Abstract
In recent years, green bonds have become an important part of the green financial system. In this paper, we investigate theoretically and empirically how green bond financing impacts corporate long-term value orientation. To study this relationship, we manually collect green bond financing data and use Python to construct a measure reflecting corporate long-term value. Using a sample of Chinese A-share bond issuing companies from 2016 to 2021, we find that (1) green bond financing can significantly promote companies to pursue long-term value, in which financing costs, management’s strategic risk-taking, and external supervision are the underlying mechanisms. (2) There is a synergistic effect between green bond financing and environmental regulation, which can jointly improve the intensity of corporate long-term value orientation. (3) The relationship between green bond financing and corporate long-term value is more significant in enterprises with heavily polluting, lower risk-taking levels, less strategic change, and lower financial mismatch risk. Our findings reveal the “corrective” effect of green bond financing on management’s strategic decision-making, which provides new empirical evidence for comprehensively and accurately evaluating the role of green bonds and promoting the development of the green bond market.
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Data availability
The datasets generated during the current study are available. The bond data are from the WIND database and CSMAR database. The data on sewage charges and environmental protection taxes are sourced from the WIND database and the “China Statistical Yearbook”; regional GDP data comes from the “China Statistical Yearbook.” Corporate green patent applications originate from the CNRDS database, and other corporate governance and financial data are obtained from the CSMAR database. The datasets used are available from the corresponding author on reasonable request.
Notes
The data comes from the China Green Bond Market Report 2021, which was jointly compiled by the Climate Bond Initiative (CBI) and the China Bond Research and Development Center of the Central Government Bond Registration and Settlement Corporation.
The most fundamental impact that green bond financing brings to firms is the credit spread advantage, which reduces corporate financing costs (Hyun et al. 2020). In addition, financing costs is the most important driving factor in organizational investment decisions (Gianfrate and Peri 2019). Therefore, we choose financing cost to reflect the degree of corporate financing constraints.
China’s green bond market is currently in the development stage, and the number of A-share listed companies in non-financial industries is relatively limited. The explanatory variable is not suitable for tail reduction, so this paper excludes its abnormal values greater than 1.
Specifically, we use the machine learning method to identify 43 words that reflect the management's short-termism in annual reports (for example, 10 seed words such as “several months” and 33 expanded words such as “several days”). Then, we divide the frequency of 43 words by the total word frequency of annual reports. Considering the dimensional differences between variables, the short-term investment preference index (INV_S) of enterprises is obtained by multiplying the above calculation results by 1000.
Unless otherwise specified, all models in this paper control for the above three fixed effects and adjust for heteroskedasticity in the standard errors of the model regression coefficients.
Notethat since China implemented the Environmental Protection Tax Law on January 1, 2018, the sewage fee system has been changed to a comprehensive environmental protection tax. Thus, the GDP-adjusted environmental protection tax revenue adjusted for GDP is used for measurement in 2018 and subsequent years.
Due to space limitation, the regression results of control variables for the robustness test are not listed. If necessary, you can obtain them from the author.
Since the beginning of 2016, China's green bond market has entered a standardized stage. To retain the samples as much as possible, under this method the sample period has been extended to 2010–2021.
There is a lack of data on the measurement indicators of strategic risk-taking and analyst attention. Therefore, the sample size for testing these two mechanisms is less than that of the main regression. This paper will present the complete results.
The sample size is less than that of the main regression because of the missing data on the new variables introduced.
The codes for heavily polluting industries are B06, B07, B08, B09, B10, C15, C17, C18, C19, C22, C25, C26, C27, C28, C29, C30, C31, C32, C33, and D44.
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Hailin Wang contributed to conceptualization, writing—review and editing, supervision, and project administration. Linlin Duan contributed to formal analysis, data curation, writing-original draft, and writing-review and editing. Hao Zeng contributed to Python software and conceptualization.
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Wang, H., Duan, L. & Zeng, H. Green bond financing, environmental regulation, and long-term value orientation: evidence from Chinese-listed companies. Environ Sci Pollut Res 30, 123335–123350 (2023). https://doi.org/10.1007/s11356-023-30986-8
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DOI: https://doi.org/10.1007/s11356-023-30986-8