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Does Economic Policy Uncertainty Affect CO2 Emissions? Empirical Evidence from the United States

descriptionPublicationkeyboard_double_arrow_right Article , Journal , Other literature type 02 Nov 2020Publisher:MDPI AGJournal:Sustainability, volume 12, page 9,108 (eissn: 2071-1050,

Authors: Wang, Qing; Xiao, Kefeng; Lu, Zhou;

doi: 10.3390/su12219108

Does Economic Policy Uncertainty Affect CO2 Emissions? Empirical Evidence from the United States

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Abstract

This paper aims to examine the effects of economic policy uncertainty (measured by the World Uncertainty Index—WUI) on the level of CO2 emissions in the United States for the period from 1960 to 2016. For this purpose, we consider the unit root test with structural breaks and the autoregressive-distributed lag (ARDL) model. We find that the per capita income promotes CO2 emissions in the long run. Similarly, the WUI measures are positively associated with CO2 emissions in the long run. Energy prices negatively affect CO2 emissions both in the short run and the long run. Possible implications of climate change are also discussed.

Related Organizations

Wuhan University
China (People's Republic of)
Tianjin University of Commerce
China (People's Republic of)
Tianjin University of Commerce
China (People's Republic of)
Wuhan University
China (People's Republic of)

Keywords

economic policy uncertainty, Environmental effects of industries and plants, World Uncertainty Index, TJ807-830, TD194-195, ARDL model, United States, Renewable energy sources, Environmental sciences, CO<sub>2</sub> emissions, GE1-350

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	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	102
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	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%