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Impact of green credit policy on sustainability performance of high-pollution enterprises

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Abstract 

Green credit is a financial service that potentially mitigates the impact of environment problems such as climate change, one of the greatest challenges of our time. The effectiveness of a policy that promotes green credit, however, remains controversial. We use China’s “Green Credit Guidelines” in 2012 as a quasi-natural experiment to identify the causal impact of green credit policy (GCP) on enterprise sustainability performance (ESP), which integrates the financial and environmental social responsibility performance of heavily polluting firms. Our PSM-DID analysis shows that GCP significantly improves ESP, especially among the small and private highly polluting firms and firms located in regions with a higher level of marketization. Furthermore, GCP leads to more green innovation which, together with government subsidies, plays a positive moderating role between GCP and ESP. Our results have important policy implications.

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Data availability

The data and materials used in this paper are available from the corresponding author on reasonable request.

Notes

  1. Note: (1) Steps for calculating the intensity of environmental regulation (ER): Firstly, $${UE}_{ij}^{S}=\left[{UE}_{ij}-\mathrm{min}\left({UE}_{j}\right)\right]/[\mathrm{max}\left({UE}_{j}\right)-\mathrm{m}\mathrm{i}\mathrm{n}({UE}_{j})]$$ is used to standardize industrial wastewater emissions per unit of output value, industrial SO2 emissions per unit of output value, and industrial soot emissions per unit of output value in each province, where $${UE}_{ij}$$ is the unit output emissions of pollutant of category $$j$$ in region $$i$$ and $${UE}_{ij}^{S}$$ is the standardized result of the index. $$\mathrm{max}\left({UE}_{j}\right)$$ denotes the maximum value of unit output emissions of pollutant of category $$j$$ in all regions, and $$\mathrm{min}\left({UE}_{j}\right)$$ denotes the minimum value of unit output emissions of pollutant of category j in all regions. Secondly, $${W}_{j}={UE}_{j}/\stackrel{-}{{UE}_{ij}}$$ is used to calculate the weights of each pollutant category, where $$\stackrel{-}{{UE}_{ij}}$$ denotes the average level of emissions per unit of output of pollutant $$j$$ for all regions in each year. Finally, the environmental regulation composite index is calculated based on: $${ER}_{i}=1/3{\sum }_{j=1}^{3}{W}_{j}{UE}_{ij}^{S}$$. (2) In this paper, the value of previous patents is considered to have declined out of consideration of factors such as technology renewal, so depreciation of previous patents is used. We use $${k}_{i,t}=(1-\delta ){k}_{i,t-1}+{r}_{i,t}$$ to measure the stock of green invention patents in firms, where $${k}_{i,t}$$ denotes the number of green invention patent applications at the end of year $$t$$ and $${r}_{i,t}$$ is the number of new patents in year t. The value of $$\delta $$ takes little effect on the results, and we take $$\delta =15\%$$.

  2. In this paper, the pollution emission intensity of the industry where the firm is located is taken as an important basis to identify the pollution attributes of the firm (see Appendix for details).

  3. In this paper, similar results are obtained by using other different matching methods, indicating that the results are robust.

  4. To alleviate potential endogeneity concern, we also lag all continuous control variables by one period, and the results show that our results are robust.

  5. The government subsidy data come from Wind database. And the government subsidies are classified as high and low according to the median value.

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Acknowledgements

The authors thank Dr. Qingwei Wang of Cardiff University for his revision of this manuscript.

Funding

This paper was financially supported by “Key Projects of Social Science Planning in Anhui Province of China” (No. AHSKZ2019D026).

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Authors and Affiliations

  1. School of Economics, Anhui University, Hefei, 230601, China

    Yan Jiang & Yaping Xu

  2. Institute of Finance and Economics Research, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Shuyue Qin

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  1. Yan Jiang
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  2. Shuyue Qin
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  3. Yaping Xu
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Contributions

YJ designed this study, analyzed the data, conducted statistical analysis, and wrote the original draft. SQ collected the sample data and participated in reviewing. YX participated in reviewing and editing.

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Correspondence to Yan Jiang.

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Appendix

Appendix

Industry emission intensity of pollution indicators are measured as follows: (1) calculate the pollution emission value of industry \(j\) pollutant \(g\) per unit of output value: \({UE}_{jg}={E}_{jg}/{Q}_{j}\), where \({E}_{jg}\) is the industry pollutant \(g\) emissions and \({Q}_{j}\) is the total industrial output value of industry \(j\); (2) linear normalization of pollutant g emissions per unit of output: \({UE}_{jg}^{^{\prime}}\); (3) the average score is calculated by equal-weighted summation of emissions per unit of output for each pollutant \(g\): \({NUE}_{jg}=\sum {UE}_{jg}^{^{\prime}}/n\); (4) according to the average industry pollution intensity index, the manufacturing industry is classified as follows: the average pollution intensity of the calendar year is greater than or equal to 0.0320, which is a high-pollution industry. The average pollution intensity of the calendar year is less than 0.0320, which is a low-pollution industry. The specific classification of industries is shown in the table.

Classification

Industries

High-pollution industries

Agriculture and food processing industry; food manufacturing; beverage manufacturing; textile industry; wood processing and wood, bamboo, rattan, palm, grass product industry; paper and paper product industry; petroleum processing, coking and nuclear fuel processing industry; chemical raw material and chemical manufacturing industry; pharmaceutical manufacturing; non-metallic mineral product industry; ferrous metal smelting and rolling processing industry; non-ferrous metal smelting and rolling processing industry; electricity, heat production, and supply industry

Low-pollution industries

Tobacco product industry; textile, clothing, shoes, hat manufacturing; leather, fur, feathers (down) and its product industry; furniture manufacturing industry; printing industry; reproduction of recording media, teaching, and sporting goods manufacturing; rubber product industry; plastic product industry; metal product industry; general equipment manufacturing industry; special equipment manufacturing; transportation equipment manufacturing; electrical machinery and equipment manufacturing; communications equipment, computers, and other electronic equipment manufacturing; instrument manufacturing; other manufacturing

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Jiang, Y., Qin, S. & Xu, Y. Impact of green credit policy on sustainability performance of high-pollution enterprises. Environ Sci Pollut Res 29, 79199–79213 (2022). https://doi.org/10.1007/s11356-022-21315-6

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