Journal
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
Volume 29, Issue 39, Pages 59712-59726Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11356-022-20043-1
Keywords
Green credit; Carbon emissions; Theoretical analysis framework; Mediating effect model; Dynamic panel threshold model
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Funding
- National Natural Science Foundation of China [72073124]
- MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS
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This paper analyzes the action paths of green credit on carbon emissions in China through empirical examination of panel data. The results show that green credit mainly inhibits carbon emissions through its impact on industrial structure, energy structure, and energy intensity. Furthermore, the study explores the role of signal formation mechanism in this process, finding heterogeneity among provinces. The findings provide policy implications for different regions.
As an important part of China's green finance, green credit is regarded as an important tool to promote China's transformation to a low-carbon economy. In order to clarify the mechanism of green credit on carbon emissions, this paper puts forward a theoretical analysis framework including functional attributes - micro subject response - key influencing factors from the macro and micro perspectives. We select the panel data of 30 provinces in China from 2005 to 2019 for an empirical test and identify the action paths of green credit on carbon emission based on the mediating effect model. Further, we consider the special mechanism of signal formation and test it based on the dynamic panel threshold model. The results show that: (1) China's green credit mainly inhibits carbon emissions through three paths: industrial structure, energy structure and energy intensity. (2) There is a signal formation mechanism for the impact of green credit on carbon emissions, which mainly acts on the two action paths of industrial structure and energy intensity. (3) The signal formation mechanism is heterogeneous in each province. According to the empirical results, we divide the provinces into three echelons and propose corresponding suggestions.
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