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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Bibliographic Details
Published inJournal of environmental management Vol. 360; p. 121212
Main Authors Wang, Yangjie, Feng, Junyi, Shinwari, Riazullah, Bouri, Elie
Format Journal Article
LanguageEnglish
Published England Elsevier Ltd 01.06.2024
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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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ISSN:0301-4797
1095-8630
DOI:10.1016/j.jenvman.2024.121212