nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

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

verfasst von : Faik Bilgili, Erhan Mugaloglu, Emrah Koçak

Erschienen in: Econometrics of Green Energy Handbook

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Impact of Energy Use Segregation on Carbon Emissions: The Role of FDI in Net Importing and Net Exporting Countries

Nächstes Kapitel The Effect of Internet Use on Air Quality: Evidence from Low-Income Countries

Nur mit Berechtigung zugänglich

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.

If a wavelet is square integrable \( \vartheta (t) \in L^{2} ({\mathbb{R}}) \), then it must satisfy \( \mathop \int \nolimits_{ - \infty }^{\infty } \vartheta (t)^{2} {\text{d}}t < \infty \).

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.

Aguiar-Conraria, L., Azevedo, N., & Soares, M. J. (2008). Using wavelets to decompose the time-frequency effects of monetary policy. Physica A: Statistical Mechanics and Its Applications, 387, 2863–2878. https://doi.org/10.1016/J.PHYSA.2008.01.063.CrossRef

Aguiar-Conraria, L., & Joana Soares, M. (2011). Business cycle synchronization and the Euro: A wavelet analysis. Journal of Macroeconomics, 33, 477–489. https://doi.org/10.1016/J.JMACRO.2011.02.005.CrossRef

Aguiar-Conraria, L., Magalhães, P. C., & Soares, M. J. (2013). The nationalization of electoral cycles in the United States: A wavelet analysis. Public Choice, 156, 387–408. https://doi.org/10.1007/s11127-012-0052-8.CrossRef

Aguiar-Conraria, L., & Soares, M. J. (2011). Oil and the macroeconomy: Using wavelets to analyze old issues. Empirical Economics, 40, 645–655. https://doi.org/10.1007/s00181-010-0371-x.CrossRef

Alam, M. S., & Paramati, S. R. (2015). Do oil consumption and economic growth intensify environmental degradation? Evidence from developing economies. Applied Economics, 47, 5186–5203. https://doi.org/10.1080/00036846.2015.1044647.CrossRef

Anand, M. K. (2016). Reforming fossil fuel prices in India: Dilemma of a developing economy. Energy Policy, 92, 139–150. https://doi.org/10.1016/j.enpol.2016.01.039.CrossRef

Apergis, N., & Payne, J. E. (2014). Renewable energy, output, CO₂ emissions, and fossil fuel prices in Central America: Evidence from a nonlinear panel smooth transition vector error correction model. Energy Economics, 42, 226–232. https://doi.org/10.1016/J.ENECO.2014.01.003.CrossRef

Bilgili, F., Koçak, E., Bulut, Ü., & Kuloğlu, A. (2017). The impact of urbanization on energy intensity: Panel data evidence considering cross-sectional dependence and heterogeneity. Energy, 133, 242–256. https://doi.org/10.1016/j.energy.2017.05.121.CrossRef

Bilgili, F., Öztürk, İ., Koçak, E., Bulut, Ü., Pamuk, Y., Muğaloğlu, E., et al. (2016). The influence of biomass energy consumption on CO₂ emissions: A wavelet coherence approach. Environmental Science and Pollution Research, 23. https://doi.org/10.1007/s11356-016-7094-2.

Blazquez, J., Maria Martin-Moreno, J., Perez, R., & Ruiz, J. (2017). Fossil fuel price shocks and CO₂ emissions: The case of Spain. The Energy Journal, 38. https://doi.org/10.5547/01956574.38.6.jmar.

Bloomberg (2016). http://www.bloomberg.com/news/articles/2016-02-22/imf-s-lagarde-says-oil-may-staylow-for-longer-than-expected-ikxowj72. Accessed 16 December, 2015.

Boyd, T. O. (2012). China’s energy reform and climate policy: The ideas motivating change. Working Paper.

Canadell, J. G., Le Quéré, C., Raupach, M. R., Field, C. B., Buitenhuis, E. T., Ciais, P., et al. (2007). Contributions to accelerating atmospheric CO₂ growth from economic activity, carbon intensity, and efficiency of natural sinks. Proceedings of the National Academy of Sciences of the United States of America, 104, 18866–18870. https://doi.org/10.1073/pnas.0702737104.CrossRef

Crowley, P. M. (2005). An intuitive guide to wavelets for economists. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.787564.

Crowley, P. M. (2007). A guide to wavelets for economists. Journal of Economic Surveys, 21, 207–267. https://doi.org/10.1111/j.1467-6419.2006.00502.x.CrossRef

de Bruyn, S., van den Bergh, J. C. J., & Opschoor, J. (1998). Economic growth and emissions: Reconsidering the empirical basis of environmental Kuznets curves. Ecological Economics, 25, 161–175. https://doi.org/10.1016/S0921-8009(97)00178-X.CrossRef

Dong, C., Dong, X., Jiang, Q., Dong, K., & Liu, G. (2017a). What is the probability of achieving the carbon dioxide emission targets of the Paris agreement? Evidence from the top ten emitters. Science of the Total Environment, 622–623, 1294–1303. https://doi.org/10.1016/j.scitotenv.2017.12.093.CrossRef

Dong, K., Sun, R., & Dong, X. (2018). CO₂ emissions, natural gas and renewables, economic growth: Assessing the evidence from China. Science of the Total Environment, 640–641, 293–302. https://doi.org/10.1016/j.scitotenv.2018.05.322.CrossRef

Dong, K., Sun, R., Li, H., & Jiang, H. (2017b). A review of China’s energy consumption structure and outlook based on a long-range energy alternatives modeling tool. Petroleum Science, 14, 214–227. https://doi.org/10.1007/s12182-016-0136-z.CrossRef

EIA (2016). International energy outlook 2016 with projections to 2040. U.S. Energy Information Administration, Washington, D.C., USA. Chapter 9. Energy Related CO2 emissions, 139–148. https://www.eia.gov/outlooks/ieo/pdf/0484(2016).pdf

EPA (2016). Global greenhouse gas emissions data, U.S energy policy agency. https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data#four. Accessed 27 April, 2018.

Garnaut, R. (2014). China’s role in global climate change mitigation. China & World Economy, 22, 2–18. https://doi.org/10.1111/j.1749-124X.2014.12081.x.CrossRef

Gençay, R., Selçuk, F., & Whitcher, B. (2002). An introduction to wavelets and other filtering methods in finance and economics. https://doi.org/10.1088/0959-7174/12/3/701.

Gençay, R., Selçuk, F., & Whitcher, B. (2005). Multiscale systematic risk. Journal of International Money and Finance, 24, 55–70. https://doi.org/10.1016/J.JIMONFIN.2004.10.003.CrossRef

Green, F., & Stern, N. (2015). China’s ‘new normal’: Better growth, better climate (pp. 1–23). Policy paper, March 2015. ESRC Centre for Climate Change Economics and Policy Grantham Research Institute on Climate Change.

Grossmann, A., & Morlet, J. (1984). Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM Journal on Mathematical Analysis (SIMA), 15, 723–736. https://doi.org/10.1137/0515056.MathSciNetCrossRefMATH

Guo, Y., Zeng, Z., Tian, J., Xu, F., Chen, L., & Zhou, A. (2017). Uncovering the strategies of green development in a Chinese province driven by reallocating the emission caps of multiple pollutants among industries. Science of the Total Environment, 607, 1487–1496. https://doi.org/10.1016/j.scitotenv.2017.06.234.CrossRef

Hallding, K., Han, G., & Olsson, M. (2009). China’s climate-and energy-security dilemma: Shaping a new path of economic growth. Journal of Current Chinese Affairs, 38(3), 119–134. https://doi.org/10.1177/186810260903800306

Hamilton, J. D. (2003). What is an oil shock? Journal of Economics, 113, 363–398. https://doi.org/10.1016/S0304-4076(02)00207-5.MathSciNetCrossRefMATH

Hammoudeh, S., Nguyen, D. K., & Sousa, R. M. (2014). Energy prices and CO₂ emission allowance prices: A quantile regression approach. Energy Policy, 70, 201–206. https://doi.org/10.1016/J.ENPOL.2014.03.026.CrossRef

He, J., & Richard, P. (2010). Environmental Kuznets curve for CO₂ in Canada. Ecological Economics, 69, 1083–1093. https://doi.org/10.1016/J.ECOLECON.2009.11.030.CrossRef

Hu, J., Kahrl, F., Yan, Q., & Wang, X. (2012). The impact of China’s differential electricity pricing policy on power sector CO₂ emissions. Energy Policy, 45, 412–419. https://doi.org/10.1016/J.ENPOL.2012.02.049.CrossRef

Hudgins, L., Friehe, C. A., & Mayer, M. E. (1993). Wavelet transforms and atmospheric turbulence. Physical Review Letters, 71, 3279–3282. https://doi.org/10.1103/PhysRevLett.71.3279.CrossRef

IEA (2017a). CO2 emissions from fuel combustion 2017-highlights. International Energy Agency, 1, 1–162. https://doi.org/10.1787/co2_fuel-2017-en.

IEA (2017b). Key world energy statistics 2017. International Energy Agency: Paris, 1–97. https://doi.org/10.1787/key_energ_stat-2017-en. https://doi.org/10.1787/co2_fuel-.

IEA (2017c). The 10th five-year plan for economic and social development of the People’s Republic Of China (2001–2005). https://www.iea.org/policies/1736-the-10th-five-year-plan-for-economic-and-social-development-of-the-peoples-republic-of-china-2001-2005. Accessed 28 April, 2018.

IEA (2017d). The 11th five-year plan for economic and social development of the People’s Republic Of China (2006-2010). https://www.iea.org/policiesandmeasures/energyefficiency/?country=China. Accessed 28 April, 2018.

IEA (2017e). The 12th five-year plan for economic and social development of the People’s Republic Of China (2011–2015). https://www.iea.org/policies/2123-the-12th-five-year-plan-for-economic-and-social-development-of-the-peoples-republic-of-china-2011-2015. Accessed 28 April, 2018.

Jammazi, R., & Aloui, C. (2012). Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling. Energy Economics, 34, 828–841. https://doi.org/10.1016/J.ENECO.2011.07.018.CrossRef

Ju, K., Su, B., Zhou, D., & Wu, J. (2017). Does energy-price regulation benefit China’s economy and environment? Evidence from energy-price distortions. Energy Policy, 105, 108–119. https://doi.org/10.1016/j.enpol.2017.02.031.CrossRef

Khalfaoui, R., Boutahar, M., & Boubaker, H. (2015). Analyzing volatility spillovers and hedging between oil and stock markets: Evidence from wavelet analysis. Energy Economics, 49, 540–549. https://doi.org/10.1016/J.ENECO.2015.03.023.CrossRef

Kim, S., & In, F. (2007). On the relationship between changes in stock prices and bond yields in the G7 countries: Wavelet analysis. Journal of International Financial Markets, Institutions & Money, 17, 167–179. https://doi.org/10.1016/J.INTFIN.2005.10.004.CrossRef

Kumar, S., & Managi, S. (2009). Energy price-induced and exogenous technological change: Assessing the economic and environmental outcomes. Resource and Energy Economics, 31, 334–353. https://doi.org/10.1016/J.RESENEECO.2009.05.001.CrossRef

Lim, K.-M., Lim, S.-Y., Yoo, S.-H., Lim, K.-M., Lim, S.-Y., & Yoo, S.-H. (2014). Oil consumption, CO₂ emission, and economic growth: Evidence from the Philippines. Sustainability, 6, 967–979. https://doi.org/10.3390/su6020967.CrossRef

Lindmark, M. (2002). An EKC-pattern in historical perspective: Carbon dioxide emissions, technology, fuel prices and growth in Sweden 1870–1997. Ecological Economics, 42, 333–347. https://doi.org/10.1016/S0921-8009(02)00108-8.CrossRef

Liu, J., Chen, S., Wang, H., & Campana, P. E. (2016). Evolution of China’s urban energy consumption structure—A case study in Beijing. Energy Procedia, 88, 88–93. https://doi.org/10.1016/J.EGYPRO.2016.06.029.CrossRef

Liu, P. C. (1994). Wavelet Spectrum analysis and ocean wind waves. Wavelet Analysis and Its Applications, 4, 151–166. https://doi.org/10.1016/B978-0-08-052087-2.50012-8.MathSciNetCrossRef

Liu, Z., Guan, D., Crawford-Brown, D., Zhang, Q., He, K., & Liu, J. (2013). A low-carbon road map for China. Nature, 500, 143–145. https://doi.org/10.1038/500143a.CrossRef

Luo, K., Hu, X., He, Q., Wu, Z., Cheng, H., Hu, Z., et al. (2017). Impacts of rapid urbanization on the water quality and macroinvertebrate communities of streams: A case study in Liangjiang New Area, China. Science of the Total Environment, 621, 1601–1614. https://doi.org/10.1016/j.scitotenv.2017.10.068.CrossRef

Ma, L., Fu, F., Li, Z., & Liu, P. (2012). Oil development in China: Current status and future trends. Energy Policy, 45, 43–53. https://doi.org/10.1016/J.ENPOL.2012.01.023.CrossRef

Madaleno, M., & Pinho, C. (2014). Wavelet dynamics for oil-stock world interactions. Energy Economics, 45, 120–133. https://doi.org/10.1016/J.ENECO.2014.06.024.CrossRef

Maji, I. K., Habibullah, M. S., Saari, M. Y., & Abdul-Rahim, A. S. (2017). The nexus between energy price changes and environmental quality in Malaysia. Energy Sources, Part B Economics, Planning, Policy, 12, 903–909. https://doi.org/10.1080/15567249.2017.1323052.CrossRef

McCollum, D. L., Jewell, J., Krey, V., Bazilian, M., Fay, M., & Riahi, K. (2016). Quantifying uncertainties influencing the long-term impacts of oil prices on energy markets and carbon emissions. Nature Energy, 1, 16077. https://doi.org/10.1038/nenergy.2016.77.CrossRef

Merrill, S., Grofman, B., & Brunell, T. L. (2008). Cycles in American national electoral politics, 1854–2006: Statistical evidence and an explanatory model. American Political Science Review, 102, 1–17. https://doi.org/10.1017/S0003055408080064.CrossRef

NDRC (2016). The 13th five-year plan for economic and social development of the People’s Republic Of China (2016-2020). National Development and Reform Commission of China. https://en.ndrc.gov.cn/policyrelease_8233/201612/P020191101482242850325.pdf. Accessed 28 April, 2018.

Nwani, C. (2017). Causal relationship between crude oil price, energy consumption and carbon dioxide (CO₂) emissions in Ecuador. OPEC Energy Review, 41, 201–225. https://doi.org/10.1111/opec.12102.CrossRef

Payne, J. E. (2012). The causal dynamics between US renewable energy consumption, output, emissions, and oil prices. Energy Sources, Part B Economics, Planning, Policy, 7, 323–330. https://doi.org/10.1080/15567249.2011.595248.CrossRef

Percival, D., & Walden, A. (2006). Wavelet methods for time series analysis.

Pereira, A. M., & Pereira, R. M. (2014). On the environmental, economic and budgetary impacts of fossil fuel prices: A dynamic general equilibrium analysis of the Portuguese case. Energy Economics, 42, 248–261. https://doi.org/10.1016/J.ENECO.2014.01.006.CrossRef

Qi, T., Winchester, N., Karplus, V. J., & Zhang, X. (2014). Will economic restructuring in China reduce trade-embodied CO₂ emissions? Energy Economics, 42, 204–212. https://doi.org/10.1016/J.ENECO.2013.12.011.CrossRef

Reboredo, J. C., Rivera-Castro, M. A., & Ugolini, A. (2017). Wavelet-based test of co-movement and causality between oil and renewable energy stock prices. Energy Economics, 61, 241–252. https://doi.org/10.1016/J.ENECO.2016.10.015.CrossRef

Rua, A., & Nunes, L. C. (2009). International comovement of stock market returns: A wavelet analysis. Journal of Empirical Finance, 16, 632–639. https://doi.org/10.1016/J.JEMPFIN.2009.02.002.CrossRef

Saboori, B., Rasoulinezhad, E., & Sung, J. (2017). The nexus of oil consumption, CO₂ emissions and economic growth in China, Japan and South Korea. Environmental Science and Pollution Research, 24, 7436–7455. https://doi.org/10.1007/s11356-017-8428-4.CrossRef

Sadorsky, P. (2009). Renewable energy consumption, CO₂ emissions and oil prices in the G7 countries. Energy Economics, 31, 456–462. https://doi.org/10.1016/J.ENECO.2008.12.010.CrossRef

Salim, R. A., & Rafiq, S. (2012). Why do some emerging economies proactively accelerate the adoption of renewable energy? Energy Economics, 34, 1051–1057. https://doi.org/10.1016/J.ENECO.2011.08.015.CrossRef

Sarkodie, S. A., & Adams, S. (2018). Renewable energy, nuclear energy, and environmental pollution: Accounting for political institutional quality in South Africa. https://doi.org/10.1016/j.scitotenv.2018.06.320.

Sarkodie, S. A., & Strezov, V. (2018). Assessment of contribution of Australia’s energy production to CO₂ emissions and environmental degradation using statistical dynamic approach. Science of the Total Environment, 639, 888–899. https://doi.org/10.1016/j.scitotenv.2018.05.204.CrossRef

Sugihara, H., & Tsuji, K. (2008). Energy conservation in urban areas. IEEJ Transactions on Electrical and Electronic Engineering, 3, 10–14. https://doi.org/10.1002/tee.20226.CrossRef

Teng, F., & Jotzo, F. (2014). Reaping the economic benefits of decarbonization for China. China World Economy, 22, 37–54. https://doi.org/10.1111/j.1749-124X.2014.12083.x.CrossRef

Torrence, C., Compo, G. P., Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79, 61–78. https://doi.org/10.1175/1520-0477(1998)079%3c0061:APGTWA%3e2.0.CO;2.CrossRef

Vacha, L., & Barunik, J. (2012). Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis. Energy Economics, 34, 241–247. https://doi.org/10.1016/J.ENECO.2011.10.007.CrossRef

Wang, K., Che, L., Ma, C., & Wei, Y.-M. (2017). The shadow price of CO₂ emissions in China’s iron and steel industry. Science of the Total Environment, 598, 272–281. https://doi.org/10.1016/j.scitotenv.2017.04.089.CrossRef

Wang, Q. (2010). Effective policies for renewable energy—The example of China’s wind power—Lessons for China’s photovoltaic power. Renewable and Sustainable Energy Reviews, 14, 702–712. https://doi.org/10.1016/J.RSER.2009.08.013.CrossRef

Wang, Q. (2015). Cheaper oil—Challenge and opportunity for climate change. Environmental Science and Technology, 49, 1997–1998. https://doi.org/10.1021/es505704u.CrossRef

Wang, Q., & Li, R. (2016). Impact of cheaper oil on economic system and climate change: A SWOT analysis. Renewable and Sustainable Energy Reviews, 54, 925–931. https://doi.org/10.1016/J.RSER.2015.10.087.CrossRef

Wen, Y. (2002). The business cycle effects of Christmas. Journal of Monetary Economics, 49, 1289–1314. https://doi.org/10.1016/S0304-3932(02)00150-2.CrossRef

Winchester, N., & Ledvina, K. (2017). The impact of oil prices on bioenergy, emissions and land use. Energy Economics, 65, 219–227. https://doi.org/10.1016/J.ENECO.2017.05.008.CrossRef

World Bank (2015). Global economic prospects, January 2015 : having fiscal space and using it (English). Global Economic Prospects. Washington, DC : World Bank Group. https://documents.worldbank.org/curated/en/444361468127152333/Global-economic-prospects-January-2015-having-fiscal-space-and-using-it

Yang, D., & Timmermans, H. (2012). Effects of fuel price fluctuation on individual CO₂ traffic emissions: Empirical findings from pseudo panel data. Procedia Social and Behavioral Science, 54, 493–502. https://doi.org/10.1016/J.SBSPRO.2012.09.767.CrossRef

Zhang, C., Wang, Y., Song, X., Kubota, J., He, Y., Tojo, J., et al. (2017a). An integrated specification for the nexus of water pollution and economic growth in China: Panel cointegration, long-run causality and environmental Kuznets curve. Science of the Total Environment, 609, 319–328. https://doi.org/10.1016/j.scitotenv.2017.07.107.CrossRef

Zhang, G., Liu, P., Gao, X., & Liu, M. (2014). Companies’ behavior of carbon emission reduction at the risk of oil price volatility. Procedia Computer Science, 31, 291–298. https://doi.org/10.1016/J.PROCS.2014.05.271.CrossRef

Zhang, J., & Xie, M. (2016). China’s oil product pricing mechanism: What role does it play in China’s macroeconomy? China Economic Review, 38, 209–221. https://doi.org/10.1016/j.chieco.2016.02.002.CrossRef

Zhang & Zhang, L. (2016). Impacts on CO₂ emission allowance prices in China: A quantile regression analysis of the Shanghai emission trading scheme. Sustainability, 8, 1195. https://doi.org/10.3390/su8111195.CrossRef

Zhang, Y.-J., Bian, X.-J., Tan, W., & Song, J. (2017b). The indirect energy consumption and CO₂ emission caused by household consumption in China: An analysis based on the input–output method. Journal of Cleaner Production, 163, 69–83. https://doi.org/10.1016/J.JCLEPRO.2015.08.044.CrossRef

Titel: The Impact of Oil Prices on CO2 Emissions in China: A Wavelet Coherence Approach
verfasst von: Faik Bilgili
Erhan Mugaloglu
Emrah Koçak
Verlag: Springer International Publishing
Buch: Econometrics of Green Energy Handbook
Print ISBN: 978-3-030-46846-0

Electronic ISBN: 978-3-030-46847-7

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-46847-7_2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"