Surrey Energy Economics Centre Energy demand and energy

Surrey Energy Economics Centre

Energy demand and energy efficiency in the OECD Energy demand and energy efficiency in the OECD countries: a stochastic demand frontier approachcountries: a stochastic demand frontier approachcountries: a stochastic demand frontier approachcountries: a stochastic demand frontier approach

IEFE IEFE decemberdecember 20092009

Massimo Filippini and Lester HuntMassimo Filippini and Lester Hunt


Outline

Motivation and goals

MethodologyMethodology

Model Specification

Data

Results

Conclusions

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Motivation and Goals

• Energy efficiency has a critical role in addressing energy security, environmental and economic issuesenvironmental and economic issues

• All OECD countries are implementing energy efficiency policies• All OECD countries are implementing energy efficiency policies

• The promotion of energy efficiency policy is also a very important • The promotion of energy efficiency policy is also a very important activity of the IEA.

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Motivation However the exact definition/ measurement of

‘energy efficiency’ remains rather vagueenergy efficiency remains rather vague

W h f d ‘ ’ With energy intensity often used as a ‘proxy’ i.e. the simple energy consumption to GDP ratio

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“Since the oil shocks of the 1970s, G8

countries have promoted energy

efficiency improvements across all

sectors of their economies. As a result

of these policies and structural

changes in their economies, these

countries have been able to decouple p

primary energy use from economic

growth. This is shown in the decline in g

overall primary energy intensity” 10


Footnote 10

Energy intensity is the amount of energy used per unit of

activity. It is commonly calculated as the ratio of energy use to y y gy

GDP. Energy intensity is often taken as a proxy for energy

efficiency, although this is not entirely accurate since changes in ff y, g y g

energy intensity are a function of changes in several factors

including the structure of the economy and energy efficiency.including the structure of the economy and energy efficiency.

M ti ti Surrey Energy Economics CentreMotivation In the 2009 version of the ‘UK Energy Sector In the 2009 version of the UK Energy Sector

Indicators’ (DECC, 2009) it states:

“Traditionally energy intensity has been used Traditionally, energy intensity … has been used

as a proxy for an energy efficiency indicator.

However, intensity trends also include changes

in the composition of energy service demand …

or structural changes …”(p. 31).

Then go on to suggest alternative measures at a

disaggregate level

But we are interest in more aggregate analysis

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Problems of this indicator E f l Energy intensity can vary between countries for several reasons:

the level of industrialization; the mix of services and manufacturing; the climate; the climate; the level of energy efficiency of the appliance and capital stock and

production processes the organization of the production and consumption processes in

space …



Goals of the paper

• To estimate the economy-wide level of energy efficiency for a

sample of OECD countries using an alternative approach based p g pp

on two branches of the literature:

frontier estimation and frontier estimation and

energy demand modelling.

• Estimation of an aggregate energy demand frontier function

in order to derive the “underlying energy efficiency” for

each country

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MethodologyAn aggregate frontier energy demand modelAn aggregate frontier energy demand model

I l f ti f ti i th i l i i l l l f • In general, a frontier function gives the maximal or minimal level of an economic indicator attainable by an economic agent.

• In the case of an aggregate energy demand function the frontier • In the case of an aggregate energy demand function the frontier gives the minimum level of energy necessary for an economy to produce any given level of goods and services.

• The distance from the frontier measures the level of energy consumption above the baseline demand, e.g. the level of energy inefficiency.

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E

An aggregate frontier energy demand model

EEobs

Energy efficiency

th Efro

EFimeasures the ability of a country to

1froE

iEF

yminimize the energy consumption

Y (GDP)

obsEi

F consumption, given a level of y

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Y (GDP)

Two approaches Surrey Energy Economics CentreTwo approachesIn the literature we can distinguish two principal types of approaches

to measure efficiency:

the econometric (parametric) approach and

the linear programming (non-parametric) approach.


Stochastic Frontier MethodsSFAF

Panel data modelsCross section models

Pooled model

RE /FE m d l

Pitt Lee

Random Effects

heterogeneity

True random True fixed

effects

Mundlak’s formulation of RE modelmodel model heterogeneity effects of RE model

Greene (2005)F i Fili i i

Kumbhakar (1993) Heshmati andK bh k (1994)

F��

Fili i i

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Farsi, Filippini, Greene (2005)

Kumbhakar(1994) Filippini,Kuenzle

(2005)

Model specification (1)Surrey Energy Economics Centre

p f ( )• it is assumed that there exists an aggregate energy demand

relationship for a panel of OECD countries, as follows

)SSHISH,ADC,D,YE(PE ititiittititit ,,,

• Eit is aggregate energy consumption per capita, • Pit is the real price of energy, • Y is GDP per capita• Yit is GDP per capita• Dt is the Underlying Energy Demand Trend (time dummies)• DCit is a dummy for climate (Köppen Geiger climate classification)DCit is a dummy for climate (Köppen Geiger climate classification)• Ai is the area of each country• ISHit is the share of the value added of the industrial sector• SSHit is the share of the value added of the service sector

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Model – Specification (2)Surrey Energy Economics Centre

• The approach used in this study is therefore based on the assumptionthat the level of the economy-wide energy efficiency can beapproximated by a one-sided non-negativepp y g

• using a log-log functional form of the above equation and applying apooled frontier approach the (symmetric) model is :

ia

itc

ttitp

ity

it acDpye

itititS

itI uvSSHISH

a symmetric disturbance capturing the effect of noise and as usual is

is interpreted as an indicator of energy efficiency and is

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assumed to be normally distributed

energy efficiency and is assumed to be half-normal distributed


Model specification The above is a pooled model The above is a pooled model

i.e. slopes and the constant (α) are homogenous

B t did id But did consider Fixed effects model

R d ff t d l Random effects model And other variations

B h h l d d l i i But argue that the pooled model is appropriate for the analysis being undertaken here

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Data Surrey Energy Economics CentreData• Unbalanced Panel data set

• 29 OECD countries (i = 1, …, 29)( )

• 1978 to 2006 (t = 1978-2006)

• where:• E = per capita aggregate energy consumption (toe);• Y = per capita GDP (thousand US2000$PPP);• P = index of real energy prices (2000=100); (all from IEA database)P = index of real energy prices (2000=100); (all from IEA database)• C = climate dummy variable where a country is characterized by a cold

climate (according to the Köppen-Geiger climate classification)• Ai is the area of each country in km2Ai is the area of each country in km2• ISH is the share of the value added of the industrial sector • SSH is the share of the value added of the service sector (all from OECD

database)

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database)

Results (1)Surrey Energy Economics Centre

Estimated coefficients (t-values in parentheses)Constant -2.247

(-7.651)

y 0.899(38.942)

p -0.272( 4 743) 0 35

0.4

Coefficients time dummies

(-4.743)

c 0.227(12.308)

a 0 0200.15

0.2

0.25

0.3

0.35

a 0.020(3.352)

ISH 0.017(9.113)

0

0.05

0.1

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

SSh 0.029(11.501)

Time dummies YesThe estimated mean average efficiency is estimated to be about

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Lamda () 2.827(8.583)

estimated to be about 78% (median 79%)

Results Surrey Energy Economics CentreResults Shows that the estimated coefficients and lambda have the Shows that the est mated coeff c ents and am da ha e the

expected signs and are statistically significant

Lambda (λ) gives information on the relative contribution of uitLambda (λ) gives information on the relative contribution of uit

and vit

- Showing that in this case the one-sided error component is l ti l lrelatively large

The estimated income elasticity is about 0.9

The estimated price elasticity is about -0.3

both not out of line with previous estimates

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ResultsSurrey Energy Economics Centre

The climate variable, DC, also appears to have an important influence on

a country’s energy demand

Also larger shares of a country’s industrial and service sectors also

increase energy consumption

The time dummies as a group are significantThe time dummies, as a group, are significant

Also as expected, the overall the trend in their coefficients is negativeg

however, they do not fall continually over the estimation period, reflecting the ‘non-linear’ impact of technical progress and other exogenous variables.

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Results (2)Surrey Energy Economics Centre

• For some countries the (negative) correlation between Energy I t it d th ti t d E Effi i i it hi h b t f Intensity and the estimated Energy Efficiency is quite high, but for some it is not and for some it is positive.

• Therefore focussing only on Energy Intensity could give a misleading picture – hence the focus should be on the estimated Energy picture hence the focus should be on the estimated Energy Efficiency from the model

• For the Period 1998-2006 we observe the following:• See Table and FiguresSee Table and Figures

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Conclusions Surrey Energy Economics CentreConclusions This research is a fresh attempt to isolate core energy efficiency for a

panel of 29 OECD countries, opposed to relying on the simple energy to

GDP ratio – or energy intensity

By estimating a measure of ‘underlying energy efficiency’ by combining

the approaches taken in energy demand modelling and frontier analysispp gy g y

The energy demand specification controls for income, price, climate,

country size structure of the economy and a common underlying energy country size, structure of the economy and a common underlying energy

demand trend, thus the ‘efficiency’ measure is obtained – in a similar way

t pr vi us rk n c st nd pr ducti n stim ti nto previous work on cost and production estimation

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ConclusionsSurrey Energy Economics Centre

The estimates for core energy efficiency using this approach

show that although for a number of countries the change in

energy intensity over time might give a reasonable indication of

efficiency improvements

this is not always the case

both over time and across countries

Italy and Greece being prime examplesFor Italy energy intensity declines over the estimation - For Italy, energy intensity declines over the estimation period suggesting an improvement in energy efficiency, whereas the estimated underlying energy efficiency falls over the periodover the period

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Conclusions Therefore unless the analysis advocated here is undertaken it is

not possible to know whether the energy intensity of a country is

a good proxy for energy efficiency or not

Hence, it is argued that this analysis should be undertaken in

order to give policy makers an additional indicator other than the

rather naïve measure of energy intensity in order to try to avoid

potentially misleading policy conclusions.

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Reference

Filippini M., Hunt L. (2009). Energy demand and energy

efficiency in the OECD countries: a stochastic demant frontier

approach, CEPE Working Paper 68, Centre for Energy Policy and

Economics, ETH Zurich, October 2009. [pdf, 897 kB]

http://www.cepe.ethz.ch/publications/workingPapers

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