Abstract
Green credit plays a crucial role in reducing energy consumption and environmental degradation in China. Using data on China’s new energy listed companies from 2007 to 2018, this study explores the impact of green credit on new energy firms’ value, as well as the mediating effects of financing constraints and external supervision on the relationship between green credit and new energy companies’ economic benefits. Our results suggest that green credit significantly improved new energy firms’ value, and this positive impact can last over the long term. The above result is robust to using alternative measures, replacement of fixed effects, exclusion of abnormal samples, and placebo test. Additional tests reveal that green credit improves new energy companies’ value by alleviating financing constraints and strengthening external supervision. Finally, green credit’s value-enhancement effect is heterogeneous, depending on corporate property rights, business life cycle, implementation of Green Credit Guideline policy, and the firms’ geographical location. Our conclusions suggest that government should not only pay attention to the continuity of green credit commitment but also the mitigation of financing constraints and improvement of external supervision for new energy companies. Moreover, heterogeneous factors should be considered to formulate and calibrate related policy rather than a one-size-fits-all policy.
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Data availability
The datasets used during the current study are available from the corresponding author on reasonable request.
Notes
Taking into account the shortage of financial resources in the development of the green industry and urging microeconomic entities to pay more attention to environmental benefits, the Chinese government has formulated a series of green credit development policies to make banks incorporate environmental factors into the credit decision-making process. One of the most representative policies, named “Opinions on Implementing Environmental Protection Policies and Regulations and Preventing Credit Risks,” jointly promulgated by China’s Environmental Protection Administration (renamed Ministry of Ecology and Environment of the People’s Republic of China in 2018), the People’s Bank of China, and the China Banking and Insurance Regulatory Commission in 2007, was used as an important market tool for environmental protection, energy conservation, and emission reduction, marking the official launch of the green credit policy. Green Credit Guidelines policy, which is issued by the China Banking and Insurance Regulatory Commission in 2012, formulated the detailed standards and norms for financial institutions to implement green credit policy, signifying a leap in the strategic level of GC development.
Figure 1 includes the following contents: first, the “Overall effect” part shows the results of the baseline regression and dynamic effect of GC on NECs’ value, aiming to explore the influence of GC on NECs’ value and the duration of this effect. The second part is “Intermediary mechanisms,” which examines the influencing mechanism of GC on NECs’ value, including internal financing constraints and external monitoring. Thirdly, the section of “Heterogeneity factors” analyzes the heterogeneous effect of GC on firm value of NECs, including the internal factors (property rights and life cycle) and external factors (the implementation of Green Credit Guidelines policy and geographic attributes) to identify which types of NECs’ value are more likely to be enhanced by GC.
We choose to use the OLS regression method based on the following two considerations: first, referring to the previous studies on firm value, e.g., Desai and Dharmapala (2009), Tambe (2014), Servaes and Tamayo (2013), and Li et al. (2018a, 2018b), we find that OLS regression method is widely used to analyze the influencing factors of enterprise value, which provides important clues and reference basis for our choice of OLS method; second, dependent variables (FV), core explanatory variables (GC), and control variable (CVs) of this study are all continuous variables, which makes classical OLS estimation method in linear regression models may be more appropriate than the truncated tail regression with a right-skewed data structure (e.g., Tobit model) and logit regression with dummy variables (e.g., Probit model).
The distribution of GC funds to provinces is executed by financial institutions, but the number of financial institutions in different regions of China varies greatly. For example, among the 31 provinces and municipalities in China, Tibet had only 723 financial institution branches as of 2019, whereas Guangdong Province, the province with the largest number of financial institution branches, has 16,959. It is generally believed that GC resources obtained by NECs located in areas with more financial institution branches tend to have more abundant GC financing than firms in other areas.
The former China Banking Regulatory Commission (CBRC) has been renamed as China Banking and Insurance Regulatory Commission in 2018. In addition, the “Report on Corporate Social Responsibility of the Banking Industry in China” began disclosing GC data in 2013. Before 2013, only data on the national “loan balance of energy conservation and environmental protection project and service” can be obtained, which accounts for an important part of the GC balance (in recent years, the proportion of loan balance of energy conservation and environmental protection project and service in GC has been consistently maintained at about 70%), which can reasonably reflect the GC development situation.
ST stands for special treatment and refers to listed companies that have had two consecutive years of negative net profits.
China’s financial system is mainly dominated by indirect financing. Direct financing accounts for a relatively small share. In 2020, the share of direct financing such as stocks and bonds in the scale of social financing in China’s capital market was only 12.59%, implying that the indirect financing system dominated by commercial banks has become the primary way of corporate external financing. This not only determines that the GC business conducted by commercial banks dominates the overall green financial system (which is an important reason why this study uses GC as the main explanatory variable), but also reflects the close correlation between GC and green finance. Therefore, we use the green finance indicator (GC3) as a proxy for GC in robustness tests.
The basis for the heterogeneity clustering is as follows: as far as the attributes of enterprises are concerned, given the widespread presence of “credit discrimination” in banking institutions, SOEs are likely to have easier access to bank loans than NSOEs because SOEs possess lower default risk due to their government-related attribute. Additionally, because of the profit-driven nature of capital, banks and other financial institutions tend to allocate credit resources to companies in growth stage due to their better development prospects and companies in mature stage as a result of their high repayment capabilities. Meanwhile, those companies in the shakeout stage usually are deemed as having high financial risk and thereby have difficulty in obtaining bank loans. These conjectures are only on a theoretical level. Whether GC has different impacts on NECs’ value for NECs with different property rights and life cycles need to be verified through empirical evidence.
Regarding the external environment of enterprises, the CBRC issued the “Green Credit Guidelines” in 2012, which detailed the standards and principles for banks carrying out GC. The implementation of this policy encouraged the concentration of bank credit resources to environmentally friendly enterprises (Xing et al. 2020), but whether it can strengthen the value-enhancement effect of GC on NECs is still unknown. Moreover, there is a serious spatial misallocation of credit resources in the developed eastern regions and the less-developed areas, including central and western regions; thus, it is valuable to examine whether the differences in geographic attributes produce significant differences in the relationship between “GC and NECs’ value.”.
Regarding the division of companies’ life cycle, drawing on the practice employed by Dickinson (2011), cash flow pattern is used to capture the cycle, considering the characteristics of listed companies in China. “Introduction stage” and “growth stage” are deemed as the “growth stage”; the definition of the maturity stage is the same as that of Dickinson (2011); the “shakeout stage” and “decline stage” are merged as the “shakeout period.” The whole life cycle is finally displayed as three stages of growth, mature, and shakeout periods.
Drawing on the National Bureau of Statistics of China, Hebei, Beijing, Tianjin, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, and Hainan are classified as the eastern region. The remaining provinces of mainland China are categorized as the central and western regions.
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Acknowledgements
The authors are grateful to the Editor, as well as the anonymous referees for valuable suggestions and comments that helped us improve our paper significantly.
Funding
This study was supported by the Humanities and Social Science Foundation of the Ministry of Education of China (grant number 20YJA790085), National Natural Science Foundation of China (grant number 71503039), Social Science Fund of Jiangsu Province (grant number 17GLB008), the Major Project of Philosophy and Social Science Research Funds for Jiangsu Universities (grant number 2020SJZDA059).
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Xiaobing Lai: conceptualization, methodology, data curation, writing—review and editing. Shujing Yue: conceptualization, formal analysis, methodology, validation. Hongtao Chen: conceptualization, writing—original draft.
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Lai, X., Yue, S. & Chen, H. Can green credit increase firm value? Evidence from Chinese listed new energy companies. Environ Sci Pollut Res 29, 18702–18720 (2022). https://doi.org/10.1007/s11356-021-17038-9
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DOI: https://doi.org/10.1007/s11356-021-17038-9