Achieving development and mitigation objectives through a decarbonization development pathway in South Africa

Abstract

Achieving the international 2 °C limit climate policy requires stringent reductions in GHG emissions by mid-century, with some countries simultaneously facing development-related challenges. South Africa is a middle-income developing country with high rates of unemployment and high levels of poverty, as well as an emissions-intensive economy. South Africa takes into account an assessment of what a fair contribution to reducing global emissions might be, and is committed to a ‘peak, plateau and decline' emissions trajectory with absolute emissions specified for 2025 and 2030, while noting the need to address development imperatives. This work utilizes an economy-wide computable general equilibrium model (e-SAGE) linked to an energy-system optimization model (TIMES) to explore improving development metrics within a 14 GtCO₂e cumulative energy sector carbon constraint through to 2050 for South Africa. The electricity sector decarbonizes by retiring coal-fired power plants or replacing with concentrated solar power, solar photovoltaics and wind generation. Industry and tertiary-sector growth remains strong throughout the time period, with reduced energy intensity via fuel-switching and efficiency improvements. From 2010 to 2050, the model results in the unemployment rate decreasing from 25% to 12%, and the percentage of people living below the poverty line decreasing from 49% to 18%. Total energy GHG emissions were reduced by 39% and per capita emissions decreased by 62%.

Policy relevance

Lower poverty and inequality are goals that cannot be subordinated to lower GHG emissions. Policy documents in South Africa outline objectives such as reducing poverty and inequality with a key focus on education and employment. In its climate policy and Intended Nationally Determined Contribution (INDC), South Africa is committed to a peak, plateau and decline GHG emissions trajectory. As in many developing countries, these policy goals require major transformations in the energy system while simultaneously increasing affordable access to safe and convenient energy services for those living in energy poverty. The modelled scenario in this work focuses on employment and poverty reduction under a carbon constraint, a novel combination with results that can provide information for a holistic climate and development policy framework. This study has focused on the long term, which is important in generating clear policy signals for the necessary large-scale investments.

Keywords:

• development pathways
• economic models
• employment
• energy models
• GHG reductions
• mitigation scenarios

Acknowledgements

This work was supported by the German Ministry for Environment under the DDPP project.

Notes

1. The 544.3 Mt CO₂e includes agriculture, hence only FOLU is denoted separately and for consistency with the South African National Greenhouse Gas Inventory, 2000–2010.

2. Capital accumulation is determined endogenously based on the previous period investment levels. New capital is allocated to sectors based on their relative profit rates. Once invested, capital becomes sector-specific. At the macro-economic level, nominal private and public consumption and investment spending are assumed to be fixed proportions of the total absorption and the real exchange rate adjusts to maintain an exogenously determined current account balance.

3. Energy is considered to be an intermediate input and the interaction between intermediates and factors is governed by a Leontief production function. To decrease the rigidity of using a Leontief production function, there is 'response elasticity' that governs the amount sectors that are able change their energy inputs by per unit of output, based on energy prices.

4. TIMES is a well-established partial equilibrium optimization energy modelling platform that was developed by IEA-ETSAP (http://www.iea-etsap.org) and is widely used by a large number of countries for energy planning and analysis.

5. Ideally a single version of SATIM (-F) would be used together with e-SAGE to ensure consistency between the two models across the whole energy sector, but this version of the linked model is still under development.

6. See Department of Agriculture, Forestry and Fisheries figures for export in agricultural, forestry and fishery products from 1996 to 2014, http://www.daff.gov.za/daffweb3/Branches/Economic-Development-Trade-Marketing/International-Trade.

7. The IRP analysis only extends through to 2030 (Department of Energy, 2013).

8. The learning is a result of global installed capacity, assuming that South Africa is a price taker for the power generation technologies. It is possible that with large share of localization in the manufacture of power plant equipment that further learning would be observed as installed capacity increases.