Can green finance credit allocation enable green innovation quality improvement -- Evidence from China's manufacturing firms

Can green finance credit allocation enable green innovation quality improvement -- Evidence from China's manufacturing firms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Can green finance credit allocation enable green innovation quality improvement -- Evidence from China's manufacturing firms Liangfeng Hao, Biyi Deng, Chuanming Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4337275/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The allocation of green financial credit plays a crucial role in establishing a market-oriented green innovation system. This study sets up a quasi-natural experiment using the Green Credit Policy (GCP) to examine the impact of green financial credit allocation on the quality of enterprise green innovation, with a focus on promoting high-quality development. The findings demonstrate that the GCP has the potential to improve the quality of green innovation in industries restricted by green credit, compared to non-green credit-restricted ones. This conclusion remains consistent after conducting thorough trend analysis and robustness tests. As China speeds up its industrial digital transformation, the fundamental drive of green credit to enable enterprises towards green innovation is also evolving. The analysis of the impact mechanism reveals that green financial credit allocation can elevate the digitization level and total factor productivity of green credit-restricted industries, leading to a higher quality of green innovation by curbing corporate shadow banking. Furthermore, additional research shows that fintech and financial regulation can strengthen the positive influence of GCP on the quality of green innovation. Moreover, regional intellectual property protection has a beneficial synergistic effect in combination with GCP. This study confirms that green credit is an effective strategy for optimizing the allocation of green financial resources and enhancing the quality of green innovation, with amplified positive effects achievable through financial technology and financial regulation. Green finance Green innovation Patent quality Financial regulation Financial technology Figures Figure 1 Figure 2 1. Introduction Given the increasingly prominent impact of global climate change, there is a growing focus on environmental protection worldwide. Significant improvement of the ecological environment necessitates not only strong end-of-pipe measures but also harnessing the enabling power of financial resources. Green finance not only facilitates redirecting financial resources from polluting to eco-friendly endeavors but also encourages the allocation of a substantial amount of social capital to green initiatives. Research by Li et al. (2018) and de Haas and Popov (2019) demonstrates that green finance, from a global standpoint, plays a significant role in promoting green innovation within enterprises [ 1 , 2 ] . As the largest developing country in the world, China is in dire need of transitioning from its traditional development approach and establishing a green technological innovation system. However, China's environmental regulatory policies have overlooked the potential role of green finance in promoting high-quality green innovation essential for fostering environmentally sustainable development [ 3 , 4 ] . Green finance refers to financial activities that support and promote environmental protection and sustainable development through various financial instruments and products. This type of financial activity is characterized by the integration of environmental and social responsibility into financial decision-making, with the main objective of taking environmental, social and governance (ESG) factors into account in financial activities in order to promote sustainable economic growth. Through capital guidance, risk management, incubation and innovation, information disclosure and policy support, green finance attempts to promote low-carbon, environmentally and socially responsible investment and financing for sustainable development. However, green finance involves a wide range of fields, mainly including green credit, green bonds, green insurance, carbon finance, etc., and it is difficult for us to explore the role of green finance in a comprehensive manner in all fields. The former China Banking Regulatory Commission (CBRC) formulated the Guidelines on Green Credit (hereinafter referred to as "the Guidelines") in 2012, which is the first domestic normative document dedicated to green credit. The Guidelines require banking financial institutions to promote green credit at a strategic level and effectively control environmental and social risks in credit business activities. Distinguishing from environmental regulatory policies characterized by administrative penalties, the Guidelines aim to guide green credit through the allocation of credit resources to restrict green innovation of enterprises and promote green transformation of the economy, which also provides a good perspective for us to analyze the role of green finance from the perspective of green credit. According to the estimation of Societe Generale Research, the balance of green credit accounts for more than 90% of the balance of all green financing. As of the first half of 2023, the green credit balance of China's 21 major banks reached 25 trillion yuan, up 33% year-on-year, ranking first in the world in terms of scale. In the same period, the balance of green bonds in China was only RMB 1.98 trillion, less than 8% of the scale of green credit, and the scale of other green financing methods was even smaller. Therefore, we believe that the findings based on green credit can represent the role of green finance to a large extent. Thus, we take the Guidelines issued by the former CBRC as the entry point and use the DID method to assess the implementation effect of GCP. Specifically, considering that the Guidelines are committed to guiding the flow of funds to environmentally friendly and sustainable projects, promoting the green transformation of the economy, and preventing environmental and social risks, this paper tries to analyze the micro impact of the GCP from the perspective of the quality of green innovation. Currently, two contrasting perspectives have surfaced from research on the influence of green finance on green innovation. One viewpoint suggests that green finance facilitates increased corporate R&D spending [ 5 ] , enhances the effectiveness of green innovation within enterprises [ 6 ] , and leads to high-quality enterprise development. Simultaneously, it can reduce corporate agency costs, improve investment efficiency, and act as credit restraints to drive green innovation among heavily polluting industries, thereby enhancing corporate environmental performance [ 4 ] and firms' green innovation performance [ 7 ] . On the other hand, an alternative perspective implies that green finance may aggravate the financing constraint faced by heavily polluting industries [ 8 ] , raising the cost of credit financing for enterprises [ 9 ] and impeding green innovation.. Specifically, the impact of green finance on the green innovation behavior of listed companies is detrimental, particularly in hindering corporate credit financing, particularly with regards to long-term borrowing [ 10 ] . Additionally, green finance may intensify financial regulation challenges and diminish the effectiveness of financial services, thereby undermining the promotion of environmentally-friendly practices within the corporate sector [ 11 ] . However, as China’s economic development shifts from "quantitative increase" to "qualitative improvement", the value of patents is no longer sufficient to reflect the innovation level of enterprises; rather, it highlights the degree of significance that enterprises place on green innovation. We employed the “Green Credit Guidelines” (GCP), introduced in China in 2012, as a quasi-natural experiment to investigate the influence of green credit policies on the green innovation of firms subject to green credit restrictions. To identify whether a listed company falls within a green credit-restricted industry, we ascertained the company's industry based on the presence of environmental and social risks classified as Category A in the Key Evaluation Indicators for Green Credit Implementation. If a listed company is associated with an industry falling under Category A, it is recognized as a green credit-restricted industry; otherwise, it is classified as a non-green credit-restricted industry. We created an interaction term involving the policy time dummy variable and the grouping dummy variable as the principal explanatory variable. This variable primarily assesses the impact of GCP on the green innovation of firms subject to green credit restrictions and those that are not, both before and after the implementation of GCP. It is important to note that current methods for measuring enterprise green innovation are somewhat limited, making it challenging to fully capture the diverse nature of corporate environmental improvement. To date, existing studies have mainly measured corporate green innovation through metrics such as the quantity of green patents [ 4 , 12 ] , As China's economy transitions from quantity-driven growth to a focus on qualitative enhancements, the emphasis is no longer solely on the sheer volume of patents, but rather on the significance of enterprises' commitment to green innovation. To address this shift, we have utilized the WIPO Green Patent List's patent classification system, and applied the knowledge breadth method to assess the quality of enterprises' green patents. By doing so, we aim to gauge the extent of green innovation within these enterprises, thereby addressing the limitations of relying solely on patent numbers to measure innovation activities. This approach ultimately yields a more accurate reflection of enterprises' achievement in the realm of green innovation. After establishing the indicators mentioned above, we discovered that the GCP has a substantial impact on driving green innovation within green credit-constrained enterprises. This finding remains robust even after addressing several potential endogeneity issues and conducting thorough tests to validate the results. The findings of the impact mechanism test indicate that green credit effectively curbs corporate shadow banking and leads to spillover effects in digital technology, thereby facilitating the transformation of corporations towards green practices. Simultaneously, green credit also enhances the total factor productivity of enterprises, which in turn supports and encourages the transition towards greener operations. Meanwhile, the existing literature often overlooks the influence of FinTech in examining how green finance facilitates entities' transition to sustainable practices. In the interconnected landscape of green finance and green technology innovation, FinTech has the potential to enhance the supportive role of green finance in empowering entities. Findings from this study indicate that FinTech can notably bolster green credit's efficacy in promoting the greening of enterprises. However, the implementation of green credit may also add complexity to financial institutions' portfolios and pose challenges to regulatory oversight, leading to increased difficulties in financial regulation. This paper also explores the moderating impact of financial regulation on these dynamics [ 11 ] . In order to enhance the precision of the regression results, we adjusted the financial regulation index to the city level based on the concentration of urban commercial banks, given that the financial regulation data are currently only available at the provincial level. The findings of this study indicate that financial regulation reinforces the positive impact of fintech. Concurrently, the enhancement of the intellectual property protection system has been shown to bolster enterprises' innovation capabilities, thereby facilitating technological advancements and industrial upgrades, and effectively mitigating the constraints related to R&D investment and innovation risks [ 13 ] . Furthermore, this paper explores the influence of the IPR protection system and establishes its role in bolstering green innovation within firms that are both enabled and constrained by green credit. In conclusion, this paper makes a meaningful contribution in three primary areas. Firstly, it provides a fresh perspective on green finance for promoting high-quality development of green innovation. Instead of solely relying on the quantity of green patent applications or authorizations, as commonly done in existing studies, this paper introduces the knowledge breadth approach to assess the quality of corporate green innovation. This method ensures a more objective and accurate measurement, important for China's progression towards high-quality development. The second contribution lies in the comprehensive examination of the impact mechanism of green credit on enterprise green innovation. In light of China's policies aimed at digitization and innovation, this paper scrutinizes the influence of green credit through the lenses of commercial credit, digital technological innovation, and total factor productivity. Lastly, this paper addresses the often overlooked impact of financial technology and regulation in studies related to green finance and entity greening transformation. By introducing city-level fintech and financial regulation indicators, the study explores how these factors can strengthen the role of green finance in promoting entity green innovation. This innovative approach adds depth to our understanding of the factors affecting the quality of green innovation in firms. The paper is structured as follows: the second part comprises the theoretical analysis and research hypotheses. The third and fourth parts detail the research design and the analysis of empirical results, respectively. The fifth part explores the impact mechanism, followed by the sixth part, which presents further discussion. The final section contains the conclusions and suggestions for countermeasures. 2. Theoretical analysis and research hypotheses 2.1 Green finance credit allocation and the quality of corporate green innovation Green financial credit allocation primarily occurs through bank credit channels to facilitate the efficient distribution of funds. This makes it more feasible for enterprises with a focus on energy conservation and environmental protection, as well as those engaged in green production activities, to secure loans and access more convenient and open financing channels. Additionally, the reduced financing costs allow for the allocation of funds to green production initiatives. The Guidelines on Green Credit, established by the former CBRC in 2012, provide valuable insight for studying green financial credit allocation. Within this system, commercial banks consider the environmental and social risks of their clients an essential factor in their ratings, credit approvals, management, and disbursement. They also identify industries subject to green credit restrictions. Poor environmental performance, high energy consumption, and excessive pollution emissions make it less likely for a company to receive credit, resulting in higher financing expenses [ 14 ] . From a perspective of long-term and sustainable development, green credit restrictions gradually prompt industries to address short-term negative impacts. The constant reduction of environmental pollution costs, the mitigation of environmental risks, and the acceleration of green transformation processes are vital [ 15 ] . Consequently, green credit policies may compel enterprises in green credit-restricted industries to engage in green innovation initiatives. Simultaneously, as green finance fortifies banks' monitoring functions concerning fund usage and possesses enduring credit monitoring, more enterprises shift from quantitative to substantive innovation [ 16 ] , to bolster their standing in credit allocation. Accordingly, research hypothesis H1 can be posited: Hypothesis 1. Green financial credit allocation exerts a positive motivational effect on the quality of green innovation in green credit-constrained industries. 2.2 Path analysis of green financial credit allocation affecting the quality of corporate green innovation The primary goal of green finance credit allocation is to boost financial support for environmentally responsible enterprises meeting green finance recognition standards, while concurrently reducing financing for heavy polluters. This ensures a reallocation of financial resources towards sustainable initiatives [ 17 ] . To secure additional financial backing, businesses in green credit-restricted industries are motivated to invest in research and development of green and clean technologies to diminish their ecological footprint [ 18 ] . Digital technology plays a pivotal role in steering companies away from traditional high-input, high-output, high-energy consumption, and high-pollution production methods, toward low-carbon, energy-efficient production modes. This shift allows enterprises to enhance production efficiency by leveraging new technologies while addressing the negative environmental impact of their operations, ultimately achieving their own green transformation [ 19 ] . Striving to navigate the pressures stemming from green credit policies, companies in green credit-restricted sectors may further enhance their digital technology capabilities and prioritize the synergy between production and environmental considerations, ultimately contributing to ecological improvement and heightened resource allocation efficiency [ 20 ] . Moreover, the strategic use of digital technology can assist heavy polluters in mitigating external environmental pressures. Based on this reasoning, the research hypothesis H2a is formulated: Hypothesis 2 a. Green finance credit allocation has the potential to enhance the quality of corporate green innovation through the advancement of digitization in green credit-restricted industries. Green finance credit policies play a crucial role in guiding the optimal allocation of resources between polluting and green industries, and in enhancing the efficiency of resource allocation within green credit-restricted industries [ 21 ] . In this context, less productive firms have an opportunity to access production resources at lower costs, allowing them to compete more effectively in the market. However, this could inhibit the flow of resources to high-productivity firms, thus limiting the market space for inefficient firms and enabling dynamic adjustments in firm size, leading to improved resource allocation efficiency [ 22 ] . Consequently, green finance credit policies can motivate enterprises to boost their total factor productivity through optimized resource allocation. An increase in total factor productivity can lead to reduced raw material consumption and waste generation, which in turn lessens the negative environmental impact. Moreover, it provides enterprises with more resources and surplus funds, enabling greater investment in green innovation activities and perpetual enhancement of the quality of green innovation driven by efficiency [ 23 ] . Accordingly, research hypothesis H2b can be formulated as follows: Hypothesis 2 b. Green finance credit allocation has the potential to enhance the quality of corporate green innovation by increasing total factor productivity in green credit-constrained industries. Green finance credit policies typically compel financial institutions to thoroughly assess the environmental track records of borrowers. If borrowers are found to have engaged in serious environmental polluting activities, they may face higher loan interest rates and more stringent loan terms [ 24 ] . Consequently, companies operating in sectors with limited access to green credit may turn to shadow banking to shore up their liquidity [ 25 ] . However, the shadow banking system is characterized by high leverage, a significant information asymmetry, and ambiguous legal entities, rendering it more risky and potentially exposing firms to liquidity dilemmas and bankruptcy risks [ 26 ] . This hinders their ability to focus on core business development and dampens the prospects for green innovation. Nevertheless, in China's strategic environment aimed at preventing real enterprises from engaging in "de-realization" and promoting the green transformation of the manufacturing industry, green finance and credit policies may compel enterprises in industries with restricted green credit access to refocus on their core business and pursue green credit support through green innovation [ 4 ] . Therefore, the influence of green finance credit policies on whether shadow banking is practiced in green credit-constrained industries remains uncertain. As a result, the research hypothesis H2c can be formulated as follows: Hypothesis 2 c. Green finance credit policies may impact the quality of green innovation in firms operating in sectors with limited green credit through the practice of corporate shadow banking, but this effect is subject to uncertainty. 2.3 Analysis of factors influencing the allocation of green financial credit to affect the quality of corporate green innovation The efficiency of green finance credit allocation may be influenced by various financial regulatory factors [ 27 ] . Specifically, financial regulators have the power to standardize the management regulations, auditing, and assessment procedures for green credit business within financial institutions. They can also supervise and evaluate the implementation of green credit policies within these institutions, ultimately playing a key role in regulating green innovation. Financial regulators can encourage financial institutions to actively and effectively engage in green financial business and investment, ensuring the quality of green credit and the feasibility of the regulatory mechanism, while also safeguarding the legitimacy of the regulation and promoting the development of enterprise green innovation. Through financial supervision, regulations can effectively govern the behaviors of financial institutions and enterprises, ultimately supporting the stability and sustainable growth of the market. The regulation of green financial credit allocation not only helps prevent the accumulation of non-performing assets and risks but also guarantees the transparency of green credit allocation and fair competition, and facilitates the establishment of a risk prevention mechanism [ 28 ] . Therefore, the research hypothesis H3a can be stated as follows: Hypothesis 3 a. Financial regulation can enhance the impact of green finance credit allocation on promoting the quality of green innovation in enterprises. Additionally, the use of financial technology (Fintech) can significantly increase the efficiency of pre-credit review and post-credit risk management for green credits [ 29 ] , ultimately facilitating access to and effective utilization of funds for environmental projects by enterprises. Fintech can collect business information from multiple channels [ 30 ] and screen the credit needs of enterprises engaged in green innovation [ 31 ] , enhancing the allocation efficiency of green credit. Furthermore, Fintech, through means such as blockchain and big data, can effectively reduce post-loan moral hazards and improve the ability of financial institutions to prevent risks [ 32 ] . It can also better manage the destination of corporate credit through technological means, effectively controlling the operational and financial risks of enterprises and providing a stable environment for innovative activities. Therefore, research hypothesis H3b can be stated as follows: Hypothesis 3 b. Fintech can strengthen the positive impact of green financial credit allocation on the quality of firms' green innovations. Protecting intellectual property is a crucial tool for driving innovation, nurturing economic growth, and ensuring fair competition, thereby offering financial returns and competitive advantages in the market. Additionally, intellectual property protection plays a vital role in advancing technological progress and fostering industrial development. A robust judicial framework for safeguarding intellectual property rights constitutes a critical institutional foundation for strategies promoting innovation-led growth [ 33 ] . Intellectual property infringement can be a serious impediment to R&D [ 34 ] , Strengthened enforcement of IPR protection by the government can enhance firms' ability to innovate, mainly by reducing R&D spillover losses and easing external financing constraints [ 35 ] . Accordingly, the research hypothesis H3c can be formulated: Hypothesis 3 c. Regional support for intellectual property protection can magnify the positive impact of green finance credit allocation on the quality of firms' green innovation efforts. 3. Research design 3.1 Sample selection and data sources Prior to 2007, China's policy emphasis was primarily on the traditional financial sector rather than the green financial sector. Therefore, this study focuses on Chinese A-share listed companies from the period of 2007 to 2021, excluding 2017, in order to more accurately assess the impact of current green credit policies on corporate green innovation. Due to data quality concerns, referring to Lu Jing et al. (2021), 2017 data were excluded and treated as consecutive years for 2016 and 2018. The listed company data was obtained from CSMAR and WIND databases, while the enterprise patent data was sourced from the WIPO Green Patent List. Financial regulation data at the provincial level was obtained from the National Bureau of Statistics, provincial statistical yearbooks, and bulletin data. To comprehensively measure the level of financial regulation at the city level, data on the number of bank financial institutions' outlets in Chinese cities was manually organized. The ratio of the number of bank outlets in cities to the number of bank outlets in the province in the current year was used to construct the financial weight at the city level, which was then multiplied by the financial regulation index at the provincial level. Additionally, the level of fintech was measured through the compilation of the distribution of commercial banks in each city in China, derived from the financial license information of the China Banking Regulatory Commission (CBRC). Furthermore, the city AI agglomeration index was measured through a specific search of AI companies by Tianyecha. This study collected and organized 10,572 "company-year" observations. To ensure the robustness of the study, various treatments were applied to the raw data, including the exclusion of financial enterprises, enterprises with delisting risk, enterprises with missing key variables, enterprises with delisting risk, and samples with insolvency and negative book value of shareholders' equity. Additionally, a two-sided 1% shrinkage treatment was performed on all continuous variables to mitigate the impact of outliers on the empirical results. Ultimately, the study resulted in 10,572 "firm-year" observations. 3.2 Model setting In reference to the work by Xin Wang and Ying Wang (2021) [ 4 ] , this study utilizes the 2012 Green Credit Guidelines formulation as a quasi-natural experiment to examine green finance credit allocation, and constructs the model in the following manner: The explanatory variable "Patent" represents the quality of firms' green innovation. The main explanatory variables include the GCP (Policy), industry attributes (Gcres), and the interaction term between the two (Policy × Gcres). Control variables are denoted as X, and ε i,t represents the random error term, where subscript i refers to the firm and t refers to the year. 3.3 Variable setting 3.3.1 Explained variables The quality of green innovation (patent) is assessed in this study. Building on the work of Jie Zhang and Wenping Zheng (2018) [ 37 ] , based on the patent classification numbers provided by the WIPO Green Patent Inventory, the knowledge breadth method is utilized to calculate the quality of green patents of enterprises and to measure their green innovations, which to a certain extent can overcome the shortcomings of reflecting the innovation activities of enterprises only by the number of patent applications: In model (2), we use Patentn,t to represent the breadth of knowledge associated with various types of patents, serving as a proxy for the quality of environmental innovation within enterprises. Here, n and t respectively refer to the patent and the year, and α denotes the proportion of major group classifications within the patent classification number. The IPC classification number format in the patent documents of Chinese enterprises at the State Intellectual Property Office typically follows the structure "Department - Major Class - Minor Class - Major Group - Group", such as "F24F11/00". The first letter of the classification number spans A-H, representing 8 major departments; the second and third digits indicate the major categories; the fourth letter denotes the minor categories, with major and minor groups separated by "/". For example, one patent may have classification numbers F24F11/00, F24F11/10, and F24F11/20, while another patent similarly has F24F11/00, F24F12/00, and F24F13/20. Although the two patents share the same number of classification codes, they differ in that the first patent utilizes only F24F11 as major group information, whereas the second patent encompasses F24F11 as minor group information and three different major group information. According to the calculation rule of model (2), this indicates that the breadth of knowledge applied in the second patent exceeds that of the former. Hence, the greater the diversity between the patent classification numbers at the major group level, the wider the knowledge scope, reflecting a higher patent quality. To address the right-skewed distribution issue of green patent data, this study employs the natural logarithm of green patent quality after adding 1 to obtain Ln(Patent + 1). 3.3.2 Core explanatory variables The main explanatory variables under consideration are the GCP, industry characteristics, and their cross-multiplier. Specifically, the variable "Policy" functions as a dummy variable, indicating the period before and after the implementation of the Guidelines. Following the implementation (2012 and beyond), the value of "Policy" becomes 1, otherwise it remains 0. Gcres denotes the industry classification for the implementation of the GCP specified by the Guidelines. In this study, the industry category to which the companies with environmental and social risks fall under in the Key Evaluation Indicators for Green Credit Implementation determines whether the listed company belongs to the green credit-restricted industry. If the company falls under category A, it is labeled as a green credit-restricted industry (Gcres = 1); if not, it is classified as a non-green credit-restricted industry (Gcres = 0). The interaction term, "Policy*Gcres," primarily assesses the impact of the GCP on green innovation within both the green credit-restricted and non-green credit-restricted industries before and after the policy's implementation. A significantly positive coefficient of the cross-multiplier term, β2, indicates the substantial advancement of green innovation in green credit-constrained industries due to the GCP. Conversely, a non-significant coefficient suggests the lack of a significant promotional effect. 3.3.3 Control variables Based on previous literature [ 38 , 39 ] , this paper incorporates the following control variables X i, t−1 : firm size (LnSize), expressed as the logarithm of the total assets of the firm; gearing ratio (LEV); net profitability of total assets (ROA); return on equity (ROE); accounts receivable-to-revenue ratio (REC); dual chairmanship and CEO position (Dual); the percentage of ownership of the first largest shareholder (LnTop1); book market capitalization ratio (BM); and firm value (LnTQ), expressed as the logarithm of the enterprise value multiple. Table 1 Descriptive statistics of main variables VarName Obs Mean Median P10 SD Min Max Ln ( Patent + 1) 10572 0.422 0.486 0.000 0.185 0.000 0.649 LnSize 10572 3.206 3.232 3.083 0.085 3.030 3.350 LEV 10572 0.539 0.567 0.352 0.137 0.130 0.786 ROA 10572 0.043 0.040 0.006 0.034 -0.027 0.136 ROE 10572 0.099 0.085 0.015 0.080 -0.060 0.337 REC 10572 0.184 0.134 0.024 0.180 0.000 0.883 Dual 10572 0.379 0.000 0.000 0.485 0.000 1.000 LnTop1 10572 3.267 3.229 2.448 0.638 1.973 4.458 BM 10572 0.771 0.795 0.419 0.242 0.225 1.220 LnTQ 10572 2.776 2.696 2.084 0.547 1.749 4.466 4. Empirical analysis 4.1 Baseline regression Table 2 displays the findings from the benchmarking study on the impact of green credit on firms’ green innovation. In columns (1) and (2), the coefficient of the cross-multiplier term DID shows a highly significant positive correlation at the 1% level. After incorporating region fixed effects, the resulting coefficient of 0.058 indicates a notable 5.8% increase in the quality of green innovations within the green credit-restricted sector following the implementation of the policy, underscoring the significant boost provided by the Guidelines in enhancing green innovation output in this sector. Conversely, the coefficient of Policy fails to demonstrate significance, with a resulting coefficient of 0.003 after adding the region fixed effect, suggesting that the Guidelines do not significantly elevate the quality of green innovation in the non-green credit-restricted sector, reflecting a marginal average increase of merely 0.3 percent. Additionally, the coefficient on Gcres shows a statistically significant positive relationship at the 10 percent level. With regional fixed effects, the resulting coefficient of 0.238 denotes a substantial 23.8 percent improvement in the green innovation quality within the green credit-restricted sector subsequent to policy implementation,indicating a clear differentiation between the green credit-restricted and non-green credit-restricted industries in terms of green innovation quality. This underscores a significant disparity in the quality of green innovation between the two sectors. Table 2 Benchmark regression results Model (1) (2) Ln ( Patent + 1) Ln ( Patent + 1) DID 0.065 *** 0.058 *** (4.55) (4.03) Policy 0.024 0.003 (0.59) (0.06) Gcres 0.120 0.238 * (1.15) (2.08) LnSize 0.278 *** 0.276 *** (6.19) (5.08) LEV 0.037 0.025 (1.54) (0.96) ROA 0.024 -0.144 (0.10) (-0.57) ROE -0.007 0.086 (-0.07) (0.87) REC -0.072 *** -0.074 *** (-5.30) (-4.89) Dual 0.018 *** 0.028 *** (4.11) (5.55) LnTop1 -0.029 *** -0.035 *** (-5.81) (-6.44) BM 0.013 0.019 (0.77) (1.08) LnTQ 0.001 0.004 (0.13) (0.48) Year Yes Yes Sector Yes Yes Area No Yes N 10572 10572 Note: t statistics in parentheses; *p < 0.1, **p < 0.05, ***p < 0.01. 4.2 Robustness tests 4.2.1 Parallel trend test Figure 1 presents the analysis of the impact of the GCP on corporate green innovation over time. Prior to the policy implementation, the estimated coefficient β within the 95% confidence interval shows no significant deviation from 0. However, one period after the policy shock, the coefficient becomes significantly different from 0, suggesting a delayed response to the policy implementation. Subsequent to this, there is a noticeable divergence in trends between the treatment group and the control group, particularly after the t + 1 period, indicating a positive promotion of enterprise green innovation by the GCP. Additionally, the parallel trend test confirms these findings. 4.2.2 Placebo test To test the reliability of the empirical findings, we introduced the placebo method to assess the robustness of the impact of the GCP. Following the guidelines, we randomly selected 9 industries as the "pseudo-treatment group" (Gcresfalse), from which we constructed the dummy variable Patentfalse = Gcresfalse*Policy for the placebo test. This experiment was then repeated 1,000 times, resulting in the p-value test plot shown in Fig. 2 . The p-value test plot. The results show that the coefficient estimates are clustered around 0 and approximately follow a normal distribution, indicating that the regression results are not affected by unobservable factors and the results are more robust. 4.4.3 Replacement of explanatory variables By utilizing the patent classification numbers from the WIPO Green Patent List, we employed the knowledge breadth method to assess the quality of enterprises' green invention patents using the formula Ln(lnva + 1), and the quality of green utility model patents using Ln(uma + 1) as proxy variables for evaluating enterprises' green innovation. The findings reveal a substantially positive regression coefficient of Ln(lnva + 1) at a significance level of 1%, while the regression coefficient of Ln(uma + 1) is found to be statistically insignificant. This can be attributed to the fact that green invention patents necessitate a higher degree of innovation, particularly in the realm of green technology, as they emphasize environmentally sustainable technological solutions and are considerably more technical and innovative compared to green utility model patents. Consequently, the quality of green invention patents serves as a more reliable indicator of corporate green innovation, which aligns with the results of the primary regression and underscores the robustness of our findings. Table 3 Robustness test based on proxy variables for corporate green innovation Model (1) (2) (3) (4) Ln ( lnva + 1) Ln ( lnva + 1) Ln ( uma + 1) Ln ( uma + 1) DID 0.043 *** 0.053 *** -0.014 -0.003 (2.59) (3.15) (-0.86) (-0.22) Policy -0.016 -0.024 0.028 0.030 (-0.34) (-0.49) (0.62) (0.64) Gcres -0.091 0.006 -0.342 **/ -0.357 *** (-0.74) (0.04) (-2.91) (-2.79) Control variable Yes Yes Yes Yes Year Yes Yes Yes Yes Sector Yes Yes Yes Yes Area No Yes No Yes N 10572 10572 10572 10572 Note: t statistics in parentheses; *p < 0.1, **p < 0.05, ***p < 0.01. 4.4.4 Change of industry definition criteria The Key Evaluation Indicators for Green Credit Implementation indicates that, aside from Class A industries, Class B industries also have adverse effects on the environment and society. Therefore, this study broadens the identification of green credit restricted industries, including category B industries. Furthermore, by referencing the "Listed Company Environmental Verification Industry Classification Management Directory" and "Listed Company Environmental Information Disclosure Guidelines" and in conjunction with the "Guidelines for Industry Classification of Listed Companies," this paper identifies the mining industry (industry code: B06, B07, B08, B09), manufacturing industry (industry code: C17, C19, C22, C25, C26, C28, C29, C30, C31), and polluting firms in electricity, heat, gas, and water production and supply (industry code: D44) as the experimental group. Non-polluting firms, after removing green firms, are used as the control group. The regression results in Table 4 show that the coefficients of the Difference-in-Differences (DID) are all significantly positive at the 1% level, consistent with the regression results in Table 2 , indicating robust results for the first and second changes in industry definition criteria. Table 4 Robustness test based on two criteria for changing industry definition Model (1) (2) Ln ( Patent + 1) Ln ( Patent + 1) DID 0.048 *** 0.048 *** (3.67) (3.59) Policy -0.019 -0.018 (-0.45) (-0.43) Gcres 0.486 *** 0.317 (3.53) (1.93) Control variable Yes Yes Year Yes Yes Sector Yes Yes Area Yes Yes N 10572 10572 Note: t statistics in parentheses; *p < 0.1, **p < 0.05, ***p < 0.01. 4.4.5 Tests based on the PSM-DID methodology To mitigate systematic differences between heavily polluting industries and other sectors and minimize estimation bias in the double-difference method, we employ the PSM-DID method for robustness testing. This involves conducting a Logit regression of control variables using a dummy variable to determine if an industry is heavily polluting, in order to obtain the propensity score value. The matching industry for the heavily polluting industry is identified as the industry with the closest propensity score value, effectively minimizing systematic differences and reducing the DID estimation bias. We specifically utilize the kernel matching method for estimation to assess the robustness of the GCP 's role in promoting firms' green innovation. Table 5 presents the regression results, which demonstrate that even after applying the PSM-DID methodology, the GCP consistently and significantly stimulates firms' green innovation activities. Additionally, the quality of firms' green innovations increases by 6.3%, aligning with the previous results and indicating robustness. Table 5 Robustness test based on PSM-DID Model (1) (2) (3) Ln ( Patent + 1) Ln ( Patent + 1) Ln ( Patent + 1) Before After DID Increment -0.060 0.003 0.063 Standard error 0.011 0.007 0.012 T-value -5.270 0.410 5.310 P-value 0.000 *** 0.679 0.000 *** Note: t statistics in parentheses; *p < 0.1, **p < 0.05, ***p < 0.01. 4.4.6 The 2008 international financial crisis and the impact of omitted variables Due to potential impacts from the 2008 international financial crisis and omitted variables, this study omitted the 2008 data sample and introduced a new variable (Punish) in model (1) to represent environmental penalties, where Punish = 1 signifies that firms are subject to such penalties, and Punish = 0 signifies that firms are not. Table 6 (Refer to Supplementary Materials) presents the results, with column (1) reporting the regression results excluding the 2008 data sample, and column (2) reporting the results introducing environmental penalties. The regression coefficients of the cross-multiplier term DID are consistently and significantly positive at the 5% level, aligning with the robust results displayed in Table 2 . Table 6 Robustness tests based on excluding the 2008 sample and environmental penalties Model (1) (2) Ln ( Patent + 1) Ln ( Patent + 1) DID 0.044 *** 0.048 *** (3.29) (3.11) Policy 0.031 0.103 ** (0.70) (2.08) Gcres 0.490 *** 0.213 (3.56) (1.49) Punish — -0.002 — (-0.11) Control variable Yes Yes Year Yes Yes Sector Yes Yes Area Yes Yes N 10507 10572 4.4.7 Environmental pollution factors and replacement sample year intervals In order to account for the impact of concurrent environmental policies, this study incorporates three variables based on Xin Wang and Ying Wang (2021) [ 38 ] . The first variable is the annual PITI index, which measures the disclosure of pollution source regulatory information in the location where the firm is registered. The second variable, Distance, represents the shortest distance between the registered location of the listed company and neighboring state-controlled air monitoring stations. To address the issue of skewed distribution, the natural logarithm of the nearest distance plus 1 (LnDistance) is used. The third variable assesses the level of haze pollution (PM2.5) in the location of the listed companies. The study focuses on Chinese listed companies from 2007 to 2021, treating the 2012 introduction of the Green Credit Guidelines as a quasi-natural experiment to examine its impact on corporate green innovation. To minimize the influence of subsequent policies, the sample is restricted to 2010–2013, based on the methodology of Ding et al. (2022) and Li Juncheng et al. (2023) [ 40 , 41 ] . Table 7 (Refer to Supplementary Materials) presents the regression results, showing that the regression coefficient of the DID cross-multiplier term is significantly positive, at least at the 5% level, consistent with previous robust results, and supporting hypothesis H1. Table 7 Robustness test based on environmental pollution factors and replacement sample year intervals Model (1) (2) (3) (4) Ln ( Patent + 1) Ln ( Patent + 1) Ln ( Patent + 1) Ln ( Patent + 1) DID 0.050 *** 0.058 *** 0.068 *** 0.075 *** (3.06) (4.07) (4.66) (3.04) Policy -0.036 0.006 -0.035 0.047 (-1.44) (0.14) (-0.84) (0.87) Gcres -0.048 0.454 *** 0.222 0.308 *** (-0.27) (8.01) (1.93) (2.87) PITI -0.001 — — — (-1.89) — — — Lndistance — -0.002 — — — (-0.65) — — PM2.5 — — 0.003 *** — — — (5.44) — Control variable Yes Yes Yes Yes Year Yes Yes Yes Yes Sector Yes Yes Yes Yes Area Yes Yes Yes Yes N 6642 10677 10085 9487 5. Analysis of impact mechanisms 5. 1 Mechanism testing based on digital transformation Based on the primary classification number of patents matched with the Statistical Classification of Digital Economy and Its Core Industries, the number of digital economy patent applications filed by listed companies in that year is obtained. This data is then logarithmized to derive Ln(Innovate + 1), which measures the level of digitalization of enterprises. To assess the reasonableness of the mediating effect, the Bootstrap method is employed to sample 500 times, yielding an estimated value of ab of Ln(Patent + 1) at 0.008, with a 95% confidence interval of (0.003, 0.014). As the confidence interval does not encompass 0, and the two-tailed test is significant (p = 0.002), it is indicative of a mediating effect with Ln(Innovate + 1) as the conduction variable. In Table 8 (Refer to Supplementary Materials), column (1) represents the total effect of green finance credit allocation on firms' green innovation. Column (2) demonstrates the effect of green financial credit allocation on firms' digital transformation, with the regression coefficient significantly positive at the 1% level, suggesting that green credit can promote the digitalization level of green credit-constrained industries. Column (3) provides the regression result of green credit and firms' digitization level on Ln(Patent + 1). The regression coefficients of Difference-in-Differences (DID) and Ln(Innovate + 1) are significantly positive at least at the 5% level, signaling that green financial credit allocation can enhance the digitization level of firms, consequently promoting the enhancement of the quality of green innovations in green credit-restricted industries, thus proving hypothesis H2a. Table 8 Mechanism test based on digital transformation Model (1) (2) (3) Ln ( Patent + 1) Ln ( Innovate + 1) Ln ( Patent + 1) DID 0.058 *** 0.410 *** 0.054 *** (4.03) (19.37) (4.18) Policy 0.003 0.148 *** -0.057 *** (0.06) (5.45) (-8.06) Gcres 0.238 ** -0.611 *** 0.235 ** (2.08) (-2.77) (2.04) Ln ( Innovate + 1) — — 0.005 *** — — (2.78) Control variable Yes Yes Yes Year Yes Yes No Sector Yes Yes Yes Area Yes Yes Yes N 10572 10372 10372 5. 2 Mechanism test based on total factor productivity of enterprises The OP and LP methods are frequently utilized in existing studies to gauge a firm's total factor productivity [ 42 , 43 ] , However, the LP method is more adaptable to handling sample loss and endogeneity issues than the OP method, and it provides more accurate estimates of a firm's total factor productivity. Thus, following the approach of Song Min et al. (2021) [ 43 ] , we employ the LP method to measure the firm's total factor productivity TFP_LP. This paper utilizes the Bootstrap method with 500 samples, resulting in an estimated indirect effect ab of Ln(Patent + 1) at 0.016, with a 95% confidence interval of (0.011 0.022). As the confidence interval does not include 0, the two-tailed test is significant (p = 0.000), suggesting the existence of a mediating effect with TFP_LP as the transmission variable. In Table 9 (Refer to Supplementary Materials), column (2) demonstrates the impact of green finance credit allocation on firms' total factor productivity, with the regression coefficient significantly positive at the 1% level. Meanwhile, column (3) represents the combined effect of green financial credit allocation and enterprise total factor productivity on enterprise green innovation quality, showing both DID and TFP_LP coefficients to be significantly positive at the 1% level. These results indicate that the GCP can enhance the total factor productivity of the green credit-restricted industry, subsequently improving the quality of enterprise green innovation, thereby supporting hypothesis H2b. Table 9 Mechanism test based on firms' total factor productivity Model (1) (2) (3) Ln ( Patent + 1) TFP_LP Ln ( Patent + 1) DID 0.058 *** 0.406 *** 0.050 *** (4.03) (17.20) (3.50) Policy 0.003 -0.221 ** 0.044 (0.06) (-2.74) (0.91) Gcres 0.238 ** -1.042 *** -0.014 (2.08) (-41.12) (-0.85) TFP_LP — 0.036 *** — (6.04) Control variable Yes Yes Yes Year Yes Yes No Sector Yes Yes Yes Area Yes Yes Yes N 10572 10392 10392 5.3 Mechanism test based on shadow banking Referring to Li Zhen et al. (2023) [ 25 ] , the study delves into the link between "green credit and corporate green innovation" by using the sum of corporate accounts receivable, notes receivable, and prepayment divided by business revenue to denote commercial credit (TC). A higher TC value signifies greater supply of corporate shadow banking. Employing the Bootstrap method for 500 samplings, the estimated Ln(Patent + 1) succinct effect ab is found to be 0.005, with a 95% confidence interval (0.003 0.007). As the confidence interval does not encompass 0 and the two-tailed test is significant (p = 0.000), this indicates a mediation effect with TC as the transmission variable. The regression results in Table 10 (Refer to Supplementary Materials) reveal that the regression coefficient of the Difference-in-Differences (DID) in column (2) is significantly negative at the 1% level, suggesting that the implementation of green financial credit policy can curtail shadow banking in listed companies. Meanwhile, the regression coefficient of DID in column (3) is significantly positive at the 1% level, and the regression coefficient of TC is significantly negative at the 1% level. These findings imply that the green financial credit policy can foster improved green innovation quality in restricted industries by restricting shadow banking activities in listed companies, thus supporting hypothesis H2c. Table 10 Mechanism test based on business credit of enterprises Model (1) (2) (3) Ln ( Patent + 1) TC Ln ( Patent + 1) DID 0.058 *** -0.083 *** 0.057 *** (4.03) (-6.33) (3.96) Policy 0.003 -0.027 0.025 (0.06) (-0.70) (0.58) Gcres 0.238 ** 0.498 *** 0.460 *** (2.08) (8.40) (6.89) TC — -0.049 *** — (-4.19) Control variable Yes Yes Yes Year Yes Yes No Sector Yes Yes Yes Area Yes Yes Yes N 10572 9396 9396 6. Further discussion 6.1 Financial regulation Drawing from the theoretical analysis of how the allocation of green financial credit may complicate financial regulation, this paper delves deeper into the influence of financial regulation on the green innovation of enterprises enabled by green credit. Previous research has relied on the ratio of regional financial regulatory expenditure to financial sector value added as a proxy for local financial regulation. However, this indicator is limited to the provincial level, leading to internal bias in evaluating the level of financial regulation at the city level due to data constraints. To address this, this study manually compiles data on the number of urban bank and financial institution outlets nationwide to estimate the level of financial regulation at the city level. It constructs financial weights for each city by using the ratio of the number of urban bank outlets to the number of bank outlets in the province and multiplies them by the financial regulation index at the provincial level to obtain the financial regulation indicator. According to the regression results in Table 11 (Refer to Supplementary Materials), the coefficients of “DID*Regulation” are both significantly positive at the 1% level, with Regulation further promoting green innovation activities in the green credit-restricted industry and enhancing the quality of green innovation. This provides evidence to support hypothesis H3a. Table 11 Impact analysis based on financial regulation, fintech and IP protection Model (1) (2) (3) Ln ( Patent + 1) Ln ( Patent + 1) Ln ( Patent + 1) DID * Regulation 0.047 *** — — (3.90) — — DID * Ln ( fintech + 1) — 0.091 *** — — (3.28) — DID * Lnprotect — — 0.043 *** — — (4.72) Policy -0.070 *** 0.096 -0.126 *** (-3.44) (1.78) (-5.30) Gcres 0.458 *** 0.240 ** 0.463 *** (7.46) (2.12) (7.55) Regulation -0.011 — — (-1.70) — — Ln ( fintech + 1) — -0.134 *** — — (-6.05) — Lnprotect — — 0.085 *** — — (3.32) Control variable Yes Yes Yes Year Yes Yes Yes Sector Yes Yes Yes Area Yes Yes Yes N 10572 10572 10572 6.2 Financial technology Given that Fintech has the potential to enhance the efficient allocation of green credit to financial resources, this study leverages the approach taken by Wu Fei et al. (2021) [ 44 ] . Initially, we utilized Python tools to scrape 48 keywords associated with "Fintech" from relevant news articles and conferences. Subsequently, we employed Baidu News Advanced Search to look for news pages containing these keywords in conjunction with cities and municipalities. By crawling the source code of Baidu News Advanced Search and extracting the number of search results, we aggregated a total of 254,456 searches containing "region + keywords" at the prefecture-level city or municipality directly under the central government. Next, we manually sorted the number of branches of each bank in each year and city using financial license information from the China Banking Regulatory Commission (CBRC) to construct the Herfindahl Index (HHI) for measuring bank competition. Additionally, we aggregated the number of AI enterprises at the city and provincial levels and applied the location entropy index to gauge AI enterprise concentration at the regional level. Multiplying these three indexes yielded the comprehensive Fintech evaluation index. To address right skewness and prevent missing values, we processed the index by adding one and taking the logarithm. Consequently, we obtained the Fintech level measure at the city level (Ln(fintech + 1)). According to the regression results in Table 11 (Refer to Supplementary Materials), the coefficient of the cross-multiplier term “DID*Ln(fintech + 1)” significantly indicates at the 1% level, supporting the notion that Fintech further advances the allocation of green financial credit, empowering businesses to enhance the quality of green innovation and rendering hypothesis H3b as valid. 6.3 Intellectual property protection Research has indicated that IPR protection systems can lead to an increase in the quantity and quality of patent applications filed by firms, consequently enhancing the level of innovation within these firms [ 45 ] . Thus, leveraging Yuan Shengchao's work (2023) [ 46 ] , this paper examines the regional level of intellectual property protection (Lnprotect), characterizing it as the average of the number of intellectual property infringement cases divided by the total population and the number of lawyers divided by the total population in each region, and then taking the logarithm. According to the regression results in Table 11 (Refer to Supplementary Materials), the coefficient of “DID*Lnprotect” is significantly positive at the 1% level, suggesting that post-implementation of green finance and credit policies, reinforcing regional intellectual property protection can effectively elevate the quality of green innovation in green credit-restricted industries. This supports the validation of hypothesis H3c. 7. Conclusions and policy recommendations Serving as a critical mechanism for market-driven resource allocation and a vital intermediary between the financial sector and the ecological environment, effectively supporting corporate green innovation is pivotal for empowering entities through financial means. Against the backdrop of China's shift from "quantitative increase" to "qualitative improvement" in green innovation, this study utilizes the formulation of the Green Credit Guidelines as a quasi-natural experiment to investigate the impact of green financial credit allocation on the quality of corporate green innovation. The study reveals that green financial credit allocation significantly enhances the quality of green innovation in industries restricted by green credit, and this conclusion remains robust across various dimensions even after rigorous testing. Furthermore, through an analysis of the influence mechanism, it is evident that green financial credit allocation elevates the quality of corporate green innovation by boosting corporate digitization levels, total factor productivity, and by curbing corporate shadow banking. Additionally, our examination of influencing factors demonstrates that fintech, financial regulation, and local intellectual property protection systems can further reinforce the positive impact of green financial credit allocation on the quality of enterprises' green innovation. Based on the findings, this paper presents the following policy implications: Firstly, it is crucial to strengthen the standards and assessment system for green credit and enhance supervision on green credit practices. Financial regulators should intensify their oversight of banks and other financial institutions to ensure that they thoroughly consider environmental factors when awarding green credit, and strictly control lending for environmentally harmful projects. Furthermore, to encourage more enterprises to engage in green innovation, regulators can provide certain policy preferences and incentives while also requiring banks and other financial institutions to regularly disclose information on their green credit operations for improved environmental monitoring. Secondly, efforts should be made to enhance green industrial ecology and establish a green ecosystem for enterprises. The government should augment its support for the green industry, guiding and financially supporting enterprises to implement green production methods and foster a low-carbon, eco-friendly, and circular economy. Enterprises themselves should actively pursue green development through technological innovation and upgrade production processes, while strengthening environmental supervision and cracking down on pollution. It is also essential for enterprises to establish an effective green management system, set clear green development goals, and shoulder responsibilities. Additionally, they should advance research and development and application of green technology, improve production efficiency, and collaborate with upstream and downstream enterprises for the advancement of a green supply chain. Lastly, enterprises should engage in environmental protection activities, fulfill social responsibilities, and enhance their social image and reputation. Thirdly, it is imperative to bolster positive incentives for fintech and financial regulation while enhancing the local intellectual property protection system. The advantages of financial science and technology should be utilized to improve the accuracy and efficiency of green credit, using big data and artificial intelligence for a more scientific and precise evaluation for green credit approval. There is a need for the formulation of more scientific and rational regulatory policies to encourage financial institutions to develop green credit business and direct more capital to the environmental protection industries and green enterprises. Strengthening supervision and assessment of financial institutions, as well as rewarding and punishing mechanisms for green credit, are vital. Moreover, fostering investments in research and development of environmental protection technologies and products will stimulate the growth of the environmental protection industry. Strict enforcement of the intellectual property protection system is essential to prevent infringement and safeguard the legitimate rights and interests of enterprises. Declarations The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics Approval This is an empirical analysisstudy. The Suzhou University of Science and Technology Research Ethics Committee has confirmed that no ethical approval is required. Author Contribution All authors contributed to the study conception and design. Liangfeng Hao designed the study and conducted the analysis. Material preparation, data collection were performed by Biyi Deng. The first draft of the manuscript was written by Biyi Deng. The final draft of the manuscript was written by Liangfeng Hao. Chuanming Yang revised the manuscript. And all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. 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Science of Science and Management of Science & Technology, 2023,44(04):60-81. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4337275","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":297861691,"identity":"78c31442-4b00-4a33-a151-458d6c59e19a","order_by":0,"name":"Liangfeng Hao","email":"","orcid":"","institution":"Suzhou University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Liangfeng","middleName":"","lastName":"Hao","suffix":""},{"id":297861694,"identity":"8d3de5ee-fcc5-4f35-b443-bcba18da482d","order_by":1,"name":"Biyi Deng","email":"","orcid":"","institution":"Suzhou University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Biyi","middleName":"","lastName":"Deng","suffix":""},{"id":297861697,"identity":"9e3b8814-1143-4d61-b68e-242a4b5fffe7","order_by":2,"name":"Chuanming Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYFAC5gYGhgMMcmzs7QeI1cII1mLMx3MmgTQtifMkHAyI08A/7WCbxI8zh9PbJBgSGH5UbCOsReJ2Yptkz43DuW3SjQcYe87cJqzFQDqxTZrhA1CLzIEEZsY2ErSks0kkGJCi5cbhBOK1AP3SbNlzJt2wDRjIB4nyC//s5IM3fhyzlpdvbz/44EcFEVqgoBlMHiBaPRDUkaJ4FIyCUTAKRhoAABS2Pp69ZbLEAAAAAElFTkSuQmCC","orcid":"","institution":"Suzhou University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Chuanming","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-04-28 09:39:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4337275/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4337275/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56117405,"identity":"707a8648-3544-42a0-8d6b-6e2a55c0205e","added_by":"auto","created_at":"2024-05-08 18:34:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15895,"visible":true,"origin":"","legend":"\u003cp\u003eParallel trend test\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4337275/v1/c43112aebc1f1c42741a6c42.png"},{"id":56117406,"identity":"ebe49492-4366-4b69-a539-f2b04e6361c5","added_by":"auto","created_at":"2024-05-08 18:34:25","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20789,"visible":true,"origin":"","legend":"\u003cp\u003ePlacebo test\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4337275/v1/0a00e152434d799392b484e1.jpeg"},{"id":56819804,"identity":"8aed9ecb-b1dc-405f-a22a-9e020574b0b6","added_by":"auto","created_at":"2024-05-21 00:46:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1435778,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4337275/v1/6c1e901b-8976-4304-87dc-aa2a6b275c3f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Can green finance credit allocation enable green innovation quality improvement -- Evidence from China's manufacturing firms","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGiven the increasingly prominent impact of global climate change, there is a growing focus on environmental protection worldwide. Significant improvement of the ecological environment necessitates not only strong end-of-pipe measures but also harnessing the enabling power of financial resources. Green finance not only facilitates redirecting financial resources from polluting to eco-friendly endeavors but also encourages the allocation of a substantial amount of social capital to green initiatives. Research by Li et al. (2018) and de Haas and Popov (2019) demonstrates that green finance, from a global standpoint, plays a significant role in promoting green innovation within enterprises\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. As the largest developing country in the world, China is in dire need of transitioning from its traditional development approach and establishing a green technological innovation system. However, China's environmental regulatory policies have overlooked the potential role of green finance in promoting high-quality green innovation essential for fostering environmentally sustainable development\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGreen finance refers to financial activities that support and promote environmental protection and sustainable development through various financial instruments and products. This type of financial activity is characterized by the integration of environmental and social responsibility into financial decision-making, with the main objective of taking environmental, social and governance (ESG) factors into account in financial activities in order to promote sustainable economic growth. Through capital guidance, risk management, incubation and innovation, information disclosure and policy support, green finance attempts to promote low-carbon, environmentally and socially responsible investment and financing for sustainable development. However, green finance involves a wide range of fields, mainly including green credit, green bonds, green insurance, carbon finance, etc., and it is difficult for us to explore the role of green finance in a comprehensive manner in all fields. The former China Banking Regulatory Commission (CBRC) formulated the Guidelines on Green Credit (hereinafter referred to as \"the Guidelines\") in 2012, which is the first domestic normative document dedicated to green credit. The Guidelines require banking financial institutions to promote green credit at a strategic level and effectively control environmental and social risks in credit business activities. Distinguishing from environmental regulatory policies characterized by administrative penalties, the Guidelines aim to guide green credit through the allocation of credit resources to restrict green innovation of enterprises and promote green transformation of the economy, which also provides a good perspective for us to analyze the role of green finance from the perspective of green credit. According to the estimation of Societe Generale Research, the balance of green credit accounts for more than 90% of the balance of all green financing. As of the first half of 2023, the green credit balance of China's 21 major banks reached 25 trillion yuan, up 33% year-on-year, ranking first in the world in terms of scale. In the same period, the balance of green bonds in China was only RMB 1.98 trillion, less than 8% of the scale of green credit, and the scale of other green financing methods was even smaller. Therefore, we believe that the findings based on green credit can represent the role of green finance to a large extent. Thus, we take the Guidelines issued by the former CBRC as the entry point and use the DID method to assess the implementation effect of GCP. Specifically, considering that the Guidelines are committed to guiding the flow of funds to environmentally friendly and sustainable projects, promoting the green transformation of the economy, and preventing environmental and social risks, this paper tries to analyze the micro impact of the GCP from the perspective of the quality of green innovation.\u003c/p\u003e \u003cp\u003eCurrently, two contrasting perspectives have surfaced from research on the influence of green finance on green innovation. One viewpoint suggests that green finance facilitates increased corporate R\u0026amp;D spending\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, enhances the effectiveness of green innovation within enterprises\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, and leads to high-quality enterprise development. Simultaneously, it can reduce corporate agency costs, improve investment efficiency, and act as credit restraints to drive green innovation among heavily polluting industries, thereby enhancing corporate environmental performance\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e and firms' green innovation performance\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. On the other hand, an alternative perspective implies that green finance may aggravate the financing constraint faced by heavily polluting industries\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, raising the cost of credit financing for enterprises\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e and impeding green innovation.. Specifically, the impact of green finance on the green innovation behavior of listed companies is detrimental, particularly in hindering corporate credit financing, particularly with regards to long-term borrowing\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Additionally, green finance may intensify financial regulation challenges and diminish the effectiveness of financial services, thereby undermining the promotion of environmentally-friendly practices within the corporate sector\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. However, as China\u0026rsquo;s economic development shifts from \"quantitative increase\" to \"qualitative improvement\", the value of patents is no longer sufficient to reflect the innovation level of enterprises; rather, it highlights the degree of significance that enterprises place on green innovation.\u003c/p\u003e \u003cp\u003eWe employed the \u0026ldquo;Green Credit Guidelines\u0026rdquo; (GCP), introduced in China in 2012, as a quasi-natural experiment to investigate the influence of green credit policies on the green innovation of firms subject to green credit restrictions. To identify whether a listed company falls within a green credit-restricted industry, we ascertained the company's industry based on the presence of environmental and social risks classified as Category A in the Key Evaluation Indicators for Green Credit Implementation. If a listed company is associated with an industry falling under Category A, it is recognized as a green credit-restricted industry; otherwise, it is classified as a non-green credit-restricted industry. We created an interaction term involving the policy time dummy variable and the grouping dummy variable as the principal explanatory variable. This variable primarily assesses the impact of GCP on the green innovation of firms subject to green credit restrictions and those that are not, both before and after the implementation of GCP. It is important to note that current methods for measuring enterprise green innovation are somewhat limited, making it challenging to fully capture the diverse nature of corporate environmental improvement. To date, existing studies have mainly measured corporate green innovation through metrics such as the quantity of green patents\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, As China's economy transitions from quantity-driven growth to a focus on qualitative enhancements, the emphasis is no longer solely on the sheer volume of patents, but rather on the significance of enterprises' commitment to green innovation. To address this shift, we have utilized the WIPO Green Patent List's patent classification system, and applied the knowledge breadth method to assess the quality of enterprises' green patents. By doing so, we aim to gauge the extent of green innovation within these enterprises, thereby addressing the limitations of relying solely on patent numbers to measure innovation activities. This approach ultimately yields a more accurate reflection of enterprises' achievement in the realm of green innovation.\u003c/p\u003e \u003cp\u003eAfter establishing the indicators mentioned above, we discovered that the GCP has a substantial impact on driving green innovation within green credit-constrained enterprises. This finding remains robust even after addressing several potential endogeneity issues and conducting thorough tests to validate the results. The findings of the impact mechanism test indicate that green credit effectively curbs corporate shadow banking and leads to spillover effects in digital technology, thereby facilitating the transformation of corporations towards green practices. Simultaneously, green credit also enhances the total factor productivity of enterprises, which in turn supports and encourages the transition towards greener operations.\u003c/p\u003e \u003cp\u003eMeanwhile, the existing literature often overlooks the influence of FinTech in examining how green finance facilitates entities' transition to sustainable practices. In the interconnected landscape of green finance and green technology innovation, FinTech has the potential to enhance the supportive role of green finance in empowering entities. Findings from this study indicate that FinTech can notably bolster green credit's efficacy in promoting the greening of enterprises. However, the implementation of green credit may also add complexity to financial institutions' portfolios and pose challenges to regulatory oversight, leading to increased difficulties in financial regulation. This paper also explores the moderating impact of financial regulation on these dynamics\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In order to enhance the precision of the regression results, we adjusted the financial regulation index to the city level based on the concentration of urban commercial banks, given that the financial regulation data are currently only available at the provincial level. The findings of this study indicate that financial regulation reinforces the positive impact of fintech. Concurrently, the enhancement of the intellectual property protection system has been shown to bolster enterprises' innovation capabilities, thereby facilitating technological advancements and industrial upgrades, and effectively mitigating the constraints related to R\u0026amp;D investment and innovation risks\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Furthermore, this paper explores the influence of the IPR protection system and establishes its role in bolstering green innovation within firms that are both enabled and constrained by green credit.\u003c/p\u003e \u003cp\u003eIn conclusion, this paper makes a meaningful contribution in three primary areas. Firstly, it provides a fresh perspective on green finance for promoting high-quality development of green innovation. Instead of solely relying on the quantity of green patent applications or authorizations, as commonly done in existing studies, this paper introduces the knowledge breadth approach to assess the quality of corporate green innovation. This method ensures a more objective and accurate measurement, important for China's progression towards high-quality development. The second contribution lies in the comprehensive examination of the impact mechanism of green credit on enterprise green innovation. In light of China's policies aimed at digitization and innovation, this paper scrutinizes the influence of green credit through the lenses of commercial credit, digital technological innovation, and total factor productivity. Lastly, this paper addresses the often overlooked impact of financial technology and regulation in studies related to green finance and entity greening transformation. By introducing city-level fintech and financial regulation indicators, the study explores how these factors can strengthen the role of green finance in promoting entity green innovation. This innovative approach adds depth to our understanding of the factors affecting the quality of green innovation in firms.\u003c/p\u003e \u003cp\u003eThe paper is structured as follows: the second part comprises the theoretical analysis and research hypotheses. The third and fourth parts detail the research design and the analysis of empirical results, respectively. The fifth part explores the impact mechanism, followed by the sixth part, which presents further discussion. The final section contains the conclusions and suggestions for countermeasures.\u003c/p\u003e"},{"header":"2. Theoretical analysis and research hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Green finance credit allocation and the quality of corporate green innovation\u003c/h2\u003e\n\u003cp\u003eGreen financial credit allocation primarily occurs through bank credit channels to facilitate the efficient distribution of funds. This makes it more feasible for enterprises with a focus on energy conservation and environmental protection, as well as those engaged in green production activities, to secure loans and access more convenient and open financing channels. Additionally, the reduced financing costs allow for the allocation of funds to green production initiatives. The Guidelines on Green Credit, established by the former CBRC in 2012, provide valuable insight for studying green financial credit allocation. Within this system, commercial banks consider the environmental and social risks of their clients an essential factor in their ratings, credit approvals, management, and disbursement. They also identify industries subject to green credit restrictions. Poor environmental performance, high energy consumption, and excessive pollution emissions make it less likely for a company to receive credit, resulting in higher financing expenses\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. From a perspective of long-term and sustainable development, green credit restrictions gradually prompt industries to address short-term negative impacts. The constant reduction of environmental pollution costs, the mitigation of environmental risks, and the acceleration of green transformation processes are vital\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Consequently, green credit policies may compel enterprises in green credit-restricted industries to engage in green innovation initiatives. Simultaneously, as green finance fortifies banks' monitoring functions concerning fund usage and possesses enduring credit monitoring, more enterprises shift from quantitative to substantive innovation\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, to bolster their standing in credit allocation. Accordingly, research hypothesis H1 can be posited:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 1.\u0026nbsp;\u003c/strong\u003eGreen financial credit allocation exerts a positive motivational effect on the quality of green innovation in green credit-constrained industries.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Path analysis of green financial credit allocation affecting the quality of corporate green innovation\u003c/h2\u003e\n\u003cp\u003eThe primary goal of green finance credit allocation is to boost financial support for environmentally responsible enterprises meeting green finance recognition standards, while concurrently reducing financing for heavy polluters. This ensures a reallocation of financial resources towards sustainable initiatives\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. To secure additional financial backing, businesses in green credit-restricted industries are motivated to invest in research and development of green and clean technologies to diminish their ecological footprint\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Digital technology plays a pivotal role in steering companies away from traditional high-input, high-output, high-energy consumption, and high-pollution production methods, toward low-carbon, energy-efficient production modes. This shift allows enterprises to enhance production efficiency by leveraging new technologies while addressing the negative environmental impact of their operations, ultimately achieving their own green transformation\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Striving to navigate the pressures stemming from green credit policies, companies in green credit-restricted sectors may further enhance their digital technology capabilities and prioritize the synergy between production and environmental considerations, ultimately contributing to ecological improvement and heightened resource allocation efficiency\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Moreover, the strategic use of digital technology can assist heavy polluters in mitigating external environmental pressures. Based on this reasoning, the research hypothesis H2a is formulated:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cstrong\u003ea.\u003c/strong\u003e Green finance credit allocation has the potential to enhance the quality of corporate green innovation through the advancement of digitization in green credit-restricted industries.\u003c/p\u003e\n\u003cp\u003eGreen finance credit policies play a crucial role in guiding the optimal allocation of resources between polluting and green industries, and in enhancing the efficiency of resource allocation within green credit-restricted industries\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In this context, less productive firms have an opportunity to access production resources at lower costs, allowing them to compete more effectively in the market. However, this could inhibit the flow of resources to high-productivity firms, thus limiting the market space for inefficient firms and enabling dynamic adjustments in firm size, leading to improved resource allocation efficiency\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Consequently, green finance credit policies can motivate enterprises to boost their total factor productivity through optimized resource allocation. An increase in total factor productivity can lead to reduced raw material consumption and waste generation, which in turn lessens the negative environmental impact. Moreover, it provides enterprises with more resources and surplus funds, enabling greater investment in green innovation activities and perpetual enhancement of the quality of green innovation driven by efficiency\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Accordingly, research hypothesis H2b can be formulated as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cstrong\u003eb.\u003c/strong\u003e Green finance credit allocation has the potential to enhance the quality of corporate green innovation by increasing total factor productivity in green credit-constrained industries.\u003c/p\u003e\n\u003cp\u003eGreen finance credit policies typically compel financial institutions to thoroughly assess the environmental track records of borrowers. If borrowers are found to have engaged in serious environmental polluting activities, they may face higher loan interest rates and more stringent loan terms\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Consequently, companies operating in sectors with limited access to green credit may turn to shadow banking to shore up their liquidity\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. However, the shadow banking system is characterized by high leverage, a significant information asymmetry, and ambiguous legal entities, rendering it more risky and potentially exposing firms to liquidity dilemmas and bankruptcy risks\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This hinders their ability to focus on core business development and dampens the prospects for green innovation. Nevertheless, in China's strategic environment aimed at preventing real enterprises from engaging in \"de-realization\" and promoting the green transformation of the manufacturing industry, green finance and credit policies may compel enterprises in industries with restricted green credit access to refocus on their core business and pursue green credit support through green innovation\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Therefore, the influence of green finance credit policies on whether shadow banking is practiced in green credit-constrained industries remains uncertain. As a result, the research hypothesis H2c can be formulated as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cstrong\u003ec.\u003c/strong\u003e Green finance credit policies may impact the quality of green innovation in firms operating in sectors with limited green credit through the practice of corporate shadow banking, but this effect is subject to uncertainty.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Analysis of factors influencing the allocation of green financial credit to affect the quality of corporate green innovation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe efficiency of green finance credit allocation may be influenced by various financial regulatory factors\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Specifically, financial regulators have the power to standardize the management regulations, auditing, and assessment procedures for green credit business within financial institutions. They can also supervise and evaluate the implementation of green credit policies within these institutions, ultimately playing a key role in regulating green innovation. Financial regulators can encourage financial institutions to actively and effectively engage in green financial business and investment, ensuring the quality of green credit and the feasibility of the regulatory mechanism, while also safeguarding the legitimacy of the regulation and promoting the development of enterprise green innovation. Through financial supervision, regulations can effectively govern the behaviors of financial institutions and enterprises, ultimately supporting the stability and sustainable growth of the market. The regulation of green financial credit allocation not only helps prevent the accumulation of non-performing assets and risks but also guarantees the transparency of green credit allocation and fair competition, and facilitates the establishment of a risk prevention mechanism\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Therefore, the research hypothesis H3a can be stated as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003cstrong\u003ea.\u003c/strong\u003e Financial regulation can enhance the impact of green finance credit allocation on promoting the quality of green innovation in enterprises.\u003c/p\u003e\n\u003cp\u003eAdditionally, the use of financial technology (Fintech) can significantly increase the efficiency of pre-credit review and post-credit risk management for green credits\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, ultimately facilitating access to and effective utilization of funds for environmental projects by enterprises. Fintech can collect business information from multiple channels\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e and screen the credit needs of enterprises engaged in green innovation\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, enhancing the allocation efficiency of green credit. Furthermore, Fintech, through means such as blockchain and big data, can effectively reduce post-loan moral hazards and improve the ability of financial institutions to prevent risks\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. It can also better manage the destination of corporate credit through technological means, effectively controlling the operational and financial risks of enterprises and providing a stable environment for innovative activities. Therefore, research hypothesis H3b can be stated as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003cstrong\u003eb.\u003c/strong\u003e Fintech can strengthen the positive impact of green financial credit allocation on the quality of firms' green innovations.\u003c/p\u003e\n\u003cp\u003eProtecting intellectual property is a crucial tool for driving innovation, nurturing economic growth, and ensuring fair competition, thereby offering financial returns and competitive advantages in the market. Additionally, intellectual property protection plays a vital role in advancing technological progress and fostering industrial development. A robust judicial framework for safeguarding intellectual property rights constitutes a critical institutional foundation for strategies promoting innovation-led growth\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Intellectual property infringement can be a serious impediment to R\u0026amp;D\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, Strengthened enforcement of IPR protection by the government can enhance firms' ability to innovate, mainly by reducing R\u0026amp;D spillover losses and easing external financing constraints\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Accordingly, the research hypothesis H3c can be formulated:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003cstrong\u003ec.\u003c/strong\u003e Regional support for intellectual property protection can magnify the positive impact of green finance credit allocation on the quality of firms' green innovation efforts.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Research design","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Sample selection and data sources\u003c/h2\u003e\n \u003cp\u003ePrior to 2007, China's policy emphasis was primarily on the traditional financial sector rather than the green financial sector. Therefore, this study focuses on Chinese A-share listed companies from the period of 2007 to 2021, excluding 2017, in order to more accurately assess the impact of current green credit policies on corporate green innovation. Due to data quality concerns, referring to Lu Jing et al. (2021), 2017 data were excluded and treated as consecutive years for 2016 and 2018. The listed company data was obtained from CSMAR and WIND databases, while the enterprise patent data was sourced from the WIPO Green Patent List. Financial regulation data at the provincial level was obtained from the National Bureau of Statistics, provincial statistical yearbooks, and bulletin data. To comprehensively measure the level of financial regulation at the city level, data on the number of bank financial institutions' outlets in Chinese cities was manually organized. The ratio of the number of bank outlets in cities to the number of bank outlets in the province in the current year was used to construct the financial weight at the city level, which was then multiplied by the financial regulation index at the provincial level. Additionally, the level of fintech was measured through the compilation of the distribution of commercial banks in each city in China, derived from the financial license information of the China Banking Regulatory Commission (CBRC). Furthermore, the city AI agglomeration index was measured through a specific search of AI companies by Tianyecha. This study collected and organized 10,572 \"company-year\" observations. To ensure the robustness of the study, various treatments were applied to the raw data, including the exclusion of financial enterprises, enterprises with delisting risk, enterprises with missing key variables, enterprises with delisting risk, and samples with insolvency and negative book value of shareholders' equity. Additionally, a two-sided 1% shrinkage treatment was performed on all continuous variables to mitigate the impact of outliers on the empirical results. Ultimately, the study resulted in 10,572 \"firm-year\" observations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Model setting\u003c/h2\u003e\n \u003cp\u003eIn reference to the work by Xin Wang and Ying Wang (2021)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, this study utilizes the 2012 Green Credit Guidelines formulation as a quasi-natural experiment to examine green finance credit allocation, and constructs the model in the following manner:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"649\" height=\"44\"\u003e\u003c/p\u003e\n \u003cp\u003eThe explanatory variable \"Patent\" represents the quality of firms' green innovation. The main explanatory variables include the GCP (Policy), industry attributes (Gcres), and the interaction term between the two (Policy × Gcres). Control variables are denoted as X, and ε\u003csub\u003ei,t\u003c/sub\u003e represents the random error term, where subscript i refers to the firm and t refers to the year.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Variable setting\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Explained variables\u003c/h2\u003e\n \u003cp\u003eThe quality of green innovation (patent) is assessed in this study. Building on the work of Jie Zhang and Wenping Zheng (2018)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, based on the patent classification numbers provided by the WIPO Green Patent Inventory, the knowledge breadth method is utilized to calculate the quality of green patents of enterprises and to measure their green innovations, which to a certain extent can overcome the shortcomings of reflecting the innovation activities of enterprises only by the number of patent applications:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eIn model (2), we use Patentn,t to represent the breadth of knowledge associated with various types of patents, serving as a proxy for the quality of environmental innovation within enterprises. Here, n and t respectively refer to the patent and the year, and α denotes the proportion of major group classifications within the patent classification number. The IPC classification number format in the patent documents of Chinese enterprises at the State Intellectual Property Office typically follows the structure \"Department - Major Class - Minor Class - Major Group - Group\", such as \"F24F11/00\". The first letter of the classification number spans A-H, representing 8 major departments; the second and third digits indicate the major categories; the fourth letter denotes the minor categories, with major and minor groups separated by \"/\". For example, one patent may have classification numbers F24F11/00, F24F11/10, and F24F11/20, while another patent similarly has F24F11/00, F24F12/00, and F24F13/20. Although the two patents share the same number of classification codes, they differ in that the first patent utilizes only F24F11 as major group information, whereas the second patent encompasses F24F11 as minor group information and three different major group information. According to the calculation rule of model (2), this indicates that the breadth of knowledge applied in the second patent exceeds that of the former. Hence, the greater the diversity between the patent classification numbers at the major group level, the wider the knowledge scope, reflecting a higher patent quality. To address the right-skewed distribution issue of green patent data, this study employs the natural logarithm of green patent quality after adding 1 to obtain Ln(Patent + 1).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Core explanatory variables\u003c/h2\u003e\n \u003cp\u003eThe main explanatory variables under consideration are the GCP, industry characteristics, and their cross-multiplier. Specifically, the variable \"Policy\" functions as a dummy variable, indicating the period before and after the implementation of the Guidelines. Following the implementation (2012 and beyond), the value of \"Policy\" becomes 1, otherwise it remains 0. Gcres denotes the industry classification for the implementation of the GCP specified by the Guidelines. In this study, the industry category to which the companies with environmental and social risks fall under in the Key Evaluation Indicators for Green Credit Implementation determines whether the listed company belongs to the green credit-restricted industry. If the company falls under category A, it is labeled as a green credit-restricted industry (Gcres = 1); if not, it is classified as a non-green credit-restricted industry (Gcres = 0). The interaction term, \"Policy*Gcres,\" primarily assesses the impact of the GCP on green innovation within both the green credit-restricted and non-green credit-restricted industries before and after the policy's implementation. A significantly positive coefficient of the cross-multiplier term, β2, indicates the substantial advancement of green innovation in green credit-constrained industries due to the GCP. Conversely, a non-significant coefficient suggests the lack of a significant promotional effect.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.3 Control variables\u003c/h2\u003e\n \u003cp\u003eBased on previous literature\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, this paper incorporates the following control variables X\u003csub\u003ei, t−1\u003c/sub\u003e: firm size (LnSize), expressed as the logarithm of the total assets of the firm; gearing ratio (LEV); net profitability of total assets (ROA); return on equity (ROE); accounts receivable-to-revenue ratio (REC); dual chairmanship and CEO position (Dual); the percentage of ownership of the first largest shareholder (LnTop1); book market capitalization ratio (BM); and firm value (LnTQ), expressed as the logarithm of the enterprise value multiple.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics of main variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVarName\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eObs\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMedian\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP10\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMin\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMax\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e + 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLnSize\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLEV\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eROE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eREC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLnTop1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLnTQ\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Empirical analysis","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e4.1 Baseline regression\u003c/h2\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e displays the findings from the benchmarking study on the impact of green credit on firms\u0026rsquo; green innovation. In columns (1) and (2), the coefficient of the cross-multiplier term DID shows a highly significant positive correlation at the 1% level. After incorporating region fixed effects, the resulting coefficient of 0.058 indicates a notable 5.8% increase in the quality of green innovations within the green credit-restricted sector following the implementation of the policy, underscoring the significant boost provided by the Guidelines in enhancing green innovation output in this sector. Conversely, the coefficient of Policy fails to demonstrate significance, with a resulting coefficient of 0.003 after adding the region fixed effect, suggesting that the Guidelines do not significantly elevate the quality of green innovation in the non-green credit-restricted sector, reflecting a marginal average increase of merely 0.3 percent. Additionally, the coefficient on Gcres shows a statistically significant positive relationship at the 10 percent level. With regional fixed effects, the resulting coefficient of 0.238 denotes a substantial 23.8 percent improvement in the green innovation quality within the green credit-restricted sector subsequent to policy implementation,indicating a clear differentiation between the green credit-restricted and non-green credit-restricted industries in terms of green innovation quality. This underscores a significant disparity in the quality of green innovation between the two sectors.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBenchmark regression results\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eModel\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.065\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.058\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(4.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(4.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePolicy\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGcres\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.238\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLnSize\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.278\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.276\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLEV\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.037\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.144\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eROE\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.086\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eREC\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.072\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.074\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-5.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-4.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(4.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLnTop1\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.029\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.035\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-5.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-6.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eBM\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLnTQ\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSector\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eArea\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10572\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10572\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eNote: t statistics in parentheses; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e4.2 Robustness tests\u003c/h2\u003e\n\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n\u003ch2\u003e4.2.1 Parallel trend test\u003c/h2\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the analysis of the impact of the GCP on corporate green innovation over time. Prior to the policy implementation, the estimated coefficient \u0026beta; within the 95% confidence interval shows no significant deviation from 0. However, one period after the policy shock, the coefficient becomes significantly different from 0, suggesting a delayed response to the policy implementation. Subsequent to this, there is a noticeable divergence in trends between the treatment group and the control group, particularly after the t\u0026thinsp;+\u0026thinsp;1 period, indicating a positive promotion of enterprise green innovation by the GCP. Additionally, the parallel trend test confirms these findings.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n\u003ch2\u003e4.2.2 Placebo test\u003c/h2\u003e\n\u003cp\u003eTo test the reliability of the empirical findings, we introduced the placebo method to assess the robustness of the impact of the GCP. Following the guidelines, we randomly selected 9 industries as the \"pseudo-treatment group\" (Gcresfalse), from which we constructed the dummy variable Patentfalse\u0026thinsp;=\u0026thinsp;Gcresfalse*Policy for the placebo test. This experiment was then repeated 1,000 times, resulting in the p-value test plot shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The p-value test plot. The results show that the coefficient estimates are clustered around 0 and approximately follow a normal distribution, indicating that the regression results are not affected by unobservable factors and the results are more robust.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n\u003ch2\u003e4.4.3 Replacement of explanatory variables\u003c/h2\u003e\n\u003cp\u003eBy utilizing the patent classification numbers from the WIPO Green Patent List, we employed the knowledge breadth method to assess the quality of enterprises' green invention patents using the formula Ln(lnva\u0026thinsp;+\u0026thinsp;1), and the quality of green utility model patents using Ln(uma\u0026thinsp;+\u0026thinsp;1) as proxy variables for evaluating enterprises' green innovation. The findings reveal a substantially positive regression coefficient of Ln(lnva\u0026thinsp;+\u0026thinsp;1) at a significance level of 1%, while the regression coefficient of Ln(uma\u0026thinsp;+\u0026thinsp;1) is found to be statistically insignificant. This can be attributed to the fact that green invention patents necessitate a higher degree of innovation, particularly in the realm of green technology, as they emphasize environmentally sustainable technological solutions and are considerably more technical and innovative compared to green utility model patents. Consequently, the quality of green invention patents serves as a more reliable indicator of corporate green innovation, which aligns with the results of the primary regression and underscores the robustness of our findings.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRobustness test based on proxy variables for corporate green innovation\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eModel\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(4)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003elnva\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003elnva\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003euma\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003euma\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.043\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.053\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePolicy\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGcres\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.342\u003csup\u003e**/\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.357\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-2.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-2.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSector\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eArea\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10572\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10572\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10572\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10572\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eNote: t statistics in parentheses; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n\u003ch2\u003e4.4.4 Change of industry definition criteria\u003c/h2\u003e\n\u003cp\u003eThe Key Evaluation Indicators for Green Credit Implementation indicates that, aside from Class A industries, Class B industries also have adverse effects on the environment and society. Therefore, this study broadens the identification of green credit restricted industries, including category B industries. Furthermore, by referencing the \"Listed Company Environmental Verification Industry Classification Management Directory\" and \"Listed Company Environmental Information Disclosure Guidelines\" and in conjunction with the \"Guidelines for Industry Classification of Listed Companies,\" this paper identifies the mining industry (industry code: B06, B07, B08, B09), manufacturing industry (industry code: C17, C19, C22, C25, C26, C28, C29, C30, C31), and polluting firms in electricity, heat, gas, and water production and supply (industry code: D44) as the experimental group. Non-polluting firms, after removing green firms, are used as the control group. The regression results in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e show that the coefficients of the Difference-in-Differences (DID) are all significantly positive at the 1% level, consistent with the regression results in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, indicating robust results for the first and second changes in industry definition criteria.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRobustness test based on two criteria for changing industry definition\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eModel\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePolicy\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGcres\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.486\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.317\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSector\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eArea\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10572\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10572\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eNote: t statistics in parentheses; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n\u003ch2\u003e4.4.5 Tests based on the PSM-DID methodology\u003c/h2\u003e\n\u003cp\u003eTo mitigate systematic differences between heavily polluting industries and other sectors and minimize estimation bias in the double-difference method, we employ the PSM-DID method for robustness testing. This involves conducting a Logit regression of control variables using a dummy variable to determine if an industry is heavily polluting, in order to obtain the propensity score value. The matching industry for the heavily polluting industry is identified as the industry with the closest propensity score value, effectively minimizing systematic differences and reducing the DID estimation bias. We specifically utilize the kernel matching method for estimation to assess the robustness of the GCP 's role in promoting firms' green innovation. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the regression results, which demonstrate that even after applying the PSM-DID methodology, the GCP consistently and significantly stimulates firms' green innovation activities. Additionally, the quality of firms' green innovations increases by 6.3%, aligning with the previous results and indicating robustness.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRobustness test based on PSM-DID\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eModel\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eBefore\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAfter\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eIncrement\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eStandard error\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eT-value\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-5.270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.310\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.679\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eNote: t statistics in parentheses; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n\u003ch2\u003e4.4.6 The 2008 international financial crisis and the impact of omitted variables\u003c/h2\u003e\n\u003cp\u003eDue to potential impacts from the 2008 international financial crisis and omitted variables, this study omitted the 2008 data sample and introduced a new variable (Punish) in model (1) to represent environmental penalties, where Punish\u0026thinsp;=\u0026thinsp;1 signifies that firms are subject to such penalties, and Punish\u0026thinsp;=\u0026thinsp;0 signifies that firms are not. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e (Refer to Supplementary Materials) presents the results, with column (1) reporting the regression results excluding the 2008 data sample, and column (2) reporting the results introducing environmental penalties. The regression coefficients of the cross-multiplier term DID are consistently and significantly positive at the 5% level, aligning with the robust results displayed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRobustness tests based on excluding the 2008 sample and environmental penalties\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eModel\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.044\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePolicy\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.103\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGcres\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.490\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.213\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePunish\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSector\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eArea\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10507\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10572\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n\u003ch2\u003e4.4.7 Environmental pollution factors and replacement sample year intervals\u003c/h2\u003e\n\u003cp\u003eIn order to account for the impact of concurrent environmental policies, this study incorporates three variables based on Xin Wang and Ying Wang (2021)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. The first variable is the annual PITI index, which measures the disclosure of pollution source regulatory information in the location where the firm is registered. The second variable, Distance, represents the shortest distance between the registered location of the listed company and neighboring state-controlled air monitoring stations. To address the issue of skewed distribution, the natural logarithm of the nearest distance plus 1 (LnDistance) is used. The third variable assesses the level of haze pollution (PM2.5) in the location of the listed companies. The study focuses on Chinese listed companies from 2007 to 2021, treating the 2012 introduction of the Green Credit Guidelines as a quasi-natural experiment to examine its impact on corporate green innovation. To minimize the influence of subsequent policies, the sample is restricted to 2010\u0026ndash;2013, based on the methodology of Ding et al. (2022) and Li Juncheng et al. (2023)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e (Refer to Supplementary Materials) presents the regression results, showing that the regression coefficient of the DID cross-multiplier term is significantly positive, at least at the 5% level, consistent with previous robust results, and supporting hypothesis H1.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRobustness test based on environmental pollution factors and replacement sample year intervals\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eModel\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(4)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.050\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.058\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.068\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.075\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(4.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(4.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePolicy\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.035\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.047\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-1.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGcres\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.454\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.308\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(8.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePITI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-1.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLndistance\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePM2.5\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSector\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eArea\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6642\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10677\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9487\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Analysis of impact mechanisms","content":"\n\u003ch3\u003e5. 1 Mechanism testing based on digital transformation\u003c/h3\u003e\n\u003cp\u003eBased on the primary classification number of patents matched with the Statistical Classification of Digital Economy and Its Core Industries, the number of digital economy patent applications filed by listed companies in that year is obtained. This data is then logarithmized to derive Ln(Innovate\u0026thinsp;+\u0026thinsp;1), which measures the level of digitalization of enterprises. To assess the reasonableness of the mediating effect, the Bootstrap method is employed to sample 500 times, yielding an estimated value of ab of Ln(Patent\u0026thinsp;+\u0026thinsp;1) at 0.008, with a 95% confidence interval of (0.003, 0.014). As the confidence interval does not encompass 0, and the two-tailed test is significant (p\u0026thinsp;=\u0026thinsp;0.002), it is indicative of a mediating effect with Ln(Innovate\u0026thinsp;+\u0026thinsp;1) as the conduction variable. In Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e (Refer to Supplementary Materials), column (1) represents the total effect of green finance credit allocation on firms' green innovation. Column (2) demonstrates the effect of green financial credit allocation on firms' digital transformation, with the regression coefficient significantly positive at the 1% level, suggesting that green credit can promote the digitalization level of green credit-constrained industries. Column (3) provides the regression result of green credit and firms' digitization level on Ln(Patent\u0026thinsp;+\u0026thinsp;1). The regression coefficients of Difference-in-Differences (DID) and Ln(Innovate\u0026thinsp;+\u0026thinsp;1) are significantly positive at least at the 5% level, signaling that green financial credit allocation can enhance the digitization level of firms, consequently promoting the enhancement of the quality of green innovations in green credit-restricted industries, thus proving hypothesis H2a.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism test based on digital transformation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eModel\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003eInnovate\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\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\u003e0.058\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.410\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.054\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(4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(19.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePolicy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.057\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(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-8.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGcres\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.238\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.611\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.235\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(2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003eInnovate\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\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\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\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\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSector\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\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\u003e10572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e5. 2 Mechanism test based on total factor productivity of enterprises\u003c/h3\u003e\n\u003cp\u003eThe OP and LP methods are frequently utilized in existing studies to gauge a firm's total factor productivity\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e, However, the LP method is more adaptable to handling sample loss and endogeneity issues than the OP method, and it provides more accurate estimates of a firm's total factor productivity. Thus, following the approach of Song Min et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e, we employ the LP method to measure the firm's total factor productivity TFP_LP. This paper utilizes the Bootstrap method with 500 samples, resulting in an estimated indirect effect ab of Ln(Patent\u0026thinsp;+\u0026thinsp;1) at 0.016, with a 95% confidence interval of (0.011 0.022). As the confidence interval does not include 0, the two-tailed test is significant (p\u0026thinsp;=\u0026thinsp;0.000), suggesting the existence of a mediating effect with TFP_LP as the transmission variable. In Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e (Refer to Supplementary Materials), column (2) demonstrates the impact of green finance credit allocation on firms' total factor productivity, with the regression coefficient significantly positive at the 1% level. Meanwhile, column (3) represents the combined effect of green financial credit allocation and enterprise total factor productivity on enterprise green innovation quality, showing both DID and TFP_LP coefficients to be significantly positive at the 1% level. These results indicate that the GCP can enhance the total factor productivity of the green credit-restricted industry, subsequently improving the quality of enterprise green innovation, thereby supporting hypothesis H2b.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism test based on firms' total factor productivity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eModel\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTFP_LP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\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\u003e0.058\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.406\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\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(4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(17.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePolicy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.221\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\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(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGcres\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.238\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.042\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.014\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(2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-41.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTFP_LP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\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\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(6.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\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\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSector\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\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\u003e10572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Mechanism test based on shadow banking\u003c/h2\u003e \u003cp\u003eReferring to Li Zhen et al. (2023)\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, the study delves into the link between \"green credit and corporate green innovation\" by using the sum of corporate accounts receivable, notes receivable, and prepayment divided by business revenue to denote commercial credit (TC). A higher TC value signifies greater supply of corporate shadow banking. Employing the Bootstrap method for 500 samplings, the estimated Ln(Patent\u0026thinsp;+\u0026thinsp;1) succinct effect ab is found to be 0.005, with a 95% confidence interval (0.003 0.007). As the confidence interval does not encompass 0 and the two-tailed test is significant (p\u0026thinsp;=\u0026thinsp;0.000), this indicates a mediation effect with TC as the transmission variable. The regression results in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e (Refer to Supplementary Materials) reveal that the regression coefficient of the Difference-in-Differences (DID) in column (2) is significantly negative at the 1% level, suggesting that the implementation of green financial credit policy can curtail shadow banking in listed companies. Meanwhile, the regression coefficient of DID in column (3) is significantly positive at the 1% level, and the regression coefficient of TC is significantly negative at the 1% level. These findings imply that the green financial credit policy can foster improved green innovation quality in restricted industries by restricting shadow banking activities in listed companies, thus supporting hypothesis H2c.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism test based on business credit of enterprises\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eModel\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTC\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\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\u003e0.058\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.083\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\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(4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-6.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePolicy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025\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(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGcres\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.238\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.498\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.460\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(2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(8.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(6.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.049\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\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-4.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\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\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSector\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\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\u003e10572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Further discussion","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Financial regulation\u003c/h2\u003e \u003cp\u003eDrawing from the theoretical analysis of how the allocation of green financial credit may complicate financial regulation, this paper delves deeper into the influence of financial regulation on the green innovation of enterprises enabled by green credit. Previous research has relied on the ratio of regional financial regulatory expenditure to financial sector value added as a proxy for local financial regulation. However, this indicator is limited to the provincial level, leading to internal bias in evaluating the level of financial regulation at the city level due to data constraints. To address this, this study manually compiles data on the number of urban bank and financial institution outlets nationwide to estimate the level of financial regulation at the city level. It constructs financial weights for each city by using the ratio of the number of urban bank outlets to the number of bank outlets in the province and multiplies them by the financial regulation index at the provincial level to obtain the financial regulation indicator. According to the regression results in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e (Refer to Supplementary Materials), the coefficients of \u0026ldquo;DID*Regulation\u0026rdquo; are both significantly positive at the 1% level, with Regulation further promoting green innovation activities in the green credit-restricted industry and enhancing the quality of green innovation. This provides evidence to support hypothesis H3a.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImpact analysis based on financial regulation, fintech and IP protection\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eModel\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003ePatent\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\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*\u003cem\u003eRegulation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.047\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\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(3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDID\u003c/em\u003e* \u003cem\u003eLn\u003c/em\u003e(\u003cem\u003efintech\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.091\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\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\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDID\u003c/em\u003e* \u003cem\u003eLnprotect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\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\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePolicy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.070\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.126\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(-3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-5.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGcres\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.458\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.240\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.463\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(7.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(7.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRegulation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\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(-1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLn\u003c/em\u003e(\u003cem\u003efintech\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.134\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\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\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-6.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLnprotect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.085\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\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\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\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSector\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\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\u003e10572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Financial technology\u003c/h2\u003e \u003cp\u003eGiven that Fintech has the potential to enhance the efficient allocation of green credit to financial resources, this study leverages the approach taken by Wu Fei et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Initially, we utilized Python tools to scrape 48 keywords associated with \"Fintech\" from relevant news articles and conferences. Subsequently, we employed Baidu News Advanced Search to look for news pages containing these keywords in conjunction with cities and municipalities. By crawling the source code of Baidu News Advanced Search and extracting the number of search results, we aggregated a total of 254,456 searches containing \"region\u0026thinsp;+\u0026thinsp;keywords\" at the prefecture-level city or municipality directly under the central government. Next, we manually sorted the number of branches of each bank in each year and city using financial license information from the China Banking Regulatory Commission (CBRC) to construct the Herfindahl Index (HHI) for measuring bank competition. Additionally, we aggregated the number of AI enterprises at the city and provincial levels and applied the location entropy index to gauge AI enterprise concentration at the regional level. Multiplying these three indexes yielded the comprehensive Fintech evaluation index. To address right skewness and prevent missing values, we processed the index by adding one and taking the logarithm. Consequently, we obtained the Fintech level measure at the city level (Ln(fintech\u0026thinsp;+\u0026thinsp;1)). According to the regression results in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e (Refer to Supplementary Materials), the coefficient of the cross-multiplier term \u0026ldquo;DID*Ln(fintech\u0026thinsp;+\u0026thinsp;1)\u0026rdquo; significantly indicates at the 1% level, supporting the notion that Fintech further advances the allocation of green financial credit, empowering businesses to enhance the quality of green innovation and rendering hypothesis H3b as valid.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Intellectual property protection\u003c/h2\u003e \u003cp\u003eResearch has indicated that IPR protection systems can lead to an increase in the quantity and quality of patent applications filed by firms, consequently enhancing the level of innovation within these firms\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Thus, leveraging Yuan Shengchao's work (2023)\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e, this paper examines the regional level of intellectual property protection (Lnprotect), characterizing it as the average of the number of intellectual property infringement cases divided by the total population and the number of lawyers divided by the total population in each region, and then taking the logarithm. According to the regression results in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e (Refer to Supplementary Materials), the coefficient of \u0026ldquo;DID*Lnprotect\u0026rdquo; is significantly positive at the 1% level, suggesting that post-implementation of green finance and credit policies, reinforcing regional intellectual property protection can effectively elevate the quality of green innovation in green credit-restricted industries. This supports the validation of hypothesis H3c.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Conclusions and policy recommendations","content":"\u003cp\u003eServing as a critical mechanism for market-driven resource allocation and a vital intermediary between the financial sector and the ecological environment, effectively supporting corporate green innovation is pivotal for empowering entities through financial means. Against the backdrop of China's shift from \"quantitative increase\" to \"qualitative improvement\" in green innovation, this study utilizes the formulation of the Green Credit Guidelines as a quasi-natural experiment to investigate the impact of green financial credit allocation on the quality of corporate green innovation. The study reveals that green financial credit allocation significantly enhances the quality of green innovation in industries restricted by green credit, and this conclusion remains robust across various dimensions even after rigorous testing. Furthermore, through an analysis of the influence mechanism, it is evident that green financial credit allocation elevates the quality of corporate green innovation by boosting corporate digitization levels, total factor productivity, and by curbing corporate shadow banking. Additionally, our examination of influencing factors demonstrates that fintech, financial regulation, and local intellectual property protection systems can further reinforce the positive impact of green financial credit allocation on the quality of enterprises' green innovation.\u003c/p\u003e \u003cp\u003eBased on the findings, this paper presents the following policy implications:\u003c/p\u003e \u003cp\u003eFirstly, it is crucial to strengthen the standards and assessment system for green credit and enhance supervision on green credit practices. Financial regulators should intensify their oversight of banks and other financial institutions to ensure that they thoroughly consider environmental factors when awarding green credit, and strictly control lending for environmentally harmful projects. Furthermore, to encourage more enterprises to engage in green innovation, regulators can provide certain policy preferences and incentives while also requiring banks and other financial institutions to regularly disclose information on their green credit operations for improved environmental monitoring.\u003c/p\u003e \u003cp\u003eSecondly, efforts should be made to enhance green industrial ecology and establish a green ecosystem for enterprises. The government should augment its support for the green industry, guiding and financially supporting enterprises to implement green production methods and foster a low-carbon, eco-friendly, and circular economy. Enterprises themselves should actively pursue green development through technological innovation and upgrade production processes, while strengthening environmental supervision and cracking down on pollution. It is also essential for enterprises to establish an effective green management system, set clear green development goals, and shoulder responsibilities. Additionally, they should advance research and development and application of green technology, improve production efficiency, and collaborate with upstream and downstream enterprises for the advancement of a green supply chain. Lastly, enterprises should engage in environmental protection activities, fulfill social responsibilities, and enhance their social image and reputation.\u003c/p\u003e \u003cp\u003eThirdly, it is imperative to bolster positive incentives for fintech and financial regulation while enhancing the local intellectual property protection system. The advantages of financial science and technology should be utilized to improve the accuracy and efficiency of green credit, using big data and artificial intelligence for a more scientific and precise evaluation for green credit approval. There is a need for the formulation of more scientific and rational regulatory policies to encourage financial institutions to develop green credit business and direct more capital to the environmental protection industries and green enterprises. Strengthening supervision and assessment of financial institutions, as well as rewarding and punishing mechanisms for green credit, are vital. Moreover, fostering investments in research and development of environmental protection technologies and products will stimulate the growth of the environmental protection industry. Strict enforcement of the intellectual property protection system is essential to prevent infringement and safeguard the legitimate rights and interests of enterprises.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eEthics Approval\u003c/h2\u003e\n\u003cp\u003eThis is an empirical analysisstudy. The Suzhou University of Science and Technology Research Ethics Committee has confirmed that no ethical approval is required.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Liangfeng Hao designed the study and conducted the analysis. Material preparation, data collection were performed by Biyi Deng. The first draft of the manuscript was written by Biyi Deng. The final draft of the manuscript was written by Liangfeng Hao. Chuanming Yang revised the manuscript. And all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eListed company data from CSMAR and WIND databases at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.csmar.com/\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wind.com.cn/\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eCorporate patent data from the WIPO Green Patent Inventory at https://www.wipo.int/.\u003c/p\u003e\n\u003cp\u003eData on financial regulation at the provincial level are obtained from the National Statistical Office at https://www.stats.gov.cn/.\u003c/p\u003e\n\u003cp\u003eDistribution number of commercial banks in each city in China, data from the financial license information of the China Banking Regulatory Commission at https://www.cbirc.gov.cn/.\u003c/p\u003e\n\u003cp\u003eArtificial Intelligence enterprise data from Tianyancha at https://m.tianyancha.com/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi Z, Liao G, Wang Z, et al. 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Science of Science and Management of Science \u0026amp; Technology, 2023,44(04):60-81.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Green finance, Green innovation, Patent quality, Financial regulation, Financial technology","lastPublishedDoi":"10.21203/rs.3.rs-4337275/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4337275/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe allocation of green financial credit plays a crucial role in establishing a market-oriented green innovation system. This study sets up a quasi-natural experiment using the Green Credit Policy (GCP) to examine the impact of green financial credit allocation on the quality of enterprise green innovation, with a focus on promoting high-quality development. The findings demonstrate that the GCP has the potential to improve the quality of green innovation in industries restricted by green credit, compared to non-green credit-restricted ones. This conclusion remains consistent after conducting thorough trend analysis and robustness tests. As China speeds up its industrial digital transformation, the fundamental drive of green credit to enable enterprises towards green innovation is also evolving. The analysis of the impact mechanism reveals that green financial credit allocation can elevate the digitization level and total factor productivity of green credit-restricted industries, leading to a higher quality of green innovation by curbing corporate shadow banking. Furthermore, additional research shows that fintech and financial regulation can strengthen the positive influence of GCP on the quality of green innovation. Moreover, regional intellectual property protection has a beneficial synergistic effect in combination with GCP. This study confirms that green credit is an effective strategy for optimizing the allocation of green financial resources and enhancing the quality of green innovation, with amplified positive effects achievable through financial technology and financial regulation.\u003c/p\u003e","manuscriptTitle":"Can green finance credit allocation enable green innovation quality improvement -- Evidence from China's manufacturing firms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-08 18:34:20","doi":"10.21203/rs.3.rs-4337275/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4c51b056-fbc7-4584-83d5-0a951f0d4393","owner":[],"postedDate":"May 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-21T00:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-08 18:34:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4337275","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4337275","identity":"rs-4337275","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}