Do green finance and green innovation affect corporate credit rating performance? Evidence from machine learning approach
This study investigates the impact of green finance (GF) and green innovation (GI) on corporate credit rating (CR) performance in Chinese A-share listed firms from 2018 to 2021. The least absolute shrinkage and selection operators (LASSOs) machine learning algorithms are first used to select the cri...
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Published in | Journal of environmental management Vol. 360; p. 121212 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
England
Elsevier Ltd
01.06.2024
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Subjects | |
Online Access | Get full text |
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Summary: | This study investigates the impact of green finance (GF) and green innovation (GI) on corporate credit rating (CR) performance in Chinese A-share listed firms from 2018 to 2021. The least absolute shrinkage and selection operators (LASSOs) machine learning algorithms are first used to select the critical drivers of corporate credit performance. Then, we applied partialing-out LASSO linear regression (POLR) and double selection LASSO linear regression (DSLR) machine learning techniques to check the impact of GF and GI on CR. The main results reveal that a 1% increase in GF diminishes CR by 0.26%, whereas GI promotes CR performance by 0.15%. Moreover, the heterogeneity analysis reveals a more significant negative effect of GF on the CR performance of heavily polluting firms, non-state-owned enterprises, and firms in the Western region. The findings raise policies for managing green finance and encouraging green innovation formation, as well as addressing company heterogeneity to support sustainability.
•Study the impact of green finance and green innovation on corporate credit rating.•Use Chinese A-share listed firms from 2018 to 2021 and machine learning techniques.•A 1% increase in green finance diminishes credit rating by 0.26%.•Impact is more pronounced in heavily polluting firms and non-state-owned firms.•Green innovation promotes credit rating performance by 0.15%. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0301-4797 1095-8630 |
DOI: | 10.1016/j.jenvman.2024.121212 |