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2020 | OriginalPaper | Chapter

2. The Impact of Oil Prices on CO2 Emissions in China: A Wavelet Coherence Approach

Authors : Faik Bilgili, Erhan Mugaloglu, Emrah Koçak

Published in: Econometrics of Green Energy Handbook

Publisher: Springer International Publishing

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Abstract

This paper observes the possible co-movements of oil price and CO2 emissions in China by following wavelet coherence and wavelet partial coherence analyses to be able to depict the short-run and long-run co-movements at both low and high frequencies. To this end, this research might provide the current literature with the output of potential short-run and long-run structural changes in CO2 emissions upon a shock (a change) in oil prices in China together with the control variables of world oil prices, fossil energy consumption and renewable consumption and urban population in China. Therefore, this research aims at determining wavelet coherencies between the variables and phase differences to exhibit the leading variable in potential co-movements. By following the time domain and frequency domain analyses of this research, one may claim that the oil prices in China have considerable negative impact on CO2 emissions at high frequencies for the periods 1960–2014 and 1971–2014 in China. Besides, one may underline as well other important output of the research exploring that the urban population and CO2 emissions have positive associations, move together for the period 1960–2014 in China. Eventually, this paper might suggest that authorities follow demand-side management policies considering energy demand behavior at both shorter cycles and longer cycles to diminish the CO2 emissions in China.

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Appendix
Available only for authorised users
Footnotes
1
Fourier Transformation (FT) can decompose any periodic and some non-periodic signal into a sine/cosine function. FT of an arbitrary signal \( z(t) \) can be written as \( B(f) = \mathop \int \nolimits_{ - \infty }^{\infty } b(t)\exp ( - i2\pi ft){\text{d}}t = \mathop \int \nolimits_{ - \infty }^{\infty } b(t)[\cos (2\pi ft) - i\,\sin (2\pi ft)]{\text{d}}t \), where \( B(f) \) is a function of frequency f and \( i = \sqrt { - 1} \) is the complex or imaginary number. Alternatively, this equation can be written with radian frequencies as \( B(w) = \mathop \int \nolimits_{ - \infty }^{\infty } b(t){\text{e}}^{ - iwt} {\text{d}}t = \mathop \int \nolimits_{ - \infty }^{\infty } b(t)[\cos (wt) - i\,\sin (wt)]{\text{d}}t \), where \( w = 2\pi f \) denotes radian frequency.
 
2
If a wavelet is square integrable \( \vartheta (t) \in L^{2} ({\mathbb{R}}) \), then it must satisfy \( \mathop \int \nolimits_{ - \infty }^{\infty } \vartheta (t)^{2} {\text{d}}t < \infty \).
 
3
The conjugate of a complex number, \( b + hi \), is simply \( b - hi \). If it has only real value rather than complex, its conjugate will be itself.
 
Literature
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Metadata
Title
The Impact of Oil Prices on CO2 Emissions in China: A Wavelet Coherence Approach
Authors
Faik Bilgili
Erhan Mugaloglu
Emrah Koçak
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-46847-7_2