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ECONOMIC REGULATORS CONFERENCENERSA & TIPSAUGUST 2012
The sensitivity of the South African industrial sector’s electricity consumption
to electricity price fluctuations
Dr Roula Inglesi-Lotz
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Structure of presentation
• Introduction• Motivation• Purpose• Methodology• Data• Results• Conclusion
Introduction
• In South Africa, choosing the correct and appropriate electricity price regime has been an issue for discussion during the last decade.
• Imperative for the policy makers to be able to understand and in a way, “predict” the reactions of consumers to price changes.
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Introduction (2)
• Different price structures as well as different electricity profiles are the main reasons why different economic sectors should be examined separately and treated accordingly.
• The South African industrial sector is responsible for an average of approximately 58% of the total electricity consumption, making this sector one of the most important ones from an energy consumption point of view.
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Motivation
• Inglesi-Lotz and Blignaut (2011) found out that among various economic sectors such as agriculture and transport, the industrial sector was the only one whose electricity consumption behaviour was found sensitive to price fluctuations on average during the period 1993 to 2006.
• Inglesi-Lotz (2011) found out that the sensitivity of electricity consumption to price fluctuations changes over time by using the Kalman filter econometric methodology for the period 1970 to 2007
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Purpose
• This paper proposes that the sensitivity of the industrial sector’s electricity consumption to price fluctuations (price elasticity) has been changing through the years.
• The purpose of this paper is to estimate the time-varying sensitivity of the industrial sector’s electricity consumption to price changes by using the Kalman filter econometric methodology for the period 1970 to 2007.
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LiteratureAuthors Year Country/region Sector Price elasticity
Pindcyck 1979 Group of countriesIndustrial & Commercial
-0.07 to -0.16
Lynk 1989 UK Industrial -0.69Caloghirou et al. 1997 Greece Industrial -0.90Beenstock et al. 1999 Israel Industrial 0.123
Hunt et al. 2003 UK Manufacturing -0.20Kamerchen, Porter 2004 Oman Industrial -0.34 to -0.55Dimitropoulos et al. 2005 UK Manufacturing -0.159
Roy et al. 2006 Country panel Industrial -0.80 to -1.76
Enevoldsen et al. 2007Denmark, Norway and
SwedenIndustrial -0.35 to -0.44
Agnolucci 2007 UK Industrial -0.60Agnolucci 2009 UK and Germany Industrial -0.64
Inglesi-Lotz and Blignaut
2011 South Africa Industrial -0.869
He et al. 2011 ChinaIndustrial & Commercial
-0.018
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Literature (2)
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• South Africa (Industrial sector)– Inglesi-Lotz and Blignaut (2011): Estimating the
price elasticity of demand for electricity by sector in South Africa.
– Ziramba (2009): Disaggregate energy consumption and industrial production in South Africa.
– Inglesi-Lotz and Blignaut (2011): South Africa’s electricity consumption: A sectoral decomposition analysis
Methodology
1. Hansen test for stability of parameters
Testing whether the coefficients are statistically varying over time or not.
2. Kalman filter estimation
Estimating the equation with the assumption of time-varying coefficients (from Hansen test).
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𝑙𝑐𝑜𝑛𝑠=𝑎0+𝑎1 𝑙𝑝+𝑎2𝑙𝑦+𝑎3𝑙𝑛+𝜀𝑡
Data
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1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
-
5.00
10.00
15.00
20.00
25.00
30.00
p
c/kW
h
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
-
200,000,000,000,000
400,000,000,000,000
600,000,000,000,000
800,000,000,000,000
1,000,000,000,000,000
1,200,000,000,000,000
1,400,000,000,000,000
1,600,000,000,000,000
y
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
-
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
n
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
-
20,000,000,000
40,000,000,000
60,000,000,000
80,000,000,000
100,000,000,000
120,000,000,000
cons
MW
h
Data (2)
Variable Source Units
Industrial electricity consumption (cons)
National Energy council and Energy Balances (DoE)
kWh
Average electricity price for the industry (p)
Energy price report (DoE) c/kWh
Real output of the industrial sector (y)
Quantec Constant 2005 prices
ZAR
Employment of the industrial sector (n)
Quantec number
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Results
Lc statistic
p-value Conclusion
ly, ln 0.192 >0.2 The null hypothesis (H0: parameter
stability) cannot be rejected the parameters are constant
Lp 0.400 0.077 The null hypothesis (H0: parameter
stability) can be rejected the parameter is not constant
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Hansen test results
Results (2)
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Kalman filter results
SSpace model
Sample 1976-2007
Included observations 32
Number of iterations to convergence 9
Variables Estimated coefficients p-values
c(1) (output coefficient) 0.690 0.000
c(2) (employment coefficient) 0.252 0.061
c(3) -4.065 0.000
Final state p-values
sv1 (price coefficient) -0.952 0.000
Results (3)
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Kalman filter results19
77
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
0
5
10
15
20
25
30
-1.15
-1.10
-1.05
-1.00
-0.95
-0.90
-0.85
electricity price price elasticity
c/kW
h
Conclusion
• The findings of the exercise show that indeed price sensitivity changed since the 1970s: it has decreased in absolute values from -1 in 1980 to -0.953 in 1990.
• Since then, the elasticity stabilised around -0.95 showing that the industrial sector has experienced an inelastic demand. In other words, the behaviour of the industrial consumers did not vary significantly in the 2000s.
• Sensitivity of consumers to price fluctuations becomes smaller in absolute terms while the real prices of electricity declined over the last half of the sample examined.
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Conclusion (2)
• As the prices increase, probably reaching the levels of the 1970s or even before, the industrial sector’s behaviour might change and the elasticity might end up at levels higher than one (elastic).
• Although, the price elasticity of electricity consumption is high, the historically low level of electricity prices was a factor for the unchanged sensitivity of the industrial sector in the last decade.
• This low level of prices is indicative made the electricity costs of the economy to contribute little in the total cost of operation of all the economic sectors.
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Thank you for your time and attention
Dr Roula Inglesi-Lotz
Department of Economics
University of Pretoria20