Developing an Energy Based Poverty Line for South Africa

1. Developing an Energy Based Poverty Line for South Africa Marcel Kohler, Bruce Rhodes, Claire Vermaak

2. Background: Poverty alleviation • The importance of energy poverty was confirmed at the World Summit on Sustainable Development in Johannesburg in 2002. The resultant Johannesburg Plan of Implementation, stresses the links between energy use and the general Millennium Development Goals that targets the provision of water, sanitation and education. SA wants to halve poverty and unemployment by 2014 (HSRC, 2008). • Energy poverty deepens general ‘human’ poverty and contributes to the handicaps that go with it such as poor health and education. • Any goals of eradicating energy poverty require an accurate reliable measure.

3. Background: Fuel Poverty • Fuel Poverty or ‘energy burden’ is measured using expenditure on energy as a proportion of income. Whilst simple, it has serious limitations as it does not consider the type of fuel purchased. Also some fuel is not purchased but still used. • If households A and B both spend 15% of their income on energy, both are classified as equally poor according to the DoE. Purchases can be very different in terms of usefulness – or the quantityof energy used by the household, rather than just its cost. Access to free basic electricity (FBE), also changes energy poverty status independent of energy expenditure. • Accounting for access to energy will give a truer representation of energy poverty. What distinguishes an energy poor household from a wealthy one is the wider range of choice in which fuels to use (more efficient, more convenient, less polluting) and which equipment and appliances to buy.

4. Dataset • 2008/2009 Department of Energy (DoE) survey. • After some exclusions/restrictions 3893 households from all nine of South Africa’s provinces. Electrified and non-electrified households in Living Standards Measure (LSM) groups 1 – 3, corresponding to household incomes of less than R1600 per month. • 50 Enumeration Areas sampled per province and 9 households per EA. Two-thirds of households are electrified and the remainder non-electrified. While this ratio is not proportionally representative of households in LSM1 – LSM3, the sample contains weights that are used throughout the analysis to make the results of the estimation nationally representative of the total population of LSM1 – LSM3 households.

5. Dataset: Access-adjusted energy poverty • The survey did NOT record quantity of energy acquired. Energy expenditure figures were converted into ‘useful’ energy quantities in kilowatt hours using Rand-to-energy intensity and efficiency conversion factors(notably Winkler, 2006; DME 2001). All prices used for a given fuel assumed constant nationally. Fuel efficiency conversions largely based on cooking. • These ‘useful’ energy values were converted to access-adjusted useful energy by using an ease of access weighting of 1 to ‘traditional’ fuel types, 2 to ‘transitional’ fuel types and 3 to ‘modern’ fuel types (Kemmlerand Spreng, 2007). Energy accessed by a flick of a switch gets the highest weighting; energy from bulky, collected and stored such as fuelwood gets the lowest rating. See next slide. • Much of the work here stems from an earlier paper: Developing Energy-based Poverty indicators for South Africa, Vermaak et al, JIE, 2009. The details were developed there.

6. Dataset: Energy Sources Definitions and weightings of energy sources • (1) Traditional energy sources: firewood, candles, other energy • (2) Transitional energy sources: gas, paraffin, coal, batteries, car batteries • (3) Modern energy sources: electricity, generators, solar • The dataset also contains an indicator for whether the household receives free basic electricity (FBE). If yes, it is assumed that they consume the full 50kWh per month.

7. Dataset: Energy Sources • Vermaak et al (2009) developed three energy poverty indicators showing that the access-adjusted version generated the highest correlations with other indicators of poverty (notably education, sanitation and house ownership). • Whilst the division of the different fuels into these categories is somewhat arbitrary Vermaak et al (2009) conducted a series of sensitivity tests and established significant robustness regards the fuel types per category. Further to this sensitivity testing was also carried out on the price-to-GJ conversion factors, reinforcing chosen methodologies.

8. Dataset: Poverty lines • The energy poverty lines below are based on 3 pre-determined minimum household energy threshold levels applied to the data. Areas of energy deprivation are then shown on GIS maps Poverty lines used: • $1.25 equiv $1.25 per day “energy threshold” equivalent: 667 kWh per capita per annum (or 0.20GJ per capita per month) • Basic needs IEA (2009) Basic needs “energy threshold”: 1200 kWh per capita per annum(or 0.36GJ per person per month) • Modern needs IEA (2009) Modern needs “energy threshold”: 2000 kWh per capita per annum (or 0.60GJ per capita per month)

9. Results Table 1: Energy expenditure as a percentage of total monthly household income, by province

10. Results

11. Results Table 2: Proportion of households at or below different energy poverty lines, by province.

12. Results

13. Results

14. Results

15. Results

16. Results

17. Results:Consumption of Energy Concentration Index This captures the extent to which households have to rely on a diversity of fuels. Calculated as the sum of the squares of the shares of different energy sources in a household's energy consumption. The maximum value is one (when a household uses only one fuel source), while the lower the value, the more the household relies on a diversity of fuel sources. It is worth noting here that a score of one may mean that only electricity is used (thereby showing energy ‘wealth’) yet by the same logic a score of one could indicate significant energy poverty (only using wood for cooking, heating, lighting).

18. Results:Consumption of Energy Concentration Index

20. Results:Average proportion of household’s monthly GJ energy use derived from each source, by province

22. Findings • The access-adjusted measure of energy poverty generally show that both the Western Cape and Gauteng province are the least energy impoverished (yellow shading) showing the lowest percentages of households below the chosen energy poverty threshold. Not in line with conventional energy burden (expenditure) indicator. • Conversely, the Northern Cape, Limpopo and Mpumulanga appear vulnerable to energy poverty (red shading). • This provides an alternative and better (?) measure of energy poverty than the conventional energy expenditure measures • Poverty lines remain arbitrary. Demand-based approach might be needed.

23. Thank you very much Tack såmycket