Explore whether firms with better external environmental disclosure and internal green innovation receive more bank loans because of green credit, and utilize a panel of 1086 listed Chinese manufacturing enterprises from 2012 to 2017 to test our hypotheses.
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Green credit policy and corporate access to bank loans in China: The role of environmental disclosure and green innovation Chao Xinga, Yuming Zhanga,*, David Tripeb aSchool of Management, Shandong University, 27 South Shanda Road, Jinan 250100, Shandong, China bSchool of Economics and Finance, Massey University, Palmerston North 4442, New Zealand ARTICLE INFO JEL classification: G21 G32 O16 Keywords: Green credit policy Environmental disclosure Green innovation Bank loan Green-washing ABSTRACT This research assesses the effectiveness of China’s green credit policy. We explore whether firms with better external environmental disclosure and internal green innovation receive more bank loans because of green credit, and utilize a panel of 1086 listed Chinese manufacturing enterprises from 2012 to 2017 to test our hy-potheses. The results suggest that firms with higher environmental disclosure quality do not obtain more loans, and only green innovation promotes access to corporate loans. We show that the underlying cause of this phenomenon is corporate green-washing, which is prevalent in soft environmental disclosure and hinders en-terprises from obtaining more loans. Our findings contribute to the literature on green credit policy, corporate green innovation, environmental disclosure, and green-washing, and provide a reference for companies, banks, and governments to make decisions. 1.Introduction Nowadays China is the world’s biggest emerging market. What makes China the focus for this study is not only its rapid economic growth but also the environmental pollution caused by industrialization. In recent years, the government of China has implemented a series of policies to protect the environment. In the financial and industrial sector, green credit policy, which was jointly designed in 2007 and formally enacted in 2012 by several central government departments,1 is a critical method for alleviating industrial pollution (CBRC, 2007; CBRC, 2012; Liu, Wang, & Cai, 2019). As a typical policy of green finance, green credit aims to reduce pollution by reallocating credit among enterprises. More concretely, from the perspective of commercial banks, green credit policy directs them to consider corporate environmental behaviour as an important consideration when they are granting loans, instead of simply focusing on financial solvency (He, Zhang, Zhong, Wang, & Wang, 2019; Zhang, Yang, & Bi, 2011). For example, the Industrial and Commercial Bank of China (ICBC), China’s biggest commercial bank, has constructed a comprehensive framework of green credit. On the one hand, it has established a specific loan portfolio for eco-friendly enterprises. On the other hand, for applicants or debtors with bad environmental performance, it has the power to refuse all loan requests or suspend any renewal of granted loans, namely “one ticket veto” (Li & Hu, 2014; ICBC, 2018). Correspondingly, from firms’ perspective, after the implementation of green credit policy, companies with contamination projects find it more difficult to obtain loan financing, but enterprises dedicated more to environment protection are advantaged (CBRC, 2007; CBRC, 2012). Similarly to many other countries, China has a bank-centered financial system in which loans are the predominant method for firms to obtain debt financing (Qiu & Shen, 2017). As banks are more interested in corporate green behaviours after the green credit policy was adopted, most enterprises in China, especially manufacturing enterprises, have been affected by the policy (Zhu, Geng, & Lai, 2010). These firms have to incorporate environmental protection into their operations for meeting banks’ requirements (He et al., 2019). Nevertheless, green credit policy does not stipulate which types of corporate green behaviour are criteria for applying for loans, and hence the impacts of specific eco-friendly characteristics on corporate loan financing are still a black box, and the policy’s effectiveness has not been explored thoroughly. Theoretically, there are two premises for policy success: firstly, banks should collect corporate environmental informa-tion, and secondly, firms have to engage in environment protection *Corresponding author. E-mail addresses: [email protected] (C. Xing), [email protected] (Y. Zhang), [email protected] (D. Tripe). 1 Including State Environmental Protection Administration (SEPA), the China Banking Regulatory Commission (CBRC), and the People’s Bank of China (PBC), etc. Contents lists available at ScienceDirect International Review of Financial Analysis journal homepage: www.elsevier.com/locate/irfa https://doi.org/10.1016/j.irfa.2021.101838 Received 31 March 2020; Received in revised form 19 May 2021; Accepted 22 June 2021
(Tolliver, Keeley, & Managi, 2020a). These two premises can be ach-ieved by two activities in a single framework developed by Tilt (2006) and Passetti, Cinquini, and Tenucci (2018), i.e., external reporting and internal reform. Therefore, in this paper, we focus on external environmental disclosure and internal green innovation. For environmental disclosure, substantive disclosure will reduce information asymmetry between firms and banks, but symbolic disclosure may exacerbate information friction (Du, Jian, Zeng, & Chang, 2018). Meanwhile, green innovation can improve corporate environmental performance and future devel-opment internally (Lin, Tsai, Hasan, & Tuan, 2018). Based on this, we try to assess the effectiveness of China’s green credit policy via a key question, that is, do both external and internal dimensions of corporate eco-friendliness help firms obtain more loans under the green credit policy? To answer the question, we undertake an empirical analysis using a panel data of 1086 listed manufacturing enterprises from 2007 to 2017 and test the effects of the green credit policy on bank loans from the perspectives of environmental disclosure and green innovation. In addition, we further explore the potential problems in this process in combination with the characteristics of these two factors. Our work contributes significantly to previous research. Firstly, we enrich studies looking at the relationship between green credit policy and corporate loan financing in China. To the best of our knowledge, this is the first paper to illustrate the relationship between green credit policy, the two types of corporate green activity, and access to bank loans. Our methodology allows us to open the black box and explain the different factors affecting banks’ loan choice. Secondly, we exploit environmental disclosure quality and green innovation performance to measure the degree of eco-friendliness more precisely and comprehen-sively, following Passetti et al. (2018) and Tilt (2006). Generally, banks are more concerned about firms’ specific green behaviours rather than high polluting industry.2 Thus, we expand the scope of corporate green behaviour. From banks’ perspectives, we also reveal their concerns regarding corporate eco-friendliness. Finally, our study increases research on green-washing and the problem of environmental disclo-sure. Through analyzing the problems and obstacles, we provide a so-lution to avoid policy failure, hence we develop a framework that connects corporate behaviours, green finance, and global environmental development. It is constructive for policymakers, banks, and enterprises. The rest of this research is arranged as follows. Section 2 details the background and hypotheses. Section 3 presents the methodology. Sec-tion 4 develops empirical analysis and results. Section 5 offers conclu-sions and implications. 2.Literature and hypotheses 2.1.Policy background and literature review The green credit policy of China was jointly designed by several central government departments in 2007 (CBRC, 2007). In early 2012, they submitted the detailed document explaining relevant organiza-tions, processes, policy terms, controls, and supervision of green credit, marking the formal implementation of this policy (CBRC, 2012). These documents compel local governments and commercial banks to concentrate directly on corporate environmental protection. On the one hand, local governments have to establish a database and then collect relevant data from local firms, on environmental issues, corporate emissions, and environment protection practice. Local governments should share the database with banks to implement the policy effectively (CBRC, 2007). Also consistent with the policy, banks should consider corporate sustainability as an important criterion in granting loans. Specifically, they are encouraged to grant loans to eco-friendly projects and firms, but not allowed to invest in any polluting project or any company with bad environmental performance, i.e. “Carrots and Sticks” (CBRC, 2012). Since 2013, the main commercial banks in China have had to submit data regarding the implementation of green credit to the CBRC, which has increased the effectiveness of the policy (CBRC, 2013). In 2016, the PBC jointly issued the Guidance on Building a Green Financial System with six ministries and commissions, and green credit is the primary practical policy (PBC, 2016). In sum, the effect and sig-nificance of China’s green credit policy have expanded rapidly in recent years. However, previous literature on green credit policy mainly focuses on environmental improvement, effectiveness evaluation, or problems in the policy (Sun, Wang, Yin, & Zhang, 2019; Weber, 2017; Zhang et al., 2011). Some authors who find green credit policy can improve the loan size of eco-friendly enterprises utilize the difference in difference (DID) model and assume that all high polluting enterprises have equally poor ability to obtain loans (e.g. Liu et al., 2019). These studies focus on the policy’s economic effect, i.e., the efficiency of loan resources allocation (He et al., 2019), but the policy success is twofold. Firstly, banks should collect environmental information, and secondly, enterprises have to actively engage in green behaviours and information submission (Xing, Zhang, & Wang, 2020; Zhang, Xing, & Tripe, 2021). As banks have the basic ability to collect information and monitor applicants (Goss & Roberts, 2011), the keys of policy success become corporate eco- friendliness and additional information submission. According to this logic, there are two ways for enterprises to improve their loans, one is transmitting more useful information to banks, and the other is paying more attention to environmental protection. These two needs cater to the framework of Passetti et al. (2018) and Tilt (2006) which divides firms’ green activities into external and internal di-mensions. External activities are represented by communicating envi-ronmental protection information with stakeholders, while internal activities are represented by practical, technological, and managerial reforms within firms (Teeter & Sandberg, 2017). In other words, firms can access more loans if they provide more information regarding environmental conservation, or enhance corporate eco-friendliness; hence, the main body of external activity is environmental disclosure, and the leading role of internal activity is green innovation. Environmental disclosure is a common and convenient method for firms to submit their environmental information to external stake-holders through specific regular reports (Zhang, Xing, & Wang, 2020). Some studies indicate the contents of environmental disclosure reports are related to past, current, and future environmental performance including non-financial information and its implementation (Dhaliwal, Li, Tsang, & Yang, 2011; Hummel & Schlick, 2016). The information in the environmental disclosure report may be abundant but confusing since a company may submit not only the substantive information but also lots of symbolic information (Clarkson, Li, Richardson, & Vasvari, 2008; Passetti et al., 2018). Even though, recent literature focusing on developed markets usually supports the view that environmental disclosure quality reduces information asymmetry and reflects envi-ronmental performance (Rezaee & Tuo, 2019; Schreck & Raithel, 2018). The environmental disclosure report is one of the handiest channels for banks to investigate a firm because it is submitted by the applicant itself without relying on other stakeholders (Li & Hu, 2014). Compared with environmental disclosure, corporate green innova-tion is usually created internally. For instance, Li, Huang, Ren, Chen, and Ning (2018) define green innovation as an internal mechanism. Green innovation refers to a strategy or activities intending improve-ments in emission control, pollution-reduction, and cost-saving, whose 2 In fact, the most important determinant of being classified as high polluting enterprise is production domain. In 2010, China Ministry of Environmental Protection (MEP) indicated sixteen production domains are the high polluted, i. e., thermal power, steel, cement, electrolytic aluminum, coal, metallurgy, chemical industry, petrochemical, building materials, paper making, brewing, pharmaceutical, fermentation, textile, leather making and mining. All listed companies belong to these domains are regarded as high polluting enterprises. Such classification is also utilized in the research of Liu et al. (2019). C. Xing et al.
scope comprises aspects of firms’ technology and management (Abbas & Sa˘gsan, 2019; Managi, Hibiki, & Shimane, 2014). Corporate green innovation performance indicates the capability of a firm’s environ-mental reform and conservation (Li et al., 2018; Zhang et al., 2020). According to the Porter Hypothesis, as environmental policies have affected enterprises, many companies’ corporate green innovation will have increased (Porter & van der Linde, 1995). On the one hand, green innovation embodies corporate sustainability (Fabrizi, Guarini, & Meliciani, 2018), while on the other hand, given that green innovation can improve companies’ financial performance (Huang & Li, 2017), it enables banks to consider both corporate environmental protection and investment value (Weber, 2012). In summary, current research on the relationship between green credit policy and corporate environmental activities is insufficient. Firstly, most studies only test the direct impact of the policy on corporate financing, with the linkages between the policy, firms, and banks less explored. We want to know what characteristics of companies are important to banks. Secondly, in this context, we focus on the role of environmental disclosure and green innovation, whereas previous studies do not connect them with the green credit policy and usually discuss them separately. Different from mature markets, China’s envi-ronmental disclosure and green innovation are still in an early stage. Whether they are effective and what the differences are between their roles in corporate financing also need to be discussed. Therefore, in this paper, we use environmental disclosure quality and green innovation performance for exploring the impact of China’s green credit policy on corporate loan financing, which is the most important innovation and difference of our research from previous literature. 2.2.Hypotheses 2.2.1.The role of environmental disclosure Stakeholder theory suggests that the pressure from stakeholders is the main factor encouraging enterprises’ environmental activities (Buysse & Verbeke, 2003; Cooper, Raman, & Yin, 2018). Several studies expand this theory and indicate that firms can also obtain many re-sources from stakeholders by meeting their demands (Brammer & Millington, 2008; El-Kassar & Singh, 2019). In the case of China’s green credit policy, the most important stakeholders of enterprises are banks since they not only directly control the resources of loan capital but also have the power to grant loans to eco-friendly enterprises. Firms with additional environmental information or a higher degree of eco- friendliness can thus receive more financial support from banks. But as stakeholder theory only provides a broad conception, the impacts of external and internal factors should be discussed in more depth. Firstly, we explore the relationship between environmental disclosure and ac-cess to bank loans. We believe there is a positive connection between environmental disclosure quality and bank loan acquisition if such disclosure can alleviate the problem of asymmetric information. Information asym-metry theory suggests that the distribution of information is unbalanced between seller and buyer, and the party holding more private informa-tion can gain in a transaction but may damage the interest of its coun-terparty (Akerlof, 1970; Strausz, 2017). This situation causes the problem of adverse selection, where the party with information disad-vantage cannot make a correct decision (Akerlof, 1970). In the financial sector, many studies find that investees are faced with serious capital rationing3 or higher capital cost because of asymmetric information and adverse selection (Biais, Foucault, & Moinas, 2015; Dell’ariccia, Laeven, & Suarez, 2017). A better disclosure policy can improve investors’ in-formation levels and reduce the negative impact of adverse selection (Roychowdhury, Shroff, & Verdi, 2019). Therefore, environmental disclosure, as an important element of non-financial disclosure, can provide banks and other institutions with more information related to environment protection (Cui, Jo, & Na, 2018; Schiemann & Sakhel, 2019). In summary, effective environmental disclosure is a significant way for banks to collect more corporate environmental information and help them make good decisions. Based on this, we further focus on disclosure quality, looking at the heterogeneity and comprehensiveness of environmental disclosure among enterprises. Environmental disclosure quality refers to informa-tion comprehensiveness, and a higher quality should indicate detailed information (Clarkson et al., 2008). Previous studies have discussed whether environmental disclosure quality can enhance information from two perspectives. Literature based on voluntary disclosure suggests that disclosure quality reflects better environmental information or performance because disclosing substantive information can increase firm value (Clarkson et al., 2008). Recent research on mature markets also supports the voluntary disclosure theory and its correlation with firm value from different perspectives. For example, Hora and Sub-ramanian (2019) and Schreck and Raithel (2018) find that high-quality environmental disclosure not only provides information to investors but also shows good environmental performance. Consistent with these studies, we suggest that higher environmental disclosure quality em-bodies better environmental information. Given that efficient environmental disclosure reduces information asymmetry, and its quality is positively related to real information, banks can evaluate applicants more efficiently and precisely because of the green credit policy. Accordingly, the order of granting loans in this case should be: 1) enterprises with higher disclosure quality; 2) enter-prises with lower disclosure quality; 3) enterprises without disclosure. Different from some authors who are interested in loan cost or loan maturity (e.g. Goss & Roberts, 2011), we primarily focus on the access to bank loans, i.e., loan size, because China’s green credit policy has affected banks’ decisions regarding granting loans, instead of other loan terms such as interest (Liu et al., 2019). To sum up, we develop the following hypothesis: H1a.China’s green credit policy improves the bank loan size of firms with higher environmental disclosure quality. However, another group of studies based on socio-political theory, illustrate a negative relationship between environmental disclosure and environmental information richness. Poor performing firms have to disclose more for legitimacy, and stakeholders usually confuse the quantity and quality of environmental disclosure (Hummel & Schlick, 2016). This phenomenon mainly occurs in an emerging market such as China and is contrary to the preceding hypothesis that better environ-mental disclosure can increase corporate loan size (Du et al., 2018). We suggest the potential reasons as green-washing behaviour in disclosure and the legitimacy risk it causes. Therefore, we further consider the format and context of environmental disclosure and discuss the negative perspective. As we argued, the voluntary disclosure theory suggests a positive relationship between environmental disclosure quality and environ-mental performance. Banks will value corporate environmental disclo-sure if the deviation between green talking and doing is not great. Nevertheless, in our research and survey, we found some companies talking more but doing less. For instance, a firm stated that the senior executives are attaching big importance to environment protection in the CSR report, but there is no information or data on concrete actions. Such behaviours are consistent with social-political theory and green- washing, a strategy that develops corporate environment image via symbolic description rather than substantive practice (Kim & Lyon, 2015). The relationship between green-washing and environmental disclo-sure is obvious but dichotomous, and the key to explaining this view-point is the form of environmental disclosure. An important source of 3E.g., investors only provide a certain proportion of capital, instead of ful-filling the aggregate of firms’ financing demands. According to our survey, Chinese banks only grant less than 20% of application amounts in some cases. C. Xing et al.
green-washing is symbolic environmental disclosure, which is greatly correlated with soft disclosure. Clarkson et al. (2008) classify environ-mental disclosure into soft and hard types. Soft environmental disclo-sure refers to claims without strong objective evidence, compiled by firms themselves. It reflects symbolic corporate environmental behav-iours. Correspondingly, hard environmental disclosure is that terms are based on relevant data and can be verified by other institutions. Such disclosure supports substantive environmental practices (Du et al., 2018). Thus, we offer the intuition that soft disclosure rather than hard disclosure may aggravate green-washing. If the magnitude of soft disclosure is greater than for hard, the firm will be treated as a green- washer. Previous literature mainly supports green-washing being harmful to firms’ stakeholders and themselves. On the one hand, green-washing misleads stakeholders’ perception of corporate environmental perfor-mance, and the reputation of firms will be impaired once stakeholders are informed of the real environmental information (Cooper et al., 2018). On the other hand, stakeholders will reduce their support and corporate performance will be worse because of the green-washing (Testa, Miroshnychenko, Barontini, & Frey, 2018). As the green credit policy is implemented, banks may handle symbolic green firms with extreme caution, because investors will also be punished by govern-ments once the investees are proven to be green-washers. In other words, green-washing implies a higher legitimacy risk. That means banks may reduce loan amounts to formalistic firms. Therefore, similar to the viewpoint of Walker and Wan (2012), we deem that green-washing contributes to neither environmental perfor-mance nor financial strength. As banks are unwilling to grant loans to green-washers, firms cannot be granted more loans if symbolic disclo-sure is a common phenomenon. In summary, we find another opposing path that the green-washing behind soft disclosure hinders firms from obtaining more loans, and we develop the following hypothesis. H1b.China’s green credit policy cannot improve the bank loan size of firms with higher environmental disclosure quality. 2.2.2.The role of green innovation Previous studies indicate that stakeholders’ pressure is a key influ-ence on corporate green innovation (Kawai, Strange, & Zucchella, 2018). Enterprises can meet the requirements of stakeholders by investing in green innovation, e.g., consumers’ demands for green products, society’s proposal for environment protection, and employees’ needs for healthy working conditions (El-Kassar & Singh, 2019; Kuna-patarawong & Martínez-Ros, 2016). At the same time, firms can benefit from green innovation (El-Kassar & Singh, 2019). According to ecolog-ical modernization theory, green innovation integrates environment conservation into modernization, and helps enterprises keep competi-tive without suffering from the environmental crisis (Huang & Li, 2017). Based on this, we believe that corporate green innovation is another factor affecting corporate access to loans by improving firms’ ecological activities. Firstly, green innovation can enhance corporate environmental performance, thus improving banks’ trust that firms have the ability to deal with environmental problems in investment activities. Multiple studies have supported the relationship between green innovation and environmental performance from different perspectives. On the one hand, Cai and Li (2018) analyze the “double externality” of green innovation. They suggest that in addition to the knowledge spillover effect, green innovation also creates an environmental spillover effect, which reduces negative environmental impacts and improves firms’ environmental performance. In addition, Huang and Li (2017) focus on green innovation as an environmental initiative, that environment protection is one of the most important aims of corporate green inno-vation, rather than a mere externality. Some other authors also find a positive correlation under specific circumstances, such as a green supply chain management framework, institutional heterogeneity, and high technology development (El-Kassar & Singh, 2019). According to these views, enterprises can achieve waste reduction and cost-saving via green innovation (Purcel, 2020). For instance, if a company has better green innovation or technological ability, it can easily make projects greener and fulfil ecological demands from banks when applying for loans. In turn, under green credit policy, banks will believe such firms and pro-vide them with more loans. Secondly, green innovation is beneficial for corporate long term development. Dynamic capability theory holds that innovation activities embody dynamic capability, which represents firms’ capacity to adapt and integrate resources and skills rapidly and dynamically, with further positive effects on corporate competitive advantages (Teece, Pisano, & Shuen, 1997). Extensive research supports banks valuing corporations with better innovation performance (Francis, Hasan, Huang, & Sharma, 2012; Plumlee, Xie, Yan, & Yu, 2015). As green innovation is a specific type of innovation, it can also increase corporate advantage. Given increasing interest in sustainable products and clean production, green innovation caters to the future demands of the market, and improves corporate development. A large number of studies have shown a positive correlation between green innovation and other organizational perfor-mance (Cai & Li, 2018; El-Kassar & Singh, 2019). Therefore, banks may concentrate on the performance-developing effect of green innovation because it enables them to reduce credit risk and possible bad debts (Weber, 2012). As green credit policy has been implemented, green innovation is an appropriate measurement for banks to give consider-ation to corporate eco-friendliness, solvency, and innovation perfor-mance. Thus, green innovation should bring enterprises better loan access. However, compared with the convenience of disclosure reports that firms submit, banks have to make more efforts to obtain green innova-tion data. This can be based on banks’ superior monitoring capability (Goss & Roberts, 2011). In fact, banks already pay attention to corporate innovation performance. As an example, Plumlee et al. (2015) find that banks can efficiently collect firms’ private innovation data. Nevertheless, banks cannot obtain total information on all types of green innovation. According to Abbas and Sa˘gsan (2019), green innovation can be clas-sified as technological or managerial, which are manifested as green patents and green management mechanisms, respectively. Local gov-ernments, banks, and other institutions find it difficult to collect com-plete quantitative green management innovation information since relevant details are preserved within corporate management processes (Abbas & Sa˘gsan, 2019). Thus, we mainly focus on technological green innovation which we measure using green patents. In addition, although environmental disclosure and green innovation are both listed in one framework, there are obvious differences between them in China. The principal distinction is they may be uncorrelated with each other. In China, the main method of disclosing firms’ envi-ronmental information is a corporate annual report and social re-sponsibility (CSR) report4 (Du et al., 2018). In such an integrated report, the information on corporate green innovation is less proactively dis-closed to stakeholders. In other words, green innovation performance may contribute little to environmental disclosure quality. This situation means that banks usually obtain green innovation information in other ways such as support from the State Intellectual Property Office of China (SIPO)5 or on-the-spot investigation (Zhang et al., 2011). To sum up, we suggest that green innovation (mainly technological) is an independent factor influencing the policy’s and banks’ concerns. Enterprises with better green innovation performance have better access to bank loans. 4Although a small number of enterprises submitted environmental infor-mation report (EI report), the contents of environment protection in EI report, annual report, and CSR report are usually the same. 5 SIPO is a central government department which administrate all affairs and data regarding intellectual property, patents and innovative practice of in-dividuals, enterprises and other institutions of China. C. Xing et al.
According to our analysis, the following hypothesis is developed: H2.China’s green credit policy improves the bank loan size of firms with greater green innovation. 3.Methodology 3.1.Data The sample for our study includes 1086 manufacturing enterprises listed on the Shanghai and Shenzhen Stock Exchanges. The reason we select manufacturing firms is that they play key roles in industrial pollution and environmental conservation and are thus important in the context of China’s green credit policy (Zhu et al., 2010). Environmental disclosure data are collected and calculated manually using corporate CSR reports and annual reports. The process is in line with that of Du et al. (2018). Specifically, we collect all the reports from every firm’s official website and read the materials regarding environmental pro-tection. According to the context, we fill out the form developed by Clarkson et al. (2008) and calculated the scores of each category. We collect general patent data from the CSMAR (China Stock Market and Accounting Research) database, including patent number, patent type, and the International Patent Classification (IPC) code of the patent. Green innovation data are manually selected based on the general patent data, and the selection criterion accords with the IPC Green Inventory of World Intellectual Property Organization (WIPO),6 that is, if a patent’ IPC code belongs to the Green Inventory, it is a green patent. Other corporate financial data are collected from the CSMAR database. We select the time span of 2007 to 2017 since the patent data from CSMAR database are only available before 2017, and the conception of green credit was firstly developed in 2007 but formally implemented in 2012; hence the period of 2007 to 2017 is suitable for our research. The total number of observations is 9711. All continuous variables are winsorized at the upper and lower 1% levels to avoid the impacts of extreme outliers (Du et al., 2018). 3.2.Models and variables 3.2.1.Models We employ the difference-in-difference model (DID) with continuous grouping variables to test our fundamental hypotheses according to Qian (2008) and Kim and Valentine (2021). The formulas are shown in Eqs. (1) and (2), where Loan is the independent variable; Disclosure, GreenInnovation, and Policy are explanatory variables; Controls_j is the jth control variable; μ, δ, and ε are individual fixed effect, time fixed effect, and random error, respectively. Similar to a moderation analysis, this model does not stipulate dummy variables of controlled and treated groups as the explanatory variables of Disclosure and GreenInnovation are not dichotomous. In other words, as we presume the policy to affect firms with environmental disclosure and green innovation, the higher levels of such factors the firms have, the more likely they are classified as the treated group (Kim & Valentine, 2021; Qian, 2008). The coefficient of β2 indicates the general effects of the explanatory variables on bank loans (the difference of Loan between treated and controlled groups), but when the policy is implemented (Policy equals to 1), the total effects become to (β1 +β2) which is the real effect of the policy. Therefore, the key to test our hypotheses is β1. We also utilize a fixed-effects regression strategy to control the unobservable characters of individuals and in-crease measurement accuracy (Kang, Baek, & Lee, 2019), as the Haus-man test in every regression showed the fixed effects strategy to be better than the random-effects. Thus, as the time is fixed, Policy is not added solely in the equations because of the perfect collinearity. In robustness tests, we further exploit the time trend analysis, post-policy analysis, and propensity score matching difference in difference models (PSM-DID). Loani,t=α+β1Disclosurei,t×Policyi,t+β2Disclosurei,t+∑γjControlsji,t+μi+δt+εi,t(1) Loani,t=α+β1GreenInnovationi,t×Policyi,t+β2GreenInnovationi,t+∑γjControlsji,t+μi+δt+εi,t(2) 3.2.2.Variables The main focus of our study is corporate access to bank loans, i.e. loan size, and thus the dependent variable is Loan, which is equal to the natural logarithm of new bank loan amount for the enterprise in a year (Lin et al., 2018; Nandy & Lodh, 2012; Qiu & Shen, 2017). We use the natural logarithm form because it measures the absolute capability of obtaining loans and can fulfil the requirement of normal distribution (Nandy & Lodh, 2012). Such an indicator is less affected by the ac-counting standard emendation of China in the sample period. We expect a positive correlation between Loan and the environmental disclosure and green innovation variables. The first explanatory variable is environmental disclosure quality. We construct an index assessing quality according to Clarkson et al. (2008). This index includes hard and soft disclosure categories and can be further divided into 7 subsections and 45 items. As we discussed, hard disclosure refers to information based on concrete evidence or data, and soft disclosure is disclosed by firms’ own narration or explanation. In the classification of Clarkson et al. (2008), hard disclosure is measured from four subsections; governance structure and management systems (6 items), credibility (10 items), performance indicators (10 items), and environmental spending (3 items), while the soft disclosure has three subsections, vision and strategy claims (6 items), environmental profile (4 items), and environmental initiatives (6 items). All terms are based on the Global Reporting Initiative guidelines, and several studies have explored relevant questions for China by this method (e.g. Du et al., 2018). Such an index thus has high reliability. The value of environ-mental disclosure quality (Disclosure) is the natural logarithm of 1 plus the sum of all items (including hard and soft disclosure). The specific contents and values of this variable are listed in Appendix A. The second explanatory variable is green innovation performance. Prior literature mainly uses questionnaire surveys, green R&D expen-diture, and green patents to measure green innovation performance (Amore & Bennedsen, 2016; Fabrizi et al., 2018; Kunapatarawong & Martínez-Ros, 2016). Compared with the other two measures, the green patent has several advantages. On one hand, time series data for green patents can be collected, whereas questionnaire surveys are difficult. On the other hand, banks mainly reward technological innovation output (patents) rather than input (expenditure) because of the high failure risk of R&D (Plumlee et al., 2015). Therefore, we select the number of green patents acquired to measure green innovation performance (Green_-Innovation) and employ the natural logarithm of 1 plus this number in regressions. Policy (Policy) is also an independent variable measuring the impact of China’s green credit policy, which equals 1 if the year is after 2012, and 0 otherwise, since the policy was formally implemented in 2012. At the same time, we control several other factors influencing bank loans, environmental disclosure, or green innovation (Chen & Matousek, 2020). First, we control firms’ financial characteristics (Bailey, Huang, & Yang, 2011; Chen & Matousek, 2020; Lin et al., 2018; Love, 2003; Meuleman & De Maeseneire, 2012). We use corporate growth ability (Growth), which equals the percentage increase in sales revenue; cash holding (Cash), which is the ratio of corporate cash holdings to total assets; asset tangibility (PPE), which is the proportion of tangible assets 6 See official website (https://www.wipo.int/classifications/ipc/en/green_i nventory/). C. Xing et al.
to total assets; financial performance (ROA), which equals the return on total assets; investment expenditure (Expend), which is measured as the amount of purchased long-term assets scaled by total assets; corporate size (Size), which equals the natural logarithm of total assets; financial leverage (Leverage), which is the asset-liability ratio; risk-taking (Risk), which is the variability of the return on total assets in previous three years. Second, we use two variables to control for the impact of conven-tional innovation (Huang & Li, 2017)7; research and development in-vestment (RD), which is the ratio of R&D to total sales; general innovation performance (Normal_Innovation), which equals the natural logarithm of 1 plus the number of normal patents. Thirdly, as both corporate financing and green innovation are affected by corporate governance (Amore & Bennedsen, 2016; Ang, Cole, & Lin, 2000; Lin, Ma, Malatesta, & Xuan, 2011), we control three governance characteristics: ownership concentration (Top), which is the shareholding percentage of the largest shareholder; management shareholding (Manager_Hold), which equals the percentage of the shares held by directors and executives; agency cost (Agency_Cost), which is equal to the sum of administrative expenses and sales expenses, scaled by sales revenue. Finally, we control for other financing methods that may crowd out corporate loans, including equity (Equity) and bond financing (Bond), which equal the amounts of equity and bond capital raised in a year, respectively. All variables and their definitions are listed in Appendix B. 3.3.Summary statistics Descriptive statistics for our sample are shown in Table 1. Panel A shows basic information for all the variables and the whole sample, while Panels B and C illustrate the summary of the sample after the policy came into effect (Policy =1) and the distribution of main vari-ables for each year. According to the first panel, the mean value of Loan is 17.529, implying that bank loans are the predominant external financing for Chinese enterprises. At the same time, the average value of Disclosure is 1.719, and the mean value of Green_Innovation is 0.718. However, such numbers increased in recent years as shown in Panel C, where the mean values of Disclosure gradually increase over time (0.743 in 2007 to 2.408 in 2017). This also applies to Green_Innovation (0.210 in 2007 to 1.115 in 2017). 4.Regression results 4.1.The baseline results The impacts of the green credit policy on access to loans for firms with different environmental disclosure quality and green innovation performance are shown in Table 2. We firstly test the impacts of the control variables in column (1). It shows that ROA, Cash, and Risk are negatively related to Loans. That means firms holding abundant cash and better performance apply less for external financing such as bank loans, and banks are unwilling to grant loans to enterprises with severe risk-taking. We also find the coefficients of Expend, Size, and Leverage to be significantly positive since large firms obtain loans more easily, and high leverage and higher investment expenditure companies have to raise more capital to maintain their operation. At the same time, the coefficient of Equity is positive, which may reflect the pecking order. As equity is the last financing method the firms should use (pecking order theory), such enterprises have obtained more loans before seeking eq-uity financing. We test our hypotheses in columns (2) to (4). They show the coefficients of Disclosure×Policy and Green_Innovation×Policy are both positive (β1), but the former is insignificant and the latter significant at the 1% level, indicating that H1a is rejected, and only hypotheses H1b and H2 are supported. Besides, as there is no evidence that eco-friendly enterprises are more reluctant or unwilling to obtain loans, both the explanatory variables should be considered in the supply of loans. These results show that banks may value internal reform rather than external communication after the green credit policy was implemented.8 4.2.Robustness tests 4.2.1.Time trend analysis Based on the above results, we further analyze the time trend effects of the DID model to explore the effects in different periods. The regression equations of trend analysis are shown in Eqs. (3) and (4). In these equations, Year_k is a list of dummy variables equal to 1 if the time is (2012 +k) year. Therefore, βk is the time trend effect, that is, the effects of the policy and corporate environmental behaviours on bank loans in (2012 +k) year. We select 2007 as the benchmark and hence β5 is excluded from the equations. If enterprises can obtain more loans because of the green credit policy, β4 to β1 should be insignificant, but β0 to β5 should significantly positive. These equations can also test the parallel assumption of the DID model that the coefficients of β4 to β1 should be uncorrelated with Loans. Loani,t=α+∑5k=4βkDisclosurei,t×Yearki,t+∑γjControlsji,t+μi+δt+εi,t(3) Loani,t=α+∑5k=4βkGreenInnovationi,t×Yearki,t+∑γjControlsji,t+μi+δt+εi,t(4) The results of the trend analysis are shown in Table 3. We interpret the coefficients into a figure (Fig. 1). The table and figure support the above findings, that all coefficients of Disclosure×Year_k are insignificant (all 90% confidence intervals in Fig. 1 (1) include zero), but those of Green_Innovation×Year_2012 to Green_Innovation×Year_2017 are signif-icantly positive (zero is excluded to the confidence intervals in Fig. 1 (2) after Year_0). It shows banks only grant more loans to firms with better green innovation performance after the policy was implemented (H1b and H2). At the same time, the parallel assumption is supported because all the interactions before 2012 are insignificant. 4.2.2.PSM-DID In our sample, the classification of firms is not random, i.e., firms can choose to disclose and innovate or not. Eqs. (1) and (2) are in line with the typical moderation analysis but not the conventional DID model because of such bias. We utilize the propensity score matching differ-ence in difference model (PSM-DID) to mitigate this problem. We firstly set two pairs of treated and control groups. The first criterion is disclosed environmental information. If a company’s environmental disclosure quality is greater than the median for every year in our sample period (N =4655), it will be a treated group firm, and others will be added to the control group. The second criterion is green innovation. The firms with green innovation higher than the median for every year in our sample period are taken as the treated group (N =4408), with the others taken as the controlled group. At the same time, we select covariates in the PSM procedure which can affect all independent and dependent vari-ables. However, as bond and equity financing do not necessarily impact green innovation and environmental disclosure, we utilize all control variables as covariates but exclude Bond and Equity. 7 We greatly thank an anonymous reviewer for the suggestion that including these key variables. 8In an unreported regression analysis, we find the coefficients are uncorre-lated to loan maturity and loan interests, supporting our conjecture that green credit policy mainly affects loan size. C. Xing et al.
We use the kernel match strategy to optimize the outcomes. After PSM, the samples for environmental disclosure and green innovation reduce to 6048 and 4220, respectively. We also test the balance between the treated and controlled groups: the unreported results show the covariates are not significantly different between these two groups. Therefore, our PSM procedure is effective, and the new samples have a similar range and are less affected by the problem of sample bias. The regression results for the PSM-DID model are shown in Table 4. The table shows that green innovation firms can obtain more loans after the green credit policy was executed, but the coefficients of environmental disclosure are insignificant. Therefore, our findings are still valid in this PSM-DID model. 4.2.3.Post-policy analysis Finally, we use a smaller sample whose time span is 2012 to 2017 to test the direct impacts of environmental disclosure and green innovation on bank loans after the green credit policy was implemented. As we expected, if the policy is effective for firms with better green innovation, they should obtain more bank loans, i.e., the relationship between bank loan and green innovation is significantly positive. The results are shown in Table 5. In this analysis, the dependent variables are lagged one year to avoid the problem of reciprocal causation. The coefficients of envi-ronmental disclosure are insignificant but those of green innovation are significantly positive at 1% level. Thus, the post-policy analysis still supports the above findings. We further test the interaction between environmental disclosure and green innovation in column (4). The interaction is negative with marginal significance at the 10% level. A possible reason is the conta-gion effect. Specifically, banks value corporate green innovation, but they are confused by environmental disclosure because its confidence may be questionable. If a firm holds higher levels of both, the banks will also suspect the authenticity of their green innovation, that is, the confidence instability passes from environmental disclosure to green innovation. Therefore, to reduce the default risk, banks will grant fewer loans to such enterprises. These results further suggest that problems exist in environmental disclosure. 4.3.Further analysis: why external disclosure becomes ineffective? The previous findings suggest that environmental disclosure is inef-fective in helping firms obtain more loans. In the hypothesis section, we argued that this may be because of the forms of disclosure, i.e., the Table 1 Summary statistics. Variable Obs Mean S.E. Min Median Max Panel A. Summary statistics of the total sample Loan 9711 17.529 6.961 0.000 19.942 24.222 Policy 9711 0.671 0.470 0.000 1.000 1.000 Disclosure 9711 1.462 1.075 0.000 1.386 3.584 Green_Innovation 9711 0.718 0.992 0.000 0.000 3.912 Growth 9711 19.491 33.545 38.847 14.083 204.208 Cash 9711 18.924 12.777 2.220 15.489 62.239 PPE 9711 38.920 15.274 9.040 38.000 75.398 ROA 9711 4.536 5.305 13.557 3.947 21.443 Expend 9711 5.801 4.798 0.201 4.463 23.311 Size 9711 22.011 1.163 19.953 21.844 25.401 Leverage 9711 42.043 19.444 5.535 41.757 86.821 Risk 9711 0.021 0.022 0.001 0.013 0.128 RD 9711 3.181 3.293 0.000 3.030 17.160 Normal_Innovation 9711 2.924 1.467 0.000 2.944 6.702 Top 9711 34.516 14.210 8.662 32.391 71.747 Manager_Hold 9711 11.205 19.871 0.000 0.130 85.018 Agency_Cost 9711 17.006 11.096 2.045 14.259 58.845 Equity 9711 3.272 8.555 0.000 0.000 47.550 Bond 9711 0.627 2.478 0.000 0.000 15.585 Panel B. Summary statistics of the sample after the policy (Policy =1) Loan 6516 17.367 7.207 0.000 19.978 24.222 Disclosure 6516 1.680 1.139 0.000 1.609 3.584 Green_Innovation 6516 0.887 1.074 0.000 0.693 3.912 Growth 6516 17.676 32.564 38.847 12.477 204.208 Cash 6516 18.374 12.332 2.220 15.044 62.239 PPE 6516 37.079 14.784 9.040 35.935 75.398 ROA 6516 4.402 5.225 13.557 3.715 21.443 Expend 6516 5.261 4.419 0.201 4.032 23.311 Size 6516 22.151 1.147 19.953 21.985 25.401 Leverage 6516 40.498 19.267 5.535 39.803 86.821 Risk 6516 0.018 0.020 0.001 0.012 0.128 RD 6516 4.198 3.169 0.000 3.620 17.160 Normal_Innovation 6516 3.285 1.290 0.000 3.258 6.702 Top 6516 33.793 14.043 8.662 31.701 71.747 Manager_Hold 6516 14.360 21.638 0.000 1.506 85.018 Agency_Cost 6516 18.054 11.354 2.045 15.280 58.845 Equity 6516 3.296 8.297 0.000 0.007 47.550 Bond 6516 0.701 2.598 0.000 0.000 15.585 Panel C. Mean values of main variables in each year Variable/year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Loan 18.824 18.536 18.489 17.438 16.784 16.539 16.777 17.215 17.862 17.804 18.002 Disclosure 0.743 0.762 0.880 1.092 1.370 1.278 1.444 1.471 1.585 1.895 2.408 Green_Innovation 0.210 0.255 0.321 0.467 0.504 0.655 0.772 0.826 0.933 1.022 1.115 Note: * p <0.1, ** p <0.05, *** p <0.01 (two-tailed). C. Xing et al.
opposing effects of hard and soft disclosure. The underlying problem is green-washing related to soft disclosure. To detect the mechanism, we first try to separate the disclosure into hard and soft parts and test their impacts on bank loans. Secondly, we connect them with green-washing and explain the findings discovered above. As illustrated, Clarkson et al. (2008) systematically define hard and soft environmental disclosure, with the former supported by concrete data, but the latter only by narrative. They also show the detailed forms to measure hard and soft disclosure as shown in Appendix A. We employ such a method. For variable Disclosure_Hard, it equals the natural loga-rithm of 1 plus the sum of values of H1 to H4 in Appendix A. Corre-spondingly, Disclosure_Soft is that of S1 to S3. The regression results are shown in Table 6. We find that hard disclosure is marginally correlated to more bank loans as its coefficient is positive at the 10% level, but the Table 2 Effects of environmental disclosure and green innovation on bank loans. (1) (2) (3) (4) Loan Loan Loan Loan Disclosure×Policy 0.049 0.047 (0.42) (0.41) Green_Innovation×Policy 0.377*** 0.378*** (2.76) (2.77) Disclosure 0.009 0.008 (0.13) (0.11) Green_Innovation 0.103 0.102 (1.29) (1.28) Growth 0.000 0.000 0.000 0.000 (0.03) (0.04) (0.28) (0.29) Cash 0.083*** 0.083*** 0.084*** 0.084*** (10.51) (10.53) (10.67) (10.69) PPE 0.002 0.002 0.002 0.002 (0.31) (0.34) (0.34) (0.36) ROA 0.028* 0.028* 0.028* 0.028* (1.73) (1.72) (1.71) (1.71) Expend 0.056*** 0.056*** 0.055*** 0.055*** (4.51) (4.51) (4.44) (4.44) Size 1.638*** 1.638*** 1.606*** 1.606*** (10.90) (10.89) (10.62) (10.62) Leverage 0.084*** 0.084*** 0.082*** 0.083*** (13.73) (13.73) (13.54) (13.54) Risk 6.521** 6.556** 6.735** 6.767** (2.26) (2.27) (2.33) (2.34) RD 0.031 0.030 0.039 0.038 (0.93) (0.91) (1.18) (1.16) Normal_Innovation 0.102* 0.101* 0.069 0.069 (1.66) (1.66) (1.07) (1.07) Top 0.005 0.005 0.004 0.004 (0.51) (0.51) (0.47) (0.47) Manager_Hold 0.006 0.006 0.006 0.006 (1.30) (1.30) (1.31) (1.31) Agency_Cost 0.017 0.017 0.014 0.014 (1.26) (1.24) (1.04) (1.03) Equity 0.068*** 0.068*** 0.068*** 0.068*** (10.21) (10.19) (10.24) (10.23) Bond 0.001 0.001 0.003 0.003 (0.09) (0.08) (0.21) (0.19) Year Effect Fixed Fixed Fixed Fixed Firm Effect Fixed Fixed Fixed Fixed Constant 19.237*** 19.257*** 18.527*** 18.551*** (5.91) (5.91) (5.65) (5.64) N 9711 9711 9711 9711 Adj. R2 0.637 0.636 0.637 0.637 F 38.33*** 35.67*** 36.34*** 34.00*** Note: t statistics in parentheses (corrected for heteroskedasticity and firm-level clustering), *** p <0.01, ** p <0.05, * p <0.1 (two-tailed). Table 3 Results of time trend analysis. Variables/k (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Year_-4 Year_-3 Year_-2 Year_-1 Year_0 Year_1 Year_2 Year_3 Year_4 Year_5 Disclosure 0.059 0.369 0.052 0.279 0.177 0.172 0.015 0.071 0.009 0.102 (0.25) (1.38) (0.23) (1.24) (1.38) (1.28) (0.11) (0.58) (0.07) (0.64) Green_Innovation 0.007 0.143 0.074 0.263 0.302* 0.357** 0.431*** 0.246* 0.298** 0.434*** (0.01) (0.28) (0.23) (1.06) (1.79) (2.42) (2.90) (1.82) (2.20) (2.90) Note: This table illustrates the results of trend analysis, where two rows indicate the regressions of Eqs. (3) and (4), and the columns show the coefficients of βk. Dependent variable for both rows is Loan. The coefficients of control variables, constants, and fixed effects are hidden for tidiness. The adjusted R-square values are 0.636 and 0.637, for Disclosure and Green_Innovation, respectively. t statistics in parentheses (corrected for heteroskedasticity and firm-level clustering), *** p <0.01, ** p <0.05, * p <0.1 (two-tailed). C. Xing et al.
impact of soft disclosure is significantly negative at the 1% level. This phenomenon exists in both the DID model and post-policy analysis (columns 1 and 3), and in the cases where the green innovation variable is added (columns 2 and 4). We also test the interaction between hard disclosure, soft disclosure, and green innovation in column 5. However, although the coefficients are negative, none are significant. Comparing the results in Table 5, it shows the contagion effect only marginally effective for total disclosure. A possible explanation is the sample, as our data contain few firms with both a high soft disclosure level and many green patents, and the dispersion may be not enough to detect the contagion effect. Nevertheless, our finding is still useful to test our conjecture that soft disclosure and hard disclosure are distinct in the green credit policy. In Table 6, we test the direct effect of green-washing. We use three indicators to measure green-washing. Firstly, we calculate the difference between the relative values of soft and hard disclosure as the degree of green-washing since green-washing is the excess of symbolic behaviours over substantive behaviours (Walker & Wan, 2012). The formula for GreenWashing is (Disclosure_Soft/16) minus (Disclosure_Hard/79) because the total scores for soft disclosure and hard disclosure are 16 and 79, respectively.9 Secondly, according to Du et al. (2018), we set a dummy variable named as GreenWashing_Dummy. If a firm’s environmental spending (section H4 of Appendix A) is less than the mean for the whole sample, but its soft disclosure quality is greater than the mean, the company is a green-washing firm and the value of GreenWashing_Dummy equals 1. This variable excludes non-financial disclosure items and focuses on the financial cost of environment protection in hard disclosure, which may be the most important concern of banks and can be more effective for measuring green-washing. Finally, we utilize post-policy analysis and a new green-washing variable decoupled from hard and soft disclosure. In line with Walker and Wan (2012), we invited an expert in corporate sustainable devel-opment to calculate the extent of green-washing (GreenWashing_Sub). The process comprised three steps. In step one, we provided the expert with 10 CSR reports and official websites of sample firms, from which he gave scores for substantive and symbolic action according to his expe-rience. The ranges of both types of action are 1 to 7, where 1 indicates no substantive or symbolic action, and 7 suggests the highest level of ac-tion. After this, the expert had a stronger perception of corporate envi-ronmental disclosure. In step two, the expert revalued the 10 CSR reports and files and continued to assess the rest of the sample. In the final step, we constructed our measure by subtracting the value of substantive action from symbolic action. The new smaller sample is of listed manufacturing firms belonging to Index China 500 (IC firms) from 2012 to 2017. There are two reasons for selecting these. On one hand, IC firms are usually in a sound financial position with stable CSR and environmental practices that exclude the noise of financial crisis or speculative activity. On the other hand, a smaller sample can ease the process of expert evaluation and avoid subjective bias resulting from a long-term assessment process. We ended up with 792 observations for 132 firms. Because there may be other characteristics of IC firms affecting corporate loan size, we use the Heckman selection model to alleviate this. In the first stage, the dependent variable is IC, which equals 1 if the firm is an IC firm. The criterion for being an IC firm is usually its financial condition and market position, hence we select six corporate characters as independent variables to test the probability of an enterprise being listed as IC firms, state-owned enterprise (SOE), corporate age (Age), corporate growth ability (Growth), financial Fig. 1.Time trend analysis. 9 See Appendix A. C. Xing et al.
performance (ROA), corporate size (Size), and asset-liability ratio (Leverage). All independent variables are lagged one year. We obtained the inverse Mills ratio (imr) from the first regression and added it into the second stage as a variable. The regression result is shown in Table 7. As the table shows, the coefficients of GreenWashing×Policy in column (1), Green-Washing_Dummy×Policy in column (2), and GreenWashing in column (3) are significantly negative at the 1% level, and that of Green-Washing_Dummy in column (4) is significant at 5% level, supporting our conjecture that a higher level of green-washing will impair the ability to obtaining bank loans. Similarly, column (5) shows that the substitute for green-washing (GreenWashing_Sub) negatively impacts corporate access to bank loans (Loan) at the 10% level. Therefore, we deem that the green-washing behind soft disclosure hinders enterprises from obtaining more loans. 5.Concluding remarks 5.1.Conclusions In this research, we use panel data for China’s manufacturing en-terprises listed in the Shanghai and Shenzhen Stock Exchanges from 2007 to 2017 and discuss the relationship between the green credit policy and access to bank loans from the perspectives of corporate external environmental disclosure and internal green innovation. We find firms with better green innovation performance can obtain more loans since green innovation is the embodiment of eco-friendliness and can enhance both the financial and environmental performance of en-terprises that are valued by banks and green credit policy. However, environmental disclosure quality is of no advantage in corporate loan financing. We connect the finding with environmental disclosure types and green-washing. It shows that soft disclosure hinders loan financing but hard disclosure quality marginally promotes corporate loan size. We find the underlying reason to be corporate green-washing, prevalent in soft disclosures but resisted by creditors. Banks can collect relevant in-formation because of their powerful monitoring and investigation capability, and thus grant fewer loans to green-washing applicants. Therefore, we conclude that the policy and banks value the firms with better internal performance rather than those with external disclosure. 5.2.Theoretical contributions Our findings contribute significantly to the previous research. Firstly, we systemically verify the effectiveness of China’s green credit policy in recent years, broadening the research such as Liu et al. (2019), Xing et al. (2020), and Zhang et al. (2021). In the green credit context, firms can improve loan conditions through green activities, but bad green performance will damage corporate loan access. Although previous literature discusses the impact of the policy on corporate financing, we explore this mechanism from the internal and external perspectives, and find the obstacles in the process. We develop an assessment of the policy and enlarge the scope of green finance and sustainable growth (Akao & Managi, 2007). Therefore, we also improve the significance of the analytical framework developed by Passetti et al. (2018). Secondly, we contribute to the literature on corporate environmental behaviours such as green innovation and environmental disclosure. Similarly to Hummel and Schlick (2016), we find the good and bad ef-fects of environmental disclosure are the “two sides of one coin”, which confirms both the voluntary disclosure theory and social-political the-ory. We also support some scholars such as Lyon and Montgomery (2013), Walker and Wan (2012), and Testa et al. (2018) who focus on the problem in disclosure. For green innovation, our research is also in line with ecological modernization theory and Huang and Li (2017), and shows that green innovation is beneficial for both corporate develop-ment and environment protection. In corporate finance, our findings support information asymmetry theory. Banks can collect information on corporate environmental disclosure and green innovation efficiently, and make positive (negative) decisions according to the good (bad) information. Finally, we reveal the problem of green-washing that hinders the policy. China’s green credit policy is typical green finance practice impacting economics and the environment (Ronaghi, Reed, & Saghaian, 2020), while the success of the green credit policy in China is vital for the achievement of UN Sustainable Development Goals (SDGs). Our work, however, finds the green-washing is an obstacle in this process. We provide our insights regarding the mechanism of such symbolic behaviours. It makes an important contribution to the literature on the problem of disclosure whose original intention should be to provide more useful information for investors (Guenther, Guenther, Schiemann, & Weber, 2016). Based on the findings, we contribute to the research on global green finance and economic development (Hassan, Oueslati, & Rousseli`ere, 2020; Nordhaus, 2019), and answers several questions in Table 4 Results of PSM-DID. (1) (4) Loan Loan Disclosure×Policy 0.022 (0.14) Green_Innovation×Policy 0.736*** (2.76) Disclosure 0.014 (0.16) Green_Innovation 0.175 (1.22) Growth 0.001 0.002 (0.31) (0.74) Cash 0.079*** 0.062*** (7.10) (4.47) PPE 0.006 0.002 (0.65) (0.22) ROA 0.024 0.016 (0.99) (0.59) Expend 0.044*** 0.053** (2.75) (2.56) Size 2.072*** 1.546*** (9.41) (4.75) Leverage 0.076*** 0.086*** (8.84) (7.75) Risk 8.410** 4.970 (2.16) (1.06) RD 0.008 0.014 (0.18) (0.24) Normal_Innovation 0.035 0.163 (0.41) (1.34) Top 0.003 0.009 (0.24) (0.46) Manager_Hold 0.002 0.008 (0.40) (1.02) Agency_Cost 0.009 0.005 (0.50) (0.21) Equity 0.063*** 0.060*** (6.96) (6.07) Bond 0.003 0.024 (0.13) (0.92) Year Effect Fixed Fixed Firm Effect Fixed Fixed Constant 28.608*** 18.392** (6.02) (2.57) N 6048 4220 Adj. R2 0.640 0.639 F 18.24*** 10.54*** Note: t statistics in parentheses (corrected for heteroskedasticity and firm-level clustering), *** p <0.01, ** p <0.05, * p <0.1 (two-tailed). C. Xing et al.
different theoretical areas of environmental policy such as policy se-lection (Tolliver, Keeley, & Managi, 2020b), effectiveness (Liu et al., 2019), and market reaction (Monasterolo & de Angelis, 2020). 5.3.Practical implications and policy recommendations This paper is also useful for practical implications and policy improvement. First, the main market actors of green credit policy, i.e., enterprises and banks, have to pay more attention to eco-friendliness for sustainable financing and development. As our results show banks value corporate substantial green behaviours such as green innovation, en-terprises should adopt an effective method to improve such perfor-mance. Nowadays, with the help of open innovation, property protection, and government subsidies, the efficiency of green innovation is higher while the cost is limited. Hence, firms have better chance to enlarge and maintain sustainable competence and obtain loans by conforming to the green credit policy. In this process, banks should also further cooperate with government departments or institutions such as SIPO, who can provide them with more credible information and reduce their search costs, thereby strengthening the implementation of green credit policies. However, banks have to continually investigate symbolic environmental behaviours, and firms should limit the pompous description of corporate green behaviours and avoid green-washing since it will hamper their access to bank loans. Second, for policymakers and relevant government departments of green credit, we develop several recommendations.10 On the one hand, we find the market mechanism under the green credit policy considers green innovation as a useful indicator measuring corporate eco- friendliness, but this characteristic is not embodied in the policy yet. Therefore, the government departments relevant for green credit should improve the policy, to establish a system of identifying green innovation and sharing such information with financial institutions and other stakeholders. This will be more convenient for banks and reduce complexity in information collection in the policy implementation. On the other hand, we also find environmental disclosure is ineffective because soft disclosure terms and green-washing are unacceptable to the banks. Even though hard disclosure is marginally useful to the green credit policy, such disclosure is still non-standardized in China. Hence, the government departments should optimize disclosure procedures to avoid the problem of green-washing while enhancing the validity of hard disclosure. Specifically, the regulatory disclosure regimes such as Table 5 Results of post-policy analysis. (1) (2) (3) (4) Loan Loan Loan Loan Disclosure 0.023 0.024 0.070 (0.25) (0.26) (0.58) Green_Innovation 0.393*** 0.393*** 0.562*** (3.20) (3.20) (3.30) Disclosure×Green_Innovation 0.106* (1.80) Growth 0.001 0.002 0.002 0.001 (0.54) (0.60) (0.59) (0.57) Cash 0.092*** 0.093*** 0.093*** 0.092*** (7.73) (7.76) (7.76) (7.72) PPE 0.010 0.011 0.011 0.011 (1.01) (1.04) (1.04) (1.05) ROA 0.002 0.003 0.003 0.003 (0.07) (0.14) (0.14) (0.13) Expend 0.026 0.023 0.023 0.025 (1.17) (1.08) (1.08) (1.15) Size 1.019*** 0.972*** 0.973*** 0.955*** (3.55) (3.38) (3.38) (3.31) Leverage 0.060*** 0.060*** 0.060*** 0.060*** (6.39) (6.32) (6.31) (6.32) Risk 0.751 1.264 1.238 1.539 (0.15) (0.25) (0.25) (0.31) RD 0.032 0.035 0.035 0.035 (0.62) (0.67) (0.68) (0.67) Normal_Innovation 0.179 0.060 0.060 0.063 (1.61) (0.49) (0.50) (0.52) Top 0.009 0.011 0.011 0.011 (0.60) (0.71) (0.71) (0.71) Manager_Hold 0.001 0.001 0.001 0.001 (0.15) (0.14) (0.14) (0.16) Agency_Cost 0.035 0.035 0.036 0.037 (1.49) (1.50) (1.51) (1.54) Equity 0.034*** 0.034*** 0.034*** 0.033*** (3.55) (3.55) (3.55) (3.53) Bond 0.012 0.011 0.011 0.011 (0.66) (0.56) (0.57) (0.57) Year Effect Fixed Fixed Fixed Fixed Firm Effect Fixed Fixed Fixed Fixed Constant 5.693 4.634 4.608 4.327 (0.87) (0.71) (0.70) (0.66) N 5430 5430 5430 5430 Adj. R2 0.663 0.664 0.664 0.664 F 11.74*** 12.39*** 11.82*** 11.30*** Note: t statistics in parentheses (corrected for heteroskedasticity and firm-level clustering), *** p <0.01, ** p <0.05, * p <0.1 (two-tailed). 10 We greatly thank two anonymous reviewers’ comments and the insightful suggestion of regulatory disclosure regimes. C. Xing et al.
Task Force on Climate-Related Financial Disclosures (TCFD) have to be adopted comprehensively. To the best of our knowledge, only a few commercial banks in China apply TCFD as at the end of 2020. As China’s government plans to design a mandatory environmental disclosure policy in 2021, it is meaningful to refer to TCFD and improve the effi-ciency of the green credit policy simultaneously. By stipulating the format and content of mandatory corporate environmental disclosure, including explicit soft and hard terms, the green credit policy can enhance currency mobility and achieve the aim of sustainable devel-opment via a finance approach. 5.4.Limitations and future research However, there are also several limitations in our study that also represent potential directions for future research. First, the listed firms in our sample are usually large enterprises. It is more difficult for small and medium-sized enterprises than listed companies to achieve green innovation, efficient information disclosure, and access to bank loans. Whether the role of green credit policy is different for these firms is very meaningful. Due to data availability, we do not include such enterprises in the sample. Similarly, because green credit policy may demonstrate heterogeneity in different areas, it is worth exploring its different Table 6 Relationship of hard disclosure, soft disclosure, and bank loans. DID model Post-policy analysis (1) (2) (3) (4) (5) Loan Loan Loan Loan Loan Disclosure_Hard×Policy 0.200* 0.183* (1.80) (1.65) Disclosure_Soft×Policy 0.805*** 0.748*** (2.81) (2.61) Disclosure_Hard 0.043 0.043 0.158* 0.158* 0.173* (0.66) (0.65) (1.74) (1.74) (1.83) Disclosure_Soft 0.076 0.066 0.392*** 0.397*** 0.388*** (0.56) (0.48) (3.12) (3.16) (2.93) Green_Innovation×Policy 0.346** (2.54) Green_Innovation 0.104 0.410*** (1.30) (3.30) Disclosure_Hard×Green_Innovation 0.085 (1.51) Disclosure_Soft×Green_Innovation 0.055 (0.58) Control Variables Yes Yes Yes Yes Yes Year Effect Fixed Fixed Fixed Fixed Fixed Firm Effect Fixed Fixed Fixed Fixed Fixed Constant 18.847*** 18.188*** 5.806 4.710 4.137 (5.81) (5.56) (0.89) (0.72) (0.63) N 9711 9711 5430 5430 5430 Adj. R2 0.637 0.637 0.664 0.664 0.665 F 33.65*** 32.16*** 11.97*** 12.03*** 11.12*** Note: t statistics in parentheses (corrected for heteroskedasticity and firm-level clustering), *** p <0.01, ** p <0.05, * p <0.1 (two-tailed). Table 7 Effect of green-washing on bank loans. DID model Post-policy analysis (1) (2) (3) (4) (5) Loan Loan Loan Loan Loan GreenWashing×Policy 5.938*** (2.59) GreenWashing_Dummy×Policy 0.558*** (2.72) GreenWashing 1.017 2.009*** (1.11) (3.08) GreenWashing_Dummy 0.056 0.408** (0.44) (2.44) GreenWashing_Sub 0.357* (1.86) imr 22.302 (1.21) Control Variables Yes Yes Yes Yes Yes Year Effect Fixed Fixed Fixed Fixed Fixed Firm Effect Fixed Fixed Fixed Fixed Fixed Constant 19.101*** 19.060*** 6.026 5.545 158.689 (5.89) (5.88) (0.92) (0.85) (1.12) N 9711 9711 5430 5430 660 Adj. R2 0.637 0.637 0.663 0.663 0.692 F 36.05*** 35.71*** 12.35*** 11.98*** 3.70*** Note: t statistics in parentheses (corrected for heteroskedasticity and firm-level clustering), *** p <0.01, ** p <0.05, * p <0.1 (two-tailed). C. Xing et al.
economic consequences in different provinces. Secondly, although we compare the difference between internal green innovation and external environmental disclosure, we make only a preliminary analysis of their interaction and the connection with green-washing, which may not reflect its full impact. As we showed, the interactions between green innovation and disclosure show only a slight effect that may be because of the sample and model settings. Continuing to discuss the interactive influences of green innovation, environmental disclosure, and green- washing in green credit policy will further extend the findings of this research. Funding This work was supported by The Major Program of the National Social Science Foundation of China [grant numbers 15ZDB157]. Declaration of Competing Interest None. Acknowledgements The authors would like to thank the editors and the anonymous re-viewers for their valuable comments and suggestions, which have greatly improved this paper. The Major Program of the National Social Science Foundation of China (No. 15ZDB157) is gratefully acknowl-edged for their provision of support for this study. Appendix A.Contents of environmental disclosure quality Item Map to GRI Mean S.E. Hard disclosure items (max score is 79) 5.319 8.396 (H1) Governance structure and management systems (max score is 6) 0.529 0.811 1. Existence of a Department for pollution control and/or management positions for environment management (0–1) 3.1 0.107 0.310 2. Existence of an environmental and/or a public issues committee in the board (0–1) 3.1 0.007 0.081 3. Existence of terms and conditions applicable to suppliers and/or customers regarding environment practices (0–1) 3.16 0.116 0.320 4. Stakeholder involvement in setting corporate environmental policies (0–1) 1.1, 3.10 0.015 0.123 5. Implementation of ISO14001 at the plant and/or firm level (0–1) 3.14, 3.20 0.186 0.389 6. Executive compensation is linked to environmental performance (0–1) 3.5 0.098 0.297 (H2) Credibility (max score is 10) 0.519 1.369 1. Adoption of GRI sustainability reporting guidelines or provision of a CERES report (0–1) 3.14 0.161 0.367 2. Independent verification/assurance about environmental information disclosed in the EP report/web (0–1) 2.20, 2.21 0.035 0.183 3. Periodic independent verifications/audits on environmental performance and/or systems (0–1) 3.19 0.054 0.225 4. Certification of environmental programs by independent agencies (0–1) 3.20 0.040 0.196 5. Product Certification with respect to environmental impact (0–1) 3.16 0.030 0.170 6. External environmental performance awards and/or inclusion in a sustainability index (0–1) 0.053 0.224 7. Stakeholder involvement in the environmental disclosure process (0–1) 1.1, 3.10 0.049 0.215 8. Participation in voluntary environmental initiatives endorsed by the Ministry of Environmental Protection of China (0–1) 3.15 0.060 0.237 9. Participation in industry specific associations/initiatives to improve environmental practices (0–1) 3.15 0.023 0.151 10. Participation in other environmental organizations/assoc. to improve environmental practices (0–1) 3.15 0.016 0.124 (H3) Environmental performance indicators (EPI) (max score is 60) 4.201 7.434 1. EPI on energy use and/or energy efficiency (0–6) EN 3, 4, 17 0.703 1.444 2. EPI on water use and/or water use efficiency (0–6) EN 5, 17 0.904 1.829 3. EPI on greenhouse gas emissions (0–6) EN 8 0.888 2.025 4. EPI on other air emissions (0–6) EN 9,10 0.341 1.277 5. EPI on TRI (land, water, air) (0–6) EN 11 0.080 0.515 6. EPI on other discharges, releases, and/or spills (not TRI) (0–6) EN 12, 13 0.345 1.250 7. EPI on waste generation and/or management (recycling, re-use, reducing, treatment and disposal) (0–6) EN 11 0.659 1.744 8. EPI on land and resources use, biodiversity, and conservation (0–6) EN 6, 7 0.045 0.315 9. EPI on environmental impacts of products and services (0–6) EN 14 0.078 0.432 10. EPI on compliance performance (e.g., reportable incidents) (0–6) EN 16 0.160 0.796 (H4) Environmental spending (max score is 3) 0.070 0.367 1. Summary of cash savings arising from environment initiatives to the company (0–1) 0.022 0.146 2. Amount spent on technologies, R&D, and/or innovations to enhance environmental performance and/or efficiency (0–1) EN 35 0.036 0.186 3. Amount spent on fines related to environmental issues (0–1) EN 16 0.012 0.110 Soft disclosure items (max score is 16) 1.481 1.451 (S1) Vision and strategy claims (max score is 6) 0.915 1.067 1. CEO statement on environmental performance in letter to shareholders and/or stakeholders (0–1) 1.1, 1.2 0.191 0.393 2. A statement of corporate environmental policy, values and principles, and environment codes of conduct (0–1) 1.1, 1.2, 3.7 0.295 0.456 3. A statement about formal management systems regarding environmental risk and performance (0–1) 3.19 0.067 0.249 4. A statement that the firm undertakes periodic reviews and evaluations of its environmental performance (0–1) 3.19 0.079 0.270 5. A statement of measurable goals in terms of future environmental performance (if not awarded under A3) (0–1) 1.1, 1.2 0.060 0.237 6. A statement about specific environmental technologies (0–1) 1.1, 1.2 0.222 0.416 (S2) Environmental profile (max score is 4) 0.247 0.492 1. A statement about the firm’s compliance (or lack thereof) with specific environmental standards (0–1) GN 8 0.079 0.270 2. An overview of the environmental impact of the industry (0–1) GN 8 0.034 0.181 3. An overview of how the business operations and/or products and services impact the environment (0–1) GN 8 0.116 0.320 4. An overview of corporate environmental performance relative to industry peers (0–1) GN 8 0.019 0.135 (S3) Environmental initiatives (max score is 6) 0.318 0.538 1. A substantive description of employee training in environmental management and operations (0–1) 3.19 0.124 0.330 2. Existence of response plans in case of environmental accidents 0.022 0.147 3. Internal environmental awards (0–1) 0.013 0.115 4. Internal environmental audits (0–1) 3.19, 3.20 0.010 0.101 5. Internal certification of environmental programs (0–1) 3.19 0.022 0.145 6. Community involvement and/or donations related to environ (0–1) SO 1, EC 10 0.127 0.332 C. Xing et al.
Note: Observation is 9711. For the terms of part H3, a point is awarded for each of six aspects, i.e., 1) presented data, 2) relative to previous data, 3) relative to future targets, 4) relative to peers/industry data, 5) absolute and normalized form, 6) presented at the disaggregated level. Appendix B.Variable specification Types Variables Abbreviation Specification Reference Expected Direction Dependent variable Loan size Loan The natural logarithm of the new bank loan amount acquired after adding 1. Nandy and Lodh (2012) – Explanatory variables Environmental disclosure quality Disclosure The natural logarithm of the quality of disclosed environmental information according to Appendix A after adding 1. Du et al. (2018) Uncertain Green innovation performance Green_Innovation The natural logarithm of acquired green patents number after added 1. Huang and Li (2017) Positive Control variables Corporate growth ability Growth The percentage increase in corporate operating revenue. Bailey et al. (2011) Uncertain Cash hold Cash The cash and cash equivalents as a percentage of total assets. Meuleman and De Maeseneire (2012) Uncertain Asset tangibility PPE The net value of fixed assets and inventory as a percentage of total assets. Chen and Matousek (2020) Positive Financial performance ROA The net profit divided by average total assets. Bailey et al. (2011) Uncertain Investment expenditure Expend The cash paid for the acquisition of long-term assets divided by total assets. Du et al. (2018) Positive Enterprise-scale Size The natural logarithm of total assets. Love (2003) Uncertain Financial leverage Leverage The total liabilities divided by total assets. Meuleman and De Maeseneire (2012) Positive Risk-taking Risk The variability of the return on total assets in previous three years John, Litov, and Yeung (2008) Negative R&D expenditure RD The ratio of R&D expenditure to total sales. Fabrizi et al. (2018) Uncertain Normal innovation Normal_Innovation The natural logarithm of normal patents plus 1. Fabrizi et al. (2018) Uncertain Ownership concentration Top The percentage of shareholding held by the largest shareholder. Lin et al. (2011) Positive Management shareholding Manager_Hold The percentage of shareholding held by all board members and the executive. Amore and Bennedsen (2016) Uncertain Agency problem cost Agency_Cost The sales and management expenses as a percentage of sales revenue. Ang et al. (2000) Negative Equity financing Equity The amounts of equity capital acquired in that year. Bolton and Freixas (2000) Positive Bond financing Bond The amounts of bond capital acquired in that year. Bolton and Freixas (2000) Negative Other variables Environmental disclosure dummy Disclosure_Dummy The value equals 1 if the corporate environmental disclosure quality is higher than the median, 0 otherwise. Du et al. (2018) Uncertain Green innovation dummy Green_Innovation_Dummy The value equals 1 if the firm’ green innovation performance is higher than the median, 0 otherwise. Huang and Li (2017) Positive Hard disclosure quality Disclosure_Hard The quality of hard environmental information according to Appendix A. Clarkson et al. (2008) Positive Soft disclosure quality Disclosure_Soft The quality of soft environmental information according to Appendix A. Clarkson et al. (2008) Negative Green-washing indicator type 1 Green_Washing The value equals [(Disclosure_Soft/16)-(Disclosure_Hard/ 79)]. Clarkson et al. (2008) Negative Green-washing indicator type 2 Green_Washing_Dummy Green-washing indicator calculated according to Du et al. (2018). Du et al. (2018) Negative Green-washing indicator type 3 Green_Washing_Sub Green-washing indicator calculated according to Walker and Wan (2012). 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