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How does climate policy uncertainty affect the carbon market?

Authors :
Su, Chi Wei
Wei, Shenkai
Wang, Yan
Tao, Ran
Source :
Technological Forecasting & Social Change; Mar2024, Vol. 200, pN.PAG-N.PAG, 1p
Publication Year :
2024

Abstract

Treating carbon emission rights as tradable commodities is a major institutional innovation in the carbon market's response to climate change. The level of carbon emissions from production activities is inextricably linked to climate policy adjustments. Thus, this article applies wavelet-based quantile-on-quantile regression to study the impact of climate policy uncertainty (CPU) on the carbon trading price (CTP) in the European Union Emissions Trading System (EU ETS). Short-term results show a mixed effect of CPU on CTP. In the medium to long term, there is a positive correlation between the two at the high quantiles of CPU. The results are supported by the theoretical mechanisms of CPU on CTP. The CTP represents the economic cost of carbon emissions, which can significantly influence the level of carbon emissions and is critical to achieving carbon neutrality. Governments should strive to reduce CPU and manage market expectations to minimise the impact of CPU on CTP. In addition, this paper can also inspire carbon market participants to help them make sound and scientific investment decisions. • Apply wavelet-based quantile-on-quantile regression to study the effect of CPU on the CTP. • The CPU is at different quantiles and time frequencies, the link between CPU and CTP is dynamic • The results are supported by the theoretical mechanisms of CPU on CTP. • The CTP can significantly influence the level of carbon emissions and is critical to achieving carbon neutrality. • Governments should strive to reduce CPU and manage market expectations to minimise the impact of CPU on CTP. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00401625
Volume :
200
Database :
Supplemental Index
Journal :
Technological Forecasting & Social Change
Publication Type :
Academic Journal
Accession number :
175032981
Full Text :
https://doi.org/10.1016/j.techfore.2023.123155