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Journal of Energy in Southern Africa

versión On-line ISSN 2413-3051
versión impresa ISSN 1021-447X

Resumen

STONE, Adrian; MERVEN, Bruno; MASEELA, Tiisetso  y  MOONSAMY, Resmun. Providing a foundation for road transport energy demand analysis: A vehicle pare model for South Africa. J. energy South. Afr. [online]. 2018, vol.29, n.2, pp.29-42. ISSN 2413-3051.  http://dx.doi.org/10.17159/2413-3051/2018/v29i2a2774.

It is key for national economic planning to build the tools to forecast energy demand from major sectors like transport in a credible way. As a starting point, this requires building a sufficiently detailed 'bottom-up' picture of technologies and their activity levels in the recent past. A vehicle parc model was developed for South Africa to feed transport demand and data on the fleet into a national energy systems model, the South African TIMES model, which is a least-cost optimisation model of the TIMES/ MARKAL family. Detailed assumptions were developed for 24 vehicle typologies that included the vintage profile, annual mileage and its relationship with age, fuel economy and its improvement over time, and occupancy and load factor. Combining these assumptions, the model was successfully calibrated over 2000-2014 with the national registration database, national fuel sales statistics and, on the freight side, with estimates of the demand for ton.km published by the University of Stellen-bosch's Department of Logistics (2014 only). A demand for passenger.km was also calculated, which agreed well with national transport surveys. A range of detailed indicators were produced for the vehicle typologies and some interesting trends observed, including the steady dieselisation of the light vehicle fleet over the study period and the stagnation of passenger car fuel economy, despite legislation in the European Union. The present study believes that this updated data-rich picture of the road transport vehicle parc will support other studies and national policy and planning initiatives.

Palabras clave : freight demand; fuel economy; mode share; greenhouse gas emissions; vehicle fleets; modelling.

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