versión On-line ISSN 2413-3051
J. energy South. Afr. vol.22 no.4 Cape Town 2011
Energy Research Centre, University of Cape Town, South Africa
Energy modelling serves as a crucial tool for informing both energy policy and strategy development. But the modelling process is faced with both sectoral energy data and structural challenges. Among all the sectors, the residential sector usually presents a huge challenge to the modelling profession due to the dynamic nature of the sector. The challenge is brought by the fact that each an every household in a region may have different energy consumption characteristics and the computing power of the available models cannot incorporate all the details of individual household characteristics. Even if there was enough computing power within the models, energy consumption is collected through surveys and as a result only a sample of a region is captured. These challenges have forced energy modellers to categorise households that have similar characteristics. Different researchers choose different methods for categorising the households. Some researchers choose to categorise households by location and climate, others choose housing types while others choose quintiles. Currently, there is no consensus on which categorisation method takes precedence over others.
In these myriad ways of categorising households, the determining factor employed in each method is what is assumed to be the driver of energy demand in that particular area of study. Many researchers acknowledge that households' income, preferences and access to certain fuels determine how households use energy. Although many researchers recognise that income is the main driver of energy demand in the residential sector, there has been no energy modelling study that has tried to categorise households by income in South Africa. This paper chose to categorise households by income because income is taken to be the main driver of energy demand in the urban residential sector. Gauteng province was chosen as a case study area for this paper. The Long-range Energy Alternatives Planning System (LEAP) is used as a tool for such analysis.
This paper will further reveal how the dynamics of differing income across the residential sector affects total energy demand in the long run. The households in Gauteng are classified into three income categories - high, middle and low income households. In addition to different income categories, the paper further investigates the energy demand of Gauteng's residential sector under three economic scenarios with five energy demand scenarios. The three economic scenarios are first economic scenario (ECO1), second economic scenario (ECO2) and third economic scenario (ECO3). The most distinguishing factor between these economic scenarios is the mobility of households from one income band to the next.
The model results show that electricity demand will be high in all the three economic scenarios. The reason for such high electrical energy demand in all the economic scenarios compared to other fuels is due to the fact that among all the provinces, Gauteng households have one of the highest electricity consumption profiles. ECO2 showed the highest energy demand in all the five energy demand scenarios. This is due to the fact that the share of high income households in ECO2 was very high, compared to the other two economic scenarios. The favourable energy demand scenarios will be the Energy Efficiency and MEPS scenarios due to their ability to reduce more energy demand than other scenarios in all the three economic scenarios.
Keywords: LEAP, final energy demand, income dynamics, scenarios, household mobility
Full text available only in pdf format.
The author expresses her gratitude to Alison Hughes who supervised her M Sc thesis, where some aspects of this paper were drawn from.
Afrane-Okese, Y. (1998). Domestic energy use database for integrated energy planning. Energy & Development Research Centre, University of Cape Town. [ Links ]
Amoetang, Y. A., and Heaton T. B. (2007): Families and households in post-apartheid South Africa: socio-demographic perspectives. Human Science Research Council. [ Links ]
COJ, (2007). City of Johannesburg State of Energy Report. [ Links ]
Cowan, B. and Mohlakoana, N. (2005). Barriers to modern energy services in low-income urban communities: Khayelitsha energy survey, 2004. Cape Town: Energy Research Centre. [ Links ]
CTMMSOE, (2006). City of Tswane Metropolitan Municipality State of Energy Report. [ Links ]
Dzioubinski, O. and Chipman, R. (1999). Pro-duction: Household energy consumption. New York, United Nations Sustainable Development of the Department of Economic and Social Affairs. [ Links ]
DME, (2005). Energy efficiency strategy of the Republic of South Africa: Pretoria. [ Links ]
EMMSOE, (2004). Ekurhuleni Metropolitan Municipality State of Energy Report. [ Links ]
Engineering News Online (2008) [ Links ]
Fields, G. (2009). Income Mobility within a generation: An Introduction to the State of the Art in America. United Nations Development Programme Regional Bureau for Latin America and the Caribbean, Cornell University and IZA. [ Links ]
GIES, (2009). Gauteng Integrated Energy Strategy. [ Links ]
GSSD, (2006). Gauteng Strategy for Sustainable Development. [ Links ]
Hair, J. F, Babin, B., Money A. and Samouel, P. (2005). Essentials of business research methods. Hoboken, NJ: Wiley. [ Links ]
Hughes, A. and Haw, M. (2007). Clean energy and development for South Africa (Vol. 1 background data, Vol. 2 scenarios, Vol. 3 results). Energy Research Centre, University of Cape Town. [ Links ]
Hunt, Lester C. & Judge, Guy & Ninomiya, Yasushi, (2003). Underlying trends and seasonality in UK energy demand: a sectoral analysis, Energy Economics, Elsevier, Vol. 25(1), pages 93-118 [ Links ]
Landau, L. and Gindrey, V, (2008). Migration Population Trends in Gauteng Province 1996-2055. Migration Studies Working Paper Series, Johannesburg, University of the Witwatersrand. [ Links ]
Louw, K. Conrad, B. Howells M., and Dekenah, M. (2008). Determinants of electricity demand for newly electrified low-income African households. Energy policy 36: 2814-2820. [ Links ]
Mathews, E. and van Wyk, S, (1996). 'Energy efficiency of formal low-cost housing in South Africa's Gauteng region.' Energy and Buildings 24(1996): 117-123. [ Links ]
Meth, C, (2008). Social income in South Africa, an economy marred by high unemployment, poverty and extreme inequality. Available at www.hsrc.ac.za/research/output/outputDocu-ments/5581_Meth_SocialincomeinSA.pdf Last accessed May 2009. [ Links ]
Morna Isaac M. P. and van Vuuren, D. , (2008). Modelling global residential sector energy demand for heating and air conditioning in the context of change. Netherlands Environmental Assessment Agency AH Bilthoven, The Netherlands. [ Links ]
Nadel, (2002). Appliance and Equipment Efficiency Standards. Annual Review of Energy and the Environment 27:159-192. [ Links ]
Prasad, G. (2006). Social issues, In Energy policies for sustainable development in South Africa: Options for the future. ISBN:.0-620-36294-4. Cape Town, Energy Research Centre. [ Links ]
Raut, L. K. (1996). Signalling Equilibrium, Inter-generational Mobility and Long-Run Growth. Manoa, United States: University of Hawaii-Manoa. [ Links ]
SARB, (2003). South African Reserve Bank Annual Economic Report 2003. [ Links ]
STATSSA, (2001). Census 2001 data. [ Links ]
STATSSA, (2007). Community Survey 2007, Basic Results: Municipalities. Statistical Release P0301. 1. Pretoria, Statistics South Africa. [ Links ]
STATSSA, (2007). Labour force survey, 2007. Statistical release P0210. [ Links ]
Wade, J., Pett, J., and Ramsay, L., (2003). Energy efficiencies in offices: assessing the situation, London: Association for the Conservation of Energy 2003 [ Links ]
Ward, S. and Schäffler, J, (2008). Gauteng 2034 Development Strategy: Energy trend paper, Gauteng Department of Economic Development White, C, Mafokane, T. and Meintjes, H. (1998). Social determinants of energy use in low income households: Gauteng Report. Doornfontein, Centre for Policy Studies. [ Links ]
Wilkenfeld, G. and Harrington, L, (1997). Appliance efficiency programs in Australia: labelling and standards. Energy and Buildings 22 (1): 81-88 [ Links ]
Winkler, H., (2006). Energy policies for sustainable development in South Africa's residential and electricity sectors: Implications for mitigating climate change. PhD Thesis. Energy Research Centre, University of Cape Town [ Links ]
World Bank, (2008). The welfare Impact of Rural electrification: The reassessment of cost and benefits: An IEG Impact Evaluation [ Links ]
Received 20 October 2010
Revised 1 August 2011