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An Alternative Reference Scenario for Global CO2Emissions from Fuel Consumption: An ARFIMA Approach

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  • José Belbute

    () (Department of Economics, University of Évora, Portugal and CEFAGE-UE, Portugal)

  • Alfredo M. Pereira

    () (Department of Economics, College of William and Mary, Williamsburg)

Abstract

We provide alternative reference forecasts for global CO2 emissions based on an ARFIMA model estimated with annual data from 1750 to 2013. These forecasts are free from additional assumptions on demographic and economic variables that are commonly used in reference forecasts, as they only rely on the properties of the underlying stochastic process for CO2emissions, as well as on all the observed information it incorporates. In this sense, these forecasts are more based on fundamentals. Our reference forecast suggests that in 2030, 2040 and 2050, in the absence of any structural changes of any type, CO2 would likely be at about 25%, 34% and 39.9% above 2010 emission levels, respectively. These values are clearly below the levels proposed by other reference scenarios available in the literature. This is important, as it suggests that the ongoing policy goals are actually within much closer reach than what is implied by the standard CO2reference emission scenarios. Having lower and more realistic reference emissions projections not only gives a truer assessment of the policy efforts that are needed, but also highlights the lower costs involved in mitigation efforts, thereby maximizing the likelihood of more widespread energy and environmental policy efforts.

Suggested Citation

  • José Belbute & Alfredo M. Pereira, 2015. "An Alternative Reference Scenario for Global CO2Emissions from Fuel Consumption: An ARFIMA Approach," CEFAGE-UE Working Papers 2015_11, University of Evora, CEFAGE-UE (Portugal).
  • Handle: RePEc:cfe:wpcefa:2015_11
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    References listed on IDEAS

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    1. 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.
    2. Lean, Hooi Hooi & Smyth, Russell, 2009. "Long memory in US disaggregated petroleum consumption: Evidence from univariate and multivariate LM tests for fractional integration," Energy Policy, Elsevier, vol. 37(8), pages 3205-3211, August.
    3. Gil-Alana, Luis A. & Loomis, David & Payne, James E., 2010. "Does energy consumption by the US electric power sector exhibit long memory behavior?," Energy Policy, Elsevier, vol. 38(11), pages 7512-7518, November.
    4. Liu, Hsiang-Hsi & Chen, Yi-Chun, 2013. "A study on the volatility spillovers, long memory effects and interactions between carbon and energy markets: The impacts of extreme weather," Economic Modelling, Elsevier, vol. 35(C), pages 840-855.
    5. Elder, John & Serletis, Apostolos, 2008. "Long memory in energy futures prices," Review of Financial Economics, Elsevier, vol. 17(2), pages 146-155.
    6. 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.
    7. 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.
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    Cited by:

    1. repec:eee:rensus:v:80:y:2017:i:c:p:990-1016 is not listed on IDEAS
    2. 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.
    3. José Belbute & Alberto Marvão Pereira, 2015. "Do Global CO2 Emissions from Fuel Consumption Exhibit Long Memory? A Fractional Integration Analysis," CEFAGE-UE Working Papers 2015_14, University of Evora, CEFAGE-UE (Portugal).

    More about this item

    Keywords

    Forecasting; reference scenario; CO2 emissions; long memory; ARFIMA.;

    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
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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