The Impact of the Green Credit Policy on the Short-Term and Long-Term Debt Financing of Heavily Polluting Enterprises: Based on PSM-DID Method.

Yan Yang, Yingli Zhang
Author Information
  1. Yan Yang: School of Economics and Management, Shanghai Ocean University, Shanghai 201306, China. ORCID
  2. Yingli Zhang: School of Economics and Management, Shanghai Ocean University, Shanghai 201306, China.

Abstract

"Green economy and sustainable development" has become the focus of contemporary world economic development. As an important part of green financial instruments, green credit has become a hot topic. This paper investigates whether the Green Credit Policy has had any impact. Does it have a binding effect on the debt financing of heavily polluting enterprises? Using the as the starting point for the implementation of the Green Credit Policy, this paper takes Chinese A-share listed enterprises from 2004 to 2020 as the research sample, and applies the propensity score matching combined with difference-in-difference (PSM-DID) method to analyze the impact of green credit policies on the long- and short-term financing scale of heavily polluting enterprises. The study found that the implementation of the Green Credit Policy significantly suppressed the long-term financing of heavily polluting enterprises, but allowed for the expansion of short-term financing for heavily polluting enterprises. Compared with the state-owned enterprises, the Green Credit Policy has a more significant impact on non-state-owned enterprises in terms of suppressing long-term financing and increasing short-term financing, suggesting that the Green Credit Policy is affected by the "credit discrimination" of non-state-owned enterprises. Therefore, the Green Credit Policy still needs to be improved. This study provides empirical evidence of the effectiveness of green credit policies in China, and offers suggestions for further green credit policies in the future.

Keywords

References

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MeSH Term

China
Economic Development
Forecasting
Propensity Score
Sustainable Development

Word Cloud

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