Data were primarily collected from the reports and websites of international organizations, including the International Renewable Energy Agency (IRENA), the International Energy Agency (IEA), and the Intergovernmental Panel on Climate Change (IPCC). Additionally, data were sourced from The Electricity Model Base for Africa (TEMBA), an existing OSeMOSYS model of African electricity supply .
2.1 Electricity Supply System Data
Data on South Sudan's existing on-grid power generation capacity, presented in Table 1, were extracted from the TEMBA dataset , which provides estimates residual capacity by power plant type from 2015. The 33MW Juba oil-fired power plant, commissioned in 2019, was also included . Data on South Sudan's off-grid renewable energy capacity were sourced from yearly capacity statistics produced by IRENA . Cost, efficiency and operational life data in Table 2 were collected from reports by IRENA [6,7,8], which provide generic estimates for these parameters by technology. These reports also provide projections of future costs for renewable energy technologies. These data are presented in Table 3 and Figure 2, where it was assumed that costs fall linearly between the data points provided by IRENA and that costs remain constant beyond 2040 when the IRENA forecasts end.
Country-specific capacity factors for solar PV, wind and hydropower were sourced from the TEMBA dataset , which provides estimated capacity factors by country for 8 time slices, the average values of which are presented in Table 2. These data were also used to estimate capacity factors for 8 time slices used in the OSeMOSYS model (see detail in Annex 1). Capacity factors for other technologies were sourced from reports by IRENA [7,9], which provide generic estimates for each technology. The costs and efficiencies of power transmission and distribution were sourced from TEMBA reference case , which provides generic cost estimates and country-specific efficiencies which consider expected efficiency improvements in the future. Techno-economic data for refineries were sourced from the IEA Energy Technology Systems Analysis Programme (ETSAP) , which provides generic estimates of costs and performance parameters, while the refinery options modelled are based on the methods used in TEMBA .
2.2 Fuel Data
The crude oil price is based on an international price forecast produced by the US Energy Information Administration (EIA), which runs to 2050 . The price was increased by 10% for imported oil to reflect the cost of importation. The price of imported HFO and LFO were calculated by multiplying the oil price by 0.8 and 1.33 respectively, based on the methods used in TEMBA . The prices of coal, natural gas and biomass were sourced from an IRENA report , which provides generic estimates for costs to 2030. Again, a linear rate of change was assumed between data points from IRENA, and the forecast was extended to 2040 using the rate of change between 2020 and 2030. Prices were then assumed constant after 2040. The cost of domestically-produced biomass was increased by 10% to estimate a cost of imported biomass.
2.3 Emissions Factors and Domestic Reserves
Emissions factors were collected from the IPCC Emission Factor Database , which provides carbon emissions factors by fuel. Domestic renewable energy potentials for solar PV, CSP and wind were collected from an IRENA-KTH working paper , which provides estimates of potential yearly generation by country in Africa. Based on country area proportions, the potentials given for Sudan in the IRENA-KTH report were divided between Sudan and South Sudan, with approximately one third of the potentials assigned to South Sudan and presented in this article. Other renewable energy potentials were sourced from Africa Oil & Power  and the World Small Hydropower Development Report , which provide estimated potentials in MW. Estimated domestic fossil fuel reserves are from a UN Environment Programme Energy Profile for South Sudan .
2.4 Electricity Demand Data
The final electricity demand projection is based on data from the TEMBA Reference Scenario dataset , which provides yearly total demand estimates from 2015-2070 under a reference case scenario.