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Updated Reference Forecasts for Global CO2 Emissions from Fossil-Fuel Consumption

Author

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  • José Belbute

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

  • 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 2014. 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 CO2 emissions, 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 23.1%, 29.1% and 33.7% 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 CO2 reference 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, 2016. "Updated Reference Forecasts for Global CO2 Emissions from Fossil-Fuel Consumption," CEFAGE-UE Working Papers 2016_08, University of Evora, CEFAGE-UE (Portugal).
  • Handle: RePEc:cfe:wpcefa:2016_08
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    References listed on IDEAS

    as
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    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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