IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v144y2020ics0301421520303761.html
   My bibliography  Save this article

Reference forecasts for CO2 emissions from fossil-fuel combustion and cement production in Portugal

Author

Listed:
  • Belbute, José M.
  • Pereira, Alfredo M.

Abstract

We provide reference forecasts for CO2 emissions from burning fuel fossil and cement production in Portugal based on an ARFIMA model approach and using annual data from 1950 to 2017. Our reference projections suggest a pattern of decarbonization that will cause the reduction of 3.3 Mt until 2030 and 5.1 Mt between 2030 and 2050. This scenario allows us to assess effort required by the new IPCC goals to ensure carbon neutrality by 2050. For this objective to be achieved it is necessary for emissions to be reduced by 39.9 Mt by 2050. Our results suggest that of these, only 8.4 Mt will result from the inertia of the national emissions system. The remaining reduction on emissions of 31.5 Mt of CO2 will require additional policy efforts. Accordingly, our results suggest that about 65.5% of the reductions necessary to achieve IPCC goals require deliberate policy efforts. Finally, the presence in the data of long memory with mean reversion suggests that policies must be persistent to ensure that these reductions in emissions are also permanent.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:enepol:v:144:y:2020:i:c:s0301421520303761
    DOI: 10.1016/j.enpol.2020.111642
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421520303761
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.enpol.2020.111642?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    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. 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.
    3. repec:pre:wpaper:201528 is not listed on IDEAS
    4. 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.
    5. Diebold, Francis X. & Rudebusch, Glenn D., 1991. "On the power of Dickey-Fuller tests against fractional alternatives," Economics Letters, Elsevier, vol. 35(2), pages 155-160, February.
    6. 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.
    7. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    8. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
    9. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    10. 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.
    11. Belbute, José M. & Pereira, Alfredo M., 2015. "An alternative reference scenario for global CO2 emissions from fuel consumption: An ARFIMA approach," Economics Letters, Elsevier, vol. 136(C), pages 108-111.
    12. 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.
    13. 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.
    14. Diebold, Francis X. & Rudebusch, Glenn D., 1989. "Long memory and persistence in aggregate output," Journal of Monetary Economics, Elsevier, vol. 24(2), pages 189-209, September.
    15. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    16. Sowell, Fallaw, 1992. "Modeling long-run behavior with the fractional ARIMA model," Journal of Monetary Economics, Elsevier, vol. 29(2), pages 277-302, April.
    17. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    18. 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.
    19. Apergis, Nicholas & Tsoumas, Chris, 2011. "Integration properties of disaggregated solar, geothermal and biomass energy consumption in the U.S," Energy Policy, Elsevier, vol. 39(9), pages 5474-5479, September.
    20. 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.
    21. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Carrilho-Nunes, Inês & Catalão-Lopes, Margarida, 2022. "The effects of environmental policy and technology transfer on GHG emissions: The case of Portugal," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 255-264.
    2. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    3. Pereira , Alfredo Marvão & Pereira, Rui Manuel, 2021. "On the Macroeconomic and Distributional Effects of the Regulated Closure of Coal-Operated Power Plants," Journal of Economic Development, The Economic Research Institute, Chung-Ang University, vol. 46(4), pages 1-30, December.
    4. Ghazala Aziz & Rida Waheed & Suleman Sarwar & Mohd Saeed Khan, 2022. "The Significance of Governance Indicators to Achieve Carbon Neutrality: A New Insight of Life Expectancy," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    5. Renquan Huang & Jing Tian, 2022. "Dynamic Scenario Analysis of Science and Technology Innovation to Support Chinese Cities in Achieving the “Double Carbon” Goal: A Case Study of Xi’an City," IJERPH, MDPI, vol. 19(22), pages 1-19, November.

    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., 2022. "ARFIMA Reference Forecasts for Worldwide CO2 Emissions and the National Dimension of the Policy Efforts to Meet IPCC Targets," Journal of Economic Development, The Economic Research Institute, Chung-Ang University, vol. 47(1), pages 1-27, March.
    2. 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.
    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. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    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. 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.
    7. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    8. 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).
    9. 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.
    10. 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.
    11. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    12. Koop, Gary & Ley, Eduardo & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian analysis of long memory and persistence using ARFIMA models," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 149-169.
    13. 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.
    14. Chong, Terence Tai-Leung, 2000. "Estimating the differencing parameter via the partial autocorrelation function," Journal of Econometrics, Elsevier, vol. 97(2), pages 365-381, August.
    15. Christian Fischer & Luis Alberiko Gil-Alana, 2005. "The Nature of the Relationship between International Tourism and International Trade: The Case of Ge," Faculty Working Papers 15/05, School of Economics and Business Administration, University of Navarra.
    16. Guglielmo Maria Caporale & Luis A. Gil‐Alana & James C. Orlando, 2016. "Linkages Between the US and European Stock Markets: A Fractional Cointegration Approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 21(2), pages 143-153, April.
    17. Carlos Pestana Barros & Luis Gil-Alana, 2006. "Eta: A Persistent Phenomenon," Defence and Peace Economics, Taylor & Francis Journals, vol. 17(2), pages 95-116.
    18. Benjamin J. C. Kim & David Karemera, 2006. "Assessing the forecasting accuracy of alternative nominal exchange rate models: the case of long memory," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(5), pages 369-380.
    19. Guglielmo Maria Caporale & Luis Gil‐Alana, 2014. "Long‐Run and Cyclical Dynamics in the US Stock Market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(2), pages 147-161, March.
    20. Luis A. Gil‐Alana & Robert Mudida & OlaOluwa S. Yaya & Kazeem A. Osuolale & Ahamuefula E. Ogbonna, 2021. "Mapping US presidential terms with S&P500 index: Time series analysis approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1938-1954, April.

    More about this item

    Keywords

    CO2 emissions; IPCC emission Targets; Long memory; ARFIMA; Portugal;
    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

    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:eee:enepol:v:144:y:2020:i:c:s0301421520303761. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    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.