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Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach


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This paper attempts to estimate a panel ‘frontier’ whole economy aggregate energy demand function for 29 countries over the period 1978 to 2006 using parametric stochastic frontier analysis (SFA). Consequently, unlike standard energy demand econometric estimation, the energy efficiency of each country is also modelled and it is argued that this represents a measure of the underlying efficiency for each country over time, as well as the relative efficiency across the 29 OECD countries. This shows that energy intensity is not necessarily a good indicator of energy efficiency, whereas by controlling for a range of economic and other factors, the measure of energy efficiency obtained via this approach is. This is, as far as is known, the first attempt to econometrically model OECD energy demand and efficiency in this way and it is arguably particularly relevant in a world dominated by environmental concerns with the subsequent need to conserve energy and/or use it as efficiently as possible. Moreover, the results 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. Therefore, unless this analysis 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 to avoid potentially misleading advice to policy makers.
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Energy demand and energy
efficiency in the OECD countries: a
stochastic demand frontier
Massimo Filippini, Lester C. Hunt
CEPE Working Paper No. 68
Oktober 2009
Zurichbergstrasse 18 (ZUE E)
CH-8032 Zurich
Energy demand and energy efficiency in the OECD countries: a
stochastic demand frontier approach
Massimo Filippini and Lester C Hunt
Centre for Energy Policy and
Economics (cepe), ETH Zurich
Department of Economics,
University of Lugano,
Surrey Energy Economics
Centre (SEEC) and Research
Group on Lifestyles Values and
Environment (RESOLVE),
Department of Economics,
University of Surrey, UK
This paper attempts to estimate a panel ‘frontier’ whole economy aggregate energy demand
function for 29 countries over the period 1978 to 2006 using stochastic frontier analysis (SFA).
Consequently, unlike standard energy demand econometric estimation, the energy efficiency of
each country is also modelled and it is argued that this represents a measure of the underlying
efficiency for each country over time, as well as the relative efficiency across the 29 OECD
countries. This shows that energy intensity is not necessarily a good indicator of energy
efficiency, whereas by controlling for a range of economic and other factors, the measure of
energy efficiency obtained via this approach is. This is, as far as is known, the first attempt to
model energy demand and efficiency in this way and it is arguably particularly relevant in a
world dominated by environmental concerns with the subsequent need to conserve energy
and/or use it as efficiently as possible. Moreover, the results 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. Therefore, unless this analysis 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 to
avoid potentially misleading advice to policy makers.
JEL: D, D2, Q, Q4, Q5.
Keywords: Energy demand; OECD; efficiency and frontier analysis; energy efficiency.
We are grateful to Olutomi Adeyemi for his assistance with the data collection. A preliminary version of the paper
was presented at the 2nd International workshop on Empirical Methods in Energy Economics (Jasper, Canada,
2009) and we are grateful to the discussant, Denise Young and other participants for their very helpful comments
and suggestions. A revised version of the paper was presented at the 10th IAEE European conference (Vienna,
Austria, 2009) and we thank participants for their additional comments and suggestions. The authors are, of
course, responsible for all errors and omissions.
1 Introduction
During the last 20 years, there has been considerable debate within energy policy about
the possible contribution from an improvement in energy efficiency and on the effectiveness of
ecological tax reforms in the alleviation of the greenhouse effect and in the decrease of the
dependency on fossil fuels. In order to design and implement effective energy policy
instruments to promote an efficient and parsimonious utilization of energy, it is necessary to
have information on energy demand price and income elasticities in addition to sound
indicators of energy efficiency.
In practical energy policy analysis, the typical indicator used is energy intensity,
defined as the ratio of energy consumption to GDP. This is highlighted by a report from the
International Energy Agency (IEA, 2009) on the Energy Efficiency Policies in the G8, which
states that since the 1970s many countries have promoted energy efficiency improvements,
which is illustrated by the decline in energy intensity. The report goes on to say that Energy
intensity is the amount of energy used per unit of activity. It is commonly calculated as the
ratio of energy use to GDP. Energy intensity is often taken as a proxy for energy efficiency,
although this is not entirely accurate since changes in energy intensity are a function of
changes in several factors including the structure of the economy and energy efficiency” (our
emphasis, p. 15). This highlights the weakness of this simple aggregate energy consumption to
GDP ratio in that it does not measure the level of ‘underlying energy efficiency’ that
characterizes an economy; hence, it is difficult to make conclusions for energy policy based
upon this simple measure.
In this paper, an alternative way to estimate the economy-wide level of energy
efficiency is proposed, by drawing on different strands of the energy economics research
literature; in particular, frontier estimation and energy demand modelling. An energy demand
frontier function is therefore estimated in order to attempt to isolate ‘underlying energy
efficiency’, by explicitly controlling for income and price effects, country specific effects,
climate effects and a common Underling Energy Demand Trend (the UEDT, capturing both
‘exogenous’ technical progress and other exogenous factors). Hence, it allows for the impact
of ‘endogenous’ technical progress’ through the price effect and ‘exogenous’ technical
progress through the UEDT.
The aim is to analyse economy wide energy efficiency; hence, the estimated model
introduced below is for aggregate energy consumption for the whole economy. Economy wide
aggregate energy demand is derived from the demand for energy services such as heat,
illumination, cooked food, hot water, transport services, manufacturing processes, etc. To
produce the desired services it is generally necessary to use a combination of energy fuels and
capital equipment such as household appliances, cars, insulated walls, machinery, etc. This
implies that the demand for energy is influenced by the level of energy efficiency of the
equipment and, generally, of the production process. For instance, some relatively new
equipment and production processes are able to provide the same level of services and products
using less energy than old equipment. This comes from research and development that
improves the thermodynamic efficiency of appliances and the capital stock, as well as
production processes – there is a technical improvement. Of course, in reality, apart from the
technological and economic factors there are a range of exogenous institutional and regulatory
factors that are important in explaining the level of energy consumption, furthermore, these
exogenous changes are unlikely to impact in a consistent rate over time. Hence, it is important
that the UEDT is specified in such a way that it is ‘non-linear’ and could increase and/or
decrease over the estimation period as advocated by Hunt et al. (2003a,b). Therefore, given a
panel data set is used this is achieved by time dummies as proposed by Griffin and Schulman
(2005) and Adeyemi and Hunt (2007).
In order to try to tease out these different influences, a general energy demand
relationship found in the standard energy demand modelling literature, relating energy
consumption to economic activity and the real energy price, is utilised for the estimation of an
aggregate energy demand function for a panel of OECD countries. Moreover, in order to
control for other important factors that vary across countries and hence can affect a country’s
energy demand, some variables related to climate, size, and structure of the economy are
introduced in the model. Thus the framework adopted here attempts to isolate the ‘underlying
energy efficiency’ for each country after controlling for income, price, climate effects,
technical progress and other exogenous factors, as well effects due to difference in area size
and in the structure of the economy. The estimated model therefore isolates the level of
underlying energy efficiency, defined with respect to a benchmark, e.g. a best practice
economy in the use of energy by estimation a ‘common energy demand’ function across
countries, with homogenous income and price elasticities, and responses to other factors, plus a
homogenous UEDT. This is seen as important, given the need to isolate the different
underlying energy efficiency across the countries.1 Consequently, once these effects are
adequately controlled for, it allows for the estimation of the underlying energy efficiency for
each country showing i) how efficiency has changed over the estimation period and ii) the
differences in efficiency across the panel of countries.
1 The UEDT includes exogenous technical progress and it could be argued that even though technologies are
available to each country they are not necessarily installed at the same rate; however, it is assumed that this results
from different behaviour across countries and reflects ‘inefficiency’ across countries; hence, it is captured by the
different (in)efficiency terms for all countries.
The paper is organized as follows. The next section, discusses the rationale and
specification of the energy demand frontier function, with the data and econometric
specification introduced in Section 3. The results of the estimation are presented in Section 4,
with a summary and conclusion in the final section.
2 An aggregate frontier energy demand model
Given the discussion above, it is assumed that there exists an aggregate energy demand
relationship for a panel of OECD countries, as follows:
Eit = E(Pit , Yit , Ci , Ai , ISHit , SSHit , Dt, EFit) (1)
where Eit is aggregate energy consumption per capita, Yit is GDP per capita, Pit is the real price
of energy, Ci is climate, Ai is the area size, ISHit is the share of value added of the industrial
sector and SSHit is the share of value added for the service sector all for country i in year t.
Further, Dt is a series of time dummy variables representing the UEDT that captures the
common impact of important unmeasured exogenous factors that influence all countries
simultaneously, e.g. general expectations of changes in international oil price, general changes
in awareness of climate change, and exogenous change in the technology. Finally, EFit is the
level of ‘underlying energy efficiency’ of the appliance and capital equipment used in an
economy. This could incorporate a number of factors that will differ across countries,
including different government regulations as well as different social behaviours, norms,
lifestyles and values. Hence, a low level of underlying energy efficiency implies an inefficient
use of energy (i.e. ‘waste energy’), so that in this situation, awareness for energy conservation
could be increased in order to reach the ‘optimal’ energy demand function. Nevertheless, from
an empirical perspective, when using OECD aggregate energy data, the aggregate level of
energy efficiency of the capital equipment and of the production processes is not observed
directly. Therefore, this underlying energy efficiency indicator has to be estimated.
Consequently, in order to estimate this economy-wide level of underlying energy efficiency
(EFit) and identify the best practice economy in term of energy utilization, the stochastic
frontier function approach introduced by Aigner et al. (1977) is used.2
The stochastic frontier function has generally been used in production theory to
measure, using an econometric approach, the economic performance of production processes.
The central concept of the frontier approach is that in general the function gives the maximum
or minimum level of an economic indicator attainable by an economic agent. For a production
function, the frontier gives the maximum level of output attainable by a firm for any given
level of inputs. In the case of an aggregate energy demand function, used here, the frontier
gives the minimum level of energy necessary for an economy to produce any given level of
energy services. In principle, the aim here is to apply the frontier function concept in order to
estimate the baseline energy demand, which is the frontier that reflects the demand of the
countries that use high efficient equipment and production process. This frontier approach
allows the possibility to identify if a country is, or is not, on the frontier. Moreover, if a country
is not on the frontier, the distance from the frontier measures the level of energy consumption
above the baseline demand, e.g. the level of energy inefficiency.
The approach used in this study is therefore based on the assumption that the level of
the economy-wide energy efficiency can be approximated by a one-sided non-negative term, so
2 Of course, the frontier function approach suggested by Aigner et al. (1977) has been developed within the
neoclassical production theory. The main goal of this literature has been to estimate production and cost frontier in
order to identify the level of productive inefficiency (allocative and technical inefficiency). In this study, the
neoclassical production theory is discarded and instead the concept of a stochastic frontier within the empirical
approach traditionally used in the estimation of economy-wide energy demand function is employed. Of course,
behind the concept of underlying energy inefficiency developed here, there is still a ‘production process’.
that a panel log-log functional form of Equation (1) adopting the stochastic frontier function
approach proposed by Aigner et al. (1977) can be specified as follows:
it uvSSHISHaDCDpye ++++++++=
where eit is the natural logarithm of aggregate energy consumption per capita (Eit), yit is the
natural logarithm of GDP per capita (Yit), pit is the natural logarithm of the real price of energy
(Pit), DCi is a cold climate dummy variable, ai is the natural logarithm of the area size of a
country measured in squared km (Ai), ISHit is the share of value added of the industrial sector,
SSHit is the share of value added for the service sector and Dt is a series of time dummy
variables. Furthermore, the error term in Equation (2) is composed of two independent parts.
The first part, vit, is a symmetric disturbance capturing the effect of noise and as usual is
assumed to be normally distributed. The second part, uit, which represents the underlying
energy level of efficiency EFit in equation (1) is interpreted as an indicator of the inefficient
use of energy, e.g. the ‘waste energy’. It is a one-sided non-negative random disturbance term
that can vary over time, assumed to follow a half-normal distribution.3 An improvement in
the energy efficiency of the equipment or on the use of energy through a new production
process will increase the level of energy efficiency of a country. The impact of technological,
organisational, and social innovation in the production and consumption of energy services on
the energy demand is therefore captured in several ways: the time dummy variables, the
indicator of energy efficiency and through the price effect.4
3 It could be argued that this is a strong assumption for EF, but it does allow the ‘identification’ of the efficiency
for each country separately.
4 In this model specification, we are assuming that the price effect is symmetric. Gately and Huntington (2002),
amongst others, discuss the possibility of specifying a demand model with asymmetric price effects and some
In summary, Equation (2) is estimated in order to estimate underlying energy efficiency
for each country in the sample. The data and the econometric specification of the estimated
equations are discussed in the next section.
3. Data and econometric specification
The study is based on an unbalanced panel data set for a sample of 29 OECD countries
(i = 1, …, 29)5 over the period 1978 to 2006 (t = 1978-2006). This data set is based on
information taken from the International Energy Agency (IEA) database “World Energy
Statistics and Balances of OECD Countries” available at and from the general
OECD database “Country profile Statistics”.
E is each country’s per capita aggregate energy consumption in tonnes of oil equivalent
(toe), Y is each country’s per capita GDP in thousand US2000$PPP, and P is each country’s
index of real energy prices (2000=100). The climate dummy variable, DC, indicates whether a
country belongs to those characterized by a cold climate (according to the Köppen-Geiger
climate classification6) and A is the area size of a country is measured in squared kilometres.
Finally, the value added of the industrial and service sectors is measured as percentage of GDP
(ISH and SSH). Descriptive statistics of the key variables are presented in Table 1.
experimentation with asymmetric prices was undertaken here, however, the model did not fit the data well. Future
research will investigate this further.
5 Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary,
Ireland, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak
Republic, Spain, Sweden, Switzerland, Turkey, the UK, and the US. For some countries, information on the share
of the industrial and service sector in the economy are only available for the years after 1990. For this reason the
data set is unbalanced.
6 See for a discussion of this classification Peel et al. (2007).
Table 1: Descriptive statistics
Variable Mean Std. Dev. Minimum Maximum
Description Name
Energy consumption per capita (toe/capita) E2.99 1.58 0.58 9.49
GDP per capita (1000 US2000$PPP/capita) Y20.63 8.44 4.19 63.36
Real Price of energy (2000=100) P99.65 16.42 53.56 170.30
Area size in km
1269850 2786260 2590 9984670
Share of industrial sector in % of GDP ISH 25.22 4.99 9.40 40.40
Share of service sector in % of GDP SSH 20.95 5.52 8.20 48.50
Climate Dummy DC 0.45 0.50 0 1
From the econometric specification perspective, the literature on the estimation of
stochastic frontier models using panel data needs to be considered. The first use of panel data
in stochastic frontier models goes back to Pitt and Lee (1981) who interpreted the panel data
random effects as inefficiency rather than heterogeneity.7 A major shortcoming of these
models is that any unobserved, time-invariant, group-specific heterogeneity is considered as
inefficiency. In order to solve this problem using panel data, Greene (2005a and 2005b)
proposed to extend the SFA model in its original form (Aigner, et al., 1977) by adding a fixed
or random individual effect in the model.8 It should be noted that these models produce
efficiency estimates that do not include the persistent inefficiencies that might remain more or
less constant over time. To the extent that there are certain sources of energy efficiency that
result in time-invariant excess energy consumption, the estimates of these models provide
relatively high levels of energy efficiency. For this reason, this study uses the original approach
proposed by Aigner, et al. (1977) so that fixed or random individual effects proposed by
Greene (2005a and 2005b) are not included in the model. Of course, by not considering the
individual effects in the econometric specification, it could result in the so-called ‘unobserved
7 Schmidt and Sickles (1984) and Battese and Coelli (1992) presented variations of this model.
8 For a successful application of these models in network industries, see Farsi, et al. (2006) and Farsi, et al. (2005).
variables bias’; e.g. a situation where correlation between observables and unobservables could
bias some coefficients of the explanatory variables. However, by introducing several
explanatory variables such as the climate, the area size, and some variables on the structure of
the economy it is possible to reduce this problem. In fact, the estimated coefficients of the
demand frontier function presented in the next section are very similar to those obtained by
estimating equation (2) by using a random or a fixed effects approach. 9 The econometric
approach used in this paper therefore has the advantage that it includes in the inefficiency term
the persistent inefficiencies that might remain more or less constant over time as well the
inefficiencies that vary over time.
Table 2 provides a summary of the model specification and a description of the
stochastic terms included in the model.
Table 2: Econometric specification of the model employed
Model Random error
Level of efficiency
ititit uv
),0(iid~ 2
uit Nu
),0(iid~ 2
vit Nv
)( itit
The country’s efficiency is estimated using the conditional mean of the efficiency term
ititit vuuE +
, proposed by Jondrow et al. (1982). The level of energy efficiency can be
expressed in the following way:
exp( it
it u
EF == (3)
where Eit is the observed energy consumption per capita and is the frontier or minimum
demand of the ith country in time t. An energy efficiency score of one indicates a country on
9 In a preliminary analysis, a version of equation (2) using the true random effects model was also estimated. As
expected, the obtained level of energy efficiency were very high (average level of efficiency higher than 90%).
the frontier (100% efficient), while non-frontier countries, e.g. countries characterized by a
level of energy efficiency lower than 100%, receive scores below one. This therefore gives the
measure of underlying energy efficiency estimated below.10
In summary, Equation (2) is estimated and Equation (3) used to estimate the
efficiency scores for each country for each year. The results from the estimation are given in
the next section.
4. Estimation results
The estimation results for frontier energy demand model, Equation (2), are given in
Table 3. This shows that the estimated coefficients and lambda have the expected signs and are
statistically significant.11
Table 3: Estimated coefficients (t-values in parentheses)
Constant -1.916
y 0.900
a 0.021
Time dummies Yes
Lamda (λ) 2.762
10 This is in contrast to the alternative indicator of energy inefficiency given by the exponential of uit. In this case,
a value of 0.2 indicates a level of energy inefficiency of 20%.
11 Lambda (λ) gives information on the relative contribution of uit and vit on the decomposed error term εit and
shows that in this case, the one-sided error component is relatively large.
For the variables in logarithmic form, the estimated coefficients can be directly
interpreted as elasticities. The estimated income elasticity and the estimated own price
elasticity are about 0.9 and -0.3 respectively, both not out of line with previous estimates. The
estimated area elasticity is about 0.02 indicating that a 10% larger country will demand 0.5%
more energy. The climate variable, DC, also appears to have an important influence on a
country’s energy demand; with countries characterized by a cold climate experiencing a higher
consumption of energy. Similarly, larger shares of a country’s industrial and service sectors
will also increase energy consumption. The time dummies, as a group, are significant and, as
expected, the overall the trend in their coefficients is negative as shown in Figure 1; however,
they do not fall continually over the estimation period, reflecting the ‘non-linear’ impact of
technical progress and other exogenous variables.
Table 4: Energy efficiency scores
min 0.522
max 0.951
mean 0.781
median 0.797 0.117
Table 4 provides descriptive statistics for the overall underlying energy efficiency
estimates of the countries obtained from the econometric estimation, showing that the mean
average efficiency is estimated to be about 78% (median 80%) nonetheless, as expected, there
is a fair degree of variation around the average. Table 5 presents the average energy efficiency
score for every country for three sub periods of the estimation period considered in the analysis
and over the whole period and Figure 2 shows that the estimated underlying energy efficiency
scores for each country over the estimation period relative to energy intensity. It should be
noted that, although presented individually for each country, the estimated efficiencies of each
country should not be taken as the precise position of each country given the stochastic
technique used in estimation. However, they do give a good relative indication of a country’s
change in efficiency over time and a country’s relative position vis-à-vis other countries.
Bearing this in mind, Table 5 and Figure 2 show that the estimated underlying energy
efficiency generally increased over the estimation period for some countries, such as Australia,
Canada, Denmark, Germany, Luxembourg, Netherlands, Norway, Sweden, the UK, and the
USA. Whereas for some countries the opposite is the case, with the estimated underlying
energy efficiency generally decreasing, such as Greece, Italy, Mexico, New Zealand, Portugal,
Spain and Turkey. Figure 2 also illustrates that the estimated underlying energy efficiency
would appear to be negatively correlated with energy intensity for most countries (i.e. the level
of energy intensity decreases with an increase of the level of energy efficiency), but with some
exceptions (discussed further below). This is to be expected in one sense. However, if this
technique were to be a useful tool for teasing out underlying energy efficiency then a perfect,
or even near perfect, negative correlation would not be expected since all the useful
information would be contained in the standard energy to GDP ratio.
Table 5: Average energy efficiency scores over time
1978 –
1987 1988 –
1997 1998 –
2006 Whole
Australia0.768 0.783 0.806 0.785
Austria0.865 0.894 0.888 0.882
Belgium0.666 0.682 0.622 0.658
Canada0.583 0.608 0.645 0.608
CzechRepn/a 0.678 0.695 0.687
Denmark0.849 0.909 0.916 0.891
Finland0.581 0.584 0.612 0.591
France0.856 0.888 0.876 0.873
Germany0.844 0.931 0.944 0.905
Greece0.911 0.838 0.755 0.838
Hungaryn/a 0.742 0.823 0.788
Ireland0.628 0.725 0.902 0.747
Italy0.937 0.931 0.908 0.926
Japan0.880 0.890 0.863 0.878
Korea0.820 0.833 0.753 0.804
Luxembourg0.561 0.632 0.719 0.635
Mexico0.902 0.902 0.869 0.892
Netherlands0.612 0.681 0.701 0.663
NewZealand0.740 0.706 0.652 0.707
Norway0.790 0.802 0.864 0.817
Polandn/a 0.571 0.740 0.673
Portugal0.882 0.813 0.696 0.800
SlovakRep.n/a 0.594 0.637 0.622
Spain0.934 0.871 0.770 0.861
Sweden0.723 0.774 0.813 0.768
Switzerlandn/a 0.931 0.933 0.932
Turkey0.880 0.800 0.718 0.802
UK0.842 0.859 0.893 0.864
USA0.545 0.642 0.720 0.633
Note: n/a represents the situation where the average is not available over
the sub-period.
Due to the unbalanced panel, some averages are calculated over a
slightly shorter period than indicated.
This is confirmed, given the average correlation coefficient between the estimated
underlying energy efficiency and energy intensity across all countries is -0.68. Within this,
there is a relatively high negative correlation for some countries, such as Australia, Austria,
Canada, Denmark, Germany, Hungary, Ireland, Luxembourg, Netherlands, Norway, Poland,
Portugal, the Slovak Republic, Sweden, the UK and the USA; whereas for some countries the
(negative) correlation is somewhat less, such as Belgium, the Czech Republic, Greece, Japan,
Korea, New Zealand, and Switzerland. Furthermore, for Italy, Mexico, and Turkey, there
appears to be a positive relationship between the energy to GDP ratio and estimated energy
efficiency. This suggests that for some countries energy intensity is a reasonable proxy for
energy efficiency, whereas for others it is a very poor proxy. Hence, unless the analysis
undertaken here is conducted it is arguably not possible to identify for which countries energy
intensity is a good proxy and for which it is a poor proxy.
Turning to the differences in estimated energy efficiency scores across the panel of
countries in the sample it can be seen from Table 5 that there is some difference over the whole
sample period. Finland, Canada, the Slovak Republic, the USA, and Luxembourg are the
estimated five least efficient countries, with Switzerland, Italy, Germany, Mexico, and
Denmark the estimated five most efficient countries.12 This is further shown in Figure 3, with
the countries re-ordered from the most efficient to the least efficient. However, although Italy
is estimated to be one of the most energy efficient countries over time its level of efficiency has
been generally declining, despite a general fall in energy intensity. This highlights that energy
intensity in this case gives a poor indication of Italy’s change in energy efficiency over time.
12 However, it should be noted that, given the unbalanced panel used in estimation, the figures for the Slovak
Republic and Switzerland are over a much shorter period.
Countries will, however, have improved (or deteriorated) at different rates; hence,
Figure 4 gives the ordered data for the latter period only, 1998-2006. This shows that the
ordering does change, with the five least efficient countries being Finland, Belgium, the Slovak
Republic, Canada and New Zealand and the five most efficient countries being Germany,
Switzerland, Denmark, Italy and Ireland. Furthermore, as shown in Table 6, and illustrated
when comparing Figure 4 and Figure 5, it can be seen that although there is generally a
negative relationship between the rankings of the estimated underlying energy efficiency and
energy intensity there is not a one to one correspondence. For example, according to the
measure of energy intensity over the period 1998-2006, Germany is ranked 12th, whereas it is
estimated to be the most efficient over the period; suggesting that Germany is relatively more
energy efficient than the simple energy intensity measure would suggest. Conversely, Greece
and Portugal are ranked 1st and 12th respectively in terms of energy intensity but are only
ranked 16th and 23rd respectively in terms of underlying energy efficiency; suggesting that
Greece and Portugal are somewhat less energy efficient than the simple energy intensity
measure suggest.
Table 6: Comparison of the Rankings for Estimated Underlying Energy
Efficiency and Energy Intensity (1998-2006)
Estimated Underlying
Energy Efficiency
(symmetric model)
Energy Intensity (Energy
GDP ratio, toe per 1000
Level Rank Level Rank
Australia0.806 14 0.130 17
Austria0.888 7 0.109 9
Belgium0.622 28 0.154 22
Canada0.645 26 0.213 29
CzechRep0.695 24 0.160 25
Denmark0.916 3 0.099 5
Finland0.612 29 0.184 28
France0.876 8 0.109 9
Germany0.944 1 0.114 12
Greece0.755 16 0.093 1
Hungary0.823 12 0.136 18
Ireland0.902 5 0.097 4
Italy0.908 4 0.093 1
Japan0.863 11 0.106 8
Korea0.753 17 0.160 25
Luxembourg0.719 20 0.156 24
Mexico0.869 9 0.112 11
Netherlands0.701 22 0.127 15
NewZealand0.652 25 0.152 21
Norway0.864 10 0.122 14
Poland0.740 18 0.142 20
Portugal0.696 23 0.114 12
SlovakRep.0.637 27 0.176 27
Spain0.770 15 0.103 7
Sweden0.813 13 0.140 19
Switzerland0.933 2 0.093 1
Turkey0.718 21 0.128 16
UK0.893 6 0.101 6
USA0.720 19 0.154 22
Note: A rank of 29 for underlying energy efficiency represents the least efficient
country by this measure, whereas a rank of 1 represents the most efficient
country. A rank of 29 for energy intensity represents the most energy intensity
country whereas a rank of 1 represents the least energy intensive country.
5. Summary and Conclusion
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 combining the approaches taken in energy demand modelling and frontier analysis, a
measure of the ‘underlying energy efficiency’ for each country is estimated. This approach has
not, as far is known, been attempted before. The energy demand specification controls for
income, price, climate country specific effects, area, industrial structure, and a underlying
energy demand trend in order to obtain a measure of ‘efficiency’ – in a similar way to previous
work on cost and production estimation – thus giving a measure of underlying energy
efficiency (reflecting the relative inefficient use of energy, i.e. ‘waste energy’).
The estimates for the core energy efficiency using this approach show that although for
a number of countries the change in energy intensity 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 examples. For Italy, energy intensity declines over the estimation
period suggesting an improvement in energy efficiency, whereas the estimated underlying
energy efficiency falls over the period.13 For Greece, energy intensity suggests that it is the
most efficient country over the latter period covered by the data, whereas the estimated
underlying energy efficiency suggests otherwise. Therefore, unless the analysis advocated here
is undertaken, it is not possible to know whether the energy intensity of a country is a good
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energy intensity in order to try to avoid potentially misleading policy conclusions.
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... But this simple aggregate energy consumption to GDP ratio does not measure the level of "underlying energy efficiency", hence, it is difficult to make conclusions for energy policy based upon this (Filippini & Hunt, 2011). Therefore to estimate electricity consumption efficiency we have used stochastic frontier analysis (SFA), which is a parametric approach initially developed by (Aigner, Lovell, & Schmidt, 1977), (Battese & Corra, 1977) and (Meeusen & Broeck, 1977). ...
... Apart from the compatibility of the data we have allowed for the random noise in the form of excluded variables, misspecification of model, luck and measurement errors as inefficiency variables. Following the studies done for estimation of energy efficiency/inefficiency by (Haider & Mishra, 2021), (Mendonca, Pereira, Alberto, & Pessanha, 2020), (Hu & Honma, 2014) (Lin & Du, 2014), (Boyd & Lee, 2019), (Chen, Barros, & Borges, 2015), (Filippini & Hunt, 2011, (Evans, Filippini, & Hunt, 2013), (Filippini, 2014), (Yu & Guo, 2016) and the most recent study by (Twerefou & Abeney, 2020) we have used SFA approach for the electricity consumption efficiency frontier estimation. ...
... From aggregate electricity demand function, using SFA approach by (Aigner, Lovell, & Schmidt, 1977), the frontier gives the minimum level (Filippini & Hunt, 2011) of energy/electricity service required for a household and any deviation from the frontier is measured as inefficiency given their socioeconomic status, household dwelling characteristics and other attributes such as type of the house, location of the household etc. Finally, the energy efficiency estimation application methodology applied by (Filippini, ...
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In this paper we have analysed the electricity consumption inefficiency of households in rural area by using stochastic frontier analysis (SFA) applying single stage one-sided approach on the primary data collected using stratified random sampling. We find the mean efficiency of the households is 77.25 percent where the stochastic demand frontier reveals government subsidy and minimum watt have a significant positive impact on household electricity consumption. Surprisingly, numbers of living room does not increase electricity consumption demand in rural areas. We found the inefficiency factors; a reduction in cut in minutes and cut frequency can reduce the inefficiency of electricity consumption by the households in rural areas. Intuitively, a reduction in cut time duration and number of times electricity is cut in a day on an average can reduce inefficiency of the households by 15 percent and 39 percent respectively. Additionally, households living in pukka floors and having inverter as secondary source of lighting could reduce their inefficiency by 11 and 7 percent respectively. Overall, we found inefficiency in electricity consumption in the rural areas lie more on the supply side constraints compared to the demand side.
... Given all the problems discussed above, one of the objectives of this study is, following the approach proposed by [10], to estimate an aggregate energy economy demand function in a panel of developing countries using Stochastic Frontier Analysis (SFA) and after controlling for a series of important economic and non-economic factors, to get a 'true' measurement of energy efficiency that is consistent with economic theory of production (which [10] refer to as 'underlying energy efficiency'). Thus, generating a more reliable energy efficiency indicator and providing valuable information to policy makers to address national and international energy, economic, and environmental issues. ...
... However, from an economic point of view it is quite important to have information on the level of overall or cost efficiency (i.e., technical and allocative efficiency). Hence [10,12] built upon the theoretical framework introduced by [13] and motivated by the notion of non-radial input specific efficiency introduced by [14], propose a way to measure energy efficiency by estimating a single conditional input demand frontier function, namely the demand function for energy. The waste use of energy (energy inefficiency) is defined as the distance between the optimal use of energy that corresponds to the cost minimising input combination to produce any given level of energy services and the observed use of energy. ...
... In particular, ref [10] use data from 1978 to 2006 to estimate what they call 'underline energy efficiency' for a panel of 29 OECD countries. They provide empirical evidence that energy intensity, at least for some of the countries, is a very poor proxy for energy efficiency according to their measure while they argue that efficiency measurement from the estimation of an energy demand function after controlling for several socio-economic factors is a more appropriate measurement of energy efficiency. ...
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This paper investigates relative aggregate energy efficiency for a panel of 39 developing countries by econometrically estimating an energy-demand function (EDF) using the stochastic frontier analysis (SFA) approach to provide relative energy efficiency scores over the period 1989 to 2008. Energy efficiency is arguably difficult to define or even conceptualise with several interpretations in the literature but here it is based on an economists’ perspective of efficiency. Hence, the estimates of `true’ energy efficiency found in the paper using this approach approximate the economically efficient use of energy capturing both technical and allocative efficiency and the results confirm that energy intensity should not be considered as a de facto standard indicator of energy efficiency. While, by controlling for a range of socio-economic factors, the measurements of energy efficiency obtained by the analysis are deemed more appropriate and hence it is argued that this analysis should be undertaken to avoid potentially misleading advice to policy makers. This study contributes to the literature since it is, as far as is known, the first attempt to apply the benchmarking parametric stochastic frontier technique to econometrically estimate energy efficiency for a large panel of only developing counties around the world. Moreover, the results from such analysis are arguably particularly relevant in a world dominated by environmental concerns, especially in the aftermath of energy price increase as a result of the unrest in Ukraine.
... Stock-outs are estimated to have increased 35% in 2020 compared to 14% in 2019. While temporary stock-outs increased from 12% to 22% in March 2022 and permanent stock-outs were estimated to be 20% during 2020 [2]. ...
... Under these arguments, it is extremely important to achieve the supply of infant and specialized formulas. Faced with this emergency and uncertainty situation, the adoption and implementation of project management techniques will help us identify and effectively attack priorities, problems, and risks, thus fulfilling the objectives of the organization (ability to resist, absorb, adapt, and recover from dangers) [2]. ...
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The changes in the environment make it necessary for organizations to prepare to face in the most effective and flexible way without negatively affecting the quality of the products or services offered, making this area of opportunity become growth and a competitive advantage against to the other companies. While Industry 4.0 has been a critical factor in overcoming the challenges of pandemic restrictions and improving productivity by providing continuous operations during the crisis, the smart supply chain has enabled a constant stock of products to reach their destination. Despite the efforts and success of the Industry 4.0 and the Intelligent Supply Chain during the pandemic to meet market needs, stockouts were still significant, making it a critical point to review project and risk management tools and techniques in its operations. Smart digital technologies and techniques have been useful in developing strategies to mitigate the effects of emergencies; however, these strategies are still highly vulnerable to crises of this magnitude. That is why the aim of this paper is to show the importance of adequate project and risk management within Industry 4.0 to achieve a flexible, intelligent, and resilient supply chain.
... Next, the differences in electricity consumption features between different groups are discussed. Finally, the conclusion and policy implications are found in Section 5. [27] integrated the Household Electricity Demand model based on the theory of household production proposed and improved by Flaig (1990) [28], Filippini and Hunt (2011) [29], and Shen, Ghatikar et al. (2014) [7]. According to Labandeira, Labeaga et al. (2012) [27], a theoretical model of household electricity demand based on the prices of electricity and alternative energies was built in this paper. ...
... Next, the differences in electricity consumption features between different groups are discussed. Finally, the conclusion and policy implications are found in Section 5. [27] integrated the Household Electricity Demand model based on the theory of household production proposed and improved by Flaig (1990) [28], Filippini and Hunt (2011) [29], and Shen, Ghatikar et al. (2014) [7]. According to Labandeira, Labeaga et al. (2012) [27], a theoretical model of household electricity demand based on the prices of electricity and alternative energies was built in this paper. ...
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Due to the wide coverage of first-tier electricity consumption and the small price difference between different tiers, the current tiered pricing for household electricity (TPHE) cannot give full play to the advantages of the increasing block electricity tariffs (IBTs). Based on the microscopic survey data provided by the Chinese General Social Survey (CGSS) in 2015, this paper innovatively uses the predicted average electricity price as the instrumental variable of electricity price to explore the influencing factors of household electricity consumption in order to solve the possible endogenous problems. Simultaneously, the samples are further grouped by income and electricity consumption, and the electricity consumption characteristics of different groups are discussed separately. The results show that, for low-income groups, the price elasticity of electricity consumption is relatively low because the electricity consumption of low-income households is concentrated on meeting the energy demand necessary for basic life, while the price elasticity of high-income groups is relatively high because the electricity consumption of the high-income households is mostly the energy demand generated by improving the quality of life.
... Additionally, technological innovation has been one of the essential factors in achieving greater energy efficiency. However, the countries' technological gap makes achieving such efficiency difficult, affecting energy intensity (Filippini & Hunt, 2011;Wei et al., 2019;Zhou et al., 2018). Note: p statistics in parentheses and * indicates statistical significance: *p < .05; ...
Previous literature does not incorporate the spillover effects of institutional factors in the analysis of the determinants of energy intensity. This research aims to empirically examine the impact of institutional and economic factors on energy intensity using spatial panel data models. Specifically, the institutional factors included are civil liberties, political corruption, and women's political empowerment. We find robust evidence that there are spillover effects from regressors on the energy intensity of countries. We find that the index of civil liberties, political corruption, and women's political empowerment reduce energy intensity. In addition, we find robust evidence that real output per capita and oil price reduce energy intensity, while manufacturing industry increase it. Our results indicate that manufacturing activity requires greater attention from policymakers and academics to mitigate the harmful aspects of energy intensity. Likewise, our results constitute a new look at the approach to mechanisms to reduce energy intensity to achieve sustainable economic development consistent with Sustainable Development Goals 7 and 12.
... 6 Furthermore, Filippini and Hunt (2015) illustrate the meanings of radial and nonradial measurements of energy efficiency in detail and extend the aggregate frontier energy demand model first presented by Filippini and Hunt (2011) 7 into an input requirement frontier model, which includes other non-energy inputs. Filippini and Hunt (2011) also propose an energy demand frontier model to explore the minimum amount of energy required for a certain amount of output when input prices are given. Furthermore, Hunt (2012, 2016) and Filippini et al. (2014) employ, for the first time, various SFA-based models to estimate energy efficiency. ...
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To estimate the performance of China in terms of energy use efficiency during the first two decades of the twenty-first century while also taking into consideration pollutant emission, this study uses a panel data set covering 30 provincial administrative regions in mainland China for the period 2000-2016. To overcome problems with the DEA-based method, this study proposes an SFA-based model that can estimate environmental energy efficiency while maintaining the regularity constraints imposed on undesirable output, by using Bayesian technique. Our empirical results show that the average value of environmental energy efficiency during the whole sample period changed from 0.7858 in 2000 to 0.7726 in 2016, with an average value of 0.7812 over the whole period. This result is in sharp contrast with findings based on the often-used GDP/energy and GDP/undesirable output indexes, both of which B Andrea Appolloni 123 Annals of Operations Research show an improving trend over same sample period. This study suggests that more sophisticated indexes should be used to evaluate meaningful energy efficiency and environmental protection-related performance.
... The regression functional form is an econometric technique which uses regression analysis to estimate the energy efficiency of buildings (Jacobs, 2001). Some interesting examples of using SFA for energy benchmarking include works by Filippini and Hunt (2011), Buck and Young (2007), and Boyd (2008). A SFA model can be formulated as Equation (2.2) where the Uk is systematic efficiency, which must be positive, whereas the Vk is statistical noise of the k th sample building. ...
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Universities play a significant role in creating a sustainable future, and green campus buildings can make a valuable contribution to the spread of sustainability education. Due to the variety and complexity of uses, performance evaluation of campus buildings has become a challenge. Using campus buildings as case studies, this thesis aims to understand the patterns of use, and to benchmark the performance of higher education buildings including several factors such as energy use, occupant satisfaction and thermal comfort. The campus building benchmarks and performance evaluation provide a guideline for university authorities to promote sustainability principles and enhance efficiency by evaluating building performances, determining feasible green initiative techniques, and forecasting future building performances. Based on a thesis by paper, this research has developed quantitative and qualitative approaches. Specifically, the methodology included a set of post-occupancy evaluations of buildings in use, based on case studies from Queensland universities including Griffith University, the University of Queensland, and Bond University. The study addresses three areas of building environmental performance assessment criteria: energy use (Chapter 2), occupant satisfaction (Chapter 3), and thermal comfort (Chapter 4) in higher education buildings. In Chapter 2, an energy benchmark system was developed for each campus building type in terms of both discipline and activity. A set of energy benchmark tables was developed to provide a guideline for university authorities and promote energy efficiency by evaluating building energy use and determining feasible energy saving techniques. In Chapter 3, green campus buildings are compared with non-green counterparts, and some areas of strength and weakness in the design and operation of green building strategies are identified. The research showed that occupant satisfaction is not necessarily higher in green buildings than that of non-green structures when comparing all building parameters. The study revealed the weaknesses of green buildings to be noise, ventilation, and artificial. Chapter 4 focuses on promoting mixed-mode ventilation to enhance both energy performance and occupant satisfaction in campus buildings. Mixed-mode ventilation is a system that uses a combination of natural and artificial ventilation. Thermal comfort models for three types of mixed-mode buildings were developed in order to promote the use of mixed-mode systems in higher education buildings. Finally, a set of frameworks and policy implications in terms of investment decision making, facility management, operational quality control, and planning and design are proposed (Chapter 5) to improve the effectiveness of green building initiatives at higher education buildings. This study sheds light on performance evaluation of campus buildings, which could be used as a reference for the design, construction and operation of sustainable campus buildings.
... The last term u c t is a non-negative (2017) Chinese Sub-industries Xie, Bai, and Wang (2018) Chinese Transport sector Oh and Hildreth (2014) US Car manufacturing industry Haider and Mishra (2021) Indian iron and steel industries Boyd and Lee (2019) U.S Manufacturing sector Haider and Ahmad Bhat (2018) Indian Paper industry. Lin and Long (2015) Chinese chemical industry Lundgren et al., 2016 Swedish manufacturing Weyman-Jones, Boucinha, and Inácio (2015) Portuguese households Otsuka (2020) Japanese industrial and commercial sector Lin and Wang (2014) Chinese iron and steel industry Cross-country analysis Filippini and Hunt (2011) 29 OECD countries Zhou, Ang, and Zhou (2012) 21 OECD countries Filippini, Hunt, and Zorić (2014) 27 EU countries Marin and Palma (2017) 10 EU countries Alarenan, Gasim, Hunt, and Muhsen (2019) Gulf Cooperation Council (GCC) countries Hu and Honma (2014) 14 OECD countries Hsiao, Hu, Hsiao, and Chang (2019) 10 countries across the Baltic Sea Sun et al. (2019) 71 countries across the globe Adom et al. (2018) 22 Africa countries Jin and Kim (2019) 21 emerging countries Sun, Edziah, Kporsu, Sarkodie, and Taghizadeh-Hesary (2021) 24 innovative countries Edziah et al. (2021) 10 developing countries one-sided error random variable represents the inefficiency of energy use in country c, taking current production technology into account at time t. Eq. (7) may be used to estimate the energy inefficiency component u c t of country c. ...
There has been concern that economic globalization will increase energy consumption and reduce energy efficiency. A slew of studies investigating this assertion have used trade, foreign investment, or both as indicators of economic globalization, with mixed findings. A number of concerns challenge the empirical literature including measurement issues, infrequent temporal variations in the data, business cycle effects and heterogeneity bias, which affect the causal ability of economic globalization. This study used global data of 141 countries to assess the effects of economic globalization on energy efficiency. Our identification strategies involved using more refined measures of economic globalization and energy efficiency, addressing infrequent temporal variations as well as business cycle effects and concerns of heterogeneity bias. Largely, economic globalization positively drives energy efficiency, but this effect suffers from upward bias without controls. We note that infrequent temporal variations in the data and business cycle effects and heterogeneity bias drive the result. Concerning the latter, the result has shown that economic globalization improves energy efficiency only in upper-middle and lower-middle income countries and not in high and lower-income countries. Our results raise serious caution about the causal abilities of existing studies. We discuss the policy implications.
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The monograph discusses the stages of the study of structure formation and the development of high-strength lightweight concrete - building materials that combine low average density and high strength. The results presented in the work are not only of practical value, but also contain various methodological techniques for the analysis of the structure formation of cement materials, including those with mineral additives. A description of the rheological anomaly that occurs in cement-mineral mixtures is given, and the leading role of the plasticizer is shown. The monograph is intended for specialists in the construction industry, pedagogical and scientific workers, graduate students.
Conference Paper
The demand for materials has significantly risen in recent decades. This considerable increase in material demand has consequently led to a further increase in global emissions. In this regard, alongside widely-adopted energy efficiency policies, designing and implementing more comprehensive resource efficiency strategies particularly material efficiency has become highly crucial for achieving net-zero emissions target. In this paper, we discuss the significant role of material use and material efficiency as a climate mitigation policy from the sustainability perspective. Therefore, it is aimed to go beyond the analysis, which has been generally focused until now, and to reveal the role and potential of materials in terms of climate policy. Our arguments build on some recent reports of international organizations and empirical findings of some recent papers. The findings reveal the following outcomes: (i) material efficiency, compared to energy efficiency, might provide a greater potential for further reductions in emissions. Therefore, meeting carbon-neutral targets also requires adopting more comprehensive material efficiency policies; (ii) traditional input intensity indicators of material efficiency should not always be considered as a good measure of efficiency improvements.
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This article considers estimation of a stochastic frontier production function-the type introduced by Aigner, Lovell, and Schmidt (1977) and Meeusen and van den Broeck (1977). Such a production frontier model consists of a production function of the usual regression type but with an error term equal to the sum of two parts. The first part is typically assumed to be normally distributed and represents the usual statistical noise, such as luck, weather, machine breakdown, and other events beyond the control of the firm. The second part is nonpositive and represents technical inefficiencythat is, failure to produce maximal output, given the set of inputs used. Realized output is bounded from above by a frontier that includes the deterministic part of the regression, plus the part of the error representing noise; so the frontier is stochastic. There also exist socalled deterministic frontier models, whose error term contains only the nonpositive component, but we will not consider them here (e.g., see Greene 1980). Frontier models arise naturally in the problem of efficiency measurement, since one needs a bound on output to measure efficiency. A good survey of such production functions and their relationship to the measurement of productive efficiency was given by F0rsund, Lovell, and Schmidt (1980).
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Frontier production functions are important for the prediction of technical efficiencies of individual firms in an industry. A stochastic frontier production function model for panel data is presented, for which the firm effects are an exponential function of time. The best predictor for the technical efficiency of an individual firm at a particular time period is presented for this time-varying model. An empirical example is presented using agricultural data for paddy farmers in a village in India.
A common distinguishing feature of engineering models is that they explicitly represent best practice technologies, while parametric/statistical models represent average practice. It is more useful to energy management or goal setting to have a measure of energy performance capable of answering the question, ÒHow close is observed performance from the industry best practice?Ó This paper presents a parametric/statistical approach to measure best practice. The results show how well a plant performs relative to the industry. A stochastic frontier regression analysis is used to model plant level energy use, separating energy into systematic effects, inefficiency, and random error. One advantage is that physical product mix can be included, avoiding the problem of aggregating output to define a single energy/output ratio to measure energy intensity. The paper outlines the methods and gives an example of the analysis conducted for a non-public micro-dataset of wet corn milling plants.
This paper estimates the effects on energy and oil demand of changes in income and oil prices, for 96 of the world's largest countries, in per-capita terms. We examine three important issues: the asymmetric effects on demand of increases and decreases in oil prices; the asymmetric effects on demand of increases and decreases in income; and the different speeds of demand adjustment to changes in price and in income. Our main conclusions are the following: (1) OECD demand responds much more to increases in oil prices than to decreases; ignoring this asymmetric price response will bias downward the estimated response to income changes; (2) demand's response to income decreases in many Non-OECD countries is not necessarily symmetric to its response to income increases; ignoring this asymmetric income response will bias the estimated response to income changes; (3) the speed of demand adjustment is faster to changes in income than to changes in price; ignoring this difference will bias upward the estimated response to income changes. Using correctly specified equations for energy and oil demand, the longrun response in demand for income growth is about 1.0 for Non-OECD Oil Exporters, Income Growers and perhaps all Non-OECD countries, and about 0.55 For OECD countries. These estimates for developing countries are significantly higher than current estimates used by the US Department of Energy. Our estimates for the OECD countries are also higher than those estimated recently by Schmalensee-Stoker-Judson (1998) and Holtz-Eakin and Selden (1995), who ignore the (asymmetric) effects of prices on demand. Higher responses to income changes, of course, will increase projections of energy and oil demand, and of carbon dioxide emissions.
Received stochastic frontier analyses with panel data have relied on traditional fixed and random effects models. We propose extensions that circumvent two shortcomings of these approaches. The conventional panel data estimators assume that technical or cost inefficiency is time invariant. Second, the fixed and random effects estimators force any time invariant cross unit heterogeneity into the same term that is being used to capture the inefficiency. Inefficiency measures in these models may be picking up heterogeneity in addition to or even instead of inefficiency. A fixed effects model is extended to the stochastic frontier model using results that specifically employ the nonlinear specification. The random effects model is reformulated as a special case of the random parameters model. The techniques are illustrated in applications to the U.S. banking industry and a cross country comparison of the efficiency of health care delivery.
This paper examines several extensions of the stochastic frontier that account for unmeasured heterogeneity as well as firm inefficiency. The fixed effects model is extended to the stochastic frontier model using results that specifically employ its nonlinear specification. Based on Monte Carlo results, we find that the incidental parameters problem operates on the coefficient estimates in the fixed effects stochastic frontier model in ways that are somewhat at odds with other familiar results. We consider a special case of the random parameters model that produces a random effects model that preserves the central feature of the stochastic frontier model and accommodates heterogeneity. We then examine random parameters and latent class models. In these cases, explicit models for firm heterogeneity are built into the stochastic frontier. Comparisons with received results for these models are presented in an application to the U.S. banking industry.
The industrial sector embodies a multifaceted production process consequently modelling the ‘derived demand’ for energy is a complex issue; made all the more difficult by the need to capture the effect of technical progress of the capital stock. This paper is an exercise in econometric modelling of industrial energy demand using panel data for 15 OECD countries over the period 1962–2003 exploring the issue of energy-saving technical change and asymmetric price responses. Although difficult to determine precisely, it is tentatively concluded that the preferred specification for OECD industrial energy demand incorporates asymmetric price responses but not exogenous energy-saving technical change.
This paper demonstrates the importance for energy demand modelling of allowing for trends and seasonal effects that are stochastic in form. Inherent underlying trends may be non-linear and reflect not only technical progress, which usually produces greater energy efficiency, but also other factors such as changes in consumer tastes and the economic structure that may be working in the opposite direction. Using quarterly unadjusted data for various sectors in the UK, it is shown that unless energy demand models are formulated so as to allow for stochastic trends and seasonals, estimates of price and income elasticities could be seriously biased.
The achievement of energy efficiency in commercial buildings is a function of the activities undertaken, the technology in place, and the extent to which those technologies are used efficiently. We study the factors that affect efficient energy use in the Canadian commercial sector by applying a stochastic frontier approach to a cross-section of Canadian commercial buildings included in the Commercial and Institutional Building Energy Use Survey (CIBEUS). Structural and climate-control features of the buildings as well as climatic conditions are assumed to determine the location of the frontier, while management-related variables including such factors as ownership type and activities govern whether or not the maximally attainable efficiency along the frontier is achieved. Our results indicate that although, on average, buildings appear to be fairly efficient, certain types of operations are more likely than others to exhibit energy efficiencies that are significantly worse than average. These results, along with those related to the effects of physical characteristics on the stochastic efficiency frontier, suggest that there is scope for focused policy initiatives to increase energy efficiency in this sector.