Green financial policy and investment-financing maturity mismatch of enterprises

Zhang, Lingxiao; Zhang, Ke; Bilan, Yuriy Article Green financial policy and investment-financing maturity mismatch of enterprises Journal of Business Economics and Management (JBEM) Provided in Cooperation with: Vilnius Gediminas Technical University (VILNIUS TECH) Suggested Citation: Zhang, Lingxiao; Zhang, Ke; Bilan, Yuriy (2024) : Green financial policy and investment-financing maturity mismatch of enterprises, Journal of Business Economics and Management (JBEM), ISSN 2029-4433, Vilnius Gediminas Technical University, Vilnius, Vol. 25, Iss. 3, pp. 590-611, https://doi.org/10.3846/jbem.2024.21609 This Version is available at: https://hdl.handle.net/10419/317695 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Published by Vilnius Gediminas Technical University ISSN 1611-1699 / eISSN 2029-4433 2024 Volume 25 Issue 3 Pages 590–611 https://doi.org/10.3846/jbem.2024.21609 GREEN FINANCIAL POLICY AND INVESTMENT-FINANCING MATURITY MISMATCH OF ENTERPRISES Lingxiao ZHANG 1, Ke ZHANG 2, Yuriy BILAN3 1Economics and Management School, Wuhan University, Wuhan, China 2Hubei Academy of Social Science, Wuhan, China 3Faculty of Social and Economic Relations, Alexander Dubcek University of Trencin, Trencin, Slovakia Article History: Abstract. Green financial policies play an important role in acceleration of China’s green transformation. Existing associated studies mainly focus on the qualitative analysis and descriptive analysis. However, it still lacks empirical studies. To explore the relationship between green finance policies and the investment and financing terms of enterprises, the effects of green financial policies on investment-financing maturity mismatch of A-share companies on Shanghai Stock Exchange and Shenzhen Stock Exchange from 2009 to 2020 were investigated in this study by a difference-in-difference (DID) model. Results demonstrate that green financial policies significantly alleviate short-term loans used as long-term investment in enterprises. Green financial policies inhibit investment-financing maturity mismatch of enterprises by increasing loan availability, lowering financing cost and increasing proportion of long-term loans of enterprises. Such effect is more obvious in enterprises with higher internal control quality and enterprises with more transparent information. Green financial policies can alleviate short-term loans used as long-term investment in non-state-owned enterprises more obviously than state-owned enterprises. Research results provide some references to alleviate debt risks of enterprises. Enterprises are recommended to seek steady development, fulfil social responsibilities and take green low-carbon social actions extensively. ■ received 3 November 2023 ■ accepted 2 April 2024 Keywords: investment-financing term, maturity mismatch, green finance policy, debt maturity structure, financing cost, short-term loans. JEL Classification: D81, G31, L23. Corresponding author. E-mail: [email protected] JOURNAL of BUSINESS ECONOMICS & MANAGEMENT 1. Introduction It is a key feature to maintain low-carbon economic development and achieve the goal of high-quality development. As a combination of environmental regulation policy and financial support policy, green financial policies have both constraint effects and incentive effects, and they have become a booster of green economic transformation (Aguilar et al., 2023; Lu et al., 2020; Linnenluecke et al., 2016). It is estimated that China needs 2,000 billion CNY of green investment every year to achieve the goal of ecological civilization construction. However, government can only afford 10%~15% of green investment due to the limited financial budget. Based on new green financial tools like green credit, green bonds and green funds, green financial policies encourage financial institutions to give prior investment to green Journal of Business Economics and Management, 2024, 25(3), 590–611 591 enterprises and green innovation which have a significant favorable effect on economic development and ecological conservation (Ahmad et al., 2023) through the fluctuation of interest rate, skewed credit and other mechanisms. The maturity of financing shall be matched with the period of return on investment (ROI). In other words, short-term funds shall be used to support current assets, while long-term funds shall be used as long-term asset financing. This is not only the basic principle for enterprises to make decisions in investment and financing, but also one of development barriers against green enterprises. On the one hand, green projects propose higher investment demands, longer ROI period, lower short-term profitability, and more obvious public benefits compared with traditional projects (Zhu et al., 2020). Additionally, capital markets in developing countries are still facing obstacles, such as imperfect capital market and imbalance between earnings and asymmetric environmental cost/benefits (Greenstone & Jack, 2015). Hence, green projects and green enterprises require remarkable capital investments to low-carbon energy industrial development, and they may face with severe financing constraints as well as investment and financing maturity mismatch (Liu & Bai, 2020; Wang et al., 2019). The key to alleviate “short-term loans used as long-term investment” in enterprises is to increase long-term debt financing which matches with projects (Zhang & Ye, 2021). In 2015, the government of China issued the Opinions on Implementation of the Third-party Environmental Pollution Control, stipulating that innovative financial service mode should provide long-term debt financing supports to investment projects of green enterprises. Moreover, it was suggested to alleviate green investment-financing maturity mismatch, information asymmetry, lack of products and analysis tools, etc. Then, whether the debt maturity structure of enterprises has been optimized and whether the investment-financing maturity mismatch of enterprises has been alleviated since 2015 after the green financial policies were implemented and the green financial system was established? How do green financial policies influence investment-financing maturity mismatch of enterprises? In the market of the credit tools, green financial policies play an important role in fund collecting, capital allocation, industry structure optimizing, and economic benefits growing (Zhou et al., 2023; Omran & Yaaqbeh, 2023; Du & Zheng, 2019). Shi and Wang (2023) analyzes corporate finance strategic planning from the perspective of China’s habitat environment and green finance. Facing with insufficient long-term funds, China Banking Regulatory Commission also emphasizes on establishing a long-term mechanism of “dare to lend, willing to lend and be able to lend” in order to solve “short-term loans used as long-term investment” of enterprises. In this context, studying the effect of green financial policies in improving “shortterm loans used as long-term investment” of enterprises not only is conducive to correct the supplying mismatch in capital markets, but also has theoretical and practical significance to achieve the long-term goal of financial system innovation. Existing studies concerning green financial policies mainly focus on regional pollution discharge efficiency, green financial credit balance and the improvement of enterprises excessive pollution, but rarely focus on short-term loans used as long-term investment of enterprises. This study attempts to explore whether green financial policies can alleviate investment-fi- nancing maturity mismatch of enterprises as well as specific influencing channels. On this basis, the internal mechanism of promoting green transition of enterprises based on optimized allocation of financial resources was explored. Results show that green financial policies alleviate investment-financing maturity mismatch of enterprises. Specifically, enterprises have a lower financing cost and a higher proportion of long-term loans after green financial policies were implemented compared to that before. According to the heterogeneity test, green financial policies achieve better effect in enterprises with higher internal control quality and 592 L. Zhang et al. Green financial policy and investment-financing maturity mismatch of enterprises enterprises with more transparent information than others. After implementation of the green financial policies, the phenomenon that short-term loans are used as long-term investments of non-state-owned enterprises is mitigated more obviously compared to that before. This study might have following marginal contributions: First, an empirical study on whether and how green financial policies affect investment-financing maturity mismatch of enterprises was carried out by using the multi-phase difference-in-difference (DID) method. Second, a heterogeneity analysis of influences of the green financial policies on investment-financing maturity mismatch of enterprises was carried out from perspectives of internal control quality, information transparency and research & development (R&D) intensity of enterprises. The remainder of this study is organized as follows. Section 2 introduces literature review. Section 3 is the research design. Section 4 analyzes empirical study results. Section 5 is a discussion. Section 6 summarizes conclusions and enlightenment. 2. Literature review In the context of industrialized green reform, people pursue sustainable development and improved well-being rather than mere economic development (He et al., 2023). Therefore, central banks and regulatory agencies in developed and developing countries have adopted green financial policies to cope with global climate challenges (Ionescu, 2021; Zhang et al., 2021). In this regard, some scientists stress the importance of appropriate information management as well as scientific research supporting green deal implementation (Olzhebayeva et al., 2023; Štreimikienė et al., 2022). From the policy perspective of green finance reform innovative test zone, Wang et al. (2021) demonstrated that green finance could decrease environmental pollution and facilitate regional green development. This agrees with conclusions of Mamun et al. (2022). As innovative financial tools, green financial policies have the generalized sense and narrowed sense. In the generalized sense, green financial policies provide financial channels for all enterprises and industries which stick to the principle of sustainable development. In the narrowed sense, the green financial policies guide financial institutions to issue green finance tools to prevent environmental risks and build a friendly society. These green finance tools mainly include green investment, green insurance, green securities and green credit (He et al., 2019; Liu et al., 2019). Linnenluecke et al. (2016) found that green financial policies include financial tools, system innovations and mode innovations which serve for energy-saving and environmental-friendly projects. Green financial policies can alleviate investment-financing maturity mismatch of enterprises through the following three micro-mechanisms. First, green financial policies mitigate debt financing cost issues of enterprises. Based on a comparative study between green enterprises and enterprises with “high energy consumption and high pollution”, Lian (2015) found that green financial policies could significantly lower debt financing cost of enterprises. Due to information asymmetry, traditional bankrollers may cause moral hazards, adverse selection and other problems when they involve in relevant projects, thus bringing transaction costs for project subjects. To assure professional information collection and processing, green finance system can successfully disclose the reasonable price, promote effective allocation of capitals and lower transaction cost. Under the implementation of green financial policies, financial resources are given firstly to enterprises and green industries with low-pollution and low-emission. Enterprises conforming to the policy evaluation standards have the higher capital availability and are easy to get financial supports. Niu et al. (2020) found that green credit policy increased financial convenience of green environmental-friendly enterprises by increasing credit supports to green industries. Journal of Business Economics and Management, 2024, 25(3), 590–611 593 Lv et al. (2021) pointed out that green financial policies can play the basic role of resource allocation, expand the traditional financial boundaries, optimize capital supply structures, and support green innovation of enterprises by relieving their financing constraints. This policy has numerous evidence of support from entrepreneurs and stakeholders. For instance, there are typical practices for privileged access to funding by social enterprises if they confirm certain criteria (Bilan et al., 2017). The same is about customers’ perceptions. In terms of their growing positive attitude toward sustainability principles, the preferences in buying decisions are made for the sake of responsible businesses (Mishchuk et al., 2023; Musova et al., 2021). Therefore, it became typical even for HRM practices (Bhattarai et al., 2023) to gain the advantages arise from stakeholders’ recognition. Some scholars believed that there’s a premium for enterprises to issue green bonds. For example, it may send a signal to investors about longterm stable return on investment (Baker et al., 2018) or a signal of environmental commitment (Flammer, 2021), thus lowering the financing cost of enterprises. Additionally, financing cost of enterprises can be lowered by issuing green bonds, lowering the transaction cost and providing interest discount (Ma et al., 2020). To sum up, banks or stakeholders are willing to provide financial supports to enterprises with good performances in social responsibility or strong consciousness of environmental responsibility at a low cost (Goss & Roberts, 2011). Second, green financial policies increase supports to enterprises in term of long-term green credit, green bonds and green funds (Wang et al., 2019). R&D, innovation and subsequent promotion of green technologies often require considerable capital investment, which is difficult to be met by traditional financing channels of enterprises. Moreover, R&D of most new technologies is greatly uncertain. There’s a high risk of investment in R&D of technologies and some traditional bankrollers are unwilling to involve in such projects. Volz (2018) pointed out that for transitional economy bodies, the green financial development should focus on “green-oriented” transition of investment, that is, transforming from the mode dominated by high-pollution and high-energy consumption investment to the mode dominated by energy-saving and environmental-friendly investment. Wang et al. (2019) found that green finance development and increasing scale of long-term debts can alleviate maturity mismatch of investment in green enterprises, and increase investment to green enterprises. Third, green financial policies mitigate debt maturity structure of enterprises. Wu and Yin (2021) proved that green credit policy increased the long-term and short-term debt financing scales, alleviated the debt maturity structure of enterprises without “high energy consumption, high pollution and excessive production capacity”, and generated significant “penalty” effect to long-term debt financing scale and debt maturity structure of these enterprises. Green bonds have an advantage of offering additional green financing sources and providing more long-term capitals for green projects. Green bonds can not only be used for construction and operation of green projects, but also pay for long-term and short-term debts like bank loans (Billah et al., 2023). Li and Liu (2020) carried out an empirical test based on green patent ownership data of listed companies and found that green finance promoted green innovation of enterprises by increasing proportion of long-term debts of enterprises. As a new financing tool, green bonds have become one of the optimal financing modes of enterprises’ green projects. This not only is beneficial for enterprises to get long-term capitals, but also can optimize debt maturity structure of enterprises (Ning & Wang, 2021). Based on the above analysis, this study plans to make an empirical test whether implementation of green financial policies can alleviate investment-financing maturity mismatch of enterprises. 594 L. Zhang et al. Green financial policy and investment-financing maturity mismatch of enterprises 3. Methodology 3.1. Model setting In January, 2015, the government of China proposed Opinions on Implementation of the Third-party Environmental Pollution Control, which stated clearly that financial policy should be preferential to green environmental-friendly projects. In September, 2015, the Overall Plan for Ecological Civilization Structural Reform issued by the government of China explicitly proposed the top-level design of China’s green finance system. Hence, the year of 2015 is viewed as a symbol for a new stage of China’s green financial policies. This study focuses on A-share listed companies in Shanghai and Shenzhen, using the initiation of the green financial policy in 2015 as the policy impact time point. It investigates the influence of the green financial policy on the short-term debt and long-term utilization of enterprises, both at an overall and mechanistic level. A generalized DID model is constructed with references to Ning and Wang (2021). On the one hand, the DID model can avoid direct comparison of influences of uncertainty factors except for other difficult-to-recognize factors on short-term loans used as long-term investment before and after the issuing of green financial policies. On the other hand, it can evaluate policy effect effectively and alleviate endogenous problem by using green financial policies as an explanatory variable. For the research theme of this study, the DID model can be set as follows: =α +α + +µ +υ +ε 01 , it it it i t it SFLI D cX (1) where SFLI represents the degree of enterprise short-term loans used as long-term investment. D is the DID variable of the product of Green and post. X refers to control variables, including enterprise size, current ratio, growth, inventory turnover ratio, executive compensation, audit quality, and board size. µ i is the fixed effect of individuals and it is used to control influences of individual heterogeneity which doesn’t change with time and is difficult to be observed. υt is the fixed effect of time and εit is a random disturbance term. 3.2. Declaration of major variables 3.2.1. Explained variable Degree of enterprise “short-term loans used as long-term investment” (SFLI): existing empirical studies have proved the universal behavior of using short-term loans as long-term investment in enterprises. SFLI is measured and used as the explained variable in this empirical study. Referring to Frank and Goyal (2003), Zhong et al. (2016), SFLI is measured according to the following equation: (cash outflow for investment activities like buying and building fixed assets – increment of long-term loans – increment of rights and interests of the current period – operational cash net flow – increment of bonds payable – cash inflow from selling of fixed assets in the current period)/total assets in the beginning of the period. 3.2.2. Explanatory variable The green financial policy is used as the core explanatory variable of this study and is measured by a dummy variable. The year of 2015 marks that China’s green financial policies enter into a new stage, having important significance. With references to Zou (2017), if the policy document is issued during January ~ October, it generates effects in the current year. If the Journal of Business Economics and Management, 2024, 25(3), 590–611 595 policy document is issued during November ~ December, it generates effects in the next year. In 2015, the government of China issued green financial policy documents in January and September. Hence, the dummy variable is set 0 before 2015, and 1 after 2015 (including 2015). According to requirements of classical DID model, the interaction term (D) of Green and Post is used as the core explanatory variable of this study. The green enterprises are defined as enterprises from industries of energy-saving and environmental protection, clean production, clean energy, ecological environment, green updating of infrastructure and green services. We refer to Abbas et al. (2023) did in their study, green enterprises were constructed via content analysis of carbon disclosure items reported in stand-alone ESG reports or firm annual reports. In addition, with references to list companies with energy-saving and environmental-protection concept proposed by Huaxi Securities, a total of 248 green enterprises are finally chosen as the treatment group, while other non-green enterprises are chosen as the control group. According to the method of dummy variable, enterprises which meet the abovementioned range of green enterprises according to main business scope are determined as the treatment group (value = 1) in this study; whereas the rest enterprises are determined as the control group (value = 0). 3.2.3. Mechanism variables Debt maturity structure of enterprises: proportion of long-term loans in total debts in the beginning of the period. Debt financing cost: quantitative proportion of debt interest expense in total debts in the beginning of the period. Increased debt size: including the increased long-term debt size (Loan_Long) and shortterm debt size (Loan_Short). The increased Loan_Long = long-term loan – long-term loans in the beginning of the period + matured long-term debts in a year, and it is adjusted by the total debts in the beginning of the period. The increased Loan_Short = short-term loan – short-term loans in the beginning of the period, and it is adjusted by the total debts in the beginning of the period. 3.2.4. Control variable For an objective analysis of influences of green financial policies on investment-financing maturity mismatch of enterprises and to prevent prejudiced estimation results by missing important variables, the enterprise size, solvency, operation capacity, development capacity and corporate governance are controlled with references to existing research results. Specifically, enterprise size is measured as the natural logarithm of total assets. Solvency is measured as the current ratio. Operation capacity is measured by the inventory turnover ratio. Development capacity is measured by the growth rate of main business incomes. Corporate governance is measured by board size, executive compensation and audit quality. Measurements of variables in this study are listed in Table 1. 3.3. Sample selection and data source To decrease the disturbance of other factors, we examine the period after the financial crisis. Thus, the year of 2009 is chosen as the initial year of sample survey. A-share companies in Shanghai Stock Exchange and Shenzhen Stock Exchange from 2009 to 2020 are chosen as research objects. The sampling period for the empirical study is finally determined from 2010 596 L. Zhang et al. Green financial policy and investment-financing maturity mismatch of enterprises to 2020 with consideration to the necessity of variable lag in calculation of SFLI. To get valid samples and mitigate research effect, the following samples are deleted: (1) enterprises of the financial industry, (2) ST and PT enterprises, (3) Enterprises which have listed for less than 2 years, (4) Enterprises with severe missing of financial data, and (5) Enterprises with obvious abnormal data (e.g. asset-liability ratio higher than 100%). Based on above screening and deletion, a total of 3,548 enterprises are collected and the cumulative sample number is 22,163. Furthermore, 1% and 99% winsorization are performed to continuous variables to avoid influences of extremums on the study. Only processed data is applied in the follow-up analysis. Annual data on listed firms come from the China Stock Market & Accounting Research (CSMAR). The database includes all listed firms on both the Shanghai and the Shenzhen Stock Exchanges and contains information about firm identifiers and debts. All empirical analyses are completed based on Stata 15.1 software. Table 1. Variable definitions Variables Signs Definition Maturity mismatch degree SLFI (cash outflow for investment activities like building fixed assets – increment of long-term loans – increment of rights and interests of the current period – operational cash net flow – increment of bonds payable – cash inflow from selling of fixed assets in the current period)/total assets in the beginning of the period Dummy variable of green enterprises Green It values 1 for green enterprises; otherwise, it values 0. Industries of green enterprises include energy saving and environmental protection, clean production, clean energy, ecological environment, green updating of infrastructure, green services, etc. Dummy variable of time Post It values 0 before 2016, and 1 after 2016. Green financial policy DProduct of Green and post Debt maturity structure Loansr Long-term debts/total debts Debt financing cost Cost Debt interest expense/total debts in the beginning of the period Increased shortterm debt size Loan Short short-term loan – short-term loans in the beginning of the period. It is adjusted by the total debts in the beginning of the period. Increased long-term debt size Loan long long-term loan – long-term loans in the beginning of the period + matured long-term debts in a year. It is adjusted by the total debts in the beginning of the period. Enterprise size Size Natural logarithm of total assets of the enterprise Liquidity ratio Liq Current assets/current liabilities Growth SG Growth rate of sales revenues Audit quality Big10 It values 1 for top 10 accounting firms in China; otherwise, it values 0. Operation capacity SAT Inventory turnover ratio Board size BDS Number of board members Executive compensation Dpay Natural logarithm of average compensation of the top 3 executives R&D intensity RDS Proportion of R&D expenses in operation revenues Journal of Business Economics and Management, 2024, 25(3), 590–611 597 3.4. Descriptive statistical analysis The basic features of major variables are shown in Table 2. The mean of SFLI is 0.09 (>0), indicating that maturity mismatch generally exists in sample enterprises. The maximum and minimum are 1.288 and –0.271, respectively, showing a great difference among sample enterprises in “short-term loans used as long-term investment”. The mean of Cost is 0.089. In view of maximum and minimum, the sample enterprises show great differences in debt financing cost and financing capacity. Similarly, the statistical distribution of other variables can be analyzed. The multicollinearity diagnosis results are also presented in Table 2. It is noticed that VIF values of all variables are lower than 10, indicating the absence of serious multicollinearity in variables. Table 2. Descriptive analysis NMean St.Dev min max VIF SFLI 22163 .090 .226 –.271 1.288 – D22163 .057 .232 0 1 1.01 Cost 22163 .089 .122 .001 .793 1.19 Size 22163 22.415 1.288 20.067 26.386 1.57 Liq 22163 2.215 3.37 .03 204.742 1.20 SG 22163 .381 .985 –.671 6.835 1.01 SAT 22163 13.777 51.228 .136 446.124 1.01 Dpay 22163 14.505 .72 12.751 16.545 1.31 Big10 22163 .582 .493 0 1 1.03 BDS 22163 8.669 1.708 5 15 1.09 An independent sample t-test in Table 3 is carried out to compare system differences of samples before and after implementation of the green financial policies. It can be seen from results that most variables have significant differences except solvency. Specifically, the mean of SFLI is 0.116 before the implementation of green financial policies, while 0.076 after, which proves that the degree of “short-term loans used as long-term investment” of enterprises decreases after implementation of the green financial policies. Similarly, the debt financing cost of sample enterprises after implementation of the green financial policies declines significantly compared to that before. In view of control variables, enterprise size increases after implementation of the green financial policies, accompanied with an increasing operation capacity of enterprises and strengthening corporate governance construction. Table 3. Independent sample t-test Variable Pre Policy After Policy T test Sample Avg. Sample Avg. SFLI 8000 0.116 14163 0.076 0.040*** Cost 8000 0.091 14163 0.088 0.003* Size 8000 22.242 14163 22.513 –0.271*** Liq 8000 2.245 14163 2.198 0.047 SG 8000 0.426 14163 0.355 0.071*** 604 L. Zhang et al. Green financial policy and investment-financing maturity mismatch of enterprises of green financial policies optimizing the debt maturity structure of enterprises, matching the investment and financing maturities of enterprises, thus mitigating the degree of SFLI. Observing the regression coefficients of other control variables, it is found that the coefficient of Size is positive under the 1% significance level, and the coefficient of Liq is positive under the 1% significance level. Above results indicate that the larger size and the more liquid assets of the enterprise, it is prone to fall in a SFLI predicament. In addition, the growth rate of the enterprise enhance the degree of SFLI. Table 8. PSM-DID estimation results (1) (2) k-nearest neighbor match 1:1 k-nearest neighbor match 1:2 D–0.027** –0.025** (–2.56) (–2.07) Size 0.096*** 0.103*** (2.75) (3.22) Liq 0.030*** 0.025*** (4.24) (4.34) SG 0.043*** 0.050*** (3.57) (5.99) SAT 0.000 0.000 (1.14) (0.98) Dpay –0.009 –0.015 (–0.49) (–0.89) Big10 –0.004 0.000 (–0.30) (0.02) BDS –0.006 –0.000 (–1.35) (–0.03) Constant –1.809*** –1.928*** (–3.11) (–3.55) Firm FE Yes Yes Year FE Yes Yes N 3703 5399 R20.118 0.128 4.3.4. Alternative measurements of key variables To investigate the sensitivity effect of variable measurement modes, the variable measurement is changed as follows: first, (cash outflow for investment activities like building fixed assets – increment of long-term loans – increment of rights and interests of the current period – operational cash net flow – increment of bonds payable – cash inflow from selling of fixed assets in the current period)/total assets is used as the substitution variable of shortterm loans used as long-term investment. Second, a DID analysis of company-year two-way fixed effect model is carried out. The corresponding results are reported in Table 9, which shows that both the improving effect and the mechanism of influence of green financial Journal of Business Economics and Management, 2024, 25(3), 590–611 605 policies on enterprises’ “short-term loans used as long-term investment” remain unchanged. This implies that research conclusions have a low sensitivity to measurement of variables. Table 9. Robustness test of substitution variables (1) (2) SFLI Cost D–0.014** –0.003** (–2.08) (–2.02) Size 0.067*** 0.001 (3.78) (0.29) Liq 0.00287* 0.005*** (1.83) (5.31) SG 0.000 –0.001 (0.00) (–0.51) SAT 0.000 0.000 (0.37) (1.26) Dpay 0.003 0.011*** (0.61) (9.56) Big10 0.002 0.006*** (0.53) (4.05) BDS 0.002 0.000 (0.09) (0.04) Constant –1.382*** –0.093 (–3.94) (–1.06) Firm FE Yes Yes Year FE Yes Yes N 22163 22163 R20.0824 0.0603 4.4. Heterogeneity analysis Internal control quality is a key factor that determines whether an enterprise can get longterm loans (Luo et al., 2021). The higher audit quality implies the higher effectiveness of internal control. This study further investigates heterogeneity characteristic performances of the internal control quality in the mismatch behaviors between green finance and financing term of enterprises. Specifically, enterprises with an audit opinion of “Standard and No Unreserved Opinion” are viewed as the high-audit-quality group. The internal control effectiveness of these enterprises is higher than that of the other enterprises. Other enterprises are viewed as the group of low internal control quality. Sub-sample regression is carried out. Results are shown in Columns (1) and (2) in Table 10. Clearly, the green financial policies can alleviate short-term loans used as long-term investment of enterprises with high internal control effectiveness more obviously. For the self-need of profit seeking and green requirements of supervisors, financial institutions which have green finance business may take the initiative to search enterprise 606 L. Zhang et al. Green financial policy and investment-financing maturity mismatch of enterprises information and seek projects with high benefits, low risks and conforming to green requirements. Heterogeneity characteristic performances of information transparency in the mismatch behaviors between green finance and financing term of enterprises are discussed in the present study. In the contract negotiation stage, banks have insufficient understanding on operation conditions of enterprises due to the serious information asymmetry. Banks often provide enterprises short-term credits rather than long-term credits in order to decrease credit risks (Diamond, 1991; Bonfim et al., 2018; Wang et al., 2020). For post-supervision cost, it can be known from the relational lending theory that banks have to give stronger and more intensive supervision over investment projects of long-term loans compared to short-term loans. After implementation of the green financial policies, enterprises with the higher information transparency can get more long-term loans, thus relieving their investment-financing maturity mismatch more effectively. Analyst tracking is used as the substitution variable of enterprise information transparency and a subsample regression is carried out. Results are shown in Columns (3) and (4) of Table 10. It is easy to see that green financial policies can alleviate investment-financing maturity mismatch of enterprises with higher information transparency better than enterprises with lower information transparency. Furthermore, enterprise samples are divided into state-owned enterprises and non-state- owned enterprises for the sub-sample regression according to properties of controlling shareholders. Results are shown in Columns (5) and (6) of Table 10. Coefficient of D in Column (5) is insignificant and coefficient of D in Column (6) is negative under 1% significance level, thus proving that influences of the green financial policies on short-term loans used as long-term investment of enterprises vary with property rights. Such influences are more obvious in non-state-owned enterprises. Table 10. Group test results (1) (2) (3) (4) (5) (6) Internal control H Internal control L Analys_H Analys_L SOE Non-SOE D–0.020** –0.247 –0.057*** 0.009 –0.009 –0.038*** (–2.40) (–1.61) (–4.51) (1.23) (–1.38) (–4.15) Size 0.121*** –0.011 0.092*** 0.153*** 0.105*** 0.141*** (4.29) (–0.46) (4.29) (5.39) (4.84) (3.98) Liq 0.003 0.030** 0.003 0.002* 0.013*** 0.002 (1.57) (2.11) (1.28) (1.83) (4.97) (1.01) SG 0.038*** 0.00817 0.035*** 0.037*** 0.041*** 0.036*** (10.48) (0.95) (9.17) (11.54) (8.54) (7.25) SAT –0.000 –0.001*** 0.000 –0.000** 0.000*** –0.000*** (–0.15) (–6.86) (0.09) (–2.32) (3.22) (–3.80) Dpay –0.019*** 0.025 –0.007 –0.022*** 0.004 –0.031*** (–2.84) (0.84) (–0.83) (–3.20) (0.42) (–3.55) Big10 –0.002 –0.030 –0.006 0.003 –0.004 –0.005 (–0.28) (–0.84) (–1.28) (0.50) (–0.74) (–0.82) Journal of Business Economics and Management, 2024, 25(3), 590–611 607 (1) (2) (3) (4) (5) (6) Internal control H Internal control L Analys_H Analys_L SOE Non-SOE BDS –0.001 –0.014 0.001 –0.003 0.001 0.001 (–0.64) (–1.00) (0.35) (–1.57) (0.46) (0.64) Constant –2.191*** 0.012 –1.780*** –2.828*** –2.312*** –2.378*** (–4.08) (0.03) (–4.87) (–4.96) (–5.02) (–3.74) Firm FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes N21581 582 12256 9907 9333 12830 R20.105 0.178 0.0939 0.0997 0.104 0.132 5. Discussions The empirical results validate the research hypothesis in the theoretical analysis and confirms the scientific preciseness of this study. According to the independent sample t-test, the investment-financing maturity mismatch of enterprises is alleviated after implementation of the green financial policies. Debt financing cost of enterprise samples after implementation of the green financial policies decreases significantly compared to that before. According to regression results of Table 4, the green financial policies alleviate both investment-financing maturity mismatch of enterprises and “short-term loans used as longterm investment” of enterprises. This is consistent with the conclusion of Ma and Hu (2020). Green finance policies likely boost banks’ willingness to provide long-term loans to enterprises by enhancing their capacity to screen, review, and monitor firms’ financial and operational information. It can be seen from regression results of Table 5 that the green financial policies alleviate short-term loans used as long-term investment of enterprises by decreasing their debt financing costs. This is because the green financial system could collect and process information professionally, disclose the reasonable price and promote effective allocation of capitals, thus decreasing transaction costs. Besides, the green financial policies increase new debt size of enterprises. It increases new growth and short-term loan size of enterprises, respectively. In particular, the increased long-term loan size further proves that the green financial policies can alleviate short-term loans used as long-term investment by increasing new loan size of enterprises. Moreover, green financial polices improve the debt maturity structure of enterprises. This finding is the same as that of Wang et al. (2019). The possible reason is that the implementation of green finance policy enables banks to utilize information disclosure, resource tilting, and reassessment of corporate projects to curb the moral hazard of the firms. As a result, the firms’ financial ratios become robust, the operational capacity is improved, and the size and share of long-term debt is significantly increased. Conclusions in Table 10 are new findings of this study. Results show that firstly, the green financial policies alleviate short-term loans used as long-term investment of enterprises with high internal control effectiveness more obviously. Secondly, the green financial policies alleviate short-term loans used as long-term investment of enterprises with higher information transparency more significantly compared to enterprises with a low information transparency. End of Table 10 608 L. Zhang et al. Green financial policy and investment-financing maturity mismatch of enterprises This conclusion may be attributed to the fact that state-owned enterprises, being mostly in a mature stage, benefit from state capital as an “implicit guarantee”. In contrast, private enterprises, although more growth-oriented, face greater uncertainty in growth and profitability. This directly intensifies the issue of private enterprises lacking long-term capital and relying on short-term debt. State-owned enterprises, in comparison to non-state-owned ones, encounter relatively lower external financing constraints. Consequently, the implementation of green finance policies is more visibly effective in alleviating short-term debt used as longterm debt for non-state-owned enterprises. Additionally, small and medium-sized financial institutions catering to private enterprises are proactively adopting green finance policies, thereby alleivating the mismatch between their investment and financing maturities. 6. Conclusions 6.1. Main findings In the strategic background that green finance drives green low-carbon development, it is necessary to study micro-implementation effect of the green financial policies. Hence, effects of the green financial policies on investment-financing maturity mismatch of enterprises of A-share companies in China and the influencing paths are studied through DID method based on their panel data from 2009–2020. Some major conclusions could be drawn: (1) the investment-financing maturity mismatch of enterprises is alleviated after implementation of the green financial policies. (2) Green financial policies alleviate investment-financing maturity mismatch of enterprises through three major ways, namely, lowering financing cost, increasing debt financing size and improving debt maturity structure. (3) Green financial policies alleviate the investment-financing maturity mismatch of non-state-owned enterprises better than state-owned enterprises. Green financial policies alleviate the investment-financing maturity mismatch of enterprises with higher internal control quality and higher information transparency more obviously than other enterprises. 6.2. Management implications This study enriches evidences of studies about investment-financing maturity mismatch and proposes the following policy suggestions: (1) provide good green institutional environment for production and management of enterprises, and provide better services for green economic growth. Implementing a series of policy measures like Instructions on Building the Green Financial System is conducive to facilitate coordinated economic and environmental development, encourage and guide financial institutions to invest credit funds to green projects (e.g. energy-saving and environmental protection), and thereby increase the proportion of long-term debts of green enterprises. (2) Increase size and efficiency of direct financing market, especially the equity financing channel. Take the initiative to implement local government’s reform of debt management system and decrease “squeezing effect” of government’s long-term financing to long-term financing of enterprises. Continue to insist in deepening reform of financial system, develop diversified financing modes of enterprises, and further provide interest subsidy support, aiming to lower financing cost of enterprises and increase availability of credit loans to enterprises. (3) Size and structure of enterprise debt are important premises of fund stability. Therefore, government shall strengthen macroscopic regulation and perfect relevant supervision policies, mitigate debt maturity structure of enterprises together, increase utilization of credit funds, consolidate foundation for enterprise Journal of Business Economics and Management, 2024, 25(3), 590–611 609 economic development, strengthen competitive edges of enterprises in middle-term and long-term financing investment, prevent risks caused by short-term loans used as long-term investment of enterprises, and thereby decrease the investment-financing maturity mismatch of enterprises. 6.3. Shortcomings and prospects This study still has some limitations. Firstly, it shall be more thorough in the empirical study based on a quasi-natural experiment. 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