KS-1648-MP043A-Energy-productivity-in-the-GCC_Evidence-From-an-International-Kuznets-Curve-Analysis1

1Energy productivity in the GCC: Evidence From an International Kuznets Curve Analysis
October 2016 / KS-1648-MP043A
Marzio Galeotti, Nicholas Howarth
and Alessandro Lanza
Energy Productivity
in the GCC:
Evidence From
an International
Kuznets Curve
Analysis

About KAPSARC
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© Copyright 2016 King Abdullah Petroleum Studies and Research Center (KAPSARC).
No portion of this document may be reproduced or utilized without the proper attribution
to KAPSARC.
Acknowledgement
This paper has been prepared as part of the joint KAPSARC-UNESCWA project ‘Energy
Productivity in the GCC’ and we would like to thank our UNESCWA colleagues for much
helpful input and advice. It is a paper which will be used as input to a joint report on
improving energy productivity in the GCC region.

GCC and compares this with G-7 countries and
Australia.
The group of advanced economies investigated
shows strong evidence of having successfully
decoupled economic growth from per-capita energy
consumption – even as per capita incomes rose.
GCC countries exhibit this trait only very weakly, if at
all. This highlights significant structural challenges
the GCC region faces in decoupling per capita
income from energy consumption in the context of
low energy prices.
This paper presents the detailed data and analysis
behind its companion Paper Energy Productivity
as a New Growth Model for the GCC (Dubey, et al.
2016). This work is part of a program of research
being conducted by KAPSARC with the United
Nations Economic and Social Commission for West
Asia (UNESCWA), aimed at providing the evidence
base, policy tools and institutional capacity to
improve energy productivity in the Gulf region.
T
his paper explores energy productivity trends
at a national level for the countries of the Gulf
Cooperation Council (GCC) and puts them in
an international context. This analysis can be used
as part of an evidence base for setting nationally-
appropriate energy productivity targets.
The need for this research is motivated by recent
volatility in oil markets, which has spurred an
urgency among GCC policy makers to find a new
growth model that reduces their dependence on
oil and gas, currently accounting for between 60
to 90 percent of government revenue. Some effort
has been made toward this goal, primarily through
diversification and energy efficiency, but with low
domestic energy prices a strong feature of GCC
markets, whether meaningful progress is being
achieved warrants closer attention. To assess this,
energy productivity offers a useful metric and strong
policy narrative.
To support this process, this paper applies an
energy productivity Kuznets curve analysis for the
Summary

A
t the macroeconomic level, energy productivity
describes how much value – generally
measured in gross domestic product or
GDP – can be produced using an amount of energy
– generally measured in tonnes of oil-equivalent.
It is thus a reflection of both what activities are
undertaken in the economy (degree of structural
diversification) as well as how energy is used
(energy efficiency). Increasing energy productivity
thus forms a useful measure of how well an
economy is performing in utilizing the energy it
consumes.
Energy productivity transition dynamics can
be empirically explored through econometric
investigation of the Energy Kuznets Curve (EKC)
hypothesis. This suggests that energy and per
capita GDP may display an inverted-U behavior
in the same way as the more commonly studied
Environmental Kuznets Curve involving pollution.
Only a handful of papers have estimated Energy
Kuznets Curves. However, various studies have
addressed issues implied by the Kuznets Curve
approach, namely the value of the income elasticity
of energy consumption and the decoupling between
energy and income.
Zilberfarb and Adams (1981) examine cross-sections
of 47 developing countries in years 1970, 1974 and
1976, and find that the elasticity of per capita energy
with respect to PPP adjusted income per capita is
greater than unity. This implies that energy intensity
– the inverse to energy productivity – increases with
income, i.e., that energy productivity declines as per
capita incomes rises.
Ang (1987) performs a cross-country analysis
of energy and national output using data for 100
countries for 1975. (Ang & Ngai, 2006) repeat the
exercise for 1997. These authors found that, while
the ratio of commercial energy consumption to
national output increased across countries as per
capita income increased in 1975, the converse
is observed in 1997. The cross-country energy
elasticity has also dropped from values well
above unity to below or close to unity, suggesting
that energy intensity at a more mature phase of
development falls as per capita incomes rises, i.e.,
that energy productivity rises with respect to per
capita incomes.
Energy is used everywhere and most forms of
energy release pollutants. Schmalensee, Stoker
and Judson (1998) were the first to explicitly refer to
the EKC literature when evaluating the relationship,
between energy use – and CO2
emissions – and
income. They use data on per capita apparent
energy consumption of fossil fuels for a panel of
96 countries over the period 1950-1990. Using a
flexible parametrization (spline function approach)
of the Kuznets relationship the authors “find clear
evidence of an ‘inverse U’ relation with a within-
sample peak between carbon dioxide emissions –
and energy use – per capita and per capita income”
(Schmalensee, Stoker and Judson, 1998, p.15).
Galli (1998) analyses long-term trends in energy
intensity for ten Asian emerging countries to test
for a non-monotonic relationship between energy
intensity and income. Energy demand functions
during 1973-1990 are estimated using a quadratic
function of log income. It is found that the long-
run coefficient on squared income is negative and
significant, indicating support for a Kuznets curve
relationship between energy intensity and income.
Suri and Duane (1998) examine the EKC hypothesis
for commercial energy consumption. They allow
for the role of international trade and for structural
change (share of manufacturing on total GDP) and
use data for 33 developed and developing countries
Evaluating energy productivity within a
Kuznets curve framework

used is for 36 nations, 20 developed and 16
developing countries, with observations from 1973 to
1997. For OECD countries, the authors find there is
limited support for a turning point in the relationship
between income and per capita energy use and/or
carbon emissions. For non-OECD nations, there is
no turning point in the relationship between income
and either energy use or carbon emissions. Instead,
they find that the relationship is positive. The
authors find that including information on the energy
mix in the regressions is important.
Luzzati and Orsini (2009) investigate the relationship
between absolute energy consumption, neither in
per capita nor in GDP units, and GDP per capita
for 113 countries over the period 1971–2004. The
econometric analysis is conducted separately for
low, medium and high income countries. A group
of nine ‘oil’ countries is also considered separately
(Bahrain, Brunei, Kuwait, Iraq, Libya, Oman, Qatar,
Saudi Arabia and the United Arab Emirates). At
the world level, their analysis suggests that energy
consumption and per capita income exhibit a
positive relationship. A glance at individual countries
gives a very heterogeneous picture, although their
analysis suggests hardly any country exhibits an
inverted-U pattern. When countries are placed
into groups according to their average income, the
estimated relationship is monotonically increasing,
within the actual values of income, both for low
and middle income countries, while it shows an
inverted-U shape with a turning point at around
$18,500 for high income countries.
A group of papers examined patterns of energy use
disaggregated across end-use sectors. Judson,
Schmalensee and Stoker (1999) estimate the Engel
curve that relates per-capita energy consumption
to per capita GDP in major economic sectors. They
find substantial differences among sectors in the
structure of country, time and income effects. In
particular, the household sector’s share of aggregate
over the period 1971-1991. They find that “exports
of manufactured goods by industrialized countries
has thus been an important factor in generating
the upward sloping portion of the EKC and imports
by industrialized countries have contributed to the
downward slope” (Suri and Duane, 1998, p.195).
Building on this work, Agras and Chapman (1999)
account for the price of energy and international
trade in a dynamic version of the basic relationship.
While they find evidence in favor of a bell-shaped
Kuznets curve, the income turning point is outside
the range of current incomes. Within that range the
relationship may appear increasing, albeit concave.
They find very high persistence in the dynamic
model, while the energy price is always statistically
significant.
Vehmas, Kaivo-oja and Luukkanen (2003) use
simple data examination, rather than econometric
estimation, to investigate the energy consumption
Kuznets behavior in the European Union, Japan,
U.S., India, China and Brazil over the period 1973-
1999. The authors find evidence of a Kuznets
pattern and distinguish between periods of weak
EKC (relative delinking), characterized by a rising but
concave relationship, and of strong EKC (absolute
delinking), where the curve actually decreases. The
behavior of Total Primary Energy Supply (TPES) per
units of GDP is also examined against per capita
GDP. It is found that the EU-15 and the U.S. are
characterized by relative, but not absolute, delinking.
Somewhat surprisingly, China features absolute
delinking, probably due to the sample period
considered, whereas the data for Brazil show neither
relative nor absolute delinking. There is no strong
EKC for Japan and India, whereas the case for a
weak EKC seems in both cases uncertain.
Richmond and Kaufmann (2006) look for a turning
point in the relationship between economic activity
and energy use and carbon emissions. The dataset
Evaluating energy productivity within a Kuznets curve framework

energy consumption tends to fall with income, the
share of transportation tends to rise and the share of
industry follows an inverse-U pattern.
Medlock and Soligo (2001) analyze a panel of 28
countries for the period 1978-1995 and find that
energy intensity follows an inverted-U shaped curve
with increasing income. Note that in their paper
traditional fuels are omitted from commercial
energy use.
Lescaroux (2011) confines attention to the
commercial energy consumption of 101 countries
between 1960 and 2006, and uses market exchange
rates for income to find that energy intensity
declines monotonically with income per capita.
Jakob, Haller and Marscinski (2012) examine a
panel of 30 developing and 21 developed countries
for 1971-2005. They investigate the effect of income
growth (market exchange rates) on total primary
energy use (including biomass) as well as individual
fuels and end-use categories for developed and
developing countries separately. They find that the
elasticity of total primary energy use with respect
to income is 0.631 for developing countries and
-0.181 – but statistically insignificant – for developed
countries.
Finally, Csereklyei, del Mar Rubio Varas and
Stern (2014) use an annual panel data set for 99
countries over the period from 1971 to 2010 for non-
commercial energy and income in PPP-adjusted
terms. They look separately at time series, cross-
sectional, panel and long-run averaged data. The
authors find that a stable relationship exists between
energy use per capita and income per capita over
the last four decades and that the elasticity of
energy with respect to income is less than unity.
This implies that energy intensity is negatively
correlated with income and that declining energy
intensity is related to economic growth. Energy
intensity does not decrease and may even increase
in the absence of growth. Thus, energy intensity has
declined globally as the world economy has grown.
One element that explains the heterogeneous
results of these papers is the inclusion of traditional
fuels such as biomass in energy consumption, on
the one hand, and the conversion of GDP in dollar
values using market exchange rates as opposed to
PPP adjustment.
Evaluating energy productivity within a Kuznets curve framework

O
ur starting point is the specification of the
determinants of aggregate (country level)
energy consumption E. Total energy use is
taken to depend upon income/GDP (Y), population
(P), and a vector of other explanatory variables (X).
Thus:
E=G(Y,P,X) (1)
The vector X may include variables such as energy
prices (e.g., oil price), technical change, structure
of the economy (e.g., share of industry value added
on GDP or the share of energy intensive industries
on total value added), openness of the economy
(exports plus imports relative to GDP), energy and
environmental policies. As it is not easy to find
satisfactory proxies of technological change, a time
trend is often used.
It is customary in the EKC literature to express the
relevant variables in per capita terms, to control
for the size of the economy. In terms of (1) this
amounts to assuming that the relationship is linear
homogenous in (Y, P). Thus:
E/P=g(Y/P,X) (2)
The Kuznets curve literature has been typically
interested in the curvature of the environment-
income relationship, in particular its possible
inverted-U shape, and in the location of the income
turning point where the curve starts declining. Here
we test a similar hypothesis that g(.) in (2)
possesses similar analytical properties. As
described in the previous section, the concepts of
the income elasticity of energy demand, energy-
GDP decoupling and Kuznets curve behavior are
all closely interrelated aspects of energy
consumption in its relationship with income. Thus,
g(.) in (2) can be used to understand elasticity and
decoupling issues.
To illustrate these concepts, in what follows we omit
the reference to other controls X so as not to clutter
the notation, though of course we include them at
the empirical stage. Let the function e = g(y), where e
= E/P and y = Y/P, be defined over the domain y ≥ 0.
Decoupling of energy and GDP is denoted by the
sign of the second derivative of g(.). That is when:
∂"
𝑒𝑒
∂" 𝑦𝑦
= 𝑔𝑔"(𝑦𝑦) < 0 (3)
We have decoupling if the function is concave, as
seen in figure 1. However, this is not enough for
a Kuznets curve. Condition (3) is necessary and
sufficient for ‘relative’ decoupling or where energy
use rises less than proportionally with income. For
‘absolute’ decoupling it is necessary that energy use
actually declines with income. In other words, the
Kuznets hypothesis implies the following:
Relative decoupling:

∂𝑒𝑒
∂𝑦𝑦
= 𝑔𝑔&
𝑦𝑦 > 0;
∂*
𝑒𝑒
∂* 𝑦𝑦
= 𝑔𝑔"(𝑦𝑦) < 0
Absolute decoupling:

∂𝑒𝑒
∂𝑦𝑦
= 𝑔𝑔&
𝑦𝑦 < 0;

∂+
𝑒𝑒
∂+ 𝑦𝑦
= 𝑔𝑔"(𝑦𝑦) < 0 (4)
These relationships are illustrated in Figure 1. The
income turning point B, divides the two decoupling
regions. Levinson (2002, p. 2) notes that: ‘‘All one
needs to do is show that there are some countries
and some pollutants for which a time series of
pollution plotted against GDP per capita shows a
downward trend”.
Figure 1 shows that in principle we can have a third
possibility: relinking. This is the region to the right of
point C, where the second derivative of the function
turns positive over the subsequent range, then an
N-shaped relationship would occur:
Methodology

∂"
𝑒𝑒
∂" 𝑦𝑦
= 𝑓𝑓"(𝑦𝑦) > 0 (5)
The decoupling issue can be alternatively studied
by looking at the income elasticity of energy
consumption. If energy use increases as income
goes up, then energy is a normal good. This
implies a positive income elasticity. If such elasticity
is positive but less than unity, then energy use
increases less rapidly than income, implying relative
decoupling. If instead energy use declines as
incomes rise, then energy is an inferior good. This
implies a negative income elasticity and absolute
decoupling.
These relationships can be written:
Relative decoupling:
∂𝑙𝑙 𝑛𝑛𝑒𝑒
∂𝑙𝑙 𝑛𝑛𝑦𝑦
=
∂𝑒𝑒
∂𝑦𝑦
𝑦𝑦
𝑒𝑒
< 1
Absolute decoupling: ∂𝑙𝑙 𝑛𝑛𝑒𝑒
∂𝑙𝑙 𝑛𝑛𝑦𝑦
=
∂𝑒𝑒
∂𝑦𝑦
𝑦𝑦
𝑒𝑒
< 0 (6)
Again, an inverted-U Kuznets curve will entail both
possibilities, with a positive less than unitary income
elasticity turning negative after the turning point.
The turning point is where the Kuznets curve stops
rising and starts to decline. This is equivalent to
stating that the first derivative turns from positive
to negative at point B, as shown in Figure 1. The
level of per capita income at which the turning
point occurs y* can be calculated by taking the first
derivative, equating it to zero and solving for per
capita income y. That is:
∂𝑒𝑒
∂𝑦𝑦
= 𝑔𝑔&
𝑦𝑦∗
= 0 (7)
An important issue is the selection of a functional
form for g(.) in (1) that accommodates all the above
possibilities and where all the above concepts can
be conveniently investigated.
The issue of the alternative options to parametrize
the Kuznets relationship has been widely discussed
Energyconsumptionpercapita
GDP per capita
Inflection point
relative delinking
Turning point
(absolute delinking)
Inflection point
relative relinkingA
B
C
Figure 1. Relative and Absolute Decoupling: The Inverted-U Kuznets Curve
Source: KAPSARC analysis
Methodology

in the environmental EKC literature. While linear
and log-linear specifications are the most common
options, other flexible parametrizations have been
proposed (Schmalensee, Stoker and Judson, 1998;
Galeotti, Lanza and Pauli, 2006). An additional
possibility are non-parametric methods. Aside from
the fact that the calculation of parameter-based
indicators such as income or population elasticities
is not feasible in that context, the main problem
is the very large sample size that nonparametric
methods typically necessitate.
To this end, to best analytically and empirically
investigate the above interrelated concepts, the
most convenient parametrization of the energy-
income relationship e = g(y) is a log-linear
polynomial function of income. Thus, (1) takes the
following form:
3
3
2
210 lnlnlnln ⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
+⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
+⎟
⎠
⎞
⎜
⎝
⎛
+=⎟
⎠
⎞
⎜
⎝
⎛
P
Y
P
Y
P
Y
P
E
αααα (8)
Note that the standard Kuznets relationship (2) is
expressed in per capita terms. There are several
issues related to this functional specification to
consider.
Firstly, in keeping with the concept of energy
productivity, we can exploit the logarithmic
properties of (8) and conveniently reformulate it as
follows:
3
3
2
210 lnlnln)1(ln ⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
+⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
+⎟
⎠
⎞
⎜
⎝
⎛
−+=⎟
⎠
⎞
⎜
⎝
⎛
P
Y
P
Y
P
Y
Y
E
αααα (9)
where energy intensity, instead of energy
consumption per capita, is the dependent variable.
The only difference is the coefficient of the linear per
capita GDP term, so that it is indifferent to estimate
either (8) or (9).
Secondly, the logarithmic specification represents
a natural framework where to investigate income
elasticities. For instance in (10) it can be seen that
the sign and size of the elasticity depends on the
signs and relative size of the coefficients α2
and α3
:
2
321 ln3ln2
ln
ln
⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
+⎟
⎠
⎞
⎜
⎝
⎛
+=
∂
∂
P
Y
P
Y
Y
E
ααα (10)
Relative and absolute delinking can be assessed
by looking at first and second derivatives of (2). This
task is not straightforward in a log-linear context.
The first derivative is given by:
⎟
⎠
⎞
⎜
⎝
⎛
⎪⎭
⎪
⎬
⎫
⎪⎩
⎪
⎨
⎧
⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
+⎟
⎠
⎞
⎜
⎝
⎛
+=⎟
⎠
⎞
⎜
⎝
⎛
⎟
⎠
⎞
⎜
⎝
⎛
∂
∂
=
∂
∂
PY
PE
P
Y
P
Y
Y
E
Y
E
Y
E
/
/
ln3ln2
ln
ln
2
321 ααα
(11)
Because E and Y are always positive, the sign of
(11) depends as in (8) upon the signs and relative
size of the coefficients α2 and α3. To compute the
income turning point we can follow this strategy: (i)
set (10) equal to zero and solve for ln(Y/P); (ii) take
the exponential of the result. Thus:
⎥
⎥
⎦
⎤
⎢
⎢
⎣
⎡ −±−
=⎥
⎦
⎤
⎢
⎣
⎡
=
∂
∂
=⎟
⎠
⎞
⎜
⎝
⎛
3
31
2
22
6
1242
exp0
ln
ln
exp
α
αααα
Y
E
P
Y
TP
(12)
Finally, we need to check the curvature by
calculating the second derivative.
⎟
⎠
⎞
⎜
⎝
⎛
⎪⎭
⎪
⎬
⎫
⎪⎩
⎪
⎨
⎧
⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
+⎟
⎠
⎞
⎜
⎝
⎛
+−=⎟
⎠
⎞
⎜
⎝
⎛
⎥
⎦
⎤
⎢
⎣
⎡
∂
∂
=
∂
∂
Y
E
P
Y
P
Y
Y
E
Y
E
Y
E
2
3212
2
2
2
ln3ln21
)(ln
)(ln
ααα
(13)
Because E and Y are always positive, the sign of (13)
depends as in (8) upon the signs and relative size of
the coefficients α2 and α3. The derivation of (11)-
(13) is discussed in Appendix 1.
Methodology

Data and Econometric Method
O
ur analysis makes use of data developed
by the OECD (Organization for Economic
Co-operation and Development) and the
United Nations Statistics Division (UNSTAT).
Data on Total Primary Energy Consumption (TPES)
and on Total Final Energy Consumption (TFEC) –
measured in million tonnes of oil equivalent (Mtoe)
– are taken from IEA World Energy Balances. The
source of population (POP in millions) and GDP
(billions of U.S. dollars) data are UNSTAT National
Accounts Main Aggregate Database. GDP data
are converted from national currencies to USD in
2005 PPP prices. The sample runs from 1971 to
2012. These data are then extended to 2014 using
energy and GDP growth rate information taken
from Enerdata through to 2014. (Enerdata has the
advantage of being more current, but our approach
has been to use official statistics where possible.)
From these sources we form a sample of
annual data for the country members of the Gulf
Cooperation Council (GCC): Bahrain, Kuwait, Oman,
Qatar, Saudi Arabia and United Arab Emirates. The
period covered therefore runs from 1971 to 2014.
As a reference we also conduct some comparative
analysis with benchmark countries, given by the G-7
group – Canada, France, Germany, Italy, Japan,
UK and U.S. – and Australia. We selected these
countries on the basis that if it were possible to
show that an EP Kuznets curve did exist, it would
be most likely to be exhibited in the world’s most
advanced economies. Thus they form a useful
reference case. We refer to this group as G-7 Plus
countries.
In Figure 2 and 3 we present descriptive statistics
for key variables over the sample period for GCC
countries. In terms of per capita GDP, Qatar is by
far the richest country, far above the GCC average,
followed by Kuwait, the UAE and Saudi Arabia, with
Oman and Bahrain the least well off in terms of per
capita income.
It is important to note that most GCC countries have
a large number of expat and low-paid migrant labor,
which has grown over the sample period. While the
effect of the 2008 economic crisis has also been
substantial, this in part explains why per capita
incomes have fallen in recent years, particularly for
the UAE, where expats (mostly low-skilled, low paid
labor) make up over 90 percent of the population.
Per capita energy consumption seems to be
converging across the GCC in the range of around
7 to 10 tonnes of oil equivalent per person per
year, with the exception of Qatar, which is among
the highest in the world at about 18 tones of oil
equivalent.
In Figure 3 we show energy productivity (LHS) and
energy consumption (RHS) both in terms of total
primary energy supply and total final consumption.
The UAE and Kuwait have the highest energy
productivity based on total primary energy supply
(TPES) in the region at around USD 7,000 per toe
in 2005 USD purchasing parity terms. Oman and
Bahrain have the lowest energy productivity at about
$5,000 and $4,000 respectively.
Energy productivity in total primary energy supply
(TPES) and total final energy consumption (TFEC)
follow a similar pattern. The difference between
total primary energy supply and total final energy
consumption is explained by the energy input
(TPES) and the energy output (TFEC) and is due
primarily to transformation processes, essentially
power generation and refinery processes.
Regarding the difference between energy
productivity based on TPES and TFEC, it emerges
that the GCC can be basically split into two groups.
UAE, Saudi Arabia and Oman have been relatively

Figure 2. GDP per capita and energy consumption per capita.
Source: OECD, UNSTAT, Enerdata, Energy consumption (Total Primary Energy Supply).
Figure 3. Energy productivity and energy consumption.
Source: OECD, UNSTAT, Enerdata.
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Bahrain Kuwait Oman Qatar Saudi Arabia UAE
EnergyConsumptionPercapita
(TPES,toe/personperyear)
GDPPercapita('000USD2005PPP)
GDP Per Capita (LHS) Energy Consumption Per Capita (RHS)
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EnergyConsumption(Mtoe)
EnergyProductivity('000USD2005PPPpertoe)
TPES Energy Productivity (LHS) TFC Energy Productivity (LHS)
Energy Consumption TPES (RHS) Energy Consumption TFC (RHS)

efficient in their transformation process, whereas
Bahrain, Kuwait and Qatar have less efficient
transformation systems (from the relative gap
between the dark and light blue energy productivity
curves). During the last decade a shift on this front
toward more efficient transformation has been
realized, particularly by Oman and Saudi Arabia.
This shift provides a motivation for looking at both
primary and final energy use when assessing
energy productivity, which we do in this paper.
Figure 4 presents the evolution of Energy
Productivity (GDP/TPES and GDP/TFEC) and per
capita GDP (GDP/POP) over the sample period
for the GCC aggregate. The time evolution of the
three variables for each GCC country is reported in
Appendix 2. It can be seen that after an initial period
of improvement in the 1970s, driven by rising oil
prices and GDP, energy productivity (EP) begins to
decline in the late 1970s, a shift downwards driven
particularly by the economic effects of the oil price
collapse of the early 80s. EP then stabilizes at the
level of the mid-late 1980s through to the current day.
The EP Kuznets hypothesis can be visually
interrogated by comparing the index measure of per
capita GDP (GDPpc) to those of EP. Up until around
1977, GDPpc rises in step with EP(TPES). In this
period energy productivity increases to high levels
off the surge in oil revenues in step with the high oil
prices following the Iranian revolution and Iran/Iraq
War. This rise in energy productivity is much more
pronounced for TPES, than for TFEC, suggesting
that the gap between TPES and TFEC widened over
this period due to relatively greater inefficiencies in
the transformation processes.
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2016
(1971=100)
RealOilPrice
EnergyProductivity,RealGDPpercapita
(1971-100)
GDP/TPES GDP/TFEC GDP per capita Oil prices (real)
Figure 4. Energy Productivity and Real Per Capita GDP 1971-2014 – GCC Countries.
Source: KAPSARC analysis based on OECD, UNSTAT, enerdata.

Energy productivity begins to fall around 1978,
suggesting that while per capita income and GDP in
the later part of the 1970s was going up, it was at a
slower rate than energy consumption which rose by
relatively more.
While per capita income gradually recovers,
picking up pace with the boom in oil prices from
2000 onwards, it is important to note that energy
productivity has remained steady and never gone
back to previous (albeit extremely high) levels.
This observation suggests that growth in GDP per
capita is being driven by a low energy productivity
economic structure, or more simply put, that the
impressive growth was very expensive in terms of
the amount of energy needed to achieve it.
The difference between (the reciprocal of) EP and
GDP per capita depicts our hypothetical energy
Kuznets curve. From this inspection of the actual
data, we can tentatively conclude that while per
capita incomes have gone up over recent decades,
energy productivity has at best remained steady.
This suggests no strong Kuznets curve behavior and
that while the GCC has improved living standards it
has been within a stable and low energy productivity
growth paradigm relative to the past, based on the
historical data.
Turing to the econometric method, we estimate
equation (9) using panel data techniques.
Panel data methods present several advantages
such as allowing the researcher to use data from
countries when the time horizon is short, so as to
increase the amount of information (degrees of
freedom) for the efficiency of estimated coefficients.
Other benefits include better power properties of
testing procedures, when compared with more
standard time series or cross-country methods, and
the fact that many of the issues studied, such as
convergence or transitions, can be considered as
being naturally suited for study in a panel context.
Of course the maintained hypothesis of panel data
approaches is that there is a degree of homogeneity
among the countries involved which may not
be reflected by the data. However, estimation
of regression models for individual countries for
short data samples may not provide reliable and
meaningful results. With these advantages and
qualifications in mind, we hold it is reasonable to
assume that the degree of similarity among the two
groups of countries – the GCC on one hand and
the G-& Plus on the other – makes the approach
we adopt robust enough for the purposes of our
analysis.
The econometric model we estimate is the following:
it
ititit
ti
it P
Y
a
P
Y
a
P
Y
a
Y
E
εγα +⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
+⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
+⎟
⎠
⎞
⎜
⎝
⎛
++=⎟
⎠
⎞
⎜
⎝
⎛
3
3
2
21 lnlnlnln
(14)
where i = 1,..N and t = 1,…T, N being the number
of countries and T the end-of-sample year.
Heterogeneity among countries in this specification
is captured by assuming that the error term of the
equation is equal to the sum of a genuine, standard
random disturbance εit
, of a country-specific (time-
invariant) effect αi, and of a time-specific (country-
invariant) effect γt
. If these two effects are treated
as fixed, i.e., deterministic, variables then we have
the so-called Fixed Effects (FE) panel regression
model; on the other hand, if they are treated as
truly random variables, then we have the Random
Effects (RE) model. The advantage of the former
technique is that the fixed effects may be correlated
with the disturbance term, a situation likely to occur
in practice. Random effects have to be assumed to
be uncorrelated with the disturbance, lest they give
rise to biased estimated model coefficients. The
outcome of Hausman tests, not reported here for
brevity, led us to strongly prefer the FE over the RE
specification.

Accordingly, we estimate (14) under a FE
specification. The so-called ‘within estimator’ can
be easily obtained by applying the OLS method to
the model where the fixed effects are captured by
appropriate dummy variables. Thus:
𝛼𝛼" = ∑ 𝛼𝛼%
&
%'( 𝐶𝐶𝐷𝐷%; 𝛾𝛾, = ∑ 𝛾𝛾-
.
-'( 𝑇𝑇𝐷𝐷- (15)
where CDs and TDs are country and time dummy
variables taking on one for the relevant country or
year and zero otherwise.
Geometrically speaking, the heterogeneity
across countries and across time allowed by this
estimation strategy pertains to the intercept of
the curve, whereas the slope coefficients (the ai
,
i=1,2,3) remain common to all countries. Allowing
for different slopes would be equivalent to running
individual country regressions. The resulting
curves will therefore have different intercepts but
share the same shape, and turning point.
In this study we adopt an energy productivity
and intensity measure using both total primary
energy supply and total final energy consumption.
The former reflects the input side of the energy
transformation sector, while the latter refers to
the output side. Technological improvements
and changes in habits related to energy use,
possibly induced by targeted policies, may affect
the efficiency with which various energy sources
are combined and used in refinery and power
generation processes, as well as the efficiency
with which energy vectors are consumed by final
(households and firms) users. In view of these
considerations we will estimate (14) by proxying E
both with TPES and with TFEC.

I
n this section we explore the empirical evidence
to assess the hypothetical case for EP Kuznets
curve behavior in GCC countries. We present the
estimated coefficients of equation (14) both in its
quadratic and its cubic versions. The results from
GCC countries are shown in Table 1.
The table shows that all coefficients are statistically
significant for all specifications. In addition they
exhibit the expected signs for an inverted-U Kuznets
curve. The significance of the third power of (the log
of) per capita GDP suggests that this is the preferred
specification for the cubic function. Finally, the overall
fit is quite good and the various test statistics point to
a satisfactory specification of the model.
In Table 2 we present the turning points of per capita
GDP and, correspondingly, energy intensity in the
four estimated models. The cubic specification is
characterized by two turning points. The corresponding
graph is N-shaped, although this fact may not be
apparent given the scaling of the two axes. In the
tables we selected the higher turning point, after
which the curve starts declining.
Does an EP Kuznets curve exist in
GCC countries?
Cubic Quadratic
TPES TFEC TPES TFEC
log(Y/P) 13.078 11.303 3.111 2.759
(3.202) (3.015) (3.739) (2.827)
[log(Y/P)]2
-2.868 -2.477 -0.402 -0.363
(-2.956) (-2.783) (-4.316) (-3.199)
[log(Y/P)]3
0.197 0.169
(2.594) (2.434)
Adjusted R2
0.804 0.692 0.796 0.686
Log L -54.630 -89.807 -60.489 -93.137
F test 22.139 12.612 21.519 12.497
[0.000] [0.000] [0.000] [0.000]
Jera-Barque 195.788 31.645 137.375 23.990
[0.000] [0.000] [0.000] [0.000]
Reset 133.734 26.427 151.362 34.879
[0.000] [0.000] [0.000] [0.000]
No obs. 264 264 264 264
Table 1. Estimation of Energy Kuznets Curve Relationships – GCC Countries.
Source: KAPSARC analysis. Notes: (i) fixed effects estimation with country and time effects (not shown); (ii) robust t-statistics in
round brackets: (iii) P-values in square brackets; (iv) the F statistic tests the null hypothesis that all coefficients are equal to zero;
(v) the Jarque-Bera statistic is a test of the normality of residuals; (vi) the Ramsey Reset statistic is a general specification test for
the log-linear regression model.

Does an EP Kuznets curve exist in GCC countries?
The income turning points for TPES and TFEC
are very similar to one another, but occur at higher
levels of per capita income under a quadratic
specification relative to the cubic one.
In our preferred specification the income turning
point at which energy productivity begins to rise
with per capita income is between $38,000-$39,000
(2005 PPP) dollars.
If we turn to the level of energy productivity
measured at the income turning point, we note
that this is lower for TFEC than for TPES. This
simply reflects the smaller energy denominator in
the case of TFEC, which is energy consumed after
transmission losses etc.
In terms of the level of energy productivity of each
country at the per capita income turning point,
we have the following ranking. Bahrain and Saudi
Arabia have the lowest energy productivity at the
turning point, then Bahrain with $3,280 per toe
(TPES), Saudi Arabia with $4,150 per toe (TPES),
while UAE and Qatar are characterized by the
highest energy productivity at the turning point.
For the GCC as a whole we can see that a per
capita income turning point for energy productivity
using TPES occurs around $39,000 to $48,000
depending on the econometric specification used.
The level of energy productivity at the income
turning point for the GCC as a whole is about
$6,500 to $7,000 per toe, in purchasing power parity
terms. This is illustrated along with the actual data
for each country in Figures 5 and 6.
Given per capita incomes are higher in all cases
than where we would expect an energy productivity
turning point to occur, this analysis suggests that
GCC countries are significantly underperforming,
in terms of energy productivity, given the living
standards that they enjoy. This supports the
general view commonly held that there is likely to
be significant scope to increase energy productivity
from investing revenues into diversification and
energy efficiency strategies in the GCC.
Bahrain Kuwait Oman Qatar KSA UAE GCC
GDPpc Energy Productivity at the income turning point (‘000 USD)
TPES/ Cubic 39,100 3.28 7.30 6.59 11.58 4.15 9.97 6.50
TFEC/Cubic 37,900 8.29 9.7 12.97 22.46 7.84 16.48 12.03
TPES/Quadratic 48,700 0.31 0.11 0.13 0.08 0.19 0.10 7.16
TFEC/Quadratic 45,900 0.12 0.08 0.07 0.04 0.11 0.06 13.18
GDP/TPES 3.55 6.84 5.29 6.67 6.92 7.04 6.05
GDP/TFEC 8.52 15.56 7.95 17.02 9.99 9.95 11.50
Table 2. Energy Kuznets Curve – GCC Countries – Turning Points.
Source: KAPSARC analysis. Notes: income turning points are expressed in thousand 2005 PPP dollars. Energy productivity is
expressed in thousand 2005 PPP dollars per toe.

Figure 5. Estimated per capita income turning points with actual data for GCC countries.
Source: KAPSARC analysis, OECD, UNSTAT, Enerdata.
Figure 6. Estimated energy productivity (TPES) at the turning point with actual data for GCC countries.
Source: KAPSARC analysis, OECD, UNSTAT, Enerdata.
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Energyconsumptionpercapita(TPES,toe/personperyear)
EnergyProductivity('000USD2005PPP)
GDP Per Capita (LHS) Energy Consumption Per Capita (RHS)
Estimated
(weak)
per
capita
income
turning
point
range
for
energy
productivity
(TPES)
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Energy Consumption TPES (RHS) Energy Consumption TFC (RHS)
Estimated energy productivity turning point for GCC countries (TPES)

Using our preferred specification of the cubic
function, we now turn to the graphical representation
of our results.
Given the panel specification of the model,
summarized by (14)-(15), the slope coefficients
(the coefficients of the per capita GDP terms) are
common to all countries but there are as many
intercepts as there are countries and years.
First, we took each individual country dummy and
the average value of the estimated coefficients
corresponding to each time dummy. Next, we
substituted these values and the estimated income
coefficients in the right hand side of (14), let per
capita income increase progressively from ln(1) to
ln(70) with 0.2 steps, set the residuals equal to zero
and obtained the resulting curves.
In Figures 7 through 9 we present energy
productivity kuznets curves for the GCC group as
a whole and for individual countries, which are
simply the Kuznets curves implied by the estimated
equation (14) (defined in terms of energy intensity)
turned upside down.
These curves represent the dollars of GDP that
a unit of energy used is able to generate or
‘produce.’ The Kuznets hypothesis suggests that
the productivity of energy worsens in the energy
intensive phase an economy goes through until,
after a certain level of (per capita) income, it
starts picking up owing to structural changes in
the economy due to increased diversification and
sophistication, as well as possibly policy-induced
energy efficiency.
The curves in Figure 8 and 9 suggest that a weak
U-shape of Energy Productivity Kuznets curve exists
for GCC countries as evidenced by the fairly flat tail on
the right hand side of the graph especially for TPES.
Figure 7. Energy Productivity Kuznets Curves for GCC Countries.
Source: KAPSARC analysis based on UNSTAT, IEA and Enerdata(1971-2014).
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15
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25
30
35
40
45
0 10 20 30 40 50 60 70 80
EnergyProductivity('000USD2005PPP/toe)
Per capita income ('000 USD 2005 PPP)
GDP/TPES GDP/TFEC
GCC energy productivity
at the per capita income
turning point (TFEC)
GCC energy productivity
at the per capita income
turning point (TPES)

Figure 8. Energy Productivity Kuznets Curve for Individual GCC Countries (TPES).
Source: KAPSARC analysis based on UNSTAT, IEA and Enerdata (1971-2014).
0.00
5.00
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15.00
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25.00
30.00
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50.00
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0
EnergyProductivity('000USD2005PPPpertoe,TFEC)
Per capita GDP ('000 USD 2005 PPP)
UAE Oman Kuwait Qatar KSA Bahrain
Energy productivity at the
(weak) per capita income
turning point (TFEC)
Figure 9. Energy Productivity Kuznets Curve for Individual GCC Countries (TFEC).
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EnergyProductivity('000USD2005PPP/toe,TPES)
Per capita GDP ('000 USD 2005 GDP PPP)
UAE Oman Kuwait Qatar Saudi Arabia Baharin
Energy productivity at the
(weak) per capita income
turning point (TPES)

Comparative analysis
Does an EP Kuznets curve exist
for advanced economies?
T
o provide a comparative benchmark to the
empirical results for GCC countries, we
extend the analysis to the group of G-7
countries and Australia. We chose this group since
if an EP Kuznets curve were to exist, we might
expect these economies to exhibit such behavior
being further along the development pathway.
Figures 10 and 11 show the estimated energy
productivity at the per capita income turning points
for these countries along with the actual data.
Further descriptive statistics for individual G-7 Plus
countries are reported in Appendix 3.
To begin, we note that average per capita incomes
are higher in GCC countries than in our selection of
advanced economies. In the GCC per capita income
ranges between around $40,000 for Bahrain, up
to about $120,000 for Qatar, while in the G-7 Plus
group ranges between approximately $25,000 in
Italy, up to around $45,000 in the U.S.
The GCC group of countries also exhibits higher per
capita energy consumption compared with the G-7
Plus group of advanced economies. Of the group,
Saudi Arabia has the lowest per capita energy
consumption at about 7 tonnes of oil equivalent per
person per year, while Qatar has one of the highest
per capita rates of energy consumption in the world
at just over 18 tones per person. In some cases
per capita energy consumption shows a distinct
“inverted U” shape, such as in the UAE and Bahrain,
and in other cases it seems to have plateaued in the
last few years.
The advanced group of countries, on the other
hand, show either a fairly strong pattern of ‘inverted
Us’ across the sample, or a downward trend of per
capita energy consumption, with Italy having the
lowest values at about 2 tonnes of oil equivalent per
person per year, while the U.S. and Canada have
are up around 7 tonnes per person. In the GCC
group of countries, per capita energy consumption is
universally increasing, in some cases such as Saudi
Araba incredibly rapidly, whereas in the G-7 Plus
group energy consumption is either flat or declining.
Turning now to a comparison of energy productivity
across the two groups (using TPES), in the GCC
energy productivity ranges between just under
$5,000 per toe in Bahrain up to around $7,000 toe
in the UAE. For the advanced group of countries,
energy productivity is lowest in Canada at about
$5,000 per toe and is highest in the U.K. at around
$12,000 per toe.
This comparative relationship between per capita
incomes and energy consumption is at the heart
of our energy productivity Kuznets curve analysis.
For instance, what are the conditions under which
a country moves into a development zone where
per capita incomes are improving strongly while
per capita energy consumption actually is falling,
leading to improvements in energy productivity?
For example, in Germany’s case energy productivity
has moved from about $5,000 per toe in the 1980s-
1990s to almost $10,000 per toe in 2014, while per
capita energy consumption has declined by about 1
toe over the same period.
Figure 12 presents the indices for EP and GDPpc
in this group of advanced countries. In almost all
years, per capita incomes and energy productivity
are rising at very close to the same rate. In the
later years, the effect of the 2008 economic crisis is
clearly visible on per capita incomes.
Turning to the econometric analysis, we follow the
same estimation strategy as before. The results
are displayed in Table 3 and the key estimates are
shown graphically in Figures 13 through to 15.

Figure 10. Estimated per capita income turning point for energy productivity (TPES) with actual data for selected
advanced countries
Source: KAPSARC analysis based on OECD, UNSTAT, Enerdata.
Figure 11. Estimated energy productivity at the per capita incomes turning point (TPES) for selected advanced
countries.
Source: KAPSARC analysis based on OECD, UNSTAT, Enerdata.
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Australia Canada France Germany Italy Japan UK United
States
Percapitaenergyconsumption(toeperperson)
GDPpercapita('000USD2005PPPperperson)
GDP per capita (LHS) Energy consumption per capita, TPES (RHS)
Estimated energy productivity per capita income turning point for energy productivity for GCC countries (TPES)
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Australia Canada France Germany Italy Japan UK United States
Energy consumption TPES (RHS) Energy Consumption TFC (RHS)
Estimated energy productivity turning point range for advanced countries

50
100
150
200
250
300
EnergyproductivityandGDPpercapita
(Index1971=100)
GDP/TPES GDP/TFEC GDP per capita
Figure 12. Energy Productivity and Real Per Capita GDP 1971-2014 – G-7 Plus Countries.
Source: KAPSARC based on OECD, UNSTAT and Enerdata.
Cubic Quadratic
TPES TFEC TPES TFEC
log(Y/P) 6.110 5.469 1.835 2.401
(1.233) (1.167) (5.517) (7.862
[log(Y/P)]2
-1.777 -1.469 -0.450 -0.516)
(-1.163) (-1.017) (-9.115) (-11.369)
[log(Y/P)]3
0.136 0.098
(0.878) (0.666)
Adjusted R2
0.953 0.967 0.953 0.967
Log L 462.107 502.822 461.726 502.574
F test 134.862 197.419 137.606 201.599
[0.000] [0.000] [0.042] [0.000]
Jarque-Bera 7.658 9.520 6.330 9.056
[0.022] [0.009] [0.712] [0.011]
Reset 0.087 0.009 0.137 0.025
[0.769] [0.924] [0.000] [0.874]
No obs. 352 352 352 352
Table 3 Estimation of Energy Kuznets Curve Relationships – G-7 Plus Countries.
Source: KAPSARC analysis.

For the advanced group of countries we find that all
the coefficients are statistically significant except for
those associated with the third power of (log) per
capita GDP. On the basis of the adjusted R-square,
the fit of all models is very satisfactory, as in the
GCC case. A ‘U-shaped’ Kuznets curve seems
to be in the data for all specifications. The only
noticeable point is the outcome of the Jarque-Bera
test signaling non-normality of the residuals in some
cases, while the Reset test points to some mild
problem of specification for the TPES-cubic model.
Table 4 shows our results for the turning points.
First of all, we observe that the values of the income
turning point of G-7 countries are significantly lower
than in the GCC group. This reflects the relatively
high per capita incomes in the GCC region relative
to the group of selected advanced countries.
The turning points of the cubic model are similar to
those of the quadratic specification. As expected, the
income turning points and energy productivities are
lower for TPES than for TFEC (Figure 7) due to the
lower value in the denominator for EP. The relative
ranking of energy productivity at the turning point is
also evident from the table. The U.S. has the lowest
energy productivity (GDP/TPES) of $1,600 (2005
GDP PPP) per toe at the per capita income turning
point of $7,700 (2005 GDP PPP), and is followed by
Canada $1,700, Australia $2,590, Germany $3,100,
France $3,500, U.K. $3,630, Japan $3,790 and finally
Italy with $5,120 at the turning point.
Canada France Germany Italy Japan UK USA Australia G-7 Plus
GDPpc Energy Productivity at the income turning point ('000USD 2005 PPP)
TPES/ Cubic 10,700 1.88 3.89 3.44 5.69 4.20 4.03 1.78 2.88 3.25
TFEC/Cubic 11,900 2.85 6.63 5.71 8.32 7.08 6.87 3.04 5.12 5.36
TPES/Quadratic 7,700 1.70 3.50 3.10 5.12 3.79 3.63 1.61 2.59 2.93
TFEC/Quadratic 10,300 2.77 6.41 5.53 8.04 6.86 6.64 2.95 4.96 5.19
GDP/TPES 2.95 5.17 4.15 7.06 10.89 3.96 5.70 6.68 5.82
GDP/TFEC 3.57 6.53 5.80 8.63 13.87 6.15 7.43 9.79 7.72
Table 4. Estimated Turning Points – G-7 Plus Countries
Source: KAPSARC analysis.

Actual 2014 energy productivity for each country
is also much higher than its energy productivity at
the estimated turning point, suggesting that all of
these countries are well to the right of the turning
point in their development process. This implies that
the structure of their economies is already geared
to a mutually reinforcing relationship between
rising energy productivity with per capita incomes.
In other words as incomes in these advanced
economies rise, they become less and less reliant
on energy consumed to drive this.
Turning to the graphical representation of the
estimated G-7 Plus EP Kuznets curves in Figures
13, 14 and 15, two things are immediately apparent
relative to GCC countries. First, the inverted-U
shape is more pronounced and increases much
more rapidly after the turning point. Second, there is
a bi-modal distribution, with a relatively less energy
productive North American country cluster on the
one hand and a more energy productive European
cluster on the other. Italy has the highest EP
Kuznets curve.
Figures 16 and 17 highlight the differences in the
estimated Kuznets curves for energy productivity
between GCC and G-7 Plus countries. The two
graphs effectively show the two distinct features
which differentiate the two groups: (i) the income
turning point occurs at lower levels for G-7 Plus than
for GCC; (ii) the growth of energy productivity after
the turning point is much slower for GCC than for
G-7 Plus countries.
Figure 13. Energy Productivity Kuznets Curve for G-7 Plus Countries.
Source: KAPSARC Analysis based on UNSTAT, IEA and Enerdata (1971-2014).
0
5
10
15
20
25
30
35
0 10 20 30 40 50 60 70 80
GDP per capita ('000 USD GDP 2005 PPP)
Energy productivity (TPES) Energy productivity (TFEC)
Energy productivity at
the (relatively strong)
per capita income
turning point
TPES TFEC

Figure 14. Energy Productivity Kuznets Individual Curves for G-7 Plus Countries (TPES).
Figure 15. Energy Productivity Kuznets Individual Curves for G-7 Plus Countries (TFEC).
0
5
10
15
20
25
30
0 10 20 30 40 50 60 70 80
EnergyProductivity('000USD2005PPPpertoe,TPES)
France Italy Germany Japan UK Canada Australia USA
per capita income
turning point
0
5
10
15
20
25
30
0 10 20 30 40 50 60 70 80
EnergyProductivity('000USD2005PPPpertoe,TFEC)
France Italy Germany Japan UK Canada Australia USA
per capita income
turning point

Figure 16. Energy Productivity (TPES) Kuznets Curve for GCC and G-7 Plus Countries
Australia
Canada
France
Germany
Italy
Japan
UK
USA
Bahrain
Kuwait
Oman
Qatar
KSA
UAE
Data
point
for
2014
Figure
17:
Energy
Productivity
Kuznets
Curve
for
GCC
and
G-‐7
Plus
Countries
Figure 17. Energy Productivity (TFEC) Kuznets Curve for GCC and G-7 Plus Countries
Australia
Canada
France
Germany
Italy
Japan
UK
USABahrain
Kuwait
Oman
QatarKSA UAE
Data
point
for
2014
igure
16:
Energy
Productivity
(TPES)
Kuznets
Curve
for
GCC
and
G-‐7
Plus
Countries
0 20 40 60 80 100 120
0
5
10
15
20
25
30
35
GCC G7 Plus Data point for 2014
EnergyProductivity(‘000USD2005PPPpertoe,TPES)
0 20 40 60 80 100 120
0
5
10
15
20
25
30
35
EnergyProductivity(‘000USD2005PPPpertoe,TFEC)
GDP per capita (‘000 USD 2005 PPP)
GDP per capita (‘000 USD 2005 PPP)
GCC G7 Plus Data point for 2014

Checking for Robustness
Additional explanatory
variables
T
he classical specification underlying
Kuznets curve relationships has the ratio
between income and population as the
only explanatory variable (aside from possible
time trends). The idea is that GDP captures and
summarizes the working of the whole economy and
is a sufficient statistic to use.
Several authors have challenged this notion, noting
that two countries with the same (per capita) GDP
may have very different economic characteristics.
For example, these could involve: sectoral
composition, income distribution or openness to
international trade. These would of course have
important consequences for energy intensity/
productivity as well as for carbon dioxide emissions
for a given level of per capita income.
Thus, besides per capita income and time trend,
one ought to include variables capturing the
structure of the economy or the degree of trade
openness. Domestic and international energy prices
present other candidate variables that could be
relevant especially in the energy producing countries
of the Gulf which rely on revenues generated from
export sales.
In view of these considerations we conducted
further analysis to test the robustness of our results
incorporating three additional variables; degree of
trade openness, share of manufacturing in total value
added and real price of oil. The price of oil, measured
in 2005 US dollars per barrel, is taken form the BP
Statistical Review of World Energy; data on import
and export of goods and services to calculate the
degree of openness are from UNSTAT. Data on
sectoral value added used to compute the index of
industrial composition is also taken from UNSTAT.
In Table 5 we present the estimated coefficients of
the extended version of equation (14) in its cubic
version for GCC countries. We also included the
share of sectoral value added in total value added
for all sectors. Being quotas which sum to one, we
include all sectoral shares but one, agriculture.
TPES TFEC
1 2 3 4 5 6 7 8
log(Y/P) 15.916 7.601 23.466 13.101 13.456 4.127 23.544 11.089
(3.394) (1.875) (5.394) (2.829) (3.576) (1.162) (6.015) (2.508)
[log(Y/P)]2
-3.498 -1.871 -5.081 -2.874 -2.979 -1.080 -5.167 -2.422
-(3.174) -(1.946) -(4.854) -(2.585) -(3.290) -(1.281) -(5.326) -(2.245)
[log(Y/P)]3
0.236 0.143 0.343 0.197 0.200 0.085 0.353 0.165
(2.783) (1.923) (4.174) (2.262) (2.823) (1.308) (4.542) (1.926)
Trade Openness -0.314 0.294 -0.514 -0.008 -0.119 0.479 -0.420 0.146
-(2.160) (3.018) -(3.219) -(0.021) -(0.839) (4.178) -(2.437) (0.378)
log(oil price) 0.008 0.076 0.103 0.152
Table 5. Estimation of Extended Energy Kuznets Curve Relationships – GCC Countries

Checking for robustness
TPES TFEC
1 2 3 4 5 6 7 8
(0.137) (1.505) (1.904) (2.755)
Manuf. Share -0.909 -0.818 0.484 0.004 -1.521 -1.352 0.170 -0.107
-(3.748) -(4.190) (2.476) (0.013) -(5.738) -(5.435) (0.818) -(0.337)
Mining Share -5.247 -5.586 -7.671 -7.947
-(5.184) -(7.599) -(7.139) -(8.577)
Constr. Share -9.106 -6.660 -10.878 -8.671
-(6.196) -(5.765) -(6.812) -(5.540)
Commerce Share -2.469 -5.419 -3.823 -6.592
-(1.247) -(3.849) -(1.864) -(3.672)
Transport Share 7.890 5.769 3.987 3.614
(4.302) (3.100) (2.322) (1.579)
Other Share -5.373 -8.245 -7.156 -9.896
-(5.593) (10.546) -(7.472) (10.639)
Adjusted R2
0.812 0.898 0.735 0.802 0.759 0.840 0.635 0.691
Log L -69.031 35.802 117.214 -54.628 -78.061 0.617 -135.247 -89.258
F test 72.126 40.825 67.295 21.103 52.633 24.751 42.591 12.088
[0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00]
Jarque-Bera 60.995 66.651 111.908 194.774 103.981 3.398 234.741 36.041
[0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00]
Reset 161.452 134.449 68.766 144.484 58.187 27.754 20.103 26.076
[0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00]
No obs. 264 264 264 264 264 264 264 264
Country Dummy yes yes yes yes yes yes yes yes
Time Dummies no yes no yes no yes no yes
Turning Points 34.90 25.10 39.70 38.90 32.70 18.30 37.90 38.70
Table 5 continued. Estimation of Extended Energy Kuznets Curve Relationships – GCC Countries
Source: KAPSARC analysis. Notes: see Table 2. The turning point for income is expressed in thousand 2005 PPP dollars
Among the additional explanatory variables,
the ones capturing the sectoral composition are
generally significant. For the other variables it
depends upon the specification. What is more
important is that the implied Kuznets curves exhibit
the expected shape and the value of the estimated
turning points for per capita GDP, as seen in the last
row of the table. These are generally in the ballpark
of those presented before. Figures presenting the
Kuznets curves corresponding to these extended
specifications are not reported to conserve on
space, but are available from the authors. Based on
this extended analysis we consider that our results
have an acceptable level of robustness.

T
he well-known bell-shaped curve was
originally suggested by Simon Kuznets to
characterize the relationship between per
capita income and income distribution. This idea was
later applied to the environment-income nexus and
the Environmental Kuznets Curve concept emerged
in the early 1990s. This work was pioneered
by Grossman and Krueger’s (1991) study of the
potential impacts of NAFTA and with Shafik and
Bandyopadhyay’s (1992) background study for the
1992 World Development Report.
These studies have been extended to CO2
as the
most important greenhouse gas at the heart of the
climate change movement. As nearly three quarters
of such emissions are related to energy consumption,
it is relevant to extend our work to look at the carbon
intensity Kuznets curve dynamics in GCC countries.
Figures 18 and 19 illustrate information on per capita
CO2
emissions and per capita incomes of individual
GCC economies. Data on carbon dioxide emissions
are taken from the IEA database CO2
from fuel
combustion.
From these graphs it can be seen that GCC countries
as a whole have per capita emissions that are
around twice as large relative to most of the G-7 Plus
countries. This reflects a combination of the currently
high proportion of the energy mix reliant on carbon-
based fuels and the energy intensive structure of
industry in most countries in the GCC, compared
with the G-7 group of countries investigated.
Implementation of the renewable plans across the
GCC region has the potential to shift this picture.
We model a CO2
Kuznets relationship in a manner
similar to the approach taken earlier in this paper
where per capita emissions are related to powers
of per capita GDP:
3
3
2
210
2
lnlnlnln ⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
+⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
+⎟
⎠
⎞
⎜
⎝
⎛
+=⎟
⎠
⎞
⎜
⎝
⎛
P
Y
P
Y
P
Y
P
CO
ββββ
(15)
A Look at Carbon Intensity
Exploiting the properties of the assumed log-linear
form we estimate a Kuznets model for the carbon
intensity of the economy, so that – in keeping with
(9) – we have:
3
3
2
210
2
lnlnln)1(ln ⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
+⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
+⎟
⎠
⎞
⎜
⎝
⎛
−+=⎟
⎠
⎞
⎜
⎝
⎛
P
Y
P
Y
P
Y
Y
CO
ββββ
(16)
Data on carbon dioxide emissions – measured in MtCO2
– are taken from the IEA.
The empirical results from estimation of (16) are
presented in Table 9.
It is important to note that, although carbon dioxide
emissions are calculated by applying appropriate
technical coefficients to the energy consumption
of coal, oil and natural gas, the results of the
Carbon Kuznets curve estimation may differ from
the Energy Kuznets curve for two reasons. First,
over time and across countries the fossil fuel mix
changes. Second, total energy consumption is
not limited to fossil fuels. With this in mind, and in
line with previous findings, we note that the cubic
model better explains the evidence for GCC and
the quadratic specification that of the G-7 Plus
countries. From Figure 20 we can see that the
carbon intensity curves have a bell-shaped profile.
As shown in Table 10, the income turning point
occurs much earlier for the G-7 Plus group than for
the GCC countries and the difference between the
two groups is much more marked than for energy
intensity/productivity, reflecting the lower share of
fossil fuels in the G-7 group.
From a practical standpoint for policymakers, what
this tells us is that carbon intensity goalsgoals – as
with energy productivity goals, but more so – will
be much harder to achieve in GCC relative to the
advanced economies of the G-7, even though per
capita incomes may be high in the GCC.

Figure 19. Per capita CO2
emissions and per capita GDP for GCC countries.
emissions and per capita GDP for G-7 Plus group of countries.
0
5
10
15
20
25
0
5
10
15
20
25
30
35
40
45
50
1980
2000
2014
1980
2000
2014
1980
2000
2014
1980
2000
2014
1980
2000
2014
1980
2000
2014
1980
2000
2014
1980
2000
2014
Australia Canada France Germany Italy Japan UK USA
CO2emissionspercapita
tCO2perpersonperyear
GDP per capita (LHS) Per capita CO2 emissions (RHS)CO2
0
5
10
15
20
25
30
35
40
0
20
40
60
80
100
120
140
160
180
200
1980
1990
2000
2010
2014
1980
1990
2000
2010
2014
1980
1990
2000
2010
2014
1980
1990
2000
2010
2014
1980
1990
2000
2010
2014
1980
1990
2000
2010
Bahrain Kuwait Oman Qatar KSA UAE
CO2emissionspercapita
tCO2perpersonperyear
GDP Per Capita (LHS) Per capita CO2 emissions (RHS)CO2

emissions and per capita GDP for GCC countries.
GCC G-7 Plus
Cubic Quadratic Cubic Quadratic
log(Y/P) 12.236 2.592 10.122 0.305
(3.157) (3.324) (1.257) (0.573)
[log(Y/P)]2 -2.726 -0.340 -3.197 -0.149
-(2.963) -(3.892) -(1.299) -(1.857)
[log(Y/P)]3 0.190 0.312
(2.650) (1.258)
Adjusted R2 0.795 0.786 0.922 0.922
Log L -43.508 -49.474 304.374 303.555
F test 20.983 20.365 78.934 80.321
[0.000] [0.000] [0.000] [0.000]
Jarque-Bera 223.964 156.785 1.425 2.252
[0.000] [0.000] [0.491] [0.324]
Reset 140.916 149.450 37.288 31.495
[0.000] [0.000] [0.000] [0.000]
No obs. 264 264 352 352
Table 9. Estimation of Carbon Kuznets Curve Relationships – GCC and G-7 Plus Countries. Notes: See Table 2.
GCC G-7 Plus
GDPpc 37,300 2,900
Carbon Intensity at the income turning point 0.367 0.879
Table 10. Estimated Turning Points – GCC and G-7 Plus Countries. Notes: see Table 3.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 10 20 30 40 50 60 70 80
Carbonintensity
(MtCO2/'000USD2005USDPPP)
GDP per capita ('000 USD 2005 PPP)
GCC G7 PLUS
CO2 intensity at the
per capita income
turning point

Conclusions and Areas for Further
Research
T
his paper provides an evidence base for
policymakers and their advisors interested in
using energy productivity as an organizing
goal to support economic diversification, energy
efficiency and innovation efforts. The policy
implications of this work are further explored in
this paper’s sister KAPSARC Discussion Paper
Energy productivity as a new growth model for
Gulf Cooperation Council Countries (Dubey, et al.
2016). In particular, this work highlights the case for
setting national level energy productivity targets and
provides an evidence base for doing so.
KAPSARC research has investigated the strengths
and limitations of using an aggregate measure
of energy productivity in making international
comparisons (Bean 2014; Gasim and Hunt 2016).
While it is useful for policymakers to compare
energy productivity relative to other countries in
setting targets, such international comparisons
should only inform part of the target setting process.
A pragmatic approach could consider other factors
such as energy productivity trends in the currency
units of the country involved, have some flexibility to
respond to major unexpected economic shifts and
be sensitive to each country’s development strategy.
Energy productivity by virtue of being a policy target
relative to GDP already has some built in flexibility
in this regard, however in practice extra attention is
important for major energy exporters to account for
factors such as shifts in oil prices.
Future research in this area could consider issues
such as setting energy productivity targets in the
context of other targets, such as those articulated
in the Saudi 2030 Vision statement, and similar
plans across the GCC region. Such work would be
strengthened by ideally being undertaken working
alongside the major agencies responsible for
the implementation of national strategies. There
is also a particular need for improved data and
further analysis on energy productivity trends
at the sector level and to better understand the
energy productivity implications of changes in the
fuel mix that might accompany a significant shift to
renewable energy.

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Appendix 1: Energy productivity
Kuznets specification
A
lthough it is convenient to fit a log-linear specification for the Kuznets relationship, the properties of
the shape of the Kuznets curve are to be studied for levels of y and x, not for their logs. In particular,
as illustrated in the main text, we are interested in the first and second derivatives of y with respect
to x. The relationship between them is the following:

x
y
xd
yd
dx
dy
ln
ln
= (A1.1)

⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛
−+= 1
ln
ln
ln
ln
)(ln
ln
2
2
22
2
xd
yd
xd
yd
xd
yd
x
y
dx
yd (A1.2)
To find the turning point we must solve the equation dy/dx = 0. Now let the general (cubic) log-linear
model be:
3
3
2
210 )(ln)(lnlnln xxxy αααα +++= (A1.3)
or:
3
3
2
210 )(ln)(ln xx
eexey αααα
=

(A1.4)
It can be shown that:
3
3
2
210 )(ln)(ln12
321
2
321
])(ln3ln2[
)/]()(ln3ln2[
xx
eexexx
xyxx
dx
dy
αααα
ααα
ααα
−
++=
++=

(A1.5)
and:
{
}
3
3
2
210 )(ln)(ln22
321
2
32132
22
321
2
321322
2
])(ln3ln21[
])(ln3ln2[)ln62(
)/(])(ln3ln21[
])(ln3ln2[)ln62(
xx
eexexx
xxx
xyxx
xxx
dx
yd
αααα
ααα
ααααα
ααα
ααααα
−
++−
++++=
++−
++++=

(A1.6)
This explains how expressions (11)-(13) in the main text were obtained.

Appendix 2: Energy productivity for
individual GCC countries
I
n this appendix we present the time evolution of energy productivity (GDP/TPES) and of per capita GDP
of individual GCC countries.
Figure 21. Energy productivity and real per capita income in Bahrain
Figure 22. Energy productivity and real per capita income in Kuwait
0
50
100
150
200
250
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
2014
Bahrain
Energy Productivity and Real GDP per capita (1971=100)
GDP/TPES GDP/POP GDP/TFEC
0
20
40
60
80
100
120
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
2014
Kuwait

Appendix 2: Energy productivity for individual GCC countries
0
50
100
150
200
250
300
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
2014
Oman
Energy Productivity and Real GDP per capita(1971=100)
Figure 23. Energy productivity and real per capita income in Oman
0
20
40
60
80
100
120
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
2014
Qatar
Figure 24. Energy productivity and real per capita income in Qatar

Appendix 2: Energy productivity for individual GCC countries
0
20
40
60
80
100
120
140
160
180
200
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
2014
Saudi Arabia
Figure 25. Energy productivity and real per capita income in Saudi Arabia
0
50
100
150
200
250
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
2014
United Arab Emirates
Figure 26. Energy productivity and real per capita income in UAE

Appendix 3: Energy productivity
selected advanced countries
0.0
50.0
100.0
150.0
200.0
250.0
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
Australia
GDP/POP GDP/TPES GDP/TFEC
Figure 27. Energy productivity and real per capita income in Australia.
0.0
50.0
100.0
150.0
200.0
250.0
Canada
Figure 28 Energy productivity and real per capita income in Canada.

Appendix 3: Energy productivity selected advanced countries
0.0
50.0
100.0
150.0
200.0
250.0
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
France
Figure 29. Energy productivity and real per capita income in France.
0.0
50.0
100.0
150.0
200.0
250.0
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
Germany
Figure 30. Energy productivity and real per capita income in Germany.
Source: KAPSARC analysis based on UNSTAT, IEA and Enerdata(1971-2014).

Figure 31. Energy productivity and real per capita income in Italy.
0.0
50.0
100.0
150.0
200.0
250.0
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
Italy
0.0
50.0
100.0
150.0
200.0
250.0
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
Japan
Figure 32. Energy productivity and real per capita income in Japan.

0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
United Kingdom
Figure 33. Energy productivity and real per capita income in the UK.
0.0
50.0
100.0
150.0
200.0
250.0
300.0
United States
Figure 34. Energy productivity and real per capita income in the U.S..

Notes

About the Project
Increasing energy productivity holds some of the greatest possibilities for enhancing
the welfare countries get out of their energy systems. It also recasts energy efficiency in
terms of boosting competitiveness and wealth, more powerfully conveying its profound
benefits to society.
KAPSARC and UNESCWA have initiated this project to explore the energy productivity
potential of the Arab region, starting with the six GCC countries.
Aimed at policymakers, the project aims to highlight the social gains from energy
productivity investments, where countries are currently at, and articulate options for
achieving improved performance in this area.
About the Authors
Nicholas Howarth
Nicholas is an applied economist with 20 years of experience
working with governments, industry and in academia. A research
fellow at KAPSARC, he coordinates the center's work on energy
productivity in cooperation with UNESCWA.
Marzio Galeotti
Marzio is a visiting researcher at KAPSARC. He is professor of
environmental and energy economics at the Università degli Studi di
Milano, and a research fellow at IEFE, Università Bocconi, Milan.
Alessandro Lanza
Alessandro is a visiting researcher at KAPSARC. He is Professor
of Energy and Environmental Policy at LUISS University, Rome
and a member of the Board of Directors of ENEA, Italy.

www.kapsarc.org

KS-1648-MP043A-Energy-productivity-in-the-GCC_Evidence-From-an-International-Kuznets-Curve-Analysis1

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