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The Higher Carbon Intensity of Loans, the Higher Non-Performing Loan Ratio: The Case of China

Rong Guan (), Haitao Zheng (), Jie Hu (), Qi Fang () and Ruoen Ren ()
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Rong Guan: School of Statistics and Mathematics, Central University of Finance and Economics, Beijing 100081, China
Jie Hu: School of Economics and Management, Beihang University, Beijing 100191, China
Qi Fang: School of Economics and Management, Beihang University, Beijing 100191, China
Ruoen Ren: School of Economics and Management, Beihang University, Beijing 100191, China

Sustainability, 2017, vol. 9, issue 4, 1-17

Abstract: In response to the call of the Chinese government to support low-carbon development, the issue has come to the view gradually as to whether the behaviors of banks’ green credit will contribute to easing their own credit risk. To reflect the behaviors of green credit of banks in detail, an indicator, named the carbon intensity of loans (CIL), is first proposed in this paper to measure the carbon emissions with association of the loans for commercial banks, on basis of the series of the input–output table. Then, a panel data model is used to explore the relationship between CIL and non-performing loan ratio, which measures the credit risk of banks. Based on the data of China’s commercial banks from 2007 to 2014, an empirical study has been conducted to investigate the impacts of CIL upon the non-performing loan ratio from a microscopic-level perspective. The result indicates that CIL has a positive effect on the non-performing loan ratio of banks. Since CIL is considered a significant indicator for the banks’ green credit, this paper comes to a conclusion that the green credit policy not only contributes to achieving of the emission-reduction targets for the society, but also promotes the development of banks’ credit risk.

Keywords: green credit policy; input–output table series; carbon intensity of loans; non-performing loan ratio (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2017
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