Study on the Impact of Supply Chain Finance on Green Innovation in Listed Manufacturing Enterprises in China: An AI-Driven Perspective

Study on the Impact of Supply Chain Finance on Green Innovation in Listed Manufacturing Enterprises in China: An AI-Driven Perspective | 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 Study on the Impact of Supply Chain Finance on Green Innovation in Listed Manufacturing Enterprises in China: An AI-Driven Perspective Xinglei Guo, Xiaoye Li, Kuiliang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9079679/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 Green innovation is a key national strategy for fostering high-quality corporate development. This paper examines the effect of supply chain finance on firms’ green innovation by employing a two-way fixed effects model and using panel data from Chinese A-share manufacturing companies covering the period from 2010 to 2023. The empirical results reveal that the advancement of supply chain finance has a statistically significant positive effect on green innovation activities. Mechanism analysis further demonstrates that supply chain finance promotes corporate green innovation through two primary channels: mitigating financial constraints and improving total factor efficiency.Additionally, the progression of artificial intelligence (AI) technology is found to reinforce the favorable influence of supply chain finance on green innovation, acting as a moderating factor that amplifies this relationship. Heterogeneity analysis indicates that the influence of supply chain finance on green innovation is especially notable in private enterprises, companies based in eastern China, and firms operating within technology-intensive sectors. This study provides theoretical support for promoting the “AI+” supply chain finance initiative and offers valuable policy insights to accelerate China’s green economic development. Green innovation Supply chain finance Financing constraints Total factor productivity Artificial intelligence technology 1. Introduction In light of international calls for sustainable development initiatives, green innovation has emerged as a key cornerstone for enterprises to sustain their long-term competitive edge(Song et al., 2023 ). Green innovation drives economic growth and social progress through innovative technologies, products, and services while safeguarding the environment and promoting sustainable development, aiming to achieve synergistic growth between environmental preservation and economic growth. Green innovation in manufacturing enterprises is widely regarded as a key engine for the long-term growth of the actual economy and a strategic pathway for achieving industrial transformation(Gao et al., 2023 ). The green innovation process involves companies adopting new technologies and securing adequate funding sources. This process can enhance resource conservation, reduce pollution, and improve ecosystems, thereby supporting sustainable economic development(Pan et al., 2024 ). Nevertheless, green innovation is inherently characterized by high risk exposure, prolonged development cycles, and dual externalities. These inherent attributes mean that it typically demands sustained financial backing throughout its entire lifecycle(Xiang et al., 2022 ). Recently, new insights into corporate green innovation have been made possible by the growth of supply chain financing. As highlighted by Gomm ( 2010 ), the optimization of financial structures and the improvement of cash flow efficiency within the supply chain ecosystem constitute the core of supply chain finance. Notably, the vigorous advancement of the supply chain economy, coupled with the promotion of a circular economy, has been formally designated as a national-level development strategy in China. As a systemic activity, green innovation requires collaborative participation from multiple stakeholders, and relying solely on individual enterprises is unlikely to achieve the desired outcomes. Supply chain networks, with their unique synergistic advantages, serve as a critical supporting platform(Junaid et al., 2022 ), connecting upstream and downstream enterprises for joint participation. Supply chain finance is an essential part of the whole supply chain and can promote things like resource coordination, information integration, and connections between partner companies. Current research on green innovation primarily focuses on two aspects. First, external environmental policies: studies like Kou et al. ( 2024 ) indicate that in regions with stringent environmental regulations, green government procurement significantly promotes corporate green innovation. However, it may also yield adverse outcomes. For instance, external environmental regulations could reduce corporate cash flow levels(Tang et al., 2020 ), with the structural effects of environmental regulations outweighing innovation compensation effects(Wang et al., 2019 ), resulting in a net negative impact that suppresses corporate green innovation in the short term. Second, internal organizational factors. Elements such as green entrepreneurial orientation(Shehzad et al., 2023 ), green investment levels(Zhang et al., 2023 ), digital transformation degree(Zhang et al., 2024 ), and CEO green background(Hu & Shi, 2025 ) significantly promote corporate green innovation. A growing body of scholarly research has focused on how supply chain financing supports business innovation, with findings indicating that it serves as an effective mechanism to support enterprises in augmenting their Innovation-related investments and inputs for research and development (R&D) (Feng et al., 2024 ). It promotes innovation by reducing financing constraints and adjusting supply chain relationships(Lee et al., 2025 ). Regarding green innovation, existing research primarily explores macro-level influence mechanisms, such as how supply chain finance can strengthen supply chain networks, meet local government green regulatory requirements, and shape green images to promote green innovation(Gu et al., 2023 ). Yet, existing research has devoted insufficient attention to the facilitative role of supply chain finance in fostering corporate green innovation—a relatively specialized research area. It also lacks in-depth exploration of the specific micro-level transmission mechanisms through which this effect operates. Digital transformation within supply chains fosters corporate green innovation via two core pathways: enhancing the supply chain's upstream and downstream integration and improving node enterprises' internal supply chain management effectiveness (Ma et al., 2024 ). Artificial intelligence (AI), has become a key driver of technological innovation today and is widely applied across numerous industrial sectors(Brynjolfsson et al., 2019 ), fundamentally transforming the innovation paradigm(Wu et al., 2025 ). AI-driven supply chain finance enables supply chain data transparency and enhances supply chain credibility, serving as a bridge and link for enterprises to collaborate on green innovation. AI technology influences multiple activities within the innovation process, supports the commercial operations of supply chain finance providers, and aids in assessing buyer creditworthiness, detecting fraud, or proposing suitable supply chain finance solutions(Ronchini et al., 2024 ). Cheng et al. ( 2025 ) found that AI-driven innovation presents new opportunities for green innovation in manufacturing, with AI literacy emerging as a key determinant of green absorption capacity. AI promotes high-quality economic development by accelerating energy transitions, fostering green technological innovation, and reducing climate policy uncertainty(Akram et al., 2024 ). Building upon existing research, this paper adopts an AI-centric perspective to explore how supply chain finance influences green innovation behavior among listed manufacturing enterprises, focusing on its underlying factors and operational mechanisms. Artificial intelligence (AI), a crucial technical advancement, is becoming a major force behind corporate green innovation and industrial upgrading. However, there has been limited scholarly exploration of how AI enables supply chain finance to promote corporate green innovation, particularly from a micro-enterprise perspective. This study introduces external factors from an AI-driven viewpoint, examining new models and mechanisms of supply chain finance empowered by technological advancements. This study contributes in two key ways: First, it enhances the understanding of the micro-level mechanisms through which supply chain finance impacts firm-level green innovation. By alleviating financing constraints, supply chain finance provides necessary capital for technological innovation, while enhancing total factor productivity strengthens core competitiveness and the capacity for green innovation activities. Second, it introduces an AI technology perspective. As a transformative and widely applicable enabling technology, AI increases supply chain finance's operational effectiveness and accuracy, thereby reinforcing its role in facilitating green innovation. The paper is organized as follows: a comprehensive overview of pertinent literature is presented in the next part, from which research hypotheses regarding the mechanisms through which supply chain finance affects green innovation are developed. Following this, the research background, data sources, and variables are introduced. The subsequent section reports and interprets the estimation results, including robustness tests. Finally, a review of the results and implications brings the work to a close. 2. Theoretical Hypothesis 2.1 Supply Chain Finance and Green Innovation The inherent characteristics of the supply chain itself can influence innovation. For instance, research has shown that supply chain network structures can influence corporate green innovation, with centrality inhibiting green innovation while structural holes promote it (Yu et al., 2025 ). Supply chain stability can effectively stimulate corporate green technological innovation(Tu et al., 2025 ). Within the financial operational link of the supply chain, key manufacturing firms tend to form long-term strategic partnerships with upstream and downstream cooperative partners. Through mutually beneficial and interoperable models within the supply chain, they effectively integrate innovation resources, thereby enhancing green technological innovation(Xue & Ai, 2025 ). Core enterprises in supply chains build collaborative innovation networks centered on supply chain finance platforms. Leveraging digital technologies to break down information barriers, these networks generate technological synergies, promote green technology upgrades and diffusion among enterprises, and thus effect the implementation efficiency of green innovation(Guo et al., 2024 ). Williamson ( 1975 ) defines transaction costs as the expenses incurred in planning, adapting, and monitoring goal attainment under alternative governance structures. With economic and social development, transaction costs can be viewed as all costs incurred during transactions. Based on transaction cost theory, supply chains reduce transaction costs by lowering uncertainty and increasing transaction frequency. Consequently, companies' non-productive expenditures decrease, and the reduced transaction costs enable enterprises to allocate more funds toward green innovation activities. A financial service model called supply chain finance was created to support the vertical integration of the supply chain system and offer financing options to supply chain players. It can enhance corporate R&D investment levels(Lu et al., 2024 ), lower agency costs(Xu et al., 2023 ), improve production efficiency, and promote green innovation growth in enterprises. Reputation theory represents a cutting-edge concept in modern Western economics. Since Adam Smith, economists have consistently recognized reputation as a crucial mechanism ensuring faithful contract fulfillment. Corporate reputation reflects stakeholders' subjective perceptions and overall evaluations of a firm, representing an emotional cognition embedded in stakeholders' minds. Reputation is vital to a firm's survival and development; a strong reputation not only benefits long-term growth but also effectively coordinates relationships between the firm and its diverse stakeholders. According to reputation theory, corporate reputation constitutes a vital component of long-term competitiveness. Supply chain finance enhances corporate reputation by providing stable financial support, enabling better fulfillment of obligations, and boosting trust among supply chain partners. A strong reputation attracts greater resource investment, thereby accelerating green innovation. In light of the aforementioned theoretical considerations, Hypothesis 1 is put forth: Corporate green innovation is significantly influenced by supply chain financing. 2.2 Supply Chain Finance, Financing Constraints and Green Innovation Owing to the typical high uncertainty and relatively low returns associated with corporate green innovation, these activities usually require abundant funding sources. However, financing constraints faced by enterprises are particularly detrimental to such initiatives(C.-H. Yu et al., 2021 ). Financing constraints refer to the phenomenon where increased financing costs or restricted access to funding channels arise during the financing process due to factors such as information asymmetry. Information asymmetry is a key issue contributing to heightened financing constraints for enterprises. Information asymmetry impacts the availability and cost of investment capital, thereby influencing investment project selection(Bilyay-Erdogan et al., 2024 ). Supply chain finance improves investment efficiency by reducing financial restrictions and information asymmetry between businesses and their external stakeholders(Dou & Zhao, 2024 ). Supply chain finance provides enterprises with more flexible and convenient financing solutions. This effectively alleviates financial constraints for businesses, thereby enhancing the resilience and stability of the supply chain(Lee et al., 2025 ). Implementing supply chain finance enhances transparency across the entire supply chain, reducing information asymmetry and lowering credit risks for banks. Financial institutions such as banks can thus develop a more holistic grasp of enterprises’ operational performance and capital requirements. This not only eases the constraints on corporate financing but also furnishes sufficient financial backing for projects focused on green innovation. Existing literature (Lou et al., 2024 )argues from the perspective of capital accessibility that evaluating supply chain information and supervising transactions can integrate and optimize financial and informational resources, thereby alleviating financing constraints and helping enterprises along the chain create greater value. On one hand, supply chain finance lowers financing barriers. On the other hand, by enhancing financing efficiency to foster continuous innovation(Ji, 2025 ). Drawing on the theoretical analysis presented above, Hypothesis 2a is formulated as follows: Supply chain finance facilitates enterprises’ green innovation through mitigating their financing constraints. 2.3 Supply Chain Finance, Total Factor Productivity and Green Innovation Total Factor Productivity (TFP) represents the additional productivity achieved with a given level of factor inputs(Solow, 1957 ). TFP reflects the contributions of technological progress, organizational innovation, specialization, and production innovation to economic growth. The greening of enterprise production methods forms the microfoundation of green development, constituting a core element in enhancing resource utilization efficiency and addressing environmental challenges. Supply chain digitalization can promote green innovation and advance green development by boosting enterprise TFP. Total Factor Productivity has emerged as a vital bridge connecting financial support with corporate green innovation. Research indicates that supply chain finance primarily enhances enterprises productivity by promoting technological innovation and reducing tax burdens(Li et al., 2025 ). By integrating the credit and resources of enterprises, supply chain finance breaks down information barriers inherent in traditional financial systems. It synthesizes diverse knowledge, technologies, and capital, thereby reducing transaction costs, improving resource integration efficiency, and further boosting enterprises' total factor productivity. Enhanced TFP signifies more efficient allocation of production factors, enabling enterprises to fully leverage their own resources to acquire necessary innovation resources or technologies from the market. This yields economic returns, equipping enterprises with adequate capital to allocate toward green innovation initiatives and consequently imparting financial impetus to their green innovation endeavors. Consequently, improved TFP reflects robust enterprise innovation capabilities and serves as a vital pathway for green innovation. Its combination with green innovation allows enterprises to acquire more sustainable competitive edges in the market. In light of the preceding theoretical study, Hypothesis 2b is put forth as follows: Supply chain finance increases total factor productivity, which encourages green innovation in businesses. 2.4 The Regulatory Role of Artificial Intelligence Technology Artificial intelligence is a new technology with the potential to significantly change how businesses operate and innovate(Agrawal et al., 2019 ). As a core driver of technological advancement, the AI technology ecosystem has transcended the boundaries of a single technology, evolving into a highly enabling strategic resource cluster. Resource-Based Theory (RBT) stands as a pivotal framework within corporate strategic management. This theory shifts focus from traditional externally-oriented strategic thinking to internal organizational resources, offering fresh perspectives for strategic formulation and competitive advantage building. According to the VRIO framework within RBT, AI possesses four strategic attributes: Value, Rarity, Inimitability, and Organization. Its deep integration with supply chain finance scenarios is catalyzing a paradigm shift in green innovation within manufacturing. RBT emphasizes that firms can build competitive advantage by integrating internal and external resources. As a strategic resource, AI not only helps enterprises optimize financial resource allocation but also drives cross-entity collaborative innovation through supply chain integration. Supply chain finance is a crucial source of funding for enterprises and a critical component in commercial trade, yet risk control within this financing model remains challenging to effectively manage(Li et al., 2022 ). Confronted with diverse risks, organizations are adopting advanced information systems within their supply chains to enhance transparency, predictive capabilities, competitiveness, and rapid decision-making(Gupta et al., 2023 ). Artificial intelligence can significantly enhance supply chain management(Toorajipour et al., 2021 ). Trawnih et al. ( 2025 ) found that AI technologies play a positive role in enhancing financial information security and supply chain partner collaboration. Cutting-edge technologies like artificial intelligence (AI) are revolutionizing the business ecosystem processes embedded in supply chain finance, enabling new economic opportunities and maximizing the efficient utilization of supply chain networks(Olan et al., 2022 ). W. Yu et al. ( 2021 )demonstrated that big data facilitates supply chain finance in optimizing internal financing and mitigating financial risks. Drawing on the foregoing theoretical analysis, Hypothesis 3 is formulated: Artificial intelligence has the potential to improve supply chain finance's impact on businesses' green innovation. 3. Research Design 3.1 Model Specification This study describes the basic effects of supply chain finance on businesses' green innovation based on micro-level data. Building upon the preceding analysis, the following fixed-effects model is constructed: $${Gripatent}_{i,t}={\propto}_{0}+{\propto}_{1}{SCF}_{i,t}+{\gamma Controls}_{i,t}+{\delta}_{i}+{\delta}_{t}+{\epsilon}_{i,t}$$ 1 Among these, Gripatent i,t indicates the level of green innovation of firm i during period t ; SCF i,t indicates the firm's supply chain finance level during period t ; Controls i,t is the control variable; i stands for the firm fixed effect; t for the year fixed effect; ℇ i,t represents the random disturbance term. The coefficient α 1 is the key focus of this paper, as its sign and significance reflect how supply chain finance levels affect corporate green innovation. 3.2 Variable Definition and Explanation Dependent variable: Corporate Green Innovation ( Gripatent ). The count of independent green patent applications filed by an enterprise within a single year is adopted as the indicator to assess its level of green innovation. Considering that green patent authorization typically takes 3–5 years, ln(green patent applications + 1) is used to measure the company's green innovation level for that year. Since design patents involve relatively low technical complexity, are fundamentally different from invention and utility model patents, and fail to accurately reflect an enterprise’s innovation level, the green patents selected are restricted exclusively to green invention and green utility model patents. Core Explanatory Variable: Supply Chain Finance ( SCF ). The frequency with which supply chain finance-related terms appear in listed firms' annual reports is counted and then logarithmically transformed to determine the amount of supply chain finance development. Specific keywords are detailed in Appendix I. Mediating Variable: Financing Constraints ( SA ). The assessment of financing constraints can be divided into two types: the single-indicator method and the multi-indicator method. Single indicators are primarily constructed using corporate financial metrics such as asset scale and interest expenses, exhibiting significant endogeneity. Multivariate indicators, however, are constructed by synthesizing multiple factors. Given that the KZ and WW indices incorporate an overabundance of endogenous financial indicators, The SA index is used in this study to quantify financial restrictions. The following is the formula: $${SA}_{i,t}=0.043\times\left({Size}_{i,t}^{2}\right)-0.04\times Age-0.737\times{Size}_{i,t}$$ 2 Total Factor Productivity ( TFP_LP ). Given that the LP method, as a semiparametric approach, effectively addresses endogeneity and sample selection issues, this study employs LP method results. Moderator: Artificial Intelligence Level ( AI ). With the rapid advancement of AI in China, numerous scholars have focused their research on the intersection of AI and innovation. Drawing on the methodological framework proposed by Grashof and Kopka ( 2023 ), the natural logarithm of (the number of patent applications pertaining to AI + 1) is used in this study to evaluate the degree of artificial intelligence (AI). These patent applications are categorized according to standard patent classification codes and filed by publicly listed enterprises. Control Variables: To ensure unbiased estimation results, We selected the following control variables: Company size ( Size ), Debt-to-equity ratio ( Lev ), Quick ratio ( Quick ), Proportion of independent directors ( Indep ), Dual directorship ( Dual ), Return on assets ( ROA ), Concentration of top shareholder ownership ( Top1 ), Company growth ( Growth ), Cash flow ratio ( Cashflow ), and Firmage ( FirmAge ). Specific variable definitions are provided in Table 1 . Table 1 Variable Names and Definitions Variable Symbol Declaration Green Innovation Gripatent Ln(Count of green patent filings submitted by the enterprise + 1) Supply Chain Finance SCF The natural logarithm of the number of times supply chain finance-related terms occur in the annual report, multiplied by 1 Financing Constraints SA SA Index Total Factor Productivity TFP Estimation using the LP method Artificial intelligence AI Ln(Number of AI Patent Applications Filed by Listed Companies + 1) Scale Size The company's total market capitalization for the year, taking the natural logarithm Asset-Liability Ratio Lev Total Liabilities for the Year / Total Assets for the Year Quick Ratio Quick (Current Assets - Inventory) / Current Liabilities Percentage of Independent Directors Indep The ratio of independent directors to total directors Combining Two Positions Dual If the Chairman is also the General Manager, the value is 1; if not, it is 0 Return on Total Assets ROA Net Profit / Total Assets Equity Concentration Top1 The ratio of the largest shareholder's shares to all shares Company Growth Potential Growth Current Year Operating Revenue / Previous Year Operating Revenue − 1 Cash Flow Ratio Cashflow Net Cash Flow / Aggregate Assets Company Establishment Period FirmAge Ln(Current Calendar Year - Company’s Founding Year + 1) 3.3 Data Sources and Sample Selection This study’s research time frame covers the period of 2010 to 2023, with the study samples consisting of manufacturing enterprises listed on China’s A-share markets. The selection of 2010 as the starting point is based on two key factors: First, the 2010 China Sustainable Development Strategy Report explicitly designated “green development and innovation” as its central theme, emphasizing the role of green innovation in driving economic transformation. Consequently, green innovation became a critical direction for enterprises adapting to evolving policy environments starting in 2010. Second, the U.S. Defense Advanced Research Projects Agency (DARPA) launched multiple research projects related to artificial intelligence in 2010, signaling the potential for rapid global advancement in AI. The CNRDS database provided the patent data used in this study; the Sina Finance website ( https://finance.sina.com.cn/ ) provided the annual reports of listed firms; and the CSMAR database provided the financial and basic corporate data. The following sample screening and processing techniques were used to ensure the authenticity and dependability of the study data: (1) Exclusion of samples from enterprises that had undergone “special treatment” (ST, PT, or delisting); (2) Samples with severe data gaps in certain years were excluded; (3) Samples of insolvent enterprises were excluded. This process yielded 10,811 observations. All continuous variables included in the study were winsorized at the 1% and 99% percentiles to reduce the impact of extreme values on empirical findings. 4. Empirical Results 4.1 Descriptive Statistics Table 2 reports the descriptive statistical results of the core variables involved in this study. After data cleaning, the final sample comprised 10,811 observations. For the dependent variable "corporate green innovation", the mean value is 0.422, and its standard deviation amounts to 0.834. Its range extended from 0.000 to 3.892, indicating significant variation in green innovation performance across firms and an overall low level of achievement. Furthermore, there is pronounced divergence in innovation performance across firms, with over half failing to achieve any innovation outcomes. The mean value of the primary explanatory variable, supply chain finance, was 0.357 with a standard deviation of 0.643. Data indicates that minimum value and mode of this variable are both 0.000, while the maximum value reaches 2.833. Such a significant disparity reveals that supply chain finance penetration in the Chinese market remains low, with over half of enterprises yet to engage in related operations. This structural imbalance precisely signals immense market opportunities and growth potential ahead. Table 2 Descriptive Statistics Deffnition N Mean SD Min Median Max Gripatent 10,811 0.422 0.834 0.000 0.000 3.892 SCF 10,811 0.357 0.643 0.000 0.000 2.833 Size 10,811 22.910 1.124 19.848 22.762 28.561 Lev 10,811 0.411 0.186 0.052 0.413 0.803 Quick 10,811 1.928 2.217 0.266 1.225 14.492 Indep 10,811 0.371 0.052 0.333 0.333 0.571 Dual 10,811 0.242 0.428 0.000 0.000 1.000 ROA 10,811 0.041 0.057 -0.578 0.038 0.220 Top1 10,811 0.333 0.141 0.076 0.313 0.758 Growth 10,811 0.218 0.703 -0.926 0.093 17.107 CashFlow 10,811 0.052 0.064 -0.226 0.049 0.266 FirmAge 10,811 2.921 0.352 1.099 2.996 3.689 4.2 Benchmark Regression Analysis The benchmark regression outcomes based on Model (1) are presented in Table 3 . This research used a stepwise regression approach to include control variables in order to increase the conclusions' robustness. As shown in Column (1), without any control variables, the core explanatory variables—supply chain finance ( SCF ) and green innovation ( Gripatent )—exhibit a significant positive correlation at the 1% level. Subsequently, the control factors (apart from year and individual effects) are then included in Column (2), where the SCF coefficient stays at 0.026 and is significant at the 5% level. Finally, once a set of control variables and individual and year fixed effects are included simultaneously, the coefficient of SCF is estimated at 0.0226 in Column (3) and remains statistically significant at the 5% significance level. This robust set of results provides strong support for research hypothesis H1. With respect to the control variables, the coefficient of Size exhibits a statistically significant positive sign, suggesting that larger enterprises can allocate resources more efficiently, invest more resources and capital into R&D, and thus more readily achieve green innovation. The coefficient for Top1 equity concentration is significantly negative, indicating that high power concentration within a company hinders the achievement of green innovation. The coefficient for Cash-flow ratio is significantly negative, suggesting that holding large amounts of liquid assets to avoid cash flow shortages is detrimental to innovation. The estimated coefficients for the remaining control variables are broadly aligned with existing literature, and thus a detailed discussion is omitted for brevity. Table 3 Baseline regression Variable (1) Gripatent (2) Gripatent (3) Gripatent SCF 0.1111 *** 0.0260 ** 0.0226 ** (0.0124) (0.0122) (0.0110) Size 0.2209 *** 0.0595 *** (0.0084) (0.0146) Lev 0.6308 *** -0.0535 (0.0612) (0.0628) Quick 0.0261 *** 0.0042 (0.0046) (0.0033) Indep 0.1020 0.3155 ** (0.1468) (0.1424) Dual 0.0229 -0.0236 (0.0181) (0.0165) ROA -0.1588 0.0394 (0.1661) (0.1128) Top1 -0.0957 * -0.1802 ** (0.0563) (0.0887) Growth 0.0273 ** 0.0014 (0.0108) (0.0061) CashFlow -0.3634 *** -0.2032 ** (0.1361) (0.1030) FirmAge -0.1561 *** -0.1221 (0.0239) (0.0808) Constant 0.3825 *** -4.4935 *** -0.6217 (0.0091) (0.1818) (0.3996) Id No No Yes Year No No Yes Obs. 10,811 10,811 10,811 R 2 0.0073 0.1092 0.6374 Note: At the 1%, 5%, and 10% levels, respectively, statistical significance is indicated by the symbols ***, **, and *. Consistent notation is used for following tables, and robust standard errors are supplied in parenthesis. 4.3 Robustness Test In order to verify the validity of the empirical results, we first replace the core explanatory variable for supply chain finance with a continuous micro-level indicator: the year-end ratio of (short-term borrowings plus notes payable) to total assets (SCF2). Re-running the regression with Model (1) generates a coefficient of 0.4117, which is statistically significant at the 1% significance level. Furthermore, to account for potential confounding factors, provincial and industry fixed effects were incorporated into the two-way fixed effects model. As shown in Columns (2) and (3) of Table 4 , the coefficient of interest remains positive and statistically significant, further corroborating the robustness of the benchmark analysis. Finally, to mitigate endogeneity concerns stemming from reverse causality, a lagged explanatory variable approach was employed. The explanatory variable was lagged by three periods (SCF t-3 ) and re-regressed. The outcomes of the regression in Table 4 's Column (4) indicate that, even accounting for lagged effects, supply chain finance continues to significantly promote corporate green innovation. Since its introduction to China in 2008, supply chain finance lacked substantial policy support for its development. It was not until 2012 that the state implemented macro policies focused on expanding its coverage in serving small and medium-sized enterprises, leading to rapid growth in the Chinese market. Accordingly, the data observations from 2010 to 2011 are excluded, and the regression is reconducted for the sub-sample period of 2012–2023. The estimation results of this sub-period test are shown in Table 4 's column (5). The baseline conclusion is consistent with the SCF coefficient of 0.0278, which is statistically significant at the 5% level. Lastly, to mitigate the endogeneity issue arising from omitted variables, this study introduces regional interaction fixed effects into the original model. Macroeconomic conditions vary across regions and may exert heterogeneous impacts on corporate green innovation over different years. Given this, a multidimensional fixed effects model is adopted to further control for the combined interaction fixed effect (Province×Year). After interaction fixed effects are included, the core explanatory variable's coefficient stays positive and statistically significant, as indicated in Column (6) of Table 4 . Table 4 Robustness test Variable (1) Gripatent (2) Gripatent (3) Gripatent (4) Gripatent (5) Gripatent (6) Gripatent SCF 0.0218 ** 0.0225 ** 0.0278 ** 0.0250 ** (0.0110) (0.0111) (0.0114) (0.0113) SCF t−3 0.0284 ** (0.0137) SCF2 0.4117 *** (0.1019) Constant -0.9507 ** -0.6167 -0.6773 * -1.0539 * -0.5797 -0.5660 (0.4653) (0.3985) (0.4029) (0.6396) (0.5329) (0.4071) Controls Yes Yes Yes Yes Yes Yes Id Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Pro No Yes No No No No Ind No No Yes No No No Pro×Year No No No No No Yes Obs. 9,845 10,811 10,811 8,029 9,231 10,811 R 2 0.6556 0.6376 0.6378 0.6832 0.6684 0.6377 5. Mechanism Analysis 5.1 Regression Analysis of Mediating Effects To verify the hypotheses pertaining to constraints on corporate financing and mechanisms underlying total factor productivity (TFP), we employ a two-step regression approach with the mechanism variables as the dependent variables. To examine the validity of Hypotheses 2a and 2b, we introduced financing constraints ( SA ) and total factor productivity ( TFP_LP ) as mediating variables ( Med ) into Model (1). Regression analyses were conducted on the explanatory variables and a series of control variables, constructing the following mediation mechanism testing model: $${Med}_{i,t}={\beta}_{0}+{\beta}_{1}{SCF}_{i,t}+\gamma{Controls}_{i,r}+{\mu}_{i}+{\lambda}_{t}+{\epsilon}_{i,t}$$ 3 Given that the correlation coefficient α1 is significant in Model (1), if the correlation coefficient in Model (3) is also significant, then the indirect mechanism through which supply chain finance enables corporate green innovation via the mediating variables of financing constraints and total factor productivity holds; otherwise, it does not hold. Based on the above analysis, the correlation coefficient α 1 in Model (1) is significantly positive, with regression results shown in Column (1) of Table 5 . As evidenced in Column (2) of Table 5 , supply chain finance exhibits a significant negative correlation with financing constraints. Enterprises with lower financing constraints often secure lower financing costs and more abundant capital, enabling them to allocate resources toward technological innovation and thus elevate green innovation levels. Hypothesis 2a is thus validated by establishing financial restrictions as a mechanism variable through which supply chain finance encourages corporate green innovation. The regression results in Column (3) of Table 5 identify a strong positive effect of supply chain finance on total factor productivity (TFP), which is statistically significant at the 1% level. As TFP continuously improves, enterprises enhance their production efficiency, thereby achieving superior economic returns. Accumulation of such economic benefits bolsters enterprises’ R&D investment capabilities, thereby providing robust support for green innovation initiatives and promoting the continuous advancement of green innovation capacity. Accordingly, total factor productivity (TFP) functions as a notable mediating pathway via which supply chain finance promotes corporate green innovation, thus verifying Hypothesis 2b. Table 5 Mediation Effect Test Variable (1) Gripatent (2) SA (3) TFP_LP SCF 0.0226 ** -0.0085 *** 0.0269 *** (0.0110) (0.0011) (0.0062) Constant -0.6217 4.3031 *** -1.1713 *** (0.3996) (0.0609) (0.2473) Controls Yes Yes Yes Id Yes Yes Yes Year Yes Yes Yes Obs. 10,811 10,811 10,811 R 2 0.6374 0.9624 0.9244 5.2 Regression Analysis of Moderating Effects $${Gripatent}_{i,t}={\theta}_{0}+{\theta}_{1}{SCF}_{i,t}+{\theta}_{2}{{AI}_{i,t}+{\theta}_{3}{SCF}_{i,t}\times{AI}_{i,t}+\gamma Controls}_{i,t}+{\mu}_{i}+{\lambda}_{t}+{\epsilon}_{i,t}$$ 4 Among the variables, AI i ,ₜ denotes the development level of artificial intelligence for firm i in period t . Drawing on the method proposed by Grashof and Kopka ( 2023 ), this variable is measured by taking the natural logarithm of (1 + the number of AI-related patent applications filed by listed companies), with the relevant patents identified through their classification codes. The interaction term between a company's supply chain finance level and AI development level is represented by SCF i,t × AI i,t . Both the explanatory variable (SCF) and moderator variable (AI) undergo mean centering. The remaining variables align with the baseline model (1). With a set of control variables included and individual and year fixed effects held constant, the benchmark regression results for the influence of supply chain finance on corporate green innovation are shown in Column (1) of Table 6 . The regression coefficient is statistically positive and significant at the 5% significance level, indicating that supply chain finance fosters the development of corporate green innovation. Building upon this, the moderator variable AI and the interaction term between AI and SCF were added, with regression conducted based on Model (4). Table 6 's Column (2) reports pertinent findings. The mean-centered interaction term (c.SCF × c.AI) shows a statistically significant positive coefficient at the 1% significance level, demonstrating that artificial intelligence (AI) exerts a pivotal positive moderating effect in the process of SCF facilitating corporate green innovation, thus confirming Hypothesis 3. Additionally, we replaced the artificial intelligence (AI) measurement indicator by counting the quantity of terms connected to AI in company annual reports, adding 1 to each count, and then applying a natural logarithm transformation. Following the re-estimation of Model (4), the findings reported in Table 6 ’s Column (3) reveal that the coefficient of the interaction term between supply chain finance and corporate green innovation continues to be statistically significant and positive, which attests to the robustness of the preceding conclusion. AI-related keywords are listed in Appendix II . Table 6 The regulatory role of Artificial intelligence SCF (1) Gripatent (2) Gripatent (3) Gripatent 0.0226 ** 0.0100 0.0135 (0.0110) (0.0105) (0.0109) AI 0.0887 *** 0.0366 *** (0.0079) (0.0099) c.SCF×c.AI 0.0257 *** 0.0188 * (0.0080) (0.0098) Constant -0.6217 (0.3996) 0.0321 (0.3971) -0.5093 (0.3978) Controls Yes Yes Yes Id Yes Yes Yes Year Yes Yes Yes Obs. 10,811 10,811 10,811 R 2 0.6374 0.6458 0.6383 6. Further Heterogeneity Analysis 6.1 Ownership Attributes Businesses with various ownership arrangements have unique circumstances and limitations when trying to obtain supply chain finance assistance. Private enterprises typically face more severe financing constraints than their state-owned counterparts. Due to ownership discrimination, the phenomenon of “credit rationing” remains prevalent, making it difficult for them to obtain bank credit funds. Therefore, it may be deduced that private companies are typically more affected by supply chain finance's ability to alleviate financial barriers and encourage corporate green innovation. Accordingly, this research partitions the full sample into state-owned enterprises (SOEs) and private enterprises based on ownership structure, and conducts separate regressions using Model (1) for each subsample. The findings in Columns (1) and (2) of Table 7 indicate that the positive effect is only statistically significant in the private enterprise subsample. 6.2 Regional Attributes China's regional economic development exhibits pronounced spatial imbalances, with these structural disparities creating differentiated constraints in financial resource allocation and technological innovation conversion. Following a regional division of the sample, the regression outcomesin columns (3) and (4) of Table 7 confirm that the promotional effect is significant only within the eastern region cohort. This is attributable to the robust economic base of the eastern region, comprehensive industrial support systems, abundant high-quality talent, and more mature financial markets. These factors enable supply chain finance to operate more efficiently, thereby providing strong support for green innovation. This regional disparity reflects China's uneven regional economic development and suggests that policymakers and financial institutions should prioritize regional coordination when promoting green innovation and supply chain finance. 6.3 Technology-Intensive Attributes The regression findings in Columns (5) and (6) of Table 7 reveal that the facilitative effect of supply chain finance on corporate green innovation is especially pronounced in technology-intensive enterprises. Because technology-intensive businesses usually hold important positions in industrial chains and maintain tight technological collaboration and cooperative R&D connections with upstream and downstream partners, this might be the cause. Supply chain finance can enhance collaboration among these firms, accelerating technology spillovers and innovation diffusion. By comparison, non-technology-intensive firms typically adopt relatively straightforward production procedures, characterized by weaker technological interdependence with upstream and downstream counterparts. This renders it challenging to realize green innovation via collaborative research and development. Table 7 Heterogeneity Analysis Variable State-owned enterprise (1) Gripatent Private enterprise (2) Gripatent Eastern enterprise(3) Gripatent Non-easternenterprise (4) Gripatent Technology-intensive (5) Gripatent non-technology-intensive (6) Gripatent SCF 0.0231 0.0256 * 0.0288 ** 0.0076 0.0350 ** 0.0133 (0.0190) (0.0133) (0.0135) (0.0188) (0.0159) (0.0151) Constant 1.4568 ** -2.4283 *** -1.9446 *** 2.3392 *** -2.0746 *** 0.7822 (0.7190) (0.4868) (0.4856) (0.7440) (0.5902) (0.5555) Controls Yes Yes Yes Yes Yes Yes Id Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Obs. 4,502 6,307 7,154 3,655 6,138 4,662 R 2 0.7008 0.5506 0.6492 0.6141 0.6737 0.5542 7. Research Findings and Policy Implications 7.1 Research Findings This study estimates the direct association between supply chain financing and corporate green innovation using a two-way fixed effects model and data from Chinese A-share listed enterprises (2010–2023). The mechanism analysis further verifies that financing constraints and total factor productivity serve as mediators, with artificial intelligence acting as a positive moderator in this process. Finally, heterogeneity tests based on ownership attributes and regional characteristics explore whether differences exist among enterprises of varying types. Empirical findings reveal: (1) Supply chain finance significantly promotes green innovation in manufacturing enterprises. Benchmark regression results indicate that a 1% increase in supply chain finance levels leads to a roughly 2.26% rise in green innovation levels (measured by green patent applications), a conclusion that holds across various robustness and endogeneity tests. Theoretical analysis suggests supply chain finance reduces transaction costs, thereby enhancing capital utilization efficiency and fostering green technological innovation. (2) The intermediary mechanism test for financing constraints indicates that a 1% increase in supply chain finance levels reduces the degree of capital constraints decreases by 0.85%. Based on information asymmetry theory, supply chain finance enhances information transparency, significantly boosting mutual trust between enterprises and financial institutions. This effectively alleviates funding constraints, diversifies capital sources, and provides enterprises with more accessible, low-cost funds for technological innovation activities, thereby promoting green innovation. This offers a key pathway to address the “funding shortage” challenge in manufacturing green innovation. The examination of the mediating mechanism for total factor productivity (TFP) indicates that a 1% increase in supply chain finance levels leads to a 2.69% growth in TFP. This result strongly supports the core tenets of endogenous growth theory, revealing technological innovation, knowledge accumulation, and sustained innovation as key drivers of sustainable economic growth. Enhanced TFP enables enterprises to mobilize internal resources and acquire external innovation factors more efficiently, thereby indirectly promoting green innovation through technological transformation and increased production efficiency. (4) Moderation effects indicate that artificial intelligence technology positively strengthens the promotional role of SCF in Green Innovation. Replacing the AI measurement method (using keyword frequency in annual reports instead of patent data) still yields a significantly positive interaction coefficient, validating the reliability of the conclusion. (5) Additional heterogeneity tests show that the facilitative effect of supply chain finance on corporate green innovation within manufacturing firms is particularly pronounced for private enterprises, firms situated in eastern China, and technology-intensive companies. 7.2 Policy Implications Alleviate enterprises' financing constraints and reduce transaction costs. Governments should fully leverage macro-regulatory functions to improve the institutional framework of supply chain finance, ensuring standardized operational processes to mitigate information asymmetry. Specifically, establishing dedicated funds to support green technology R&D could be considered. Targeted support policies should incentivize collaborative innovation among upstream and downstream entities in industrial chains, promoting sustainable development across the entire value chain through resource integration and optimized factor allocation. Simultaneously, incentive measures such as tax breaks and financing facilitation should be implemented to effectively enhance enterprises' capital accumulation capabilities and provide financial support for green transformation. Accelerate technological innovation and cultivate scientific and technological talent. Enterprises should accelerate the development of artificial intelligence, the Internet of Things, and blockchain, integrate digital technologies into supply chains, and focus on absorbing and utilizing new knowledge and technologies to rapidly master cutting-edge expertise. Leveraging supply chain finance, they should increase investment in R&D for innovative technologies, enhance total factor productivity, and provide financial, technological, and talent-driven momentum for green innovation. Concurrently, proactive preferential policies should be introduced to attract top domestic and international talent for employment and entrepreneurship, driving local enterprise development and fostering the output of more outstanding technical professionals. Promote private enterprise development and build a coordinated regional ecosystem. Financial institutions like banks should precisely target the numerous financing challenges faced by private enterprises—such as limited financing channels and persistently high financing costs—to effectively alleviate their difficulties in accessing affordable capital. This will inject robust momentum into their sustained, healthy, and stable development, fostering a virtuous cycle and high-quality growth across the entire economic system. Transfer payments should guide financial institutions to establish supply chain finance divisions in central and western regions, lowering financing barriers for private enterprises and mitigating regional resource disparities. Enterprises in eastern regions should be encouraged to deepen exchanges and cooperation with their counterparts in central and western areas. Local governments should adhere to the core principles of targeted policies and tailored approaches based on local conditions. They should fully leverage their unique advantages and potential, conduct in-depth analyses of the industrial chains, operational models, challenges, and opportunities faced by local enterprises, and then develop comprehensive strategies for enterprise supply chain finance and green innovation based on local realities. 8. Discussion This study uses data from China's A-share listed manufacturing enterprises from 2010 to 2023 to empirically investigate the effect of supply chain financing (SCF) on corporate green innovation. The findings substantiate our core hypothesis (H1), demonstrating that the development of SCF significantly promotes corporate green innovation. This result aligns with existing literature, which suggests that SCF facilitates innovative activities by optimizing capital flows and integrating information resources. However, our research extends this understanding by not only confirming the positive relationship but also, more importantly, uncovering the underlying micro-level mechanisms and boundary conditions. First, the mechanism tests reveal that SCF primarily drives green innovation through two key channels: improving total factor productivity (TFP) (H2b) and easing financial limitations (H2a). These findings carry important theoretical implications. On one hand, they support information asymmetry theory, showing that SCF enhances information transparency across the supply chain, effectively reducing credit risks for external financial institutions. This, in turn, provides vital capital for firms—especially private enterprises facing significant financing difficulties—allowing them to engage in high-investment, long-cycle green R&D. On the other hand, the mediating role of TFP highlights the efficiency-enhancing function of SCF, resonating with the core principles of endogenous growth theory. By facilitating the synergy and integration of knowledge, technology, and capital within the supply chain, SCF improves firms' resource allocation and innovation conversion efficiency. This enables firms to more effectively direct limited resources toward green innovation, thus evolving from merely receiving financial "transfusions" to building internal "hematopoietic" capacity. Second, a key contribution of this study lies in identifying the moderating role of artificial intelligence (AI) technology (H3). The findings demonstrate that AI considerably amplifies SCF's beneficial impact on green innovation. The Resource-Based View (RBV) can be used to interpret this finding. AI technology, which is a strategic resource that is valuable, rare, and uncopyable, greatly improves risk assessment accuracy, transaction automation, and decision-making intelligence when combined with SCF. This not only validates the feasibility of "AI+" empowered SCF but also demonstrates how digital technology amplifies its role in supporting sustainable innovation by optimizing the underlying architecture of financial services. In doing so, it provides new micro-level evidence for understanding the synergy between digital technology and green finance. Furthermore, the heterogeneity analysis deepens our understanding of the boundary conditions of this relationship. The promoting effect of SCF is more pronounced among private enterprises, firms in eastern China, and technology-intensive industries. This aligns with the specific challenges and advantages faced by these groups. Private enterprises, due to their "financing dilemma," are more responsive to SCF as an alternative financing channel. The mature financial markets and well-developed industrial infrastructure in the eastern region provide a conducive environment for the efficient operation of SCF. Technology-intensive firms, with strong technological linkages to upstream and downstream partners, are better positioned to leverage SCF for collaborative innovation. These findings suggest that the policy effectiveness of SCF is not uniform; rather, it is significantly influenced by firms' internal and external institutional and environmental factors. In a broader context, this study positions the interaction between SCF, AI, and green innovation within China’s macro-strategic framework for achieving the "Dual Carbon" goals and high-quality development. Our findings suggest that developing intelligent SCF, underpinned by digital technologies like AI, is not only an effective tool for alleviating financing constraints and improving operational efficiency but also a crucial policy lever for enabling corporate green transformation. It offers a viable pathway for achieving the dual goals of "greening" and "digitalization" in economic activities. There are, of course, a number of limitations to this study that suggest directions for further investigation. First, the measurement of SCF relies primarily on text analysis; future studies could incorporate more granular micro-survey data or case studies to capture its operational substance more accurately. Second, the mechanisms of AI's moderating role warrant further investigation. For example, does AI influence green innovation primarily through improved risk pricing, process optimization, or enhanced data sharing along the supply chain? Future research could disaggregate these different dimensions of AI application. Third, while this study focuses on the manufacturing sector, future work could extend the framework to other industries, such as agriculture or services, to test its generalizability. Finally, with the rise of ESG (Environmental, Social, and Governance) investing, exploring how SCF influences corporate ESG performance represents a promising avenue for future research. Declarations Consent for Publication: I attest that, as an open access journal, Future Business Journal charges an article processing fee for each paper that is approved for publication. I consent to paying this fee in full if my article is accepted for publication by submitting it. This manuscript's data, figures, and results have not been published elsewhere, nor are they being considered by another publisher. The final version of the work has been read and approved by all authors, who also consent to its submission. Ethics Approval and Consent to Participate: This study did not involve human participants, animal subjects, or any clinical/field sampling. As such, no ethics approval or consent to participate was required for this research. Funding: This work was supported by the Shandong Academy of Social Sciences (SASS), grant number (22BCXJ05). The APC was funded by 22BCXJ05. Author Contribution Conceptualization, Xinglei Guo and Xiaoye Li ; methodology, Xinglei Guo; software, Xiaoye Li; validation, Kuiliang Li; formal analysis, Xinglei Guo and Xiaoye Li; investigation, Kuiliang Li; resources, Xinglei Guo; data curation, Xinglei Guo; writing—original draft preparation, Xinglei Guo and Xiaoye Li; writing—review and editing, Kuiliang Li; visualization, Xiaoye Li; supervision, Xinglei Guo and Kuiliang Li; project administration, Xinglei Guo; funding acquisition, Xinglei Guo. All authors have read and agreed to the published version of the manuscript. Acknowledgement We would like to once again extend our heartfelt thanks to the Shandong Academy of Social Sciences (SASS) for their generous financial support. Additionally, we are deeply grateful to the professors at the University of the Chinese Academy of Sciences for their invaluable feedback and suggestions, which have greatly contributed to the improvement of this manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9079679","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609482448,"identity":"d7f563c8-d73e-4ffd-9b14-6a43b34d3b43","order_by":0,"name":"Xinglei Guo","email":"","orcid":"","institution":"University of Jinan","correspondingAuthor":false,"prefix":"","firstName":"Xinglei","middleName":"","lastName":"Guo","suffix":""},{"id":609482449,"identity":"513e64aa-39e6-4bf4-9282-19ff842f2caf","order_by":1,"name":"Xiaoye Li","email":"data:image/png;base64,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","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Xiaoye","middleName":"","lastName":"Li","suffix":""},{"id":609482450,"identity":"643e7e1f-c4bb-4952-8603-cb119e5c7e99","order_by":2,"name":"Kuiliang Li","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kuiliang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-03-10 06:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9079679/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9079679/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105564368,"identity":"27fda2d7-4472-49d8-88dd-54589918b53a","added_by":"auto","created_at":"2026-03-27 12:49:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1454375,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9079679/v1/b5f25a09-6385-4d00-a2c6-3becdcd5abf0.pdf"},{"id":105305279,"identity":"8571f309-693f-4dc8-8ae5-1181a82b4559","added_by":"auto","created_at":"2026-03-24 14:28:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18042,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9079679/v1/8d04cb1f0dfdf41e6c0fccfd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on the Impact of Supply Chain Finance on Green Innovation in Listed Manufacturing Enterprises in China: An AI-Driven Perspective","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn light of international calls for sustainable development initiatives, green innovation has emerged as a key cornerstone for enterprises to sustain their long-term competitive edge(Song et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Green innovation drives economic growth and social progress through innovative technologies, products, and services while safeguarding the environment and promoting sustainable development, aiming to achieve synergistic growth between environmental preservation and economic growth. Green innovation in manufacturing enterprises is widely regarded as a key engine for the long-term growth of the actual economy and a strategic pathway for achieving industrial transformation(Gao et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The green innovation process involves companies adopting new technologies and securing adequate funding sources. This process can enhance resource conservation, reduce pollution, and improve ecosystems, thereby supporting sustainable economic development(Pan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, green innovation is inherently characterized by high risk exposure, prolonged development cycles, and dual externalities. These inherent attributes mean that it typically demands sustained financial backing throughout its entire lifecycle(Xiang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recently, new insights into corporate green innovation have been made possible by the growth of supply chain financing. As highlighted by Gomm (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), the optimization of financial structures and the improvement of cash flow efficiency within the supply chain ecosystem constitute the core of supply chain finance. Notably, the vigorous advancement of the supply chain economy, coupled with the promotion of a circular economy, has been formally designated as a national-level development strategy in China. As a systemic activity, green innovation requires collaborative participation from multiple stakeholders, and relying solely on individual enterprises is unlikely to achieve the desired outcomes. Supply chain networks, with their unique synergistic advantages, serve as a critical supporting platform(Junaid et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), connecting upstream and downstream enterprises for joint participation. Supply chain finance is an essential part of the whole supply chain and can promote things like resource coordination, information integration, and connections between partner companies.\u003c/p\u003e \u003cp\u003eCurrent research on green innovation primarily focuses on two aspects. First, external environmental policies: studies like Kou et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) indicate that in regions with stringent environmental regulations, green government procurement significantly promotes corporate green innovation. However, it may also yield adverse outcomes. For instance, external environmental regulations could reduce corporate cash flow levels(Tang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with the structural effects of environmental regulations outweighing innovation compensation effects(Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), resulting in a net negative impact that suppresses corporate green innovation in the short term. Second, internal organizational factors. Elements such as green entrepreneurial orientation(Shehzad et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), green investment levels(Zhang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), digital transformation degree(Zhang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and CEO green background(Hu \u0026amp; Shi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) significantly promote corporate green innovation. A growing body of scholarly research has focused on how supply chain financing supports business innovation, with findings indicating that it serves as an effective mechanism to support enterprises in augmenting their Innovation-related investments and inputs for research and development (R\u0026amp;D) (Feng et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It promotes innovation by reducing financing constraints and adjusting supply chain relationships(Lee et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Regarding green innovation, existing research primarily explores macro-level influence mechanisms, such as how supply chain finance can strengthen supply chain networks, meet local government green regulatory requirements, and shape green images to promote green innovation(Gu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Yet, existing research has devoted insufficient attention to the facilitative role of supply chain finance in fostering corporate green innovation\u0026mdash;a relatively specialized research area. It also lacks in-depth exploration of the specific micro-level transmission mechanisms through which this effect operates.\u003c/p\u003e \u003cp\u003eDigital transformation within supply chains fosters corporate green innovation via two core pathways: enhancing the supply chain's upstream and downstream integration and improving node enterprises' internal supply chain management effectiveness (Ma et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Artificial intelligence (AI), has become a key driver of technological innovation today and is widely applied across numerous industrial sectors(Brynjolfsson et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), fundamentally transforming the innovation paradigm(Wu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). AI-driven supply chain finance enables supply chain data transparency and enhances supply chain credibility, serving as a bridge and link for enterprises to collaborate on green innovation. AI technology influences multiple activities within the innovation process, supports the commercial operations of supply chain finance providers, and aids in assessing buyer creditworthiness, detecting fraud, or proposing suitable supply chain finance solutions(Ronchini et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Cheng et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that AI-driven innovation presents new opportunities for green innovation in manufacturing, with AI literacy emerging as a key determinant of green absorption capacity. AI promotes high-quality economic development by accelerating energy transitions, fostering green technological innovation, and reducing climate policy uncertainty(Akram et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBuilding upon existing research, this paper adopts an AI-centric perspective to explore how supply chain finance influences green innovation behavior among listed manufacturing enterprises, focusing on its underlying factors and operational mechanisms. Artificial intelligence (AI), a crucial technical advancement, is becoming a major force behind corporate green innovation and industrial upgrading. However, there has been limited scholarly exploration of how AI enables supply chain finance to promote corporate green innovation, particularly from a micro-enterprise perspective. This study introduces external factors from an AI-driven viewpoint, examining new models and mechanisms of supply chain finance empowered by technological advancements. This study contributes in two key ways: First, it enhances the understanding of the micro-level mechanisms through which supply chain finance impacts firm-level green innovation. By alleviating financing constraints, supply chain finance provides necessary capital for technological innovation, while enhancing total factor productivity strengthens core competitiveness and the capacity for green innovation activities. Second, it introduces an AI technology perspective. As a transformative and widely applicable enabling technology, AI increases supply chain finance's operational effectiveness and accuracy, thereby reinforcing its role in facilitating green innovation. The paper is organized as follows: a comprehensive overview of pertinent literature is presented in the next part, from which research hypotheses regarding the mechanisms through which supply chain finance affects green innovation are developed. Following this, the research background, data sources, and variables are introduced. The subsequent section reports and interprets the estimation results, including robustness tests. Finally, a review of the results and implications brings the work to a close.\u003c/p\u003e"},{"header":"2. Theoretical Hypothesis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Supply Chain Finance and Green Innovation\u003c/h2\u003e \u003cp\u003eThe inherent characteristics of the supply chain itself can influence innovation. For instance, research has shown that supply chain network structures can influence corporate green innovation, with centrality inhibiting green innovation while structural holes promote it (Yu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Supply chain stability can effectively stimulate corporate green technological innovation(Tu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Within the financial operational link of the supply chain, key manufacturing firms tend to form long-term strategic partnerships with upstream and downstream cooperative partners. Through mutually beneficial and interoperable models within the supply chain, they effectively integrate innovation resources, thereby enhancing green technological innovation(Xue \u0026amp; Ai, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Core enterprises in supply chains build collaborative innovation networks centered on supply chain finance platforms. Leveraging digital technologies to break down information barriers, these networks generate technological synergies, promote green technology upgrades and diffusion among enterprises, and thus effect the implementation efficiency of green innovation(Guo et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWilliamson (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) defines transaction costs as the expenses incurred in planning, adapting, and monitoring goal attainment under alternative governance structures. With economic and social development, transaction costs can be viewed as all costs incurred during transactions. Based on transaction cost theory, supply chains reduce transaction costs by lowering uncertainty and increasing transaction frequency. Consequently, companies' non-productive expenditures decrease, and the reduced transaction costs enable enterprises to allocate more funds toward green innovation activities. A financial service model called supply chain finance was created to support the vertical integration of the supply chain system and offer financing options to supply chain players. It can enhance corporate R\u0026amp;D investment levels(Lu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), lower agency costs(Xu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), improve production efficiency, and promote green innovation growth in enterprises. Reputation theory represents a cutting-edge concept in modern Western economics. Since Adam Smith, economists have consistently recognized reputation as a crucial mechanism ensuring faithful contract fulfillment. Corporate reputation reflects stakeholders' subjective perceptions and overall evaluations of a firm, representing an emotional cognition embedded in stakeholders' minds. Reputation is vital to a firm's survival and development; a strong reputation not only benefits long-term growth but also effectively coordinates relationships between the firm and its diverse stakeholders. According to reputation theory, corporate reputation constitutes a vital component of long-term competitiveness. Supply chain finance enhances corporate reputation by providing stable financial support, enabling better fulfillment of obligations, and boosting trust among supply chain partners. A strong reputation attracts greater resource investment, thereby accelerating green innovation.\u003c/p\u003e \u003cp\u003eIn light of the aforementioned theoretical considerations, Hypothesis 1 is put forth: Corporate green innovation is significantly influenced by supply chain financing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Supply Chain Finance, Financing Constraints and Green Innovation\u003c/h2\u003e \u003cp\u003eOwing to the typical high uncertainty and relatively low returns associated with corporate green innovation, these activities usually require abundant funding sources. However, financing constraints faced by enterprises are particularly detrimental to such initiatives(C.-H. Yu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Financing constraints refer to the phenomenon where increased financing costs or restricted access to funding channels arise during the financing process due to factors such as information asymmetry. Information asymmetry is a key issue contributing to heightened financing constraints for enterprises. Information asymmetry impacts the availability and cost of investment capital, thereby influencing investment project selection(Bilyay-Erdogan et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Supply chain finance improves investment efficiency by reducing financial restrictions and information asymmetry between businesses and their external stakeholders(Dou \u0026amp; Zhao, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Supply chain finance provides enterprises with more flexible and convenient financing solutions. This effectively alleviates financial constraints for businesses, thereby enhancing the resilience and stability of the supply chain(Lee et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Implementing supply chain finance enhances transparency across the entire supply chain, reducing information asymmetry and lowering credit risks for banks. Financial institutions such as banks can thus develop a more holistic grasp of enterprises\u0026rsquo; operational performance and capital requirements. This not only eases the constraints on corporate financing but also furnishes sufficient financial backing for projects focused on green innovation. Existing literature (Lou et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)argues from the perspective of capital accessibility that evaluating supply chain information and supervising transactions can integrate and optimize financial and informational resources, thereby alleviating financing constraints and helping enterprises along the chain create greater value. On one hand, supply chain finance lowers financing barriers. On the other hand, by enhancing financing efficiency to foster continuous innovation(Ji, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDrawing on the theoretical analysis presented above, Hypothesis 2a is formulated as follows: Supply chain finance facilitates enterprises\u0026rsquo; green innovation through mitigating their financing constraints.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Supply Chain Finance, Total Factor Productivity and Green Innovation\u003c/h2\u003e \u003cp\u003eTotal Factor Productivity (TFP) represents the additional productivity achieved with a given level of factor inputs(Solow, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1957\u003c/span\u003e). TFP reflects the contributions of technological progress, organizational innovation, specialization, and production innovation to economic growth. The greening of enterprise production methods forms the microfoundation of green development, constituting a core element in enhancing resource utilization efficiency and addressing environmental challenges. Supply chain digitalization can promote green innovation and advance green development by boosting enterprise TFP. Total Factor Productivity has emerged as a vital bridge connecting financial support with corporate green innovation. Research indicates that supply chain finance primarily enhances enterprises productivity by promoting technological innovation and reducing tax burdens(Li et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). By integrating the credit and resources of enterprises, supply chain finance breaks down information barriers inherent in traditional financial systems. It synthesizes diverse knowledge, technologies, and capital, thereby reducing transaction costs, improving resource integration efficiency, and further boosting enterprises' total factor productivity. Enhanced TFP signifies more efficient allocation of production factors, enabling enterprises to fully leverage their own resources to acquire necessary innovation resources or technologies from the market. This yields economic returns, equipping enterprises with adequate capital to allocate toward green innovation initiatives and consequently imparting financial impetus to their green innovation endeavors. Consequently, improved TFP reflects robust enterprise innovation capabilities and serves as a vital pathway for green innovation. Its combination with green innovation allows enterprises to acquire more sustainable competitive edges in the market.\u003c/p\u003e \u003cp\u003eIn light of the preceding theoretical study, Hypothesis 2b is put forth as follows: Supply chain finance increases total factor productivity, which encourages green innovation in businesses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 The Regulatory Role of Artificial Intelligence Technology\u003c/h2\u003e \u003cp\u003eArtificial intelligence is a new technology with the potential to significantly change how businesses operate and innovate(Agrawal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As a core driver of technological advancement, the AI technology ecosystem has transcended the boundaries of a single technology, evolving into a highly enabling strategic resource cluster. Resource-Based Theory (RBT) stands as a pivotal framework within corporate strategic management. This theory shifts focus from traditional externally-oriented strategic thinking to internal organizational resources, offering fresh perspectives for strategic formulation and competitive advantage building. According to the VRIO framework within RBT, AI possesses four strategic attributes: Value, Rarity, Inimitability, and Organization. Its deep integration with supply chain finance scenarios is catalyzing a paradigm shift in green innovation within manufacturing. RBT emphasizes that firms can build competitive advantage by integrating internal and external resources. As a strategic resource, AI not only helps enterprises optimize financial resource allocation but also drives cross-entity collaborative innovation through supply chain integration.\u003c/p\u003e \u003cp\u003eSupply chain finance is a crucial source of funding for enterprises and a critical component in commercial trade, yet risk control within this financing model remains challenging to effectively manage(Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Confronted with diverse risks, organizations are adopting advanced information systems within their supply chains to enhance transparency, predictive capabilities, competitiveness, and rapid decision-making(Gupta et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Artificial intelligence can significantly enhance supply chain management(Toorajipour et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Trawnih et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that AI technologies play a positive role in enhancing financial information security and supply chain partner collaboration. Cutting-edge technologies like artificial intelligence (AI) are revolutionizing the business ecosystem processes embedded in supply chain finance, enabling new economic opportunities and maximizing the efficient utilization of supply chain networks(Olan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). W. Yu et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)demonstrated that big data facilitates supply chain finance in optimizing internal financing and mitigating financial risks.\u003c/p\u003e \u003cp\u003eDrawing on the foregoing theoretical analysis, Hypothesis 3 is formulated: Artificial intelligence has the potential to improve supply chain finance's impact on businesses' green innovation.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Design","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Model Specification\u003c/h2\u003e\n\u003cp\u003eThis study describes the basic effects of supply chain finance on businesses' green innovation based on micro-level data. Building upon the preceding analysis, the following fixed-effects model is constructed:\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$${Gripatent}_{i,t}={\\propto}_{0}+{\\propto}_{1}{SCF}_{i,t}+{\\gamma Controls}_{i,t}+{\\delta}_{i}+{\\delta}_{t}+{\\epsilon}_{i,t}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eAmong these, \u003cem\u003eGripatent\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e indicates the level of green innovation of firm \u003cem\u003ei\u003c/em\u003e during period \u003cem\u003et\u003c/em\u003e; \u003cem\u003eSCF\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e indicates the firm's supply chain finance level during period \u003cem\u003et\u003c/em\u003e; \u003cem\u003eControls\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e is the control variable; \u003cem\u003ei\u003c/em\u003e stands for the firm fixed effect; \u003cem\u003et\u003c/em\u003e for the year fixed effect; \u003cem\u003eℇ\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e represents the random disturbance term. The coefficient \u003cem\u003e\u0026alpha;\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e is the key focus of this paper, as its sign and significance reflect how supply chain finance levels affect corporate green innovation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Variable Definition and Explanation\u003c/h2\u003e\n\u003cp\u003eDependent variable: Corporate Green Innovation (\u003cem\u003eGripatent\u003c/em\u003e). The count of independent green patent applications filed by an enterprise within a single year is adopted as the indicator to assess its level of green innovation. Considering that green patent authorization typically takes 3\u0026ndash;5 years, ln(green patent applications\u0026thinsp;+\u0026thinsp;1) is used to measure the company's green innovation level for that year. Since design patents involve relatively low technical complexity, are fundamentally different from invention and utility model patents, and fail to accurately reflect an enterprise\u0026rsquo;s innovation level, the green patents selected are restricted exclusively to green invention and green utility model patents.\u003c/p\u003e\n\u003cp\u003eCore Explanatory Variable: Supply Chain Finance (\u003cem\u003eSCF\u003c/em\u003e). The frequency with which supply chain finance-related terms appear in listed firms' annual reports is counted and then logarithmically transformed to determine the amount of supply chain finance development. Specific keywords are detailed in Appendix I.\u003c/p\u003e\n\u003cp\u003eMediating Variable: Financing Constraints (\u003cem\u003eSA\u003c/em\u003e). The assessment of financing constraints can be divided into two types: the single-indicator method and the multi-indicator method. Single indicators are primarily constructed using corporate financial metrics such as asset scale and interest expenses, exhibiting significant endogeneity. Multivariate indicators, however, are constructed by synthesizing multiple factors. Given that the KZ and WW indices incorporate an overabundance of endogenous financial indicators, The SA index is used in this study to quantify financial restrictions. The following is the formula:\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$${SA}_{i,t}=0.043\\times\\left({Size}_{i,t}^{2}\\right)-0.04\\times Age-0.737\\times{Size}_{i,t}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eTotal Factor Productivity (\u003cem\u003eTFP_LP\u003c/em\u003e). Given that the LP method, as a semiparametric approach, effectively addresses endogeneity and sample selection issues, this study employs LP method results.\u003c/p\u003e\n\u003cp\u003eModerator: Artificial Intelligence Level (\u003cem\u003eAI\u003c/em\u003e). With the rapid advancement of AI in China, numerous scholars have focused their research on the intersection of AI and innovation. Drawing on the methodological framework proposed by Grashof and Kopka (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), the natural logarithm of (the number of patent applications pertaining to AI\u0026thinsp;+\u0026thinsp;1) is used in this study to evaluate the degree of artificial intelligence (AI). These patent applications are categorized according to standard patent classification codes and filed by publicly listed enterprises.\u003c/p\u003e\n\u003cp\u003eControl Variables: To ensure unbiased estimation results, We selected the following control variables: Company size (\u003cem\u003eSize\u003c/em\u003e), Debt-to-equity ratio (\u003cem\u003eLev\u003c/em\u003e), Quick ratio (\u003cem\u003eQuick\u003c/em\u003e), Proportion of independent directors (\u003cem\u003eIndep\u003c/em\u003e), Dual directorship (\u003cem\u003eDual\u003c/em\u003e), Return on assets (\u003cem\u003eROA\u003c/em\u003e), Concentration of top shareholder ownership (\u003cem\u003eTop1\u003c/em\u003e), Company growth (\u003cem\u003eGrowth\u003c/em\u003e), Cash flow ratio (\u003cem\u003eCashflow\u003c/em\u003e), and Firmage (\u003cem\u003eFirmAge\u003c/em\u003e). Specific variable definitions are provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eVariable Names and Definitions\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSymbol\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDeclaration\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\u003eGreen Innovation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGripatent\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLn(Count of green patent filings submitted by the enterprise\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSupply Chain\u003c/p\u003e\n\u003cp\u003eFinance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSCF\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe natural logarithm of the number of times supply chain finance-related terms occur in the annual report, multiplied by 1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFinancing\u003c/p\u003e\n\u003cp\u003eConstraints\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSA\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSA Index\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal Factor\u003c/p\u003e\n\u003cp\u003eProductivity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimation using the LP method\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eArtificial\u003c/p\u003e\n\u003cp\u003eintelligence\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLn(Number of AI Patent Applications Filed by Listed Companies\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eScale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe company's total market capitalization for the year, taking the natural logarithm\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsset-Liability\u003c/p\u003e\n\u003cp\u003eRatio\u003c/p\u003e\n\u003c/td\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\u003eTotal Liabilities for the Year / Total Assets for the Year\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuick Ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eQuick\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Current Assets - Inventory) / Current Liabilities\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage of\u003c/p\u003e\n\u003cp\u003eIndependent\u003c/p\u003e\n\u003cp\u003eDirectors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eIndep\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe ratio of independent directors to total directors\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCombining Two Positions\u003c/p\u003e\n\u003c/td\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\u003eIf the Chairman is also the General Manager, the value is 1; if not, it is 0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReturn on Total Assets\u003c/p\u003e\n\u003c/td\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\u003eNet Profit / Total Assets\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEquity Concentration\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eTop1\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe ratio of the largest shareholder's shares to all shares\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCompany Growth Potential\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurrent Year Operating Revenue / Previous Year Operating Revenue\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCash Flow Ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eCashflow\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNet Cash Flow / Aggregate Assets\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCompany Establishment Period\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eFirmAge\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLn(Current Calendar Year - Company\u0026rsquo;s Founding Year\u0026thinsp;+\u0026thinsp;1)\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=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Data Sources and Sample Selection\u003c/h2\u003e\n\u003cp\u003eThis study\u0026rsquo;s research time frame covers the period of 2010 to 2023, with the study samples consisting of manufacturing enterprises listed on China\u0026rsquo;s A-share markets. The selection of 2010 as the starting point is based on two key factors: First, the 2010 China Sustainable Development Strategy Report explicitly designated \u0026ldquo;green development and innovation\u0026rdquo; as its central theme, emphasizing the role of green innovation in driving economic transformation. Consequently, green innovation became a critical direction for enterprises adapting to evolving policy environments starting in 2010. Second, the U.S. Defense Advanced Research Projects Agency (DARPA) launched multiple research projects related to artificial intelligence in 2010, signaling the potential for rapid global advancement in AI. The CNRDS database provided the patent data used in this study; the Sina Finance website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://finance.sina.com.cn/\u003c/span\u003e\u003c/span\u003e) provided the annual reports of listed firms; and the CSMAR database provided the financial and basic corporate data. The following sample screening and processing techniques were used to ensure the authenticity and dependability of the study data: (1) Exclusion of samples from enterprises that had undergone \u0026ldquo;special treatment\u0026rdquo; (ST, PT, or delisting); (2) Samples with severe data gaps in certain years were excluded; (3) Samples of insolvent enterprises were excluded. This process yielded 10,811 observations. All continuous variables included in the study were winsorized at the 1% and 99% percentiles to reduce the impact of extreme values on empirical findings.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Empirical Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the descriptive statistical results of the core variables involved in this study. After data cleaning, the final sample comprised 10,811 observations. For the dependent variable \"corporate green innovation\", the mean value is 0.422, and its standard deviation amounts to 0.834. Its range extended from 0.000 to 3.892, indicating significant variation in green innovation performance across firms and an overall low level of achievement. Furthermore, there is pronounced divergence in innovation performance across firms, with over half failing to achieve any innovation outcomes. The mean value of the primary explanatory variable, supply chain finance, was 0.357 with a standard deviation of 0.643. Data indicates that minimum value and mode of this variable are both 0.000, while the maximum value reaches 2.833. Such a significant disparity reveals that supply chain finance penetration in the Chinese market remains low, with over half of enterprises yet to engage in related operations. This structural imbalance precisely signals immense market opportunities and growth potential ahead.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeffnition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuick\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCashFlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.689\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Benchmark Regression Analysis\u003c/h2\u003e \u003cp\u003eThe benchmark regression outcomes based on Model (1) are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This research used a stepwise regression approach to include control variables in order to increase the conclusions' robustness. As shown in Column (1), without any control variables, the core explanatory variables\u0026mdash;supply chain finance (\u003cem\u003eSCF\u003c/em\u003e) and green innovation (\u003cem\u003eGripatent\u003c/em\u003e)\u0026mdash;exhibit a significant positive correlation at the 1% level. Subsequently, the control factors (apart from year and individual effects) are then included in Column (2), where the SCF coefficient stays at 0.026 and is significant at the 5% level. Finally, once a set of control variables and individual and year fixed effects are included simultaneously, the coefficient of SCF is estimated at 0.0226 in Column (3) and remains statistically significant at the 5% significance level. This robust set of results provides strong support for research hypothesis H1.\u003c/p\u003e \u003cp\u003eWith respect to the control variables, the coefficient of \u003cem\u003eSize\u003c/em\u003e exhibits a statistically significant positive sign, suggesting that larger enterprises can allocate resources more efficiently, invest more resources and capital into R\u0026amp;D, and thus more readily achieve green innovation. The coefficient for \u003cem\u003eTop1\u003c/em\u003e equity concentration is significantly negative, indicating that high power concentration within a company hinders the achievement of green innovation. The coefficient for \u003cem\u003eCash-flow\u003c/em\u003e ratio is significantly negative, suggesting that holding large amounts of liquid assets to avoid cash flow shortages is detrimental to innovation. The estimated coefficients for the remaining control variables are broadly aligned with existing literature, and thus a detailed discussion is omitted for brevity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1111\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0260\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0226\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(0.0124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0110)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2209\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0595\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0146)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6308\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0628)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuick\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0261\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0033)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3155\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1468)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1424)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0165)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1661)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1128)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0957\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1802\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0563)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0887)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0273\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0061)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCashFlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.3634\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2032\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1030)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1561\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0808)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3825\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.4935\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6217\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(0.0091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1818)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.3996)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eId\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: At the 1%, 5%, and 10% levels, respectively, statistical significance is indicated by the symbols ***, **, and *. Consistent notation is used for following tables, and robust standard errors are supplied in parenthesis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Robustness Test\u003c/h2\u003e \u003cp\u003eIn order to verify the validity of the empirical results, we first replace the core explanatory variable for supply chain finance with a continuous micro-level indicator: the year-end ratio of (short-term borrowings plus notes payable) to total assets (SCF2). Re-running the regression with Model (1) generates a coefficient of 0.4117, which is statistically significant at the 1% significance level. Furthermore, to account for potential confounding factors, provincial and industry fixed effects were incorporated into the two-way fixed effects model. As shown in Columns (2) and (3) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the coefficient of interest remains positive and statistically significant, further corroborating the robustness of the benchmark analysis. Finally, to mitigate endogeneity concerns stemming from reverse causality, a lagged explanatory variable approach was employed. The explanatory variable was lagged by three periods (SCF\u003csub\u003et-3\u003c/sub\u003e) and re-regressed. The outcomes of the regression in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e's Column (4) indicate that, even accounting for lagged effects, supply chain finance continues to significantly promote corporate green innovation. Since its introduction to China in 2008, supply chain finance lacked substantial policy support for its development. It was not until 2012 that the state implemented macro policies focused on expanding its coverage in serving small and medium-sized enterprises, leading to rapid growth in the Chinese market. Accordingly, the data observations from 2010 to 2011 are excluded, and the regression is reconducted for the sub-sample period of 2012\u0026ndash;2023. The estimation results of this sub-period test are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e's column (5). The baseline conclusion is consistent with the SCF coefficient of 0.0278, which is statistically significant at the 5% level. Lastly, to mitigate the endogeneity issue arising from omitted variables, this study introduces regional interaction fixed effects into the original model. Macroeconomic conditions vary across regions and may exert heterogeneous impacts on corporate green innovation over different years. Given this, a multidimensional fixed effects model is adopted to further control for the combined interaction fixed effect (Province\u0026times;Year). After interaction fixed effects are included, the core explanatory variable's coefficient stays positive and statistically significant, as indicated in Column (6) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0218\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0225\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0278\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0250\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0113)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCF\u003csub\u003et\u0026minus;3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0284\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4117\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.9507\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.6167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6773\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.0539\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.5797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.5660\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(0.4653)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.3985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.4029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.6396)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.5329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.4071)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eId\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePro\u0026times;Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6377\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":"5. Mechanism Analysis","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Regression Analysis of Mediating Effects\u003c/h2\u003e \u003cp\u003eTo verify the hypotheses pertaining to constraints on corporate financing and mechanisms underlying total factor productivity (TFP), we employ a two-step regression approach with the mechanism variables as the dependent variables.\u003c/p\u003e \u003cp\u003eTo examine the validity of Hypotheses 2a and 2b, we introduced financing constraints (\u003cem\u003eSA\u003c/em\u003e) and total factor productivity (\u003cem\u003eTFP_LP\u003c/em\u003e) as mediating variables (\u003cem\u003eMed\u003c/em\u003e) into Model (1). Regression analyses were conducted on the explanatory variables and a series of control variables, constructing the following mediation mechanism testing model:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${Med}_{i,t}={\\beta}_{0}+{\\beta}_{1}{SCF}_{i,t}+\\gamma{Controls}_{i,r}+{\\mu}_{i}+{\\lambda}_{t}+{\\epsilon}_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eGiven that the correlation coefficient \u003cem\u003eα1\u003c/em\u003e is significant in Model (1), if the correlation coefficient in Model (3) is also significant, then the indirect mechanism through which supply chain finance enables corporate green innovation via the mediating variables of financing constraints and total factor productivity holds; otherwise, it does not hold.\u003c/p\u003e \u003cp\u003eBased on the above analysis, the correlation coefficient \u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e in Model (1) is significantly positive, with regression results shown in Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. As evidenced in Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, supply chain finance exhibits a significant negative correlation with financing constraints. Enterprises with lower financing constraints often secure lower financing costs and more abundant capital, enabling them to allocate resources toward technological innovation and thus elevate green innovation levels. Hypothesis 2a is thus validated by establishing financial restrictions as a mechanism variable through which supply chain finance encourages corporate green innovation. The regression results in Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e identify a strong positive effect of supply chain finance on total factor productivity (TFP), which is statistically significant at the 1% level. As TFP continuously improves, enterprises enhance their production efficiency, thereby achieving superior economic returns. Accumulation of such economic benefits bolsters enterprises\u0026rsquo; R\u0026amp;D investment capabilities, thereby providing robust support for green innovation initiatives and promoting the continuous advancement of green innovation capacity. Accordingly, total factor productivity (TFP) functions as a notable mediating pathway via which supply chain finance promotes corporate green innovation, thus verifying Hypothesis 2b.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation Effect Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003cp\u003eSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003cp\u003eTFP_LP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0226\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0085\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0269\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(0.0110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0062)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.6217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3031\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.1713\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(0.3996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0609)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.2473)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eId\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9244\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=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Regression Analysis of Moderating Effects\u003c/h2\u003e \u003cp\u003e \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${Gripatent}_{i,t}={\\theta}_{0}+{\\theta}_{1}{SCF}_{i,t}+{\\theta}_{2}{{AI}_{i,t}+{\\theta}_{3}{SCF}_{i,t}\\times{AI}_{i,t}+\\gamma Controls}_{i,t}+{\\mu}_{i}+{\\lambda}_{t}+{\\epsilon}_{i,t}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the variables, \u003cem\u003eAI\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e,ₜ denotes the development level of artificial intelligence for firm \u003cem\u003ei\u003c/em\u003e in period \u003cem\u003et\u003c/em\u003e. Drawing on the method proposed by Grashof and Kopka (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), this variable is measured by taking the natural logarithm of (1\u0026thinsp;+\u0026thinsp;the number of AI-related patent applications filed by listed companies), with the relevant patents identified through their classification codes. The interaction term between a company's supply chain finance level and AI development level is represented by \u003cem\u003eSCF\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026times; AI\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e. Both the explanatory variable (SCF) and moderator variable (AI) undergo mean centering. The remaining variables align with the baseline model (1). With a set of control variables included and individual and year fixed effects held constant, the benchmark regression results for the influence of supply chain finance on corporate green innovation are shown in Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The regression coefficient is statistically positive and significant at the 5% significance level, indicating that supply chain finance fosters the development of corporate green innovation. Building upon this, the moderator variable AI and the interaction term between AI and SCF were added, with regression conducted based on Model (4). Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e's Column (2) reports pertinent findings. The mean-centered interaction term (c.SCF \u0026times; c.AI) shows a statistically significant positive coefficient at the 1% significance level, demonstrating that artificial intelligence (AI) exerts a pivotal positive moderating effect in the process of SCF facilitating corporate green innovation, thus confirming Hypothesis 3.\u003c/p\u003e \u003cp\u003eAdditionally, we replaced the artificial intelligence (AI) measurement indicator by counting the quantity of terms connected to AI in company annual reports, adding 1 to each count, and then applying a natural logarithm transformation. Following the re-estimation of Model (4), the findings reported in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026rsquo;s Column (3) reveal that the coefficient of the interaction term between supply chain finance and corporate green innovation continues to be statistically significant and positive, which attests to the robustness of the preceding conclusion. AI-related keywords are listed in \u003cspan refid=\"Sec27\" class=\"InternalRef\"\u003eAppendix II\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe regulatory role of Artificial intelligence\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\u003eSCF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0226\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0135\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0109)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0887\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0366\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0099)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ec.SCF\u0026times;c.AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0257\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0188\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0098)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.6217\u003c/p\u003e \u003cp\u003e(0.3996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0321\u003c/p\u003e \u003cp\u003e(0.3971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.5093\u003c/p\u003e \u003cp\u003e(0.3978)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eId\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6383\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 Heterogeneity Analysis","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Ownership Attributes\u003c/h2\u003e \u003cp\u003eBusinesses with various ownership arrangements have unique circumstances and limitations when trying to obtain supply chain finance assistance. Private enterprises typically face more severe financing constraints than their state-owned counterparts. Due to ownership discrimination, the phenomenon of \u0026ldquo;credit rationing\u0026rdquo; remains prevalent, making it difficult for them to obtain bank credit funds. Therefore, it may be deduced that private companies are typically more affected by supply chain finance's ability to alleviate financial barriers and encourage corporate green innovation. Accordingly, this research partitions the full sample into state-owned enterprises (SOEs) and private enterprises based on ownership structure, and conducts separate regressions using Model (1) for each subsample. The findings in Columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e indicate that the positive effect is only statistically significant in the private enterprise subsample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Regional Attributes\u003c/h2\u003e \u003cp\u003eChina's regional economic development exhibits pronounced spatial imbalances, with these structural disparities creating differentiated constraints in financial resource allocation and technological innovation conversion. Following a regional division of the sample, the regression outcomesin columns (3) and (4) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e confirm that the promotional effect is significant only within the eastern region cohort. This is attributable to the robust economic base of the eastern region, comprehensive industrial support systems, abundant high-quality talent, and more mature financial markets. These factors enable supply chain finance to operate more efficiently, thereby providing strong support for green innovation. This regional disparity reflects China's uneven regional economic development and suggests that policymakers and financial institutions should prioritize regional coordination when promoting green innovation and supply chain finance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Technology-Intensive Attributes\u003c/h2\u003e \u003cp\u003eThe regression findings in Columns (5) and (6) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e reveal that the facilitative effect of supply chain finance on corporate green innovation is especially pronounced in technology-intensive enterprises. Because technology-intensive businesses usually hold important positions in industrial chains and maintain tight technological collaboration and cooperative R\u0026amp;D connections with upstream and downstream partners, this might be the cause. Supply chain finance can enhance collaboration among these firms, accelerating technology spillovers and innovation diffusion. By comparison, non-technology-intensive firms typically adopt relatively straightforward production procedures, characterized by weaker technological interdependence with upstream and downstream counterparts. This renders it challenging to realize green innovation via collaborative research and development.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eState-owned\u003c/p\u003e \u003cp\u003eenterprise\u003c/p\u003e \u003cp\u003e(1)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003cp\u003eenterprise\u003c/p\u003e \u003cp\u003e(2)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003cp\u003eenterprise(3)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-easternenterprise\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTechnology-intensive\u003c/p\u003e \u003cp\u003e(5)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003enon-technology-intensive\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003cp\u003eGripatent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0256\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0288\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0076\u003c/p\u003e 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\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.4868)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.4856)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.7440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.5902)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.5555)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eId\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5542\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":"7. Research Findings and Policy Implications","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Research Findings\u003c/h2\u003e \u003cp\u003eThis study estimates the direct association between supply chain financing and corporate green innovation using a two-way fixed effects model and data from Chinese A-share listed enterprises (2010\u0026ndash;2023). The mechanism analysis further verifies that financing constraints and total factor productivity serve as mediators, with artificial intelligence acting as a positive moderator in this process. Finally, heterogeneity tests based on ownership attributes and regional characteristics explore whether differences exist among enterprises of varying types.\u003c/p\u003e \u003cp\u003eEmpirical findings reveal: (1) Supply chain finance significantly promotes green innovation in manufacturing enterprises. Benchmark regression results indicate that a 1% increase in supply chain finance levels leads to a roughly 2.26% rise in green innovation levels (measured by green patent applications), a conclusion that holds across various robustness and endogeneity tests. Theoretical analysis suggests supply chain finance reduces transaction costs, thereby enhancing capital utilization efficiency and fostering green technological innovation. (2) The intermediary mechanism test for financing constraints indicates that a 1% increase in supply chain finance levels reduces the degree of capital constraints decreases by 0.85%. Based on information asymmetry theory, supply chain finance enhances information transparency, significantly boosting mutual trust between enterprises and financial institutions. This effectively alleviates funding constraints, diversifies capital sources, and provides enterprises with more accessible, low-cost funds for technological innovation activities, thereby promoting green innovation. This offers a key pathway to address the \u0026ldquo;funding shortage\u0026rdquo; challenge in manufacturing green innovation. The examination of the mediating mechanism for total factor productivity (TFP) indicates that a 1% increase in supply chain finance levels leads to a 2.69% growth in TFP. This result strongly supports the core tenets of endogenous growth theory, revealing technological innovation, knowledge accumulation, and sustained innovation as key drivers of sustainable economic growth. Enhanced TFP enables enterprises to mobilize internal resources and acquire external innovation factors more efficiently, thereby indirectly promoting green innovation through technological transformation and increased production efficiency. (4) Moderation effects indicate that artificial intelligence technology positively strengthens the promotional role of SCF in Green Innovation. Replacing the AI measurement method (using keyword frequency in annual reports instead of patent data) still yields a significantly positive interaction coefficient, validating the reliability of the conclusion. (5) Additional heterogeneity tests show that the facilitative effect of supply chain finance on corporate green innovation within manufacturing firms is particularly pronounced for private enterprises, firms situated in eastern China, and technology-intensive companies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Policy Implications\u003c/h2\u003e \u003cp\u003eAlleviate enterprises' financing constraints and reduce transaction costs. Governments should fully leverage macro-regulatory functions to improve the institutional framework of supply chain finance, ensuring standardized operational processes to mitigate information asymmetry. Specifically, establishing dedicated funds to support green technology R\u0026amp;D could be considered. Targeted support policies should incentivize collaborative innovation among upstream and downstream entities in industrial chains, promoting sustainable development across the entire value chain through resource integration and optimized factor allocation. Simultaneously, incentive measures such as tax breaks and financing facilitation should be implemented to effectively enhance enterprises' capital accumulation capabilities and provide financial support for green transformation.\u003c/p\u003e \u003cp\u003eAccelerate technological innovation and cultivate scientific and technological talent. Enterprises should accelerate the development of artificial intelligence, the Internet of Things, and blockchain, integrate digital technologies into supply chains, and focus on absorbing and utilizing new knowledge and technologies to rapidly master cutting-edge expertise. Leveraging supply chain finance, they should increase investment in R\u0026amp;D for innovative technologies, enhance total factor productivity, and provide financial, technological, and talent-driven momentum for green innovation. Concurrently, proactive preferential policies should be introduced to attract top domestic and international talent for employment and entrepreneurship, driving local enterprise development and fostering the output of more outstanding technical professionals.\u003c/p\u003e \u003cp\u003ePromote private enterprise development and build a coordinated regional ecosystem. Financial institutions like banks should precisely target the numerous financing challenges faced by private enterprises\u0026mdash;such as limited financing channels and persistently high financing costs\u0026mdash;to effectively alleviate their difficulties in accessing affordable capital. This will inject robust momentum into their sustained, healthy, and stable development, fostering a virtuous cycle and high-quality growth across the entire economic system. Transfer payments should guide financial institutions to establish supply chain finance divisions in central and western regions, lowering financing barriers for private enterprises and mitigating regional resource disparities. Enterprises in eastern regions should be encouraged to deepen exchanges and cooperation with their counterparts in central and western areas. Local governments should adhere to the core principles of targeted policies and tailored approaches based on local conditions. They should fully leverage their unique advantages and potential, conduct in-depth analyses of the industrial chains, operational models, challenges, and opportunities faced by local enterprises, and then develop comprehensive strategies for enterprise supply chain finance and green innovation based on local realities.\u003c/p\u003e \u003c/div\u003e"},{"header":"8. Discussion","content":"\u003cp\u003eThis study uses data from China's A-share listed manufacturing enterprises from 2010 to 2023 to empirically investigate the effect of supply chain financing (SCF) on corporate green innovation. The findings substantiate our core hypothesis (H1), demonstrating that the development of SCF significantly promotes corporate green innovation. This result aligns with existing literature, which suggests that SCF facilitates innovative activities by optimizing capital flows and integrating information resources. However, our research extends this understanding by not only confirming the positive relationship but also, more importantly, uncovering the underlying micro-level mechanisms and boundary conditions.\u003c/p\u003e \u003cp\u003eFirst, the mechanism tests reveal that SCF primarily drives green innovation through two key channels: improving total factor productivity (TFP) (H2b) and easing financial limitations (H2a). These findings carry important theoretical implications. On one hand, they support information asymmetry theory, showing that SCF enhances information transparency across the supply chain, effectively reducing credit risks for external financial institutions. This, in turn, provides vital capital for firms\u0026mdash;especially private enterprises facing significant financing difficulties\u0026mdash;allowing them to engage in high-investment, long-cycle green R\u0026amp;D. On the other hand, the mediating role of TFP highlights the efficiency-enhancing function of SCF, resonating with the core principles of endogenous growth theory. By facilitating the synergy and integration of knowledge, technology, and capital within the supply chain, SCF improves firms' resource allocation and innovation conversion efficiency. This enables firms to more effectively direct limited resources toward green innovation, thus evolving from merely receiving financial \"transfusions\" to building internal \"hematopoietic\" capacity.\u003c/p\u003e \u003cp\u003eSecond, a key contribution of this study lies in identifying the moderating role of artificial intelligence (AI) technology (H3). The findings demonstrate that AI considerably amplifies SCF's beneficial impact on green innovation. The Resource-Based View (RBV) can be used to interpret this finding. AI technology, which is a strategic resource that is valuable, rare, and uncopyable, greatly improves risk assessment accuracy, transaction automation, and decision-making intelligence when combined with SCF. This not only validates the feasibility of \"AI+\" empowered SCF but also demonstrates how digital technology amplifies its role in supporting sustainable innovation by optimizing the underlying architecture of financial services. In doing so, it provides new micro-level evidence for understanding the synergy between digital technology and green finance.\u003c/p\u003e \u003cp\u003eFurthermore, the heterogeneity analysis deepens our understanding of the boundary conditions of this relationship. The promoting effect of SCF is more pronounced among private enterprises, firms in eastern China, and technology-intensive industries. This aligns with the specific challenges and advantages faced by these groups. Private enterprises, due to their \"financing dilemma,\" are more responsive to SCF as an alternative financing channel. The mature financial markets and well-developed industrial infrastructure in the eastern region provide a conducive environment for the efficient operation of SCF. Technology-intensive firms, with strong technological linkages to upstream and downstream partners, are better positioned to leverage SCF for collaborative innovation. These findings suggest that the policy effectiveness of SCF is not uniform; rather, it is significantly influenced by firms' internal and external institutional and environmental factors.\u003c/p\u003e \u003cp\u003eIn a broader context, this study positions the interaction between SCF, AI, and green innovation within China\u0026rsquo;s macro-strategic framework for achieving the \"Dual Carbon\" goals and high-quality development. Our findings suggest that developing intelligent SCF, underpinned by digital technologies like AI, is not only an effective tool for alleviating financing constraints and improving operational efficiency but also a crucial policy lever for enabling corporate green transformation. It offers a viable pathway for achieving the dual goals of \"greening\" and \"digitalization\" in economic activities.\u003c/p\u003e \u003cp\u003eThere are, of course, a number of limitations to this study that suggest directions for further investigation. First, the measurement of SCF relies primarily on text analysis; future studies could incorporate more granular micro-survey data or case studies to capture its operational substance more accurately. Second, the mechanisms of AI's moderating role warrant further investigation. For example, does AI influence green innovation primarily through improved risk pricing, process optimization, or enhanced data sharing along the supply chain? Future research could disaggregate these different dimensions of AI application. Third, while this study focuses on the manufacturing sector, future work could extend the framework to other industries, such as agriculture or services, to test its generalizability. Finally, with the rise of ESG (Environmental, Social, and Governance) investing, exploring how SCF influences corporate ESG performance represents a promising avenue for future research.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eConsent for Publication:\u003c/strong\u003e \u003cp\u003eI attest that, as an open access journal, \u003cb\u003eFuture Business Journal\u003c/b\u003e charges an article processing fee for each paper that is approved for publication. I consent to paying this fee in full if my article is accepted for publication by submitting it. This manuscript's data, figures, and results have not been published elsewhere, nor are they being considered by another publisher. The final version of the work has been read and approved by all authors, who also consent to its submission.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e \u003cp\u003eThis study did not involve human participants, animal subjects, or any clinical/field sampling. As such, no ethics approval or consent to participate was required for this research.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by the Shandong Academy of Social Sciences (SASS), grant number (22BCXJ05). The APC was funded by 22BCXJ05.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Xinglei Guo and Xiaoye Li ; methodology, Xinglei Guo; software, Xiaoye Li; validation, Kuiliang Li; formal analysis, Xinglei Guo and Xiaoye Li; investigation, Kuiliang Li; resources, Xinglei Guo; data curation, Xinglei Guo; writing\u0026mdash;original draft preparation, Xinglei Guo and Xiaoye Li; writing\u0026mdash;review and editing, Kuiliang Li; visualization, Xiaoye Li; supervision, Xinglei Guo and Kuiliang Li; project administration, Xinglei Guo; funding acquisition, Xinglei Guo. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to once again extend our heartfelt thanks to the Shandong Academy of Social Sciences (SASS) for their generous financial support. Additionally, we are deeply grateful to the professors at the University of the Chinese Academy of Sciences for their invaluable feedback and suggestions, which have greatly contributed to the improvement of this manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ePatent Data: Sourced from the CNRDS (China National Research Data Service) database, which provides information on patents and related intellectual property; Annual Reports of Listed Companies: These were obtained from the Sina Finance website (https://finance.sina.com.cn/), where the financial and operational details of listed companies are publicly disclosed; Basic Corporate Information and Financial Data: These were derived from the CSMAR (China Stock Market \u0026amp; Accounting Research) database, which contains comprehensive data on Chinese listed companies, including financial statements, stock market information, and company profiles. The link to the publicly archived dataset analyzed during the study period is: https://www.kdocs.cn/l/cahCmBGifg5N.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgrawal, A., Gans, J., \u0026amp; Goldfarb, A. (2019). \u003cem\u003eThe economics of artificial intelligence: An agenda\u003c/em\u003e. University of Chicago Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkram, R., Li, Q., Srivastava, M., Zheng, Y., \u0026amp; Irfan, M. (2024). Nexus between green technology innovation and climate policy uncertainty: Unleashing the role of artificial intelligence in an emerging economy. \u003cem\u003eTechnological Forecasting and Social Change\u003c/em\u003e, \u003cem\u003e209\u003c/em\u003e, 123820.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilyay-Erdogan, S., Danisman, G. O., \u0026amp; Demir, E. (2024). 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This paper examines the effect of supply chain finance on firms\u0026rsquo; green innovation by employing a two-way fixed effects model and using panel data from Chinese A-share manufacturing companies covering the period from 2010 to 2023. The empirical results reveal that the advancement of supply chain finance has a statistically significant positive effect on green innovation activities. Mechanism analysis further demonstrates that supply chain finance promotes corporate green innovation through two primary channels: mitigating financial constraints and improving total factor efficiency.Additionally, the progression of artificial intelligence (AI) technology is found to reinforce the favorable influence of supply chain finance on green innovation, acting as a moderating factor that amplifies this relationship. Heterogeneity analysis indicates that the influence of supply chain finance on green innovation is especially notable in private enterprises, companies based in eastern China, and firms operating within technology-intensive sectors. This study provides theoretical support for promoting the \u0026ldquo;AI+\u0026rdquo; supply chain finance initiative and offers valuable policy insights to accelerate China\u0026rsquo;s green economic development.\u003c/p\u003e","manuscriptTitle":"Study on the Impact of Supply Chain Finance on Green Innovation in Listed Manufacturing Enterprises in China: An AI-Driven Perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 14:23:28","doi":"10.21203/rs.3.rs-9079679/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":"4380259a-725b-4fe3-b0b3-d75c02c6154f","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T01:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 14:23:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9079679","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9079679","identity":"rs-9079679","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}