the enterprise.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eFinancing Constraints (FC): Following Kaplan and Zingales (1997)[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], the KZ index is used to measure the level of financing constraints faced by the enterprise.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eFinancial Misalignment (FM): According to Wang and Ma (2023)[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], the degree to which an enterprise's capital cost deviates from the industry average is used as a proxy for financial misalignment. This is measured by the ratio of interest expenses in financial costs to total liabilities, excluding accounts payable.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEnvironmental Investment (INV): Following Zhang et al. (2019)[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], environmental investment is calculated by summing expenditures directly related to environmental protection, such as desulfurization, denitration, wastewater treatment, waste gas treatment, dust removal, and energy-saving projects, as reported in the construction in progress section of an enterprise\u0026rsquo;s annual report. The ratio of the annual increase in environmental investment to the enterprise\u0026rsquo;s size is used to measure environmental investment. To improve the readability of subsequent regression coefficients, this variable is multiplied by 100.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e3.2.4 Regulatory Distance\u003c/div\u003e\u003cp\u003eFollowing Liao and Zhang (2024)[\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e], the core variable for geographical distance from the regional Environmental Protection Bureau (EPB) to the enterprise is constructed. This requires detailed location data for both China's county-level EPBs and the enterprises. The location data for the enterprise offices are obtained from the CSMAR database. For the geographical information of EPBs, we manually collected the location data of county-level EPBs across China using Baidu Maps and extracted their latitude and longitude coordinates. Subsequently, we calculated the straight-line distance from each enterprise to the nearest EPB in its region, using this distance as a proxy for regulatory distance. A larger regulatory distance indicates weaker environmental regulatory intensity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e3.2.5 Control Variables\u003c/div\u003e\u003cp\u003eTo control for other factors influencing Enterprise Green Total Factor Productivity (EGTFP), the following firm characteristics were included in the analysis:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEnterprise Age (age): Measured by the number of years since the enterprise's establishment.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLeverage Ratio (lev): Calculated as the total liabilities at the end of the year divided by total assets at the end of the year.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eReturn on Assets (roa): Measured by dividing net profit by the average balance of total assets.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eNet Profit Growth Rate (profit): Measured by the difference between current and previous period net profits divided by the net profit of the previous period.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCash Flow Ratio (cash): Represented by the cash flow from operating activities divided by total assets.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eShareholder Concentration (top 1): Measured by the shareholding percentage of the largest shareholder.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRevenue Growth Rate (growth): Calculated as the ratio of the current year's operating revenue to the previous year's operating revenue minus one.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRelative Firm Value (tobinq): Depicted using Tobin's Q ratio.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eTo examine the relationship between green finance and EGTFP, we constructed a multidimensional fixed-effects model covering firm, time, industry, and province dimensions. The specific model configuration is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{\\text{E}\\text{G}\\text{T}\\text{F}\\text{P}}_{\\text{i}\\text{t}}={{\\alpha\\:}}_{0}+{{\\alpha\\:}}_{1}{\\text{d}\\text{i}\\text{d}}_{\\text{i}\\text{t}}+\\sum\\:{\\alpha\\:}{\\text{X}}_{\\text{i}\\text{t}}+{{\\lambda\\:}}_{\\text{i}}+{{\\lambda\\:}}_{\\text{t}}+{{\\lambda\\:}}_{\\text{i}\\text{n}\\text{d}}+{{\\lambda\\:}}_{\\text{p}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\#\\end{array}\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{E}\\text{G}\\text{T}\\text{F}\\text{P}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003erepresents the green total factor productivity of firm i in year t.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{d}\\text{i}\\text{d}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the treatment variable for the green finance policy.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\alpha\\:}}_{1}\\)\u003c/span\u003e\u003c/span\u003eis the coefficient measuring the impact of green finance on EGTFP.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\lambda\\:}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e、\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\lambda\\:}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e、\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\lambda\\:}}_{\\text{i}\\text{n}\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e和\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\lambda\\:}}_{\\text{p}}\\)\u003c/span\u003e\u003c/span\u003erepresent the fixed effects controlled for firms, years, industries, and provinces respectively.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the random error term.\u003c/p\u003e\u003cp\u003eConsidering the theoretical mechanisms through which green finance affects EGTFP, we reference Chen et al. (2020) to construct the following mechanism test model[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{\\text{M}}_{\\text{i}\\text{t}}={{\\beta\\:}}_{0}+{{\\beta\\:}}_{1}{\\text{d}\\text{i}\\text{d}}_{\\text{i}\\text{t}}+\\sum\\:{\\beta\\:}{\\text{X}}_{\\text{i}\\text{t}}+{{\\lambda\\:}}_{\\text{i}}+{{\\lambda\\:}}_{\\text{t}}+{{\\lambda\\:}}_{\\text{i}\\text{n}\\text{d}}+{{\\lambda\\:}}_{\\text{p}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\#\\end{array}\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e represents the four mechanism variables: GTI, Financing Constraints (FC), Financial Misalignment (FM), and Environmental Investment (INV).\u003c/p\u003e\u003cp\u003eFurther, we constructed the following model to discuss the moderating effect of regulatory distance on the relationship between green finance and EGTFP:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{\\text{E}\\text{G}\\text{T}\\text{F}\\text{P}}_{\\text{i}\\text{t}}={{\\upvartheta\\:}}_{0}+{{\\upvartheta\\:}}_{1}{\\text{d}\\text{i}\\text{d}}_{\\text{i}\\text{t}}+{{\\upvartheta\\:}}_{2}{\\text{d}\\text{i}\\text{d}}_{\\text{i}\\text{t}}\\times\\:{\\text{D}\\text{I}\\text{S}}_{\\text{i}\\text{t}}+\\sum\\:{\\upvartheta\\:}{\\text{X}}_{\\text{i}\\text{t}}+{{\\lambda\\:}}_{\\text{i}}+{{\\lambda\\:}}_{\\text{i}\\text{n}\\text{d}}+{{\\lambda\\:}}_{\\text{p}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\#\\end{array}\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{D}\\text{I}\\text{S}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the proxy variable for regulatory distance, specifically the geographical distance from the firm to the nearest Environmental Protection Bureau in its region. As regulatory distance data is cross-sectional, we only include firm, industry, and province fixed effects in Eq.\u0026nbsp;(3).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Baseline Results\u003c/h2\u003e\u003cp\u003eThe regression in Eq.\u0026nbsp;(1) tests the impact of green finance on EGTFP, with the results shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Specifically:\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\u003eStatistical Characteristics of Core 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=\"left\" 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\u003eVariables\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\u003esd\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\u003e\u003cem\u003eEGTFP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.284\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGTI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.1881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.6582\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.837\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eINV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11,761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.264\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDIS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e101.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eage\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003elev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eroa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eprofit\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-42.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.274\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ecash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003etop1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e71.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003egrowth\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.475\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003etobinq\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.464\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\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\u003eBaseline Regression Results of Green Finance Policy on EGTFP\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\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eFE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003edid\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0718\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0064\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0082\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0082\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0014)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6036\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8366\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8316\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9917\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0065)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0059)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControl\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003efirm\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eyear\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eindustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eprovince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eadj. R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9733\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors in parentheses. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eColumn (1) presents the Ordinary Least Squares (OLS) estimation results. Columns (2) and (3) show the results with random effects and fixed effects, incorporating firm and time dimensions. Column (4) includes all fixed effects and control variables.\u003c/p\u003e\u003cp\u003eIn Columns (1) to (4), the estimated coefficients of did are significantly positive at the 1% level, indicating that the implementation of green finance policies enhances EGTFP, thereby supporting Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eWhile previous studies have explored the impact of green finance on EGTFP, their conclusions are primarily based on macro-regional data and often rely on the problematic Green Finance Development Index, which suffers from significant endogeneity. Examples include studies by Lee and Lee (2022), Tong et al. (2022), Yue et al. (2024), and Feng et al. (2024)[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Some researchers have examined the effects of green finance on firm-level total factor productivity but without integrating environmental factors, as seen in studies by Zhao et al. (2023)[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e] and Zhou et al. (2023)[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Our study, based on a specific green finance policy implemented by the Chinese government, effectively mitigates the endogeneity concerns associated with indicator selection. Furthermore, by considering both firm-level energy inputs and pollution outputs in the measurement of EGTFP, our research provides a more accurate reflection of firms' green development. As such, our study addresses the limitations of previous research and offers more concrete policy implications.\u003c/p\u003e\u003cp\u003eOur findings reveal how green finance enhances enterprise operational performance and strengthens pollution control, thus improving EGTFP. First, green finance, with its dual focus on finance and sustainability, not only provides essential financial support for firms' green transformation and industrial upgrading but also optimizes resource allocation. This enables investments in green innovations that offset the short-term operational pressures of environmental expenditures, thereby supporting sustained growth in operational performance. Second, by internalizing environmental costs into investment decisions, green finance incentivizes firms to actively engage in green and low-carbon projects. This shift not only enhances enterprise social responsibility but also redirects financial resources from high-pollution sectors to environmentally friendly enterprises, creating a positive financial incentive for pollution control. As green finance increasingly supports low-carbon initiatives, it promotes the rational allocation of financial resources, encouraging polluting enterprises to actively participate in environmental governance. Consequently, green finance facilitates the transition of firms to more sustainable, efficient, and green development models, significantly boosting EGTFP.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Robustness Tests\u003c/h2\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Parallel Trends Test\u003c/h2\u003e\u003cp\u003eBuilding on the research by Beck et al. (2010)[\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], a parallel trend test was performed, which confirmed the absence of pre-existing trends in EGTFP prior to the implementation of green finance policies. The dynamic effects of these policies on EGTFP are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Prior to the policy implementation, there were no significant differences in EGTFP between the pilot and non-pilot regions. However, after the policy rollout, EGTFP in the pilot regions experienced a notable increase, thereby supporting the validity of the parallel trend assumption.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2. Double Machine Learning (DDML)\u003c/h2\u003e\u003cp\u003eTo evaluate the robustness of the effect of green finance on EGTFP, we utilize the Double Machine Learning (DDML) method introduced by Chernozhukov et al. (2018)[\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Unlike traditional causal inference techniques, DDML integrates machine learning models into the auxiliary equations, mitigating regularization bias. This approach not only mitigates the dimensionality problem caused by an excessive number of control variables but also improves the accuracy of nonlinear estimates through non-parametric feature selection [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. In our analysis, we employed four methods\u0026mdash;random forest, Lasso regression, gradient boosting, and neural networks\u0026mdash;to predict the results of both the primary and auxiliary regressions. The sample split ratio was set at 1:4, and squared terms of the control variables were included. As presented in columns (1) to (4) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the coefficient estimates for the \"did\" variable remain significantly positive, confirming the robustness of the green finance effect on EGTFP.\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\u003eResults of the DDML Analysis\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\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003erf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003elassocv\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003egradboost\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ennet\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003edid\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0111\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0109\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0107\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0111\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0029)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0004\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0004\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0005\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0004\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControl\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003efirm\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eyear\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eindustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eprovince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors in parentheses. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3. Propensity Score Matching (PSM)\u003c/h2\u003e\u003cp\u003eConsidering the significant differences between enterprises supported by green finance policies in pilot regions and those outside the scope of these policies, we apply kernel matching to select the PSM samples and re-assess the research model using DID regression. The results in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e further support the baseline findings.\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\u003eResults of Robustness Checks\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\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePSM-DID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGML\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInteractive Fixed Effects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eExclusion of Interference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKey Polluting Industries\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003edid\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0082\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0063\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0086\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0070\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0189\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0126\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0059)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003efeigaishui\u003c/em\u003e\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\u003e0.0699\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003e(0.0010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9919\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5668\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9906\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9329\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1357\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0214\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0097)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0066)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0062)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0103)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControl\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003efirm\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eyear\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eindustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eprovince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3588\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eadj. R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8579\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Standard errors in parentheses. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\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\u003e4.2.4. GML Index\u003c/h2\u003e\u003cp\u003eAs described in Section 3.1.1, we use the GML index as a proxy variable for EGTFP to investigate the impact of the did variable on the dynamic rate of change in EGTFP. The estimated value of did in column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is 0.0063 and is significant at the 1% level. This not only strengthens the baseline finding regarding green finance's effect on EGTFP but also reflects its dynamic enhancement effect on the high-quality development of enterprises.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003e4.2.5. Changes in Interaction Fixed Effects\u003c/h2\u003e\u003cp\u003eTo account for the heterogeneity of time trends across industries, we introduce interaction fixed effects for time and industry as a robustness check, controlling for industry characteristics that may evolve over time. The estimated DID coefficient in column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is 0.0086 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), further confirming the positive impact of green finance on EGTFP.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003e4.2.6. Excluding External Environmental Disturbances\u003c/h2\u003e\u003cp\u003eWe address the potential influence of external environmental factors on the baseline results from two perspectives. First, to account for the impact of COVID-19 on enterprise performance, production decisions, and governance responsibilities, we limit the sample period to 2011\u0026ndash;2019. Second, the formal implementation of the Environmental Protection Tax Law in 2018 may distort the effect of green finance pilot policies on EGTFP, potentially confounding our regression results. To control for this possible interference, we include a dummy variable for the environmental tax reform in the regression model. The results from these two approaches are reported in columns (4) and (5) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The coefficient of the core explanatory variable remains statistically significant and retains its original sign, suggesting that the core conclusion holds robust even after accounting for potential external disturbances.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\u003ch2\u003e4.2.7. Key Polluting Enterprises\u003c/h2\u003e\u003cp\u003eThere may be some degree of correlation between green finance policies and the classification of enterprises as \"key polluting enterprises.\" These policies are likely more inclined to support non-key polluting enterprises in promoting their transition to greener practices, while key polluting enterprises may face stricter financing restrictions. To address this, we conducted an analysis excluding key polluting enterprises. The results presented in column (6) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that, after removing the influence of key polluting enterprises on the policy effects, the positive impact of green finance policies on EGTFP remains statistically significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section3\"\u003e\u003ch2\u003e4.2.8. Placebo Test\u003c/h2\u003e\u003cp\u003eTo conduct a placebo test, we randomly assigned treatment and control groups while maintaining the original policy timing. A number of enterprises equal to those covered by the green finance policy were randomly selected from the sample to form a random treatment group. The virtual policy effects of this random grouping were estimated based on Eq.\u0026nbsp;(1), with 1,000 simulations performed. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates that the green finance policy had no effect on EGTFP in this random grouping. Regarding the significance of the random results, the majority of p-values exceeded 0.1, indicating non-significance at the 10% level, thereby passing the placebo test.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Further Analysis","content":"\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Heterogeneity Analysis\u003c/h2\u003e\u003cdiv id=\"Sec32\" class=\"Section3\"\u003e\u003ch2\u003e5.1.1. Enterprise-Level Heterogeneity\u003c/h2\u003e\u003cp\u003e(1) Background of Enterprise Executives in Environmental Protection\u003c/p\u003e\u003cp\u003eDifferences in the backgrounds, experiences, and values of executive teams across various enterprises may result in varying responses and effects when confronted with green finance policies [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. By examining the heterogeneity associated with whether enterprise executives have an environmental background, we can gain deeper insights into the implementation effects and mechanisms of green finance policies across different types of enterprises. This understanding will help policymakers better formulate and adjust policies to facilitate the green transformation and sustainable development of enterprises. To capture this heterogeneity, we introduce a dummy variable, \"X1.\" If \"X1\" equals 1, it indicates that the enterprise executives have an environmental background; otherwise, \"X1\" equals 0. The coefficient of \"did\u0026times;X1\" in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e is 0.0059 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that when enterprise executives have an environmental background, the impact of green finance policies on EGTFP is more pronounced. A plausible explanation is that executives with an environmental background are more likely to integrate environmental protection and sustainable development into the enterprise\u0026rsquo;s long-term strategic planning, thereby fostering innovations in green technologies, products, and services [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. This strategic shift directly contributes to improvements in EGTFP.\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\u003eResults of Heterogeneity Analysis\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\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eEnterprise Level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eRegional Level\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnvironmental Protection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eState-Owned Enterprise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeavy Pollution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEastern Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMarketization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDIF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003edid\u0026times;X1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0059\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0028)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003edid\u0026times;X2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0088\u003csup\u003e***\u003c/sup\u003e\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\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0020)\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\"\u003e\u003cp\u003e\u003cem\u003edid\u0026times;X3\u003c/em\u003e\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\u003e0.0098\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\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\"\u003e\u0026nbsp;\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\u003e(0.0030)\u003c/p\u003e\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\"\u003e\u003cp\u003e\u003cem\u003edid\u0026times;X4\u003c/em\u003e\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\u003cp\u003e0.0050\u003csup\u003e***\u003c/sup\u003e\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\"\u003e\u0026nbsp;\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\u003cp\u003e(0.0018)\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\"\u003e\u003cp\u003e\u003cem\u003edid\u0026times;X5\u003c/em\u003e\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\u003e0.0060\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003e(0.0017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003edid\u0026times;X6\u003c/em\u003e\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0078\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0014)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9828\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9912\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9917\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9923\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9923\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9918\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0064)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0059)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControl\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003efirm\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eyear\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eindustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eprovince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eadj. R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9733\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Standard errors in parentheses. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(2) Nature of Ownership\u003c/p\u003e\u003cp\u003eState-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs) exhibit significant differences in terms of their nature, ownership structure, and management practices. As a critical economic pillar of the state, SOEs often receive guidance and support from national policies, particularly with respect to the implementation of green finance policies, which may enhance their access to policy incentives and financial support. In contrast, non-SOEs may face greater challenges in securing policy benefits and funding. We introduce a dummy variable, \"X2,\" where a value of 1 indicates that the enterprise is an SOE, and a value of 0 indicates a non-SOE. The positive and statistically significant coefficient of \"did\u0026times;X2\" in column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicates that green finance has a stronger promoting effect on the EGTFP of SOEs. Specifically, SOEs are more likely to achieve greener transformations and improve green total factor productivity more rapidly due to their advantages in policy support and resource acquisition. In contrast, non-SOEs must strengthen internal management, enhance technological innovation capabilities, and explore diversified financing channels to overcome the challenges posed by green finance policies.\u003c/p\u003e\u003cp\u003e(3) Heavily Polluting Enterprises\u003c/p\u003e\u003cp\u003eGreen finance policies are designed to guide enterprises toward greener, low-carbon, and environmentally friendly development through financial mechanisms. For heavily polluting enterprises, these policies may impose stricter loan conditions and higher financing costs to limit high-pollution, high-energy-consuming production activities and encourage technological upgrades and green transitions [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. We introduce a dummy variable, \"X3,\" to reflect the pollution type of the enterprise. If the enterprise is heavily polluting, \"X3\" is set to 1; otherwise, it is set to 0. The coefficient of \"did\u0026times;X3\" in column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e is 0.0098 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that green finance policies have a more significant improvement effect on EGTFP for heavily polluting enterprises. This effect may stem from the requirement that financial institutions conduct rigorous evaluations of enterprises' environmental performance during the loan approval process, which involves implementing stricter loan conditions or denying loans to those failing to meet environmental standards. This policy constraint mechanism effectively incentivizes heavily polluting enterprises to increase their investments in environmental protection and technological innovation, thereby enhancing their EGTFP.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\u003ch2\u003e5.1.2. Regional-Level Heterogeneity\u003c/h2\u003e\u003cp\u003e(1) Economic Development Level\u003c/p\u003e\u003cp\u003eLocal governments in economically developed regions often demonstrate stronger execution and higher efficiency when implementing green finance policies. These regions are better positioned to swiftly establish and refine green finance policy frameworks, facilitating effective policy implementation. Additionally, financial institutions in economically advanced areas tend to have a denser presence and more abundant resources, which better supports the implementation of green finance policies. As a result, the policy effects of green finance may vary depending on the development level of the region in which the enterprise operates. The significant economic disparities between the eastern and central/western regions of China provide a valuable context for exploring the implications of these differences on policy effectiveness [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. To capture the economic development level of the region where the enterprise is located, we introduce a dummy variable, \"X4.\" If the enterprise is situated in eastern China, \"X4\" is equal to 1, indicating a more favorable economic environment; if located in central or western China, \"X4\" is equal to 0. The coefficient of \"did\u0026times;X4\" in column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e is 0.0050 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that green finance policies have a more significant impact on EGTFP in eastern regions. The industrial structure in eastern regions is relatively optimized, with a higher proportion of high-tech and green industries. These sectors have a more pressing demand for green finance policies and can derive greater benefits from them. In contrast, the industrial structure in central and western regions may still be more reliant on traditional, energy-intensive, and polluting industries, making the transition to greener practices more challenging and potentially resulting in a weaker response to green finance policies.\u003c/p\u003e\u003cp\u003e(2) Marketization Level\u003c/p\u003e\u003cp\u003eThe degree of marketization influences enterprise behavior [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]and subsequently affects the promotional impact of green finance on EGTFP. Drawing on a series of studies, including Fan et al. (2003)[\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], we utilize marketization indices across various provinces to represent the marketization level of the regions where enterprises operate. We create a dummy variable, \"X5,\" using the median of the marketization index as a cutoff. If the marketization index of the enterprise's region exceeds the median, \"X5\" is set to 1, indicating a high degree of marketization; otherwise, \"X5\" is set to 0.\u003c/p\u003e\u003cp\u003eThe coefficient of \"did\u0026times;X5\" in column (5) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e is 0.0060 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that green finance is more effective in promoting EGTFP growth in regions with higher marketization levels. On the one hand, enterprises in highly marketized areas face greater market competition, which drives them to adopt new technologies and processes to enhance production efficiency and resource utilization. On the other hand, the regulatory framework in these regions is generally more comprehensive, with stricter oversight of environmental pollution and resource waste. The combination of green finance policies and robust environmental regulations effectively constrains pollution behaviors, incentivizing enterprises to increase environmental investments and improve EGTFP.\u003c/p\u003e\u003cp\u003e(3) Digital Inclusive Finance Level\u003c/p\u003e\u003cp\u003eDigital inclusive finance, through its extensive coverage and efficient financial services, can provide enterprises facing financing difficulties with more accessible and cost-effective funding channels [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. This characteristic aligns closely with the goals of green finance, facilitating the green transitions of enterprises and enhancing EGTFP. We measure the level of digital inclusive finance in the region where the enterprise operates using an index compiled by Peking University's Digital Finance Research Center. Similarly, we introduce a dummy variable, \"X6,\" using the median of the digital inclusive finance index as a benchmark. If the index exceeds the median, \"X6\" is set to 1, indicating a high level of digital inclusive finance; otherwise, \"X6\" is set to 0. The coefficient of \"did\u0026times;X6\" in column (6) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e is 0.0078 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The results suggest that in regions with high levels of digital inclusive finance, green finance significantly enhances EGTFP. This may be due to enterprises in areas with well-developed digital inclusive finance finding it easier to access support from green finance, making the policy effects more pronounced. In contrast, enterprises in regions with lower levels of digital inclusive finance may face challenges such as higher financing costs, which limit the effectiveness of green finance policies and the improvement of EGTFP.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Impact Mechanism Analysis\u003c/h2\u003e\u003cp\u003eThis section further investigates the mechanisms through which green finance influences the enhancement of green total factor productivity (EGTFP) from four perspectives: Green Technology Innovation (GTI), financing constraints, financial mismatch, and environmental investment. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the regression results from Eq.\u0026nbsp;(2).\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\u003eResults of the Impact Mechanism Analysis\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\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGTI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFinancing Constraints\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFinancial Mismatch\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnvironmental Investment\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGTI\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\u003eFM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eINV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003edid\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.8982\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9832\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.1398\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0214\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0559)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0725)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0528)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0073)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8139\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4.8319\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.3867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0366\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.2422)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.3320)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.2420)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0350)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControl\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003efirm\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eyear\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eindustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eprovince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11761\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eadj. R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors in parentheses. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eColumn (1) illustrates the transmission mechanism of GTI, where the coefficient of the DID variable is 1.8982 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating a significant positive impact of green finance on EGTFP. This result suggests that green finance enhances enterprises' green innovation capabilities, thereby improving EGTFP. Column (2) reports the results related to financing constraints, where the coefficient is 0.9832 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This finding implies that green finance alleviates the financing constraints faced by manufacturing enterprises, motivating them to transition towards green and low-carbon practices, which, in turn, improves EGTFP. Column (3) demonstrates the results indicating that green finance mitigates financial mismatch and influences EGTFP, with a coefficient of -0.1398 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This suggests that green finance reduces the misallocation of financial resources among manufacturing firms, enhancing the efficiency of financial resource allocation and driving sustained growth in EGTFP. In Column (4), the coefficient for DID is 0.0214 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that green finance drives EGTFP growth by increasing environmental investment by enterprises. Overall, this analysis supports Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, highlighting the mechanisms through which green finance enhances EGTFP.\u003c/p\u003e\u003cp\u003eFrom the perspective of the Green Technology Innovation (GTI) mechanism, Schumpeter's theory of innovation suggests that technological innovation is a key driver of economic growth. Under the influence of green finance policies, enterprises invest in research and development of environmentally friendly technologies, improvement of production processes, and development of green products [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. These efforts not only reduce resource consumption and environmental pollution but also enhance production efficiency and product quality, thereby boosting EGTFP.\u003c/p\u003e\u003cp\u003eWith regard to the financing constraint mechanism, green finance policies compel manufacturing enterprises, particularly those in heavily polluting industries, to improve their production methods to reduce energy and resource consumption[\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. Under these policies, banks and investment institutions are more likely to provide loans to enterprises that meet green standards, thus limiting financial support for polluting firms. This shift constrains production decisions, facilitates the exit of polluting projects, and promotes the entry of green projects, ultimately improving EGTFP.\u003c/p\u003e\u003cp\u003eIn terms of financial mismatch, green finance policies direct capital towards green, low-carbon, and environmentally friendly sectors, thereby reducing excessive investment in traditional high-pollution and high-energy-consuming industries and decreasing financial misallocation [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. This mechanism aligns with the theory of industrial structural upgrading, which emphasizes the importance of transitioning to a more rational and efficient industrial structure for sustained economic growth. By optimizing the allocation of financial resources, green finance policies facilitate the shift from polluting industries to clean industries, contributing to the growth of EGTFP.\u003c/p\u003e\u003cp\u003eFinally, environmental investment plays a crucial role in enabling enterprises to achieve green development and enhance EGTFP. By increasing environmental investments, enterprises can internalize the negative externalities associated with environmental pollution as part of their operational costs, thereby incentivizing the adoption of more environmentally friendly production methods [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Green finance policies, through incentives such as policy benefits and tax breaks, encourage enterprises to increase their environmental investments. This enables them to meet their environmental and social responsibilities while simultaneously improving both performance and efficiency.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec35\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Moderating Effect Analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the regression results for Eq.\u0026nbsp;(3) under two different fixed effects specifications. The coefficients for \"did\u0026times;DIS\" in both Column (1) and Column (2) are \u0026minus;\u0026thinsp;0.0081 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that regulatory distance (DIS) negatively moderates the impact of green finance on EGTFP, thus supporting Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As previously mentioned, we measure regulatory distance using the proximity of enterprises to their respective regional environmental protection bureaus. An increase in regulatory distance significantly weakens the effect of green finance on EGTFP. This study incorporates regulatory distance into the research framework on green finance and enterprise green transformation, extending the work of Hu et al. (2021) [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]and Liao and Zhang (2024)[\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e], which primarily focus on the influence of regulatory distance on enterprise innovation activities.\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 the Moderating Effect of Regulatory Distance\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\"\u003e\u003cp\u003eVariables\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\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003edid\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0447\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0449\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0046)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003edid\u0026times;DIS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0081\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0081\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0026)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1266\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1263\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0060)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControl\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003efirm\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eindustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eprovince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12584\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eadj. R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Standard errors in parentheses. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe moderating effects revealed in this analysis illustrate the complex role of regulatory distance in the implementation of green finance policies and their impact on EGTFP. We offer the following explanations: From the perspective of information asymmetry and regulatory costs, increased regulatory distance may exacerbate the information asymmetry between enterprises and environmental protection agencies. A greater distance complicates the agencies' ability to access real-time and accurate information regarding enterprise environmental behaviors, thus increasing regulatory costs and challenges [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. This information asymmetry diminishes the effectiveness of green finance policy implementation, hindering its potential to promote enterprise green transformation.\u003c/p\u003e\u003cp\u003eRegarding the externality effects of increased geographical distance, enterprises may reduce their investments in green technologies and management due to a perceived decrease in regulatory pressure as the regulatory distance widens, thereby weakening the effectiveness of green finance policies on EGTFP enhancement. Additionally, in regions with more lenient environmental regulations, enterprises may find it easier to evade regulatory responsibilities and obligations for green transformation [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. This results in a distance decay effect on the efficacy of green finance policy implementation, ultimately limiting its ability to effectively promote EGTFP.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusion and Policy Implications","content":"\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Conclusion\u003c/h2\u003e\u003cp\u003eThis study investigates the impact of green finance on green total factor productivity (EGTFP) from the micro perspective of manufacturing enterprises, using the 2017 implementation of China's green finance reform and innovation pilot zones as an exogenous event. The empirical results indicate that green finance significantly enhances EGTFP, highlighting the positive incentive effects of green finance policies on both enterprise performance and environmental protection. The heterogeneity analysis at the enterprise level reveals that the effectiveness of green finance policies in enhancing EGTFP is particularly pronounced in enterprises with environmentally conscious executives, state-owned enterprises, and heavily polluting industries. From a regional perspective, the positive effects are more substantial in economically developed areas, regions with higher levels of marketization, and areas with advanced digital inclusive finance.\u003c/p\u003e\u003cp\u003eThe mechanism analysis shows that green finance improves enterprises' levels of green technology innovation (GTI), increases financing constraints for manufacturing enterprises, reduces financial resource misallocation, and enhances environmental investment intensity, all of which contribute to the improvement of EGTFP. Additionally, the study identifies a negative moderating effect of regulatory distance on the relationship between green finance and EGTFP, suggesting that an increase in regulatory distance weakens the policy effectiveness of green finance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec38\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Policy Implications\u003c/h2\u003e\u003cp\u003eFirst and foremost, it is crucial to actively promote green finance, with a primary focus on enhancing green total factor productivity (EGTFP). Green finance provides the necessary capital for enterprises to transition to greener practices. It also holds polluting enterprises accountable, encouraging them to incorporate environmental requirements into their development strategies. This drives them to quickly adjust their production methods. Therefore, the government should increase financial support, broaden the scope of green finance, and promote the use of financial instruments such as green loans and green bonds. Green finance should be aligned with clean production, energy conservation, and carbon reduction projects. By leveraging advanced technologies, these efforts can help promote energy savings and emissions reductions, ultimately guiding enterprises toward a more sustainable development path.\u003c/p\u003e\u003cp\u003eSecond, considering that green finance influences EGTFP through four mechanisms\u0026mdash;green technology innovation (GTI), financing constraints, financial mismatch, and environmental investment\u0026mdash;we propose four targeted recommendations:\u003c/p\u003e\u003cp\u003eGreen Finance Policy\u0026thinsp;+\u0026thinsp;Enterprise Green Technology Innovation: The government should introduce more specific incentive policies, such as tax reductions and research subsidies, to encourage enterprises to invest more in GTI. Additionally, establishing a dedicated GTI fund could prioritize support for innovative and market-potential green technology projects. The government can also create information exchange platforms to foster collaboration between financial institutions and enterprises.\u003c/p\u003e\u003cp\u003eGreen Finance Policy\u0026thinsp;+\u0026thinsp;Financing Constraints: A robust credit rating and risk assessment system for green projects should be established to improve their access to financing. Financial institutions need to improve their risk management for green projects to ensure safe and effective use of funds. Moreover, optimizing the credit structure to allocate more resources to environmental sectors will help alleviate financing constraints for green projects.\u003c/p\u003e\u003cp\u003eGreen Finance Policy\u0026thinsp;+\u0026thinsp;Financial Mismatch: Regulatory oversight of financial institutions\u0026rsquo; green finance activities should be strengthened to prevent misallocation of resources. A more scientific approach to allocating green finance resources should be developed to ensure funds are directed toward clean sectors. Financial institutions should set appropriate financing scales and interest rates based on the unique risks and needs of green projects.\u003c/p\u003e\u003cp\u003eGreen Finance Policy\u0026thinsp;+\u0026thinsp;Enterprise Environmental Investment: The government should support environmental enterprises through mechanisms such as issuing green bonds to raise funds for environmental projects. Policies should also encourage private sector participation in the construction and management of environmental investment projects, helping to ease the financial burden on enterprises.\u003c/p\u003e\u003cp\u003eThird, since regulatory distance reduces the effectiveness of green finance policies, it is essential to strengthen the role of environmental protection agencies in policy enforcement. Regulatory bodies should focus on manufacturing enterprises, especially publicly listed companies, that are geographically distant from regulatory oversight. Increased on-site inspections, along with the innovative use of technologies such as satellite remote sensing, should be employed to enhance regulatory effectiveness. This will ensure stricter compliance with environmental regulations. Additionally, the government should develop a digital regulatory system that leverages big data, cloud computing, and the Internet of Things. This system will comprehensively track environmental data from enterprises and enable real-time monitoring of key indicators such as the operation of environmental facilities and pollutant emissions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec39\" class=\"Section2\"\u003e\u003ch2\u003e6.3 Research Limitations\u003c/h2\u003e\u003cp\u003eThis study is subject to limitations due to the insufficient disclosure of enterprise environmental information, which restricts the direct acquisition of detailed environmental indicators at the enterprise level. As a result, we used industry-level data on energy consumption and pollutant emissions as proxies, constructing an indirect framework for environmental data at the enterprise level through reasonable estimation methods. However, such indirect estimation methods inevitably introduce potential biases, which may affect the accuracy of EGTFP calculations. To address this limitation, future research could focus on improving methods for obtaining energy consumption and environmental pollution data at the enterprise level. Advanced programming languages, such as Python, with their powerful data processing and analysis capabilities, could be employed to develop efficient data scraping and cleaning tools. These tools could facilitate the direct collection of original data on enterprise environmental performance from various channels and sources, thereby improving the accuracy and reliability of EGTFP calculations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConsent to Publish Declaration\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: not applicable.\u003c/p\u003e\n\u003cp\u003eConsent to Participate Declaration\u003c/p\u003e\n\u003cp\u003eConsent to Participate declaration: not applicable.\u003c/p\u003e\n\u003cp\u003eEthics Statement\u003c/p\u003e\n\u003cp\u003eEthics declaration: not applicable.\u003c/p\u003e\n\u003cp\u003eClinical Trial Declaration\u003c/p\u003e\n\u003cp\u003eOur study does not fall under the category of a clinical trial, so it is not applicable.\u003c/p\u003e\n\u003cp\u003eClinical Trial Number\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eFunding Statement\u003c/p\u003e\n\u003cp\u003eThis study is funded by the National Social Science Foundation of China. The project is titled \u0026quot;Research on the Financing Mechanism and Risk Governance of the Pig Industry Chain\u0026quot; and has the project number 21XGL007.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the support from the National Social Science Foundation of China for the project entitled \u0026quot;Research on Financing Mechanisms and Risk Governance in the Hog Industry Chain\u0026quot; (Grant No. 21XGL007), and we would like to thank the College of Management, Sichuan Agricultural University for the use of data analysis software provided by the Management Experiment Center of the College.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.Z. and Y.Q. contributed to the conceptualization, methodology design, and empirical strategy. K.G. conducted the formal analysis, data processing, and robustness testing. G.W. collected and curated the data, and participated in the interpretation of empirical results. C.X. contributed to the literature review and assisted in drafting and editing the manuscript. Y.W. supervised the entire project, provided critical revisions, and served as the corresponding author. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYANG Y., GUO H., CHEN L., LIU X., GU M.Y., KE X.L. Regional analysis of the green development level differences in Chinese mineral resource-based cities. Resources Policy, \u003cstrong\u003e61\u003c/strong\u003e, 261-272, \u003cstrong\u003e2019\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eMENTES M. Sustainable development economy and the development of green economy in the Eur-opean Union. 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Applied Economics, \u003cstrong\u003e50\u003c/strong\u003e (12), 1378-1394, \u003cstrong\u003e2018\u003c/strong\u003e. \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":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Green Finance, Environmental Regulation, Enterprise Green Total Factor Productivity, Regulatory Distance, Green Technological Innovation, Manufacturing Firms","lastPublishedDoi":"10.21203/rs.3.rs-6963369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6963369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGreen finance represents a transformative policy innovation designed to incentivize corporate environmental stewardship and accelerate sustainable economic development. However, the effectiveness of green finance in enhancing firm-level environmental performance remains underexplored, particularly considering the role of regulatory oversight. This study investigates how green finance policies impact Enterprise Green Total Factor Productivity (EGTFP), incorporating the moderating effect of regulatory distance. Using a Difference-in-Differences (DID) approach and the 2017 Green Finance Reform and Innovation Pilot Zones in China as a quasi-natural experiment, we analyze A-share listed manufacturing firms from 2010 to 2022. Our findings reveal that green finance initiatives significantly boost EGTFP, especially among firms with environmentally-experienced executives, state-owned ownership, heavy pollution profiles, and those operating in more economically developed, market-oriented, and digitally inclusive regions. Mechanism analyses indicate that green finance policies foster green technological innovation, mitigate financing constraints, and promote environmental investments, thereby internalizing environmental costs. Furthermore, greater regulatory distance weakens the positive effects of green finance on firms' green productivity. These results highlight the necessity of bridging financial instruments and environmental regulatory frameworks to maximize the effectiveness of green finance. Strengthening coordination between financial institutions and regulatory authorities is crucial to advancing the green transformation of the manufacturing sector.\u003c/p\u003e","manuscriptTitle":"Carrot or Stick? 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