Literature DB >> 35122200

Does financing constraints impact the Chinese companies' pollutants emissions? Evidence from a sample selection bias corrected model based on Chinese company-level panel data.

Xiaoguang Zhou1, Xinmeng Tang2.   

Abstract

Most Chinese companies face financing constraints and thus lack sufficient funding for operations and investments that would better control their pollutant emissions. A sample selection bias corrected model is constructed to study the impact of financing constraints on the Chinese companies' pollutant emissions using company-level emissions data. The empirical results revealed that financing constraints increase the pollutant emissions of the Chinese companies, including the emissions of industrial wastewater, industrial solid waste, and sulfur dioxide. The heterogeneity analysis showed that the impacts of financing constraints on the pollutant emissions of companies operating in highly polluting industries and non-state-owned companies are more significant. And compared with internal financing, bank financing can better mitigate the impact of financing constraints on pollutant emissions through green loan projects. The results are stable after controlling for other important company factors and testing the robustness using the subdivided regression. Several political implications are drawn based on these findings that can help control the pollutant emissions of Chinese companies from the perspective of financing constraints.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Entities:  

Keywords:  Chinese company; Financing constraints; Pollutant emission; Sample selection bias corrected model

Mesh:

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Year:  2022        PMID: 35122200     DOI: 10.1007/s11356-022-18907-7

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  1 in total

1.  Is the R&D Expenditure of Listed Companies Green? Evidence from China's A-Share Market.

Authors:  Boyu Li; Lishan Li; Tianlei Pi
Journal:  Int J Environ Res Public Health       Date:  2022-09-22       Impact factor: 4.614

  1 in total

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