A “hard” versus “soft” science dichotomy? Comparing engineering and social science approaches to electricity planning in sub-Saharan Africa

Scientific disciplines have frequently been separated into “hard” and “soft” sciences, usually referring to natural and social science, respectively. Such a dichotomy tends to mask the fact that certain problems necessarily encompass both the natural and the social science realm. How to best electrify sub-Saharan Africa constitutes such a problem. The region’s unique energy resource patterns and financial constraints require specifically optimised planning solutions for successful electrification. At the same time, sub-Saharan African electrification is governed by complex, highly politicised institutions and has multi-layered socio-economic implications that need to be considered at the planning stage.

Scholars have thus produced a methodologically broad range of research on electrification planning in sub-Saharan Africa. Using a systematic literature search approach covering all 49 countries in the region, this work identifies 306 relevant journal articles. Roughly 60% of these articles can be broadly categorised as social science, roughly 40% as engineering research. Reviewing these articles reveals that neither approach is more holistic than the other. On average, social science articles consider more different decision criteria than engineering planning approaches, manage to distil a greater variety of necessary prerequisites for successful electrification and cover 10% more sub-Saharan African countries. Yet engineering approaches on average analyse more technology solutions per article than social science, and consider almost 5 times as many concrete decision alternatives. The number of studies published per year is found to have quickly increased for both approaches, especially since the early 2000s.

The literature review suggests that although such practice is currently rare, it appears beneficial to closer combine engineering and social science approaches to electricity planning in sub-Saharan Africa. This could yield encompassing planning methods ensuring both the possibility of large-scale quantitation for decision support, as well as explicitly considering the complex and often qualitative implications of different decision alternatives.