Estimating on-road vehicle fuel economy in Africa

This research paper proposes a methodology for estimating in-use vehicle fuel economy in sub-Saharan African cities, using Nairobi as a case study to address data gaps caused by incomplete official records and the prevalence of informal transport.

• Estimating fuel economy in sub-Saharan African (SSA) cities is hindered by official data that is often "incomplete, inaccurate, inconsistent and outdated," as well as the prevalence of informal transport (matatus, bodabodas, and tuktuks) which are frequently unregistered, old, poorly maintained, and overloaded.
• Using Nairobi as a case study, the researchers applied general linear modelling (GLM) and artificial neural network (ANN) models to analyze vehicle characteristics. The GLM was found to be the superior method for predicting fuel economy based on vehicle characteristics.
• The study found that fuel economy for specific vehicle types in Nairobi—bodabodas (4.6 ± 0.4 L/100 km), tuktuks (8.7 ± 4.6 L/100 km), passenger cars (22.8 ± 3.0 L/100 km), and matatus (33.1 ± 2.5 L/100 km)—is 2–3 times worse than in the countries from which these vehicles are imported.

APA

Stockholm Environment Institute (2019). Estimating on-road vehicle fuel economy in Africa. https://www.sei.org/publications/estimating-on-road-vehicle-fuel-economy-in-africa/

Chicago

Stockholm Environment Institute. Estimating on-road vehicle fuel economy in Africa. 2019. https://www.sei.org/publications/estimating-on-road-vehicle-fuel-economy-in-africa/.

Wikipedia

{{cite report |author=Stockholm Environment Institute |title=Estimating on-road vehicle fuel economy in Africa |date=26 March 2019 |url=https://www.sei.org/publications/estimating-on-road-vehicle-fuel-economy-in-africa/ |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{stockholmenvironmentinstitute2019estimating, author = {{Stockholm Environment Institute}}, title = {{Estimating on-road vehicle fuel economy in Africa}}, institution = {Stockholm Environment Institute}, year = {2019}, month = mar, url = {https://www.sei.org/publications/estimating-on-road-vehicle-fuel-economy-in-africa/}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }