IDEAS home Printed from https://ideas.repec.org/p/mde/wpaper/0125.html
   My bibliography  Save this paper

ARFIMA Reference Forecasts for Worldwide CO2 Emissions and the National Dimension of the Policy Efforts to Meet IPCC Targets

Author

Listed:
  • José M. Belbute
  • Alfredo Marvão Pereira

Abstract

We use an ARFIMA approach to develop reference scenario projections for CO2 emissions worldwide and for seven different regions. Our objective is to determine the magnitude of the policy efforts necessary to achieve the IPCC emissions reductions goals. For worldwide emissions, the aggregate policy effort required to achieve the 2050 goals is equivalent to 97.4% of 2010 emissions. This policy effort is frontloaded as about 60% of such efforts would have to occur by 2030. In order to achieve the IPCC target the policy efforts in the cases of the USA, EU(28), Russia, and Japan - which account for 32% of worldwide emissions, are lower and less frontloaded than the IPCC goals themselves. In the case of China, India and the ROW, which account for 68% of worldwide emissions, additional policy efforts are necessary to achieve reductions in emissions of 105.0%, 156.0% and 111.4%, of the 2010 levels, respectively. In the case of India, policy efforts are not only rather severe but also rather dramatically frontloaded, as about 74% of the policy efforts would have to occur by 2030. Our results suggest that the policies toward decarbonization must consider the specific regional characteristics of emissions. Given the differences in the inertia of emissions in the different regions a one-size fits all approach is not the best approach.

Suggested Citation

  • José M. Belbute & Alfredo Marvão Pereira, 2019. "ARFIMA Reference Forecasts for Worldwide CO2 Emissions and the National Dimension of the Policy Efforts to Meet IPCC Targets," GEE Papers 0125, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Aug 2019.
  • Handle: RePEc:mde:wpaper:0125
    as

    Download full text from publisher

    File URL: https://www.gee.gov.pt//RePEc/WorkingPapers/GEE_PAPERS_125.pdf
    File Function: First version, 2019
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
    2. Apergis, Nicholas & Tsoumas, Chris, 2012. "Long memory and disaggregated energy consumption: Evidence from fossils, coal and electricity retail in the U.S," Energy Economics, Elsevier, vol. 34(4), pages 1082-1087.
    3. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    4. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    5. Uwe Hassler & Paulo M.M. Rodrigues & Antonio Rubia, 2016. "Quantile Regression for Long Memory Testing: A Case of Realized Volatility," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(4), pages 693-724.
    6. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    7. Richard S J Tol, 2018. "The Economic Impacts of Climate Change," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 12(1), pages 4-25.
    8. Carlos Barros & Luis Gil-Alana & Fernando Perez de Gracia, 2016. "Stationarity and Long Range Dependence of Carbon Dioxide Emissions: Evidence for Disaggregated Data," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 63(1), pages 45-56, January.
    9. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    10. José M. Belbute & Alfredo M. Pereira, 2017. "Do global CO emissions from fossil-fuel consumption exhibit long memory? a fractional-integration analysis," Applied Economics, Taylor & Francis Journals, vol. 49(40), pages 4055-4070, August.
    11. Marco Barassi & Matthew Cole & Robert Elliott, 2011. "The Stochastic Convergence of CO 2 Emissions: A Long Memory Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 49(3), pages 367-385, July.
    12. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    13. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Belbute, José M. & Pereira, Alfredo M., 2020. "Reference forecasts for CO2 emissions from fossil-fuel combustion and cement production in Portugal," Energy Policy, Elsevier, vol. 144(C).
    2. Geoffrey Ngene & Ann Nduati Mungai & Allen K. Lynch, 2018. "Long-Term Dependency Structure and Structural Breaks: Evidence from the U.S. Sector Returns and Volatility," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 21(02), pages 1-38, June.
    3. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    4. José Manuel Belbute & Alfredo Marvão Pereira, 2016. "Does final energy demand in Portugal exhibit long memory? A fractional integration analysis," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 15(2), pages 59-77, August.
    5. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    6. Ngene, Geoffrey & Tah, Kenneth A. & Darrat, Ali F., 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, Elsevier, vol. 34(C), pages 61-73.
    7. Choi, Kyongwook & Yu, Wei-Choun & Zivot, Eric, 2010. "Long memory versus structural breaks in modeling and forecasting realized volatility," Journal of International Money and Finance, Elsevier, vol. 29(5), pages 857-875, September.
    8. Elkin Castaño & Santiago Gallón & Karoll Gómez, 2010. "Estimation Biases, Size and Power of a Test on the Long Memory Parameter in ARFIMA Models," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 73, pages 131-148.
    9. Ana Pérez & Esther Ruiz, 2002. "Modelos de memoria larga para series económicas y financieras," Investigaciones Economicas, Fundación SEPI, vol. 26(3), pages 395-445, September.
    10. S. Lardic & V. Mignon, 2002. "Term premium and long-range dependence in volatility : A FIGARCH-M estimation on some Asian countries," THEMA Working Papers 2002-26, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    11. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    12. Silverberg, Gerald & Verspagen, Bart, 1999. "Long Memory in Time Series of Economic Growth and Convergence," Research Memorandum 015, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    13. Guglielmo Maria Caporale & Luis A. Gil-Alana & Manuel Monge, 2019. "Energy Consumption in the GCC Countries: Evidence on Persistence," CESifo Working Paper Series 7470, CESifo.
    14. Henryk Gurgul & Tomasz Wójtowicz, 2006. "Long-run properties of trading volume and volatility of equities listed in DJIA index," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 16(3-4), pages 29-56.
    15. José M. Belbute & Alfredo Marvão Pereira, 2016. "Updated Reference Forecasts for Global CO2 Emissions from Fossil-Fuel Consumption," Working Papers 170, Department of Economics, College of William and Mary.
    16. Morten Ørregaard Nielsen & Per Houmann Frederiksen, 2005. "Finite Sample Comparison of Parametric, Semiparametric, and Wavelet Estimators of Fractional Integration," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 405-443.
    17. González-Pla, Francisco & Lovreta, Lidija, 2019. "Persistence in firm’s asset and equity volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    18. Castano Vélez, Elkin & Gallón Gómez, Santiago Alejandro & Gómez Portilla, Karoll, 2011. "Sesgos en estimación, tamano y potencia de una prueba sobre el parámetro de memoria larga en modelos ARFIMA," Revista Lecturas de Economía, Universidad de Antioquia, CIE, February.
    19. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    20. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.

    More about this item

    Keywords

    CO2 emissions; IPCC emission targets; long memory; ARFIMA;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mde:wpaper:0125. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joana Almodovar (email available below). General contact details of provider: https://edirc.repec.org/data/geegvpt.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.