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Carbon Dioxide Emission-Intensity in Climate Projections: Comparing the Observational Record to Socio-Economic Scenarios

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  • Max Roser

Abstract

The large span of long-run projected temperature changes in climate projections does not predominately originate from uncertainty across climate models; instead it is the wide range of different global socio-economic scenarios and the implied energy production that results in high uncertainty about climate change. It is therefore important to assess the observational tracking of these scenarios. For the first time observations over two decades are available against which the initial sets of socio-economic scenarios used in IPCC reports can be assessed. Here we compare these socio-economic scenarios created in both 1992 and 2000 against the recent observational record to investigate the coupling of economic growth and fossil-fuel CO2 emissions. We find that the growth rate in fossil fuel CO2 emission intensity – fossil fuel CO2 emissions per GDP – over the 2000s exceeds the projections of all main emission scenarios. Proposing a method to disaggregate differences in global growth rates to country-by-country contributions, we find that the relative discrepancy is driven by high growth rates in Asia and Eastern Europe, in particular in Russia and China. The growth of emission intensity over the 2000s highlights the relevance of unforeseen local shifts in projections on a global scale.

Suggested Citation

  • Max Roser, 2016. "Carbon Dioxide Emission-Intensity in Climate Projections: Comparing the Observational Record to Socio-Economic Scenarios," Economics Series Working Papers 810, University of Oxford, Department of Economics.
  • Handle: RePEc:oxf:wpaper:810
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    Cited by:

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    7. Jinkai Li & Jingjing Ma & Wei Wei, 2020. "Analysis and Evaluation of the Regional Characteristics of Carbon Emission Efficiency for China," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
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    11. Jingwen Yi & Yuchen Zhang & Kaicheng Liao, 2021. "Regional Differential Decomposition and Formation Mechanism of Dynamic Carbon Emission Efficiency of China’s Logistics Industry," IJERPH, MDPI, vol. 18(24), pages 1-25, December.
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    13. Li-Ming Xue & Shuo Meng & Jia-Xing Wang & Lei Liu & Zhi-Xue Zheng, 2020. "Influential Factors Regarding Carbon Emission Intensity in China: A Spatial Econometric Analysis from a Provincial Perspective," Sustainability, MDPI, vol. 12(19), pages 1-26, October.
    14. David I. Stern, 2017. "How accurate are energy intensity projections?," Climatic Change, Springer, vol. 143(3), pages 537-545, August.
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    More about this item

    Keywords

    Climate; Energy; Scenarios; Emission Intensity;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • 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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