iefe 2012 -energy demand final - fondazione eni enrico mattei

1

The Demand for Energyof Italian Households

Ivan Faiella

Joint IEFEJoint IEFE--FEEM Seminar, 22nd March 2012FEEM Seminar, 22nd March 2012

2

Italian Households Energy Demand

ResearchResearch questionsquestions

1. What is the consumptionconsumption of energy of Italian Households?

2. What have been the main driversmain drivers of energy demand in the past?

3. Is heterogeneityheterogeneity relevant in explaining energy demand?

4. How changes in demographic structurechanges in demographic structure will affect energy demand in the next decades?

5. How changes in energy prices and surface changes in energy prices and surface temperaturestemperatures will affect energy demand in the next decades?

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Households Energy Expenditure: main conclusions

1. Italian households use a third of total final energy demand (47 Mtoe, about 2 toe per HH and 0.8 toe per individual): 80 per cent is used for heating/cooking and transportation.

2. Households Energy bill amounts to about 12 per cent of total expenditure (in 2008), is more unequally distributed than other items and its relevance is increasing (energy poverty).

3. Households Energy expenditure show different patterns according to households’ characteristics.

4. Simulation exercise (NOT FORECASTS!): changes in demographic structure, in energy prices and surface temperatures will reduce the mean energy demand in the next decades - with the increasing number of HH contrasting the aging effect. Energy efficiency improvements will probably sustain this reduction.

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5


0

5

10

15

20

25

30

35

40

45

50

55

19711972197319741975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008

Mto

e

Petroleum products (HH transport)ElectricitySolid Fuels&RenewablesGasPetroleum Products (heating) estimates

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Households Energy Intensity

0

10

20

30

40

50

60

70

80

90

100

19711972197319741975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008

Toe

per

M€

(200

0)

Petroleum products (HH transport)ElectricitySolid Fuels&RenewablesGasPetroleum Products

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Relative decoupling of HHs Energy Demand

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30

35

40

45

50

5519

7119

7219

7319

7419

7519

7619

7719

7819

7919

8019

8119

8219

8319

8419

8519

8619

8719

8819

8919

9019

9119

9219

9319

9419

9519

9619

9719

9819

9920

0020

0120

0220

0320

0420

0520

0620

0720

08

50

60

70

80

90

100

110

HHs energy intensity (toe per M€ 2000)

HHs energy demand (Mtoe)

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Drivers of HHs energy demand: Energy Prices…

80

90

100

110

120

130

140

150

160

170

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Pric

e in

dex

(200

2=10

0)Petrol

Gasoil (motor fuel)

Gasoil (heating)

Natural gas

Electricity

Price level (excluding energy)

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… and demographics

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Change in E/CChange in C/HHChange in HH

High crude oil prices High crude oil pricesLow crude oil prices

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Italian Households Energy Expenditure

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Households Energy Expenditure

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Households Energy Expenditure: trends

Ratio of energy expenditure to total expenditure (1997-2008)

private transport

heating

electricity

0%

1%

2%3%

4%

5%

6%7%

8%

9%

10%11%

12%

13%

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

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Households Energy Expenditure: stylised fact 1

0%

1%

2%

3%

4%

5%

6%

7%

8%

0 20 40 60 80 100age class of the RP

heating private transport electricity

Ratio of energy expenditure to total expenditure (2008)

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0%

1%

2%

3%

4%

5%

6%

7%

8%

1 2 3 4+household size


Households Energy Expenditure: stylised facts 2


15

0%

1%

2%

3%

4%

5%

6%

7%

1 2 3 4 5 6 7 8 9 10tenth of equivalent expenditure




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4,4% 3,6% 2,9%

5,2%5,6%

6,1%

1,9% 2,2% 3,2%

0%

2%

4%

6%

8%

10%

12%

14%

North Center South

geographical area




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Households Energy Expenditure: distribution

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A model for households Energy Expenditure

1.Prices (P): in the short term, energy demand is rather inelastic, because of the low degree of substitution, while in the medium term, the rise of energy prices induce households to invest in energy-efficient appliances (or switch to less expensive fuels). The efficiency gain might be crowded-out by an increase in energy demand a phenomenon known in the literature as rebound effect.

2.Demographics (Zi): energy demand increases with the number of household members and with the presence of pre-school children and elderly people that spend more time at home. Households where members commute using private transportation demand more energy and those living in small towns have less options to use public transportation.

3.Climatic conditions (T): the use of energy is also affected by the change in surface temperature. The increase in the temperatures reduces heating demand but increase the request for cooling and this can primarily affect households residing in Southern Italy. For example in 2006 the peak demand for electricity was registered - for the first time in Italy - during summer.

(i= i-th Household. j=1,2,3: heating, transport and electricity)

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A model for households Energy Expenditure

(i= i-th Household. j=1,2,3: heating, transport and electricity)

SURE estimate of a quasi-quadratic Almost Ideal Demand System (QAIDS):

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A model for households Energy Expenditure(95 per cent confidence intervals)

Heating

-15% -10% -5% 0% 5% 10% 15%

Age

Age squared

Employed

Self employed

College degree

More than 2 HH members

Dependants (age<6or age>74)

More than 4 rooms

Houseow ner

Expenditure < 20th perc

Expenditure > 20th perc

North

South

Surface temperature

Surface temperature (South)

log(expenditure)

log(expenditure) squared

Number of durables

log of price for heating (gas)

log (price for electricity)

log (price for liq fuels)

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Electricity

-8,0% -6,0% -4,0% -2,0% 0,0% 2,0%

Age

Age squared

Employed

Self employed

College degree



More than 4 rooms

Houseowner



North

South

Surface temperature


log(expenditure)


Number of durables





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Private transport

-5% 0% 5% 10% 15% 20% 25% 30%

Age

Age squared

Employed

Self employed

College degree



More than 4 rooms

Houseowner



North

South

Surface temperature


log(expenditure)


Number of durables





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Households Energy Expenditure: 2020 and 2030 simulation

«All models are wrong; some models are useful», Cox

«Energy forecasts are not worth even the cost of the cheapest acid paper on which they get printed», Smil

Caveats

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Households Energy Expenditure: 2020 and 2030 simulation

Simulation of energy-related shares on households expenditure using the structural relations estimated.

• Energy prices changes (electricity, gas and liquid fuels) incorporate IEA scenarios for 2020 and 2030.

• Surface temperatures modifications incorporates OECD (2008) projections for 2020 and 2030.

• Demographic structure is modified (raking ratio technique):Age x Sex distribution in 2020 and 2030, according to Istat (2009). Household size using Istat and survey data to estimate the trend in the reduction of household size (and the increase in households number)

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Households Energy Expenditure: simulation results

• Energy-related shares decrease by 1.8 percentage points (pp) in 2020 and of 2.2 pp in 2030. The reduction is largely driven by the effect of climate change and prices (there is a major reduction in the share of heating).

• The increase in surface temperatures reduces the demand for heating and apparently counterbalances the higher demand for electricity induced by the request of cooling.

• Unless a major structural shift in households behaviour takes place, the aging of the Italian population is going to exercise a downward pressure on energy use (demand for electricity and heating will be more than compensated by the decrease in the demand for private transportation).

• The distribution of the shares in 2020 and 2030 becomes more unequal (this is determined by both demographics, prices dynamics and higher temperatures).

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Improvements and research agenda

1. Data: Temperatures should be modeled at a more disaggregated level (at least at nuts1 level); some indicator of energy efficiency could be included;

2. Model: stochastic simulation;

3. Current research: compute energy demand and GHG emissions from energy expenditure and price data: possibility to study HHsemissions at micro level (Cingano e Faiella, 2012, An analysis of a Carbon Tax on transport; Faiella, 2013, Households' GHG emissions at the Micro Level: A Tool for Smartest Climate Policies )

4. Future research: analysis of energy poverty (role of gas and electricity bonus in alleviating it, aggregate and distributive effects of increasing costs to support RES-E);

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The Demand for Energyof Italian Households

Ivan Faiella

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iefe 2012 -energy demand final - fondazione eni enrico mattei

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