An alternative reference scenario for global CO2 emissions from fuel consumption: An ARFIMA approach

In this note, we establish an alternative reference scenario based on an ARFIMA model estimated using global CO2 emissions from 1750 to 2013. These new reference forecasts are free from additional assumptions on demographic and economic variables, often used in most reference forecasts. Instead, we only rely on the properties of the underlying stochastic process for global CO2 emissions that are, in this sense, closer to fundamentals. Our reference forecasts 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 easier to reach than what is implied by the standard reference scenarios. Having lower and more realistic reference emissions projections gives a truer assessment of the policy efforts that are needed, and highlights the lower costs involved in mitigation efforts, thereby maximizing the likelihood of more widespread environmental policy efforts.

[1]  J. Cuñado,et al.  Persistence, Mean-Reversion and Non-Linearities in CO2 Emissions: The Cases of China, India, UK and US , 2015 .

[2]  Luis A. Gil-Alana,et al.  Does energy consumption by the US electric power sector exhibit long memory behavior , 2010 .

[3]  J. Elder,et al.  Long memory in energy futures prices , 2008 .

[4]  Danièle Revel,et al.  World Energy Outlook Special Report 2015: Energy and Climate Change , 2015 .

[5]  P. Holtberg,et al.  International Energy Outlook 2016 With Projections to 2040 , 2016 .

[6]  E. Lanzi,et al.  OECD environmental outlook to 2050 : the consequences of inaction , 2012 .

[7]  R. Elliott,et al.  The Stochastic Convergence of CO2 Emissions: A Long Memory Approach , 2010 .

[8]  J. Greet,et al.  Trends in global CO2 emissions: 2012 report , 2012 .

[9]  Nicholas Apergis,et al.  Long memory and disaggregated energy consumption: Evidence from fossils, coal and electricity retail in the U.S. , 2012 .

[10]  Russell Smyth,et al.  Long memory in US disaggregated petroleum consumption: Evidence from univariate and multivariate LM tests for fractional integration , 2009 .

[11]  C. Barros,et al.  Long Memory in German Energy Price Indices , 2012, SSRN Electronic Journal.

[12]  Energy Agency World Energy Outlook 2007 : China and India Insights , 2007 .

[13]  Nicholas Apergis,et al.  Integration properties of disaggregated solar, geothermal and biomass energy consumption in the U.S , 2011 .

[14]  Luis A. Gil-Alana,et al.  Evidence of long memory behavior in U.S. renewable energy consumption , 2012 .

[15]  Hsiang-Hsi Liu,et al.  A study on the volatility spillovers, long memory effects and interactions between carbon and energy markets: The impacts of extreme weather , 2013 .