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The transformation of the energy system is a highly complex process involving many dimensions. Energy system models help to understand the process and to define either target systems or policy measures. Insights derived from the social sciences are not sufficiently represented in energy system models, but address crucial aspects of the transformation process. It is, therefore, necessary to develop approaches to integrate results from social science studies into energy system models. Hence, as a result of an interdisciplinary discourse among energy system modellers, social scientists, psychologists, economists and political scientists, this article explains which aspects should be considered in the models, how the respective results can be collected and which aspects of integration into energy system models are conceivable to provide an overview for other modellers. As a result of the discourse, five facets are examined: Investment behaviour (market acceptance), user behaviour, local acceptance, technology innovation and socio-political acceptance. Finally, an approach is presented that introduces a compound of energy system models (with a focus on the macro and micro-perspective) as well as submodels on technology genesis and socio-political acceptance, which serves to gain a more fundamental knowledge of the transformation process.

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This paper focuses on the energy economics and environmental impacts of solar water heaters (SWH) in the Gauteng Province and compares the results with other technology options for residential water heating with regard to the different income groups. The critical energy situation in South Africa and the highly coal dependent energy generation demonstrates the need to shift to a more sustainable way of living. The residential sector proves to be an optimal starting point to implement new technologies, especially for water heating. The residential hot water demand calculation shows that the annual demand in Gauteng is about 188 million cubic meters. In order to satisfy this demand, different technologies are investigated in this paper, where SWHs lie in focus. Due to the vast income inequality in Gauteng, and also in South Africa, it is obvious that there cannot be one single optimal solution suitable to all households. Therefore, this paper focuses on the differentiation of the residential sector into income groups to show the divergence in warm water demand and the applicability of alternative technologies. In order to analyse appropriate solutions for all income groups, low-cost alternatives are also analysed. The economic analysis shows that although SWHs have higher investment costs than conventional technologies, the payback periods are relatively short (between 3 and 4 years) for high and mid income groups. The payback periods will be even shorter when the planned electricity price tariff increase comes into effect. Furthermore, SWH utilisation has the additional effect of reducing the overall electricity demand up to 70% and greenhouse gas emissions significantly. In addition, SWHs are the most cost-effective water heating technology to reduce greenhouse gas emissions for mid and high income groups with negative abatement costs.It is concluded that the SWHs are the most suitable option to decrease fossil energy consumption and reduce the household’s expenditure for energy services, especially for mid and high income groups. For lower income groups the utilisation of solar energy can increase the access to energy services and living quality and, therewith, lessen the financial burden to meet their energy needs.

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Since the signing of the 2030 Agenda for Sustainable Development by the United Nations Member States and the Yellow vest movement, it is clear that emission‐reducing policies should consider their distributional impacts to ensure a sustainable and equitable growth compatible with the Paris Agreement goals. To this end, the design of environmental and energy policies should be accompanied by an interdisciplinary analysis that includes potential effects on distinct groups of society (defined by income, age, or location), regions, and sectors. This work synthesizes common modeling frameworks used to assess technical, socio‐economic, and environmental aspects in policy analysis and the recent progress to portray distributional impacts in each of them. Furthermore, the main indicators produced by each method are highlighted and a critical review pointing to gaps and limitations that could be addressed by future research is presented.