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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Notes
- 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.
Abbreviations
- AR:
-
Autoregressive
- ARMA:
-
Autoregressive moving average
- CO2:
-
Carbon dioxide
- CWP:
-
Cross wavelet power
- CWT:
-
Continuous wavelet transformation
- FT:
-
Fourier transformation
- GDP:
-
Gross domestic product
- GHG:
-
Greenhouse gasses
- MA:
-
Moving average
- MEPS:
-
Minimum energy performance standards
- MW:
-
Megawatts
- UNFCCC:
-
United Nations Framework Convention on Climate Change
- VAT:
-
Value-added tax
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See Figs. 2.6, 2.7 and Table 2.2.
a Wavelet coherency (oil price, CO2 per capita), 1960–2014. b 1–3-frequency band, 1960–2014. c 3–8-frequency band, 1960–2014
a Wavelet partial coherency (oil price, CO2 per capita || world oil price), 1960–2014. b 1–3-frequency band, 1960–2014. c 3–8-frequency band, 1960–2014
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Bilgili, F., Mugaloglu, E., Koçak, E. (2020). The Impact of Oil Prices on CO2 Emissions in China: A Wavelet Coherence Approach. In: Shahbaz, M., Balsalobre-Lorente, D. (eds) Econometrics of Green Energy Handbook. Springer, Cham. https://doi.org/10.1007/978-3-030-46847-7_2
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