South Africa After Paris—Fracking Its Way to the NDCs?

South Africa is facing the triple challenge of (a) fuelling economic development and meeting the growing energy demand; (b) increasing the reliability of the electricity system; and (c) ensuring that domestic greenhouse gas emissions peak no later than 2030 to meet its nationally determined contributions (NDC) under the 2015 Paris Agreement. Recently discovered domestic shale gas reserves are being considered as a potential new energy source to provide clean, reliable and cheap electricity while mitigating greenhouse gas emissions relative to the dominant coal sector. In order to determine if shale gas can play a viable role in solving South Africa's energy trilemma, we apply a country-level version of the integrated assessment model MESSAGEix to analyze and quantify the interdependencies between shale gas, the energy system and South Africa's greenhouse gas emissions trajectory. The data and scripts to generate this study will be made available at https://github.com/tum-ewk/message_ix_south_africa following the publication of this manuscript. Our results indicate that shale gas extraction costs must be below 3 USD/GJ for this energy source to reach a significant share in the fuel mix; this is well below current cost estimates. If, however, low-cost shale gas is available, both coal and low-carbon sources are replaced by natural gas. Whether carbon dioxide emissions increase or decrease as a result depends on the stringency of the climate change mitigation policy in place: without carbon pricing, natural gas replaces coal and mitigates harmful emissions; under high carbon prices, power generation from coal is phased out in any case, and natural gas competes with zero-carbon renewables, leading to an increase of emissions compared to a no-shale scenario

<i>d2ix</i>: A Model Input-Data Management and Analysis Tool for MESSAGE_<i>ix</i>

Bottom-up integrated assessment models, like MESSAGEix, depend on the description of the capabilities and limitations of technological, economical and ecological parameters, and their development over long-time horizons. Even small models of a few nodes, technologies and model years require input-data sets involving several hundred thousand data points. Such data sets quickly become incomprehensible, which makes error detection, collaborative working and the interpretation of results challenging, especially for non-self-created models. In response to the resulting need for manageable, comprehensible, and traceable representation of input-data, we developed a Python-based spreadsheet interface (d2ix) that enables presentation and editing of model input-data in a concise form. By increasing accessibility and transparency of the model input-data, d2ix reduces barriers to entry for new modellers and simplifies collaborative working. This paper describes the methodology and introduces the open-source Python-package d2ix. The package is available under the Apache License, Version 2.0 on GitHub