Data for: Least-Cost Pathways towards 100% Electrification in East Africa by 2030
This archive contains: 1) Calibrated input data for each country at a 1*1 km^2 resolution, ready to perform new runs with new parameters in the model; 2) Spatially explicit output data for each scenario, where scenarios follow the following naming criteria: Country_el.tier.urban_el.tier.rural_dieselprice_scenario.code where Country is the corresponding country, el.tier are World Bank electrification tiers for urban and rural areas, respecitvely, dieselprice is either high (1.30 USD/l) or low (0.90 USD/l), and scenarionumber is the resulting nomenclature from combinating such parameters, including the digit (1, 2, or 3) referring to the regional generation mix considered. 3) Summary output data for each scenario, including just the summary statistics at the country level; 4) Specs_files, which contain the parameters used for calibrating the input data for each regional mix scenario (1, 2, or 3). 5) The source code of the OnSSET model to replicate or modify the analysis. Changelog v2 (29/07/19): - Update to the peer-reviewed verison of the results. - OnSSET model included in the archive to allow for a prompt replication Refer to the documentation of the OnSSET model (http://www.onsset.org/) for guidance, implementation procedure, and caveats. For support in the use of the OnSSET model refer to the contacts page in the model's website. For questions and issues relative to the data contact email@example.com
Steps to reproduce
Requirements: Python 3.5+. Use calibrated data for the country of interest as inputs; manually define the desired technological or cost parameters; freely define specs file, electrification tiers and diesel price during the script run.