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The effect of African growth on future global energy, emissions, and regional development


Today Africa is a small emitter, but it has a large and faster-than-average growing population and per capita income that could drive future energy demand and, if unconstrained, emissions. This paper uses a multi-model comparison to characterize the potential future energy development for Continental and Sub-Saharan Africa under different assumptions about population and income. Our results suggest that population and economic growth rates will strongly influence Africa’s future energy use and emissions. We show that affluence is only one face of the medal and the range of future emissions is also contingent on technological and political factors. Higher energy intensity improvements occur when Africa grows faster. In contrast, climate intensity varies less with economic growth and it is mostly driven by climate policy. African emissions could account for between 5 % and 20 % of global emissions, with Sub-Saharan Africa contributing between 4 % and 10 % of world emissions in 2100. In all scenarios considered, affluence levels remain low until the middle of the century, suggesting that the population could remain dependent on traditional bioenergy to meet most residential energy needs. Although the share of electricity in final energy, electric capacity and electricity use per capita all rise with income, even by mid-century they do not reach levels observed in developed countries today.

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  1. 1.

    The individual models use different criteria to map countries to regions. GCAM and IPAC focus on geographic proximity and as such all regions include contiguous countries. REMIND and WITCH aggregate countries to regions based on their development and therefore include North Africa with Middle East and South Africa with Korea and Australia (WITCH) and with the Rest of the World (REMIND).

  2. 2.

    The GDP per capita scenarios have been developed using the methodology described in Hawksworth (2006). The methodology is based on a Solow-Swan model with capital and quality adjusted labor as input factor and exogenous assumption about future Total Factor Productivity (TFP) growth. Population scenarios are from the UN, historic GDP and investment information are from the Penn World Tables and data on education levels are from Barro and Lee (2010). Variations in the speed of growth are obtained by varying the TFP growth of the US. The other regions are assumed to converge to the technology frontier at a slow or fast speed. All models used in the comparison exercise, represent economic entities in Market Exchange Rate (MER). As a consequence, the Purchasing Parity Power (PPP) GDP per capita scenarios have been converted into MER using a projection of the PPP to MER ratio for the 21st century.

  3. 3.

    As noticed in other studies for Africa, this share is significantly larger than the expected share of China or India (Cilliers and Moyer 2011).

  4. 4.

    Table 1 shows the results of scenario implementation in the individual models.

  5. 5.

    In REMIND the efficiency parameters are assumed to change at the same rate as labor efficiency, plus an additional adjustment factor is applied that varies per region and final energy type and results in continuity of past trends and a converging behavior between regions (EJ/capita over GDP PPP/capita) (Luderer et al. 2013).

  6. 6.

    Traditional biomass is usually defined as unprocessed fuelwood, agricultural residues, and animal dung, as well as charcoal, normally combusted on open fires or in very inefficient stoves. Traditional biomass is represented as a function of GDP per capita in the WITCH and GCAM models, and exogenous assumptions are employed in REMIND.

  7. 7.

    Electrification rates are driven by numerous factors. Some mechanisms that influence electrification rates, e.g. in the case of REMIND, include a) possibility of substitution between transport energy, electricity, and non-electric energy for stationary end uses within the nested Constant Elasticity of Substitution production function, b) calibration of the energy efficiencies of the stationary electricity CES leaf, c) provision of energy through numerous competing technologies characterized by different efficiencies, lifetimes, investment costs, fixed and variable operation and maintenance costs, learning rates, etc.

  8. 8.

    Reasons motivating differences in the deployment of solar and nuclear across models include a) in REMIND solar (and wind) technologies are characterized by endogenous technological change through learning-by-doing, where investment costs decrease by pre-specified rates for each doubling of cumulated capacity, b) solar is not considered in the WITCH model, c) in REMIND and IPAC a sharper increase of gas and coal prices in the second half of the century is observed.

  9. 9.

    Global energy intensity growth rates range between −2.2 and −1.1 %, while carbon intensity rates range between −0.1 and 0.9 %.

  10. 10.

    We assume 5 GJ/capita of final energy is needed for meeting basic domestic cooking and electricity needs, as stipulated by the UN Secretary-General’s advisory group (AGECC 2010).


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This work was funded by Stiftung Mercator (

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Correspondence to Katherine Calvin.

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This article is part of a Special Issue on “The Impact of Economic Growth and Fossil Fuel Availability on Climate Protection” with Guest Editors Elmar Kriegler, Ottmar Edenhofer, Ioanna Mouratiadou, Gunnar Luderer, and Jae Edmonds.

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Calvin, K., Pachauri, S., De Cian, E. et al. The effect of African growth on future global energy, emissions, and regional development. Climatic Change 136, 109–125 (2016).

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  • Gross Domestic Product
  • Total Factor Productivity
  • Climate Policy
  • Energy Intensity
  • Final Energy