Due to difficulties in accessing detailed energy modelling and usage data that are required to estimate energy needs that are responsive to local circumstances, we propose an equivalence scale approach to the determination of required energy consumption. Our method requires the estimation of energy equivalence scales that are used to rescale reference household energy consumption and, thus, yield household-specific energy requirements. We apply the method using readily available income and expenditure data, finding that estimated required energy is well above actual energy expenditure for low- and middle-income households, which is consistent with an expectation that basic energy needs for poor households may not be met. The proposed approach is general enough to incorporate other features that might be deemed relevant and available in other settings, and, therefore, can be used to examine the affordability component of SDG 7, undertake energy poverty analysis or design appropriate policies.
BednarD.J.ReamesT.G. (2020). “Recognition of and response to energy poverty in the United States.” Nature Energy5(6): 432–439. https://doi.org/10.1038/s41560-020-0582-0.
2.
BEIS and BRE (2018). Fuel poverty methodology handbook. Department for Business, Energy & Industrial Strategy (BEIS) and Building Research Establishment (BRE), London, UK.
3.
BerkouwerS.B. (2020). “Electric heating and the effects of temperature on household electricity consumption in South Africa.” The Energy Journal41(4). https://doi.org/10.5547/01956574.4L4.sber.
4.
BlackorbyC.DonaldsonD. (1993). “Adult-equivalence scales and the economic implementation of interpersonal comparisons of well-being.” Social Choice and Welfare10(4): 335–361. https://doi.org/10.1007/BF00182510.
5.
BoardmanB. (1991). Fuel poverty: from cold homes to affordable warmth. London, UK: Belhaven Press.
6.
BRE (2015). A technical description of the BRE domestic energy model, version 1.1. Building Research Establishment (BRE), UK. https://www.bre.co.uk/filelibrary/bredem/BREDEM-2012-specification.pdf.
7.
ChakravartyS.TavoniM. (2013). “Energy poverty alleviation and climate change mitigation: is there a trade off?” Energy Economics40: S67–S73. https://doi.org/10.1016/j.eneco.2013.09.022.
8.
CharlierD.LegendreB. (2019). “A multidimensional approach to measuring fuel poverty.” The Energy Journal40(2): 27–53. https://doi.org/10.5547/01956574.40.2.bleg.
9.
DCLG (2009). English Housing Survey (EHS). Department for Communities and Local Government (DCLG)London, UKhttps://www.gov.uk/government/collections/english-housing-survey.
10.
DeatonA. (1987). The analysis of household surveys: a microeconometric approach to development policy. Washington, DC: World Bank.
11.
DeatonA.GroshM. (2000). “Consumption.” In GroshM.GlewweP. (editors), “Designing household survey questionnaires for developing countries,” World Bank, Washington, DC91–134.
12.
DeatonA.MuellbauerJ. (1980). Economics and consumer behavior. Cambridge University Press. https://doi.org/10.1017/CB09780511805653.
13.
DOE (2017). South African Energy Price Report 2017. Department of Energy (DOE), Pretoria, South Africa.
14.
DOE (2018). South African energy efficiency label: appliance sales trainingfor the retail sector. Department of Energy (DOE), Pretoria, South Africa. https://www.savingenergy.org.za/wp-content/uploads/2018/05/Energy-Label-Learner-Guide.pdf.
15.
DongY. (2010). “Endogenous regressor binary choice models without instruments, with an application to migration.” Economics Letters107(1): 33–35. https://doi.org/10.1016/j.econlet.2009.12.017.
16.
EngelE. (1895). “Die productions- und consumtionsverhaltnisse des königreichs sachsen.” In E. Engel (editor), “Die Leb-enskosten belgischer Arbeiter-Familien, früher und jetzt,” Dresden, Heinrich.
17.
HagenaarsA.J.de VosK.ZaidiM.A. (1994). Poverty statistics in the late 1980s: research based on micro-data. Eurostat (Statistical office of the European Communities).
18.
HayfieldT.RacineJ.S. (2008). “Nonparametric econometrics: the np package.” Journal of Statistical Software27(5): 1–32. https://doi.org/10.18637/jss.v027.i05.
19.
HeindlP. (2015). “Measuring fuel poverty: general considerations and application to German household data.” FinanzArchiv: Public Finance Analysis71(2): 178–215. https://doi.org/10.1628/001522115X14285723527593.
20.
HernandezD. (2016). “Understanding ‘energy insecurity’ and why it matters to health.” Social Science & Medicine167: 1–10. https://doi.org/10.1016/j.socscimed.2016.08.029.
21.
HerreroS.T. (2017). “Energy poverty indicators: a critical review of methods.” Indoor and Built Environment26(7): 10181031. https://doi.org/10.1177/1420326X17718054.
22.
HillsJ. (2011). Fuel poverty: the problem and its measurement. Department of Energy and Climate Change (DECC), London, UK.
23.
HillsJ. (2012). Getting the measure of fuel poverty: final report of the fuel poverty review. Department of Energy and Climate Change (DECC), London, UK.
24.
IchimuraH. (1993). “Semiparametric least squares (SLS) and weighted SLS estimation of single-index models.” Journal of Econometrics58(1-2): 71–120. https://doi.org/10.1016/0304-4076(93)90114-K.
25.
ImbensG.W.NeweyW.K. (2009). “Identification and estimation of triangular simultaneous equations models without additivity.” Econometrica77(5): 1481–1512. https://doi.org/10.3982/ECTA7108.
KochS.F. (2018). “Catastrophic health payments: does the equivalence scale matter?” Health Policy and Planning33(8): 966–973. https://doi.org/10.1093/heapol/czy072.
28.
KochS.F.Tshiswaka-KashalalaG. (2018). “The demand for reproductive health care.” Journal of African Economies27(4): 405–429. https://doi.org/10.1093/jae/ejx042.
29.
KyprianouI.SerghidesD.VaroA.GouveiaJ.KopevaD.MurauskaiteL. (2019). “Energy poverty policies and measures in 5 EU countries: a comparative study.” Energy and Buildings196: 46–60. https ://doi.org/10.1016/j.en-build.2019.05.003.
30.
LazearE.P.MichaelR.T. (1980). “Family size and the distribution of real per capita income.” American Economic Review70(1): 91–107. https://doi.org/10.3386/w0230.
31.
LegendreB.RicciO. (2015). “Measuring fuel poverty in France: which households are the most fuel vulnerable?” Energy Economics49: 620–628. https://doi.org/10.1016/j.eneco.2015.01.022.
32.
LeibbrandtM.FinnA.OosthuizenM. (2016). “Poverty, inequality, and prices in post-apartheid South Africa.” In ArndtC.McKayA.TarpF. (editors), “Growth and Poverty in Sub-Saharan Africa,” Oxford University Press, Oxford, UK393–417. https://doi.org/10.1093/acprof:oso/9780198744795.003.0017.
33.
LewbelA. (2012). “Using heteroscedasticity to identify and estimate mismeasured and endogenous regressor models.” Journal of Business & Economic Statistics30(1): 67–80. https://doi.org/10.1080/07350015.2012.643126.
34.
LewbelA.PendakurK. (2008). “Equivalence Scales.” In DurlaufS. N.BlumeL. E. (editors), “The New Palgrave Dictionary of Economics: Volume 1—8,” Palgrave MacmillanUK, London1832–1835. https://doi.org/10.1057/978-1-349-95121-5_2056-1.
35.
LiK.LloydB.LiangX.-J.WeiY.-M. (2014). “Energy poor or fuel poor: what are the differences?” Energy Policy68: 476–481. https://doi.org/10.1016/j.enpol.2013.11.012.
36.
LiQ.RacineJ. (2004). “Cross-validated local linear nonparametric regression.” Statistica Sinica14: 485–512.
37.
LiQ.RacineJ.S. (2007). Nonparametric econometrics: theory and practice. Princeton University Press, Princeton, NJ, US.
38.
LiddellC.MorrisC.McKenzieS.RaeG. (2012). “Measuring and monitoring fuel poverty in the UK: national and regional perspectives.” Energy Policy49: 27–32. https://doi.org/10.1016/j.enpol.2012.02.029.
39.
MemmottT.CarleyS.GraffM.KoniskyD.M. (2021). “Sociodemographic disparities in energy insecurity among low-income households before and during the COVID-19 pandemic.” Nature Energy6: 186–193. https://doi.org/10.1038/s41560-020-00763-9.
40.
MeyerJ.P. (2000). “A review of domestic hot-water consumption in South Africa.” R & D Journal16(3): 55–61.
41.
MohrT.M. (2018). “Fuel poverty in the US: evidence using the 2009 Residential Energy Consumption Survey.” Energy Economics74: 360–369. https://doi.org/10.1016/j.eneco.2018.06.007.
42.
NelsonJ.A. (1988). “Household economies of scale in consumption: theory and evidence.” Econometrica56(6): 1301–1314. https://doi.org/10.2307/1913099.
43.
NicholsonJ.L. (1976). “Appraisal of different methods of estimating equivalence scales and their results.” Review of Income and Wealth22(1): 1–11. https://doi.org/10.1111/j.1475-4991.1976.tb01138.x.
44.
NtaintasisE.MirasgedisS.TourkoliasC. (2019). “Comparing different methodological approaches for measuring energy poverty: evidence from a survey in the region of Attika, Greece.” Energy Policy125: 160–169. https://doi.org/10.1016/j.enpol.2018.10.048.
45.
OkushimaS. (2017). “Gauging energy poverty: a multidimensional approach.” Energy137: 1159–1166. https://doi.org/10.1016/j.energy.2017.05.137.
46.
PachauriS.SprengD. (2004). “Energy use and energy access in relation to poverty.” Economic and Political weekly39(3): 271–278.
47.
PapadaL.KaliampakosD. (2018). “A stochastic model for energy poverty analysis.” Energy Policy116: 153–164. https://doi.org/10.1016/j.enpol.2018.02.004
48.
PapadaL.KaliampakosD. (2020). “Being forced to skimp on energy needs: a new look at energy poverty in Greece.” Energy Research & Social Science64: 101450. https://doi.org/10.1016/j.erss.2020.101450.
49.
PapkeL. E.WooldridgeJ.M. (1996). “Econometric methods for fractional response variables with an application to 401(k) plan participation rates.” Journal of Applied Econometrics11(6): 619–632. https://doi.org/10.1002/(SICI)1099-1255(199611)11:6<619::AID-JAE418>3.0.CO;2-1.
50.
PendakurK. (1999). “Semiparametric estimates and tests of base-independent equivalence scales.” Journal of Econometrics88(1): 1–40. https://doi.org/10.1016/S0304-4076(98)00020-7.
51.
PollakR.A.WalesT.J. (1979). “Welfare comparisons and equivalence scales.” American Economic Review: Papers and Proceedings 69(2): 216–221.
52.
R Core Team (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing Vienna, Austria.
53.
RacineJ.LiQ. (2004). “Nonparametric estimation of regression functions with both categorical and continuous data.” Journal of Econometrics119(1): 99–130. https://doi.org/10.1016/S0304-4076(03)00157-X.
54.
RademaekersK.YearwoodJ.FerreiraA.PyeS.HamiltonI.AgnolucciP.GroverD.KarásekJ.AnisimovaN. (2016). Selecting indicators to measure energy poverty. Trinomics Rotterdam, The Netherlands.
55.
RaoN.D.MinJ. (2018). “Decent living standards: material prerequisites for human wellbeing.” Social Indicators Research138(1): 225–244. https://doi.org/10.1007/s11205-017-1650-0.
56.
RobinsonC. (2019). “Energy poverty and gender in England: a spatial perspective.” Geoforum104: 222–233. https://doi.org/10.1016/j.geoforum.2019.05.001.
57.
SchatzE.MadhavanS.CollinsonM.Gomez-OliveF.X.RalstonM. (2015). “Dependent or productive? A new approach to understanding the social positioning of older South Africans through living arrangements.” Research on Aging37(6): 581–605. https://doi.org/10.1177/0164027514545976.
58.
SetlhaoloD.XiaX. (2015). “Optimal scheduling of household appliances with a battery storage system and coordination.” Energy and Buildings94: 61–70. https://doi.org/10.1016/j.enbuild.2015.02.051.
59.
SetlhaoloD.XiaX.ZhangJ. (2014). “Optimal scheduling of household appliances for demand response.” Electric Power Systems Research116: 24–28. https://doi.org/10.1016/j.epsr.2014.04.012.
60.
StatsSA (2017a). Living Conditions Survey 2014-2015 [dataset]. Version 1.1. Statistics South Africa (Stats SA) Pretoria: Statistics South Africa (Stats SA) [producer]. Cape Town: DataFirst [distributor], 2017
61.
StatsSA (2017b). Living Conditions Survey 2014/2015 metadata. Statistics South Africa (Stats SA) Pretoria, South Africa.
62.
United Nations (2021). The Sustainable Development Goals Report 2021. New York: United Nations.
63.
WangM.-C.Van RyzinJ. (1981). “A class of smooth estimators for discrete distributions.” Biometrika68(1): 301–309. https://doi.org/10.1093/biomet/68.1.301.
64.
WelschH.BiermannP. (2017). “Energy affordability and subjective well-being: evidence for European countries.” The Energy Journal38(3): 159–176. https://doi.org/10.5547/01956574.38.3.hwel.
65.
YatchewA.SunY.DeriC. (2003). “Efficient estimation of semiparametric equivalence scales with evidence from South Africa.” Journal of Business & Economic Statistics21(2): 247–257. https://doi.org/10.1198/073500103288618936.
66.
YeY.KochS.F. (2021). “Measuring energy poverty in South Africa based on household required energy consumption.” Energy Economics103: 105553. https://doi.org/10.1016/j.eneco.2021.105553.
67.
YeY.KochS.F.ZhangJ. (2018). “Determinants of household electricity consumption in South Africa.” Energy Economics75: 120–133. https://doi.org/10.1016/j.eneco.2018.08.005.
68.
YeY.KochS.F.ZhangJ. (2020). “Modelling required energy consumption with equivalence scales.” Economic Research Southern Africa Working Paper No 809.
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