Post on 29-Jan-2023
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The importance of energy quality in energy intensive manufacturing: Evidence from panel cointegration and panel FMOLS
Brantley Liddle Centre for Strategic Economic Studies Victoria University Level 13, 300 Flinders Street Melbourne, VIC 8001 Australia +61 3 9919 1094 (phone) +61 3 9919 1350 (fax) btliddle@alum.mit.edu
Published in Energy Economics, Vol.34, pp. 1819-1825.
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The importance of energy quality in energy intensive manufacturing: Evidence from panel
cointegration and panel FMOLS
ABSTRACT
This paper expands on the panel GDP-energy cointegration modeling literature; it does so by using data disaggregated along sectoral lines and adjusting energy consumption for the quality of the energy source (e.g., electricity is of higher quality than oil, which is of higher quality than coal) in order to examine the role of energy quality in the five most energy intensive manufacturing sectors (iron and steel, non-ferrous metals, non-metallic minerals, chemicals, and pulp and paper). The database was constructed by combining energy consumption (and energy price data to construct the energy quality index) from the IEA with economic data (value added, labor employed, and physical capital services) from the OECD’s Structural Analysis Database. In addition to finding the variables analyzed are panel I(1) and cointegrated for each sector-based panel, the long-run elasticity estimates (from panel FMOLS) indicate the importance of energy quality—primarily the shift toward the use of high quality electricity—in these energy-intensive manufacturing sectors. In each case, the elasticity for energy quality is greater than that for conventionally measured energy consumption—sometimes orders of magnitude greater. Indeed, the elasticity of conventionally measured energy consumption is insignificant to very small for three of the five sectors. Also, when using the energy quality measure, the importance of energy consumption relative to the other production factors stands out. Such results are useful for both energy-GDP cointegration/causality modelers and CGE modelers, who may need to estimate elasticities.
Keywords: Energy intensive manufacturing; Panel cointegration and Panel FMOLS; energy
quality index; disaggregated energy consumption; elasticity estimation
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1. Introduction and literature review
There is a substantial and growing literature employing cointegration modeling to
examine the energy-GDP relationship at a national scale (see reviews by Payne 2010a and
2010b and Ozturk 2010); among the most popular models is one based on an aggregate
neoclassical production function. Among the consensuses emerging from this vast literature
are: (i) models should be multivariate to avoid the missing variable bias; (ii) analyses should
employ panel data to improve the power of unit root and cointegration tests that otherwise
can be impaired by the short time spans typically available for single countries; and (iii)
investigators should determine the magnitude, sign, and significance of elasticities, rather
than just performing Granger-type causality tests (see the concluding sections of the two
Payne reviews).
This paper borrows those three consensus ideas along with two seldom used ones: (i)
to use data disaggregated along sectoral lines, and (ii) to adjust energy consumption for the
quality of the energy source (e.g., electricity is of higher quality than oil, which is of higher
quality than coal); those five ideas are used to examine with OECD panel data the role of
energy quality in the five most energy intensive manufacturing sectors. The finding that
accounting for energy quality does indeed impact the production function elasticity estimates
should be of interest for both energy-GDP cointegration/causality modelers and CGE
modelers (who may need to estimate elasticities too).
Perhaps the first to use the aggregate production function model (GDP as a log-linear
function of energy, labor, and capital) was Stern (2000); he focused on the US, found
cointegration among the four variables, and tested for Granger-causality (but did not estimate
elasticities). Recently, several papers have used panel data for such an aggregate model and
have estimated elasticities via methods like panel Fully Modified OLS (FMOLS) and panel
Dynamic OLS (DOLS). For example, Narayan and Smyth (2008) focused on G7 countries;
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Lee et al. (2008) focused on OECD countries; Lee and Chang (2008) on Asian countries; and
three papers from Apergis and Payne (2009a, 2009b, and 2010) considered Central
American, former Soviet, and South American countries, respectively. All six studies found
the production variables to be cointegrated, and the long-run energy elasticity estimations (all
statistically significant) ranged from 0.12 to 0.42.
Very few energy-GDP cointegration studies, however, have used disaggregated data
and the production function model (perhaps, only this one and Soytas and Sari 2007).
Furthermore, to my knowledge, no studies using disaggregated data and the production
function model have (i) used data disaggregated at a greater level than manufacturing (i.e.,
ISIC two-digit or higher resolution) or (ii) estimated elasticities. Soytas and Sari (2007)
considered manufacturing in Turkey, and determined that electricity consumption, capital,
labor, and value added are integrated order one and cointegrated. (They performed Granger-
causality tests but did not estimate elasticities.)
Neither Sari et al. (2008), who focused on industrial output in the US, nor Ziramba
(2009), who focused on manufacturing in South Africa, considered capital, but both did
estimate elasticities via the autoregressive distributed lag approach. Sari et al. (2008), in
addition to focusing on industrial production, appeared to use aggregate employment and
coal consumption (as opposed to industrial employment and consumption). They found those
three variables to be cointegrated, but their estimated long-run coefficients for employment
and coal to be negative, significant, and large, and their estimated short-run coefficients for
both to be insignificant. Ziramba (2009) analyzed manufacturing production and
manufacturing employment, and separately, both electricity and oil consumption (again, it is
not clear whether that consumption was economy-wide or industrial consumption). Ziramba
found both sets of variables to be cointegrated (production, employment, and electricity; and
production, employment, and oil); but for both models, employment’s elasticity is highly
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insignificant (p-value 0.7 or higher), as is oil’s elasticity. Electricity’s elasticity is significant
and equal to 0.70.1
The Stern (2000) paper used a quality weighted index of energy, an inclusion
apparently motivated by the results of a still earlier paper, Stern (1993). Stern (1993)
considered both a conventional measure of energy consumption and a quality weighted index
of energy (that paper, also based on the US, used an aggregate production function model, but
did not test for cointegration). Consideration of a quality weighted index is important because
some forms of energy can produce more work than others: a unit of electricity is of higher
quality than a unit of oil, which itself is of higher quality than a unit of coal. Also, arguably
reflecting these differences in productivity, electricity tends to be the most expensive energy
source, followed by oil, and coal tends to be among the least expensive energy sources.
Berndt (1978) describes this situation as follows: “the different prices of energy forms per
Btu illustrate the fact that end-users of energy are concerned not only with the Btu heat
content of the various energy types, but also with other attributes.” Or according to Stern
(1993), “quality weighted final energy use … is likely to be a superior measure of the energy
input to economic activity as it will reflect better the productivity of the uses to which energy
is put.” Indeed, Stern (1993) found energy consumption to be endogenous when measured
conventionally, but found that energy quality weighted consumption Granger-caused GDP.
Despite the results of Stern (1993 and 2000) and the well known energy ladder
phenomenon (i.e., the shift away from biomass and coal and toward oil and electricity as
countries develop), very few energy-GDP cointegration/causality studies have considered
1 Bowden and Payne (2009), employing US data, used the Toda-Yamamoto causality tests, and thus, did not test for cointegration (they did not estimate elasticities either). In addition, they appeared to analyze aggregate real GDP, gross fixed capital formation, and civilian employment with industrial primary energy consumption. Thus, as with Sari et al. (2008) and Ziramba (2009), they were not technically using a sectoral-level production function model.
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quality adjusted energy consumption since then.2 Oh and Lee (2004) applied Stern’s methods
to Korea and arrived at similar conclusions. Similarly, Warr and Ayres (2010) replicated
Stern’s model for the US using their own measure of exergy (i.e., the amount of energy
available for useful work) in place of Stern’s energy quality index, and found both short- and
long-run causality running from either of their energy quality measures to GDP but not vice
versa.
2. Data, models, and methods
The database used in this paper was constructed by combining energy consumption
data (and energy price data to construct the energy quality index) from the IEA’s Energy
Balances with economic data (value added, labor employed, and physical capital formation)
from the Structural Analysis Database (STAN) published by the OECD. Table 1 shows, for
each manufacturing sector, the sector designation used for the remainder of the paper, the
sector classification (based on the ISIC Rev. 3) used to assemble the database, and the
average energy intensity (in thousand tons of oil equivalent, ktoe, per million yr-2005 US$)
for OECD countries in 2005 for that sector. This analysis focuses on the five most energy
intensive manufacturing sectors: iron and steel, non-ferrous metals, non-metallic minerals,
chemicals, and pulp and paper.
Table 1
Despite the increased importance of the service sector, seen both in an increasing
share of GDP and share of trade flows and in the phenomenon of deindustrialization, energy
intensive manufacturing is not unimportant in OECD countries. Table 2 shows the share of
manufacturing value added attributable to each of the five most energy intensive sectors for
individual OECD countries in 2005. On average, these five sectors account for nearly one-
quarter of OECD manufacturing value added. The non-ferrous metal sector (e.g., aluminium
2 There is a separate strand of the literature that focuses on electricity consumption; again, see the survey by Payne (2010b).
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smelting) tends to be concentrated in areas with inexpensive energy. Australia, Canada,
Norway, and the US produced 70% of the OECD’s primary aluminium in 2006. Although
more evenly distributed across the OECD than smelting activity, iron and steel is a sector in
which several OECD countries are substantial net importers (e.g., Belgium, Finland, and
Japan); whereas, other countries are substantial net exporters (e.g., Canada, Poland, US). By
contrast, chemicals, non-metallic minerals (e.g., ceramics, cement, and glass), and pulp and
paper are sectors in which value-added has increased more or less steadily and uniformly in
all OECD countries.
Table 2
2.1 Models
3I take a constant return to scale Cobb-Douglas production function used elsewhere in
the energy-GDP literature (e.g., Lee et al. 2008); depending on whether conventionally
measured energy consumption or the energy quality index is used, there are two equations for
each sector:
( )γαβtitititi LEKVA ,,,, = (1)
( )γαβtitititi LEQKVA ,,,, = (2)
Where VAi,t is value added for country i in time period t, Ki,t is capital formation/services, Ei,t
is conventional energy consumption, Li,t is labor employed, and EQi,t is energy quality
(defined in Equation 5 below). Taking natural logs forms the following two models, which
are estimated for each of five energy intensive manufacturing sectors:
3 The translog production function is a highly flexible form (e.g., unlike the Cobb-Douglas function, it does not assume an elasticity of substitution among inputs to be equal to unity) that is often used in empirical analyses (e.g., Smyth et al. 2011). However, because the variables analyzed here will be shown to be I(1) and nonlinear transformations of such variables introduce statistical problems, a translog estimation would require the first-differencing of the variables. When a first-difference, translog model was estimated, the hypothesis of the Cobb-Douglas form could be rejected only for pulp and paper; and even for that panel, all of the cross-product terms were insignificant.
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titititititi LEKbaVA ,,,,, lnlnlnln εγαβ +++++= (3)
titititititi LEQKbaVA ,,,,, lnlnlnln εγαβ +++++= (4)
Where the constants a and b are the country and time fixed effects, respectively, and ε is the
error term. The time span of the data4 and the countries included depend on data availability
and are described for each sector in Table 3 below. Value added and gross fixed capital
formation5 are in real yr-2000 US$, and labor is the number of persons engaged (from
STAN). Conventional energy consumption is in thousand tons of oil equivalent (ktoe), and
the energy quality index is described below. All variables are in natural log form, and all
variables are flows.
Table 3
Berndt (1978) proposed a Divisia index in which the consumption of the individual
fuel types is weighted by their expenditure shares, i.e., the differences in prices reflect the
differences in energy quality or productivity. Stern (1993 and 2000) used this approach in his
analysis of energy and economic growth in the US. Borrowing from Berndt and Stern, I use
the following formula to calculate quality weighted final energy use, EQi,t:
ln , ln . ∑∑ ∑ ln ln (5)
Where P are the prices of the fuels i, and E are the quantities consumed (in ktoe) for
each fuel in final energy use. The (n = 5) fuel types are electricity, oil, natural gas, coal, and
combustible renewables and waste. Like Stern (1993), I assume the price of combustible
renewables and waste is equal to 60% of the coal price.
4 The IEA price series begins in 1978. 5 It is the well-used convention in the literature (perhaps beginning with Soytas and Sari 2007) to use real gross fixed capital formation—a flow, akin to capital services—rather than capital stock. In addition to capital stock data not being readily available, Soytas and Sari (2007) argued that if one assumes a constant depreciation rate, then changes in fixed investment closely follow changes in the capital stock.
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The energy mix for industry as a whole in the OECD changed dramatically over
1974-2007. The share of oil and coal consumption fell from 32% to 15% and from 19% to
13%, respectively, while the share of electricity rose from 17% to 31% to become the main
source of energy in industry.
As an initial indication of the importance of energy quality, the quality index was
calculated for each of the five energy intensive manufacturing sectors using data from the
OECD as a whole. Figure 1 displays the ratio of energy quality to conventionally measured
energy consumption for each of those five sectors; thus, a value greater than unity means that
energy quality is larger than energy consumption (as was the case in Stern’s analysis of the
US—see Figure 2 in Stern 1993). Figure 1 shows that energy quality was greater than energy
consumption (at least from the 1990s onward) for all sectors but non-metallic minerals, and
energy quality was mostly increasing faster than energy consumption for all five sectors from
the mid-1980s onward.
Figure 1
2.2 Methods
The first step is to determine whether all the variables are integrated of the same
order. A number of panel unit root tests have been developed to determine the order of
integration of panel variables. These tests typically extend to a panel model the Augmented
Dickey-Fuller (ADF) unit root framework, where the first difference of a series is regressed
on the one-period lag of that series and on a selected number of additional lagged first
difference terms to control for autocorrelation.
Im, Pesaran and Shin (2003) developed a test (IPS) that allows for a heterogeneous
autoregressive unit root process across cross-sections by testing a statistic that is the average
of the individual (i.e., for each cross-sectional unit) ADF statistics. Maddala and Wu (1999)
proposed a panel unit root test based on Fisher (1932) that, like Im et al., allows for
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individual unit roots, but improves upon Im et al. by being more general and more
appropriate for unbalanced panels. Maddala and Wu’s test (ADF-Fisher) is based on
combining the p-values of the test statistic for a unit root in each cross-sectional unit, is non-
parametric, and has a chi-square distribution.
Both of the above tests assume that the cross-sections are independent; yet, for
variables like GPD per capita, cross-sectional dependence is possible to likely for panels of
similar countries because of, for example, regional and macroeconomic linkages. Indeed,
using the Pesaran (2004) test for cross-sectional dependence (CD test), cross-sectional
independence can be rejected for several variables (results shown in Appendix Table A.1).
Pesaran (2007) determined that when cross-sectional dependence is high, the first-
generation tests tend to over-reject the null. More recently, panel unit root tests—so-called
second-generation tests—have been developed that relax this independence assumption.
Among the most commonly used so far is that of Pesaran (2007), which is based on the IPS
test, allows for cross-sectional dependence to be caused by a single (unobserved) common
factor, and is valid for both unbalanced panels and panels in which the cross-section and time
dimensions are of the same order of magnitude. All three tests assume the null hypothesis of
nonstationarity.
If all the variables are integrated of the same order, the next step is to test for
cointegration. Engle and Granger (1987) pointed out that a linear combination of two or more
nonstationary series may be stationary. If such a stationary linear combination exists, the
nonstationary series are said to be cointegrated. The stationary linear combination is called
the cointegrating equation and may be interpreted as a long-run equilibrium relationship
among the variables.
The Pedroni (1999, 2004) heterogeneous panel cointegration test is an extension to
panel data of the Engle-Granger framework. The test involves regressing the variables along
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with cross-section specific intercepts, and examining whether the residuals are integrated
order one (i.e., not cointegrated). Pedroni proposes two sets of test statistics: (i) a panel test
based on the within dimension approach (panel cointegration statistics), of which four
statistics are calculated: the panel v-, rho-, PP-, and ADF-statistic; and (ii) a group test based
on the between dimension approach (group mean panel cointegration statistics), of which
three statistics are calculated: the group rho-, PP-, and ADF-statistic. Pedroni (1999) further
showed that the panel ADF and group ADF statistics have the best small-sample properties of
the seven test statistics, and thus, provide the strongest single evidence of cointegration.
If the variables are shown to be cointegrated, then the FMOLS estimator from Pedroni
(2000) produces asymptotically unbiased estimates of the long-run elasticities and efficient,
normally distributed standard errors. In addition, the FMOLS uses a semi-parametric
correction for endogeneity and residual autocorrelation, and the FMOLS estimator is a group
mean or between-group estimator that allows for a high degree of heterogeneity in the panel.6
3. Pre-testing results
Table 4 shows the results of the panel unit root tests. For no variable is a panel unit
root in levels rejected by both tests; in addition, for all the variables, panel unit roots in first
differences are rejected by both tests. (Several additional first-generation unit root tests were
applied as well. None of those results conflict with the ones displayed.) Thus, there is no
evidence that any variable is I(2); at the same time I(1) integration cannot be ruled out for any
variable.
Table 4
6 DOLS is another method for estimating cointegrated relationships. FMOLS requires fewer assumptions and tends to be more robust (Pedroni 2000). However, for the case of a single regressor, Kao and Chiang (2000) concluded that DOLS had a smaller bias than FMOLS. But because DOLS involves adding lead and lagged difference terms of the independent variables, in cases in which there are several independent variables and limited time observations—as is the case here—DOLS can result in severe loss in degrees of freedom; and thus, FMOLS is our preferred estimation method.
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Table 5 displays the panel cointegration test results for both models (using energy and
energy quality) for each of the five sectors. For all variants the two ADF-test statistics reject
the no cointegration null. Thus, given the several previous findings of cointegration of the
production variables discussed above (using both aggregate and more disaggregated data), the
results in Table 5 provide no plausible reason to reject cointegration for the panels used here.
Table 5
3.1 Additional cointegration tests
Pedroni (1999, 2004) recommended subtracting out common time effects (in both
cointegration tests and estimations) to capture a limited form of cross-sectional dependency.
Pedroni (2000) further argued that “… in many cases cross sectional dependence does not
play as large a role as one might anticipate once common time dummies have been
included… .” In addition, Pedroni (2000) claimed that to deal with dynamic cross sectional
dependence in cases in which time dummies are not sufficient requires that the time series
dimension be considerably larger than the cross sectional dimension. Furthermore, a
consensus in the theoretical or applied literature has yet to emerge on how best to deal with
cross sectional dependence in both cointegration testing and estimation (Wagner and
Hlouskova 2010).
Yet, the second-generation Westerlund (2007) cointegration test was applied as well.
The Westerlund test’s results are particularly sensitive to the setting of lag and lead lengths
when the time dimension is short (Persyn and Westerlund 2008)—as is the case with this
data. Thus, the failure of that test to reject the null of no cointegration for some of the panels
(i.e., non-metallic minerals, pulp and paper) seems likely to be the result of the small panel
size (and thus, limited lag and lead length possibilities) rather than the presence of cross-
sectional dependence (results not shown). Indeed, the motivation for developing the test was
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to avoid an incorrect failure to reject, rather than to avoid an incorrect rejection (Westerlund
2007).
Lastly, the Pedroni (1999, 2004) cointegration test assumes a single cointegrating
vector—a reasonable assumption since the models used here are based on the single equation
Cobb-Douglas production function. However, there are so-called systems cointegration
methods that allow for the possibility of more than one cointegrating vector. Yet, it is well-
known that systems methods perform poorly when the number of cross-sections is relatively
large compared to the time dimension (Banerjee et al. 2003 and Wagner and Hlouskova
2010); thus, such methods seem particularly inappropriate for the chemicals panel. A systems
cointegration test proposed by Maddala and Wu (1999)—a panel version of Johansen (1988)
cointegration test7—confirmed a single cointegrating vector for the other four panels (results
not shown).
4. Estimation results and discussion
Table 6 reports the long-run elasticity estimates from FMOLS for the five
sectors/panels. The table demonstrates the importance of accounting for energy quality in the
analysis of these energy intensive sectors, and, by extension, the importance for
production/value added of the technological switch to higher quality energy (i.e., electricity).
For each of the sectors the elasticity for energy when conventionally measured is either small
(less than 0.1) or highly insignificant. Indeed, the elasticity of conventionally measured
energy is insignificant for iron and steel and non-ferrous metals and very small for pulp and
paper. However, in each case when energy quality is measured, the elasticity for energy is
considerably larger (never less than 0.19) than that for conventionally measured energy, and
for iron and steel, non-ferrous metals, and pulp and paper, the elasticity for energy quality is
larger than the elasticity for either physical capital or labor services.
7 This test, like Pedroni (1999, 2004), assumes cross sectional independence.
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Table 6
Figure 2 displays the average productivity/efficiency of energy quality (value
added/energy quality) for each country and for each sector. For the three energy intensive
sectors in which all OECD countries are active (chemicals, non-metallic minerals, and pulp
and paper), there is not much variation among the countries in energy quality productivity.
However, for the two sectors in which the geographic distribution of production is more
concentrated (iron and steel and non-ferrous metals), Figure 2 indicates a much greater
degree of energy quality productivity variation. (By contrast, labor productivity levels are
extremely uniform among the countries for all five sectors.)
Figure 2
Ultimately one may want to analyze/compare the different elasticity estimations and
intensities across the five energy-intensive manufacturing sectors. However, because of data
limitations, the five panels vary substantially in cross sectional make-up and time span,
making such cross-sectoral analysis/comparisons difficult to perilous. Furthermore, to really
appreciate what makes certain sectors more energy or labor or capital intensive than other
sectors, one would need a greater understanding of the particular technologies employed, as
well as more detailed data, and probably a more flexible production model than Cobb-
Douglas; thus, such an analysis is left for future work.
5. Conclusions
This paper applied panel cointegration and panel FMOLS estimations to OECD panel
data that was disaggregated along the lines of the five most energy intensive manufacturing
sectors (iron and steel, non-ferrous metals, non-metallic minerals, chemicals, and pulp and
paper) using the Cobb-Douglas production model framework. In addition, the energy
consumption data was adjusted for the quality of the energy source (e.g., electricity is of
higher quality than oil, which is of higher quality than coal). The results indicate the
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importance of energy quality—primarily the shift toward the use of high quality electricity—
in these energy intensive manufacturing sectors. In each case the elasticity for energy quality
is greater than that for conventionally measured energy consumption—sometimes orders of
magnitude greater. Also, when using the energy quality measure, the importance of energy
consumption relative to the other production factors (capital and labor) stands out. Such
results are useful for both energy-GDP cointegration/causality modelers and CGE modelers,
who may need to estimate elasticities.
The importance of electricity to productivity in these energy intensive sectors has
implications for the impacts of carbon policy on the geographic locations of those sectors.
For countries in which electricity generation is not carbon intensive (i.e., countries that rely
on renewable sources), a carbon tax would not necessarily be damaging to even the most
energy intensive industries. However, a carbon tax could lead to the loss of energy intensive
manufacturing in countries with carbon intensive electricity generation.
An obvious next step in the analysis would be to use a more flexible production
model like the translog one. However, such a model’s estimations would need to be in first
differences since the variables are panel I(1), and most likely additional time observations
would be needed to estimate statistically significant second order and cross-product terms,
and thus, to demonstrate the inadequacy of the Cobb-Douglas framework.
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Table 1. Sector classification and average OECD energy intensity. Sector ISIC Classification 2005 Energy intensity Iron and Steel 271 & 2731 1.548 Non-ferrous metals 272 & 2732 0.672 Non-metallic minerals 26 0.438 Chemical and chemical products 24 0.344 Paper, pulp, and printing 21 & 22 0.268 Wood and wood products 20 0.200 Food and tobacco 15 & 16 0.123 Textile and leather 17, 18, & 19 0.099 Transport equipment 34 & 35 0.044 Fabricated metal products including machinery 28, 29, 30, 31, & 32 0.034 Construction 45 0.014 Note: Energy intensity is in ktoe per million yr-2005 US$.
Table 2. Manufacturing value added share for each of five energy intensive sectors. Data from 2005. Chemicals Iron &
steel Non‐ferrous
metals Non‐metallic minerals
Pulp & paper
Total for 5
Australia 3.9% 2.2% 3.4% 2.6% 7.0% 19.1%Austria 6.4% 4.8% 1.8% 5.2% 7.0% 25.3%Belgium 17.6% 5.3% 1.4% 4.5% 6.6% 35.4%Canada 4.3% 1.7% 8.8% 14.8%Denmark 9.9% 0.5% 0.3% 3.0% 6.5% 20.3%Finland 5.6% 3.9% 1.1% 3.1% 15.4% 29.2%France 9.1% 2.5% 0.9% 4.4% 7.1% 24.0%Germany 9.4% 2.8% 1.3% 2.7% 6.3% 22.5%Greece 4.7% 5.8% 5.3% 15.8%Hungary 7.9% 1.3% 1.3% 3.5% 4.4% 18.3%Ireland 30.0% 2.7% 12.5% 45.2%Italy 6.1% 2.3% 0.8% 5.2% 5.4% 19.9%Japan 6.8% 5.8% 1.7% 2.6% 6.7% 23.5%Korea 8.0% 8.3% 1.5% 3.2% 3.8% 24.7%Netherlands 12.3% 2.4% 8.5% 23.2%Norway 0.4% 1.4% 1.0% 3.2% 6.1%Poland 5.4% 2.0% 0.5% 4.7% 5.7% 18.3%Portugal 4.6% 1.5% 0.7% 9.4% 8.0% 24.1%Spain 8.1% 3.7% 1.4% 7.2% 8.0% 28.5%Sweden 10.7% 3.7% 1.1% 1.8% 10.0% 27.4%Switzerland 17.0% 2.3% 6.8% 26.1%UK 8.7% 1.0% 0.7% 2.8% 10.1% 23.3%USA 9.7% 1.7% 1.0% 2.5% 11.5% 26.4% OECD average 9.4% 3.0% 1.2% 3.7% 7.6% 23.5%
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Table 3. Time span and countries for each panel. Chemicals Iron & steel Non‐ferrous metals Non‐metallic minerals Pulp & paper1990‐2006 1980‐2006 1980‐2006 1980‐2006 1978‐2007
AUT AUT AUT AUT AUTBEL BEL BEL BEL BELCAN FIN FIN CAN CANDNK ITA ITA DNK DNKESP NOR NOR FIN ESPGBR SWE USA FRA FINIRE USA GBR FRAITA ITA ITA NLD NLD NLDSWE NOR NORUSA SWE SWE
USA
Table 4. Panel unit root tests. Levels First differences Panel Variables ADF-Fisher Pesaran ADF-Fisher Pesaran Chemicals VA 0.26 0.16 0.00 0.01 K 0.38 0.10 0.00 0.00 L 0.48 0.57 0.00 0.00 E 0.03 0.99 0.00 EQ 0.75 0.96 0.00 0.01 Iron & steel VA 0.00 0.16 0.00 K 0.00 0.10 0.00 L 0.17 0.97 0.00 0.00 E 0.82 0.95 0.00 0.00 EQ 0.03 0.79 0.00 Non-ferrous metals VA 0.01 0.27 0.00 K 0.09 0.18 0.00 0.00 L 0.73 0.82 0.00 0.00 E 0.08 0.32 0.00 0.00 EQ 0.06 0.68 0.00 0.00 Non-metallic minerals VA 0.14 0.57 0.00 0.00 K 0.01 0.27 0.00 L 0.77 0.57 0.00 0.01 E 0.02 0.32 0.00 EQ 0.00 0.22 0.00 Pulp & paper VA 0.51 0.68 0.00 0.00 K 0.06 0.00 0.00 L 0.99 0.99 0.00 0.00 E 0.88 0.47 0.00 0.00 EQ 0.92 0.08 0.00 0.00 Notes: The values in the table are the probabilities of rejecting the null of no unit root. The ADF-Fisher test assumes cross-sectional independence; whereas, the Pesaran test allows for cross-sectional dependence. The Akaike information criterion was used to select the lag lengths.
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Table 5. Pedroni panel cointegration test results. Panels
Within dimension (Panel) ADF-Stat
Between dimension (Group) ADF-Stat
VA, E, K, L Chemicals -1.74** -3.55**** Iron & steel -3.03*** -2.35*** Non-ferrous metals -1.51* -1.98** Non-metallic minerals -1.81** -2.20** Pulp & paper -2.20** -2.16**
VA, EQ, K, L Chemicals -2.14** -2.79*** Iron & steel -2.85*** -3.35**** Non-ferrous metals -2.37*** -1.86** Non-metallic minerals -2.31** -2.69*** Pulp & paper -1.67** -2.18** Note: Statistical significance is indicated by: **** p <0.001, *** p <0.01, ** p<0.05, and * p<0.10.
Table 6. Long run elasticity estimates from panel FMOLS. Panel Variables Coefficient T-statistic Variables Coefficient T-statistic Chemicals E 0.0367 2.32 EQ 0.190 2.63 K 0.163 3.83 K 0.171 3.46 L 0.696 5.38 L 0.549 3.44 Iron & steel E -0.062 -0.83 EQ 0.343 1.89 K 0.042 0.70 K 0.101 1.66 L 0.143 3.73 L 0.241 4.06 Non-ferrous metals E 0.316 0.53 EQ 0.568 4.85 K 0.043 0.99 K 0.074 2.03 L 1.307 5.06 L 0.516 3.77 Non-metallic minerals E 0.063 5.23 EQ 0.197 8.72 K 0.207 8.23 K 0.240 8.82 L 0.484 11.62 L 0.215 11.14 Pulp & paper E 0.0098 2.83 EQ 0.301 5.94 K 0.235 5.88 K 0.239 5.87 L 0.174 3.43 L 0.251 4.67 Note: T-critical of 3.29, 2.58, 1.96, and 1.64 correspond to the following levels of statistical significance: p <0.001, p <0.01, p<0.05, and p<0.10, respectively.
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0.8
0.9
1
1.1
1.2
1.3
1.4
Iron & steel
Pulp & paper
Non‐ferrous metals
Chemicals
Non‐metallic minerals
Figure 1. The ratio of an energy quality index to energy consumption for the five most energy
intensive manufacturing sectors using data from the OECD as a whole and spanning 1978-
2008.
24
12
13
14
15
16
17
18
DNK ESP ITA BEL NLD FRA USA AUT NOR SWE FIN CAN
LN (V
A/EQ)
Pulp & Paper
12
13
14
15
16
17
18
IRE DNK AUT ESP ITA GBR SWE BEL CAN FIN USA NLD
LN(VA/EQ)
Chemicals
10
11
12
13
14
15
16
17
18
USA ITA AUT BEL SWE FIN NOR
LN(VA/EQ)
Iron & Steel
12
13
14
15
16
17
18
USA AUT ITA BEL NOR SWE FIN
LN(VA/EQ)
Nonferrous metals
12
13
14
15
16
17
18
AUT NOR ESP ITA FRA CAN GBR SWE NLD USA FIN DNK BEL
LN(VA/EQ)
Nonmetallic minerals
Figure 2. Average energy quality productivity (value added/energy quality) variation across countries for five energy intensive manufacturing sectors.
25
Appendix Table A.1. Cross-section Dependence: Absolute value mean correlation coefficients and Pesaran (2004) CD test VariablesPanels VA E EQ K LChemicals 0.55
(10.9)*** 0.49(1.1)
0.47(4.7)***
0.36 (8.8)***
0.58(2.3)*
Iron & steel 0.32 (7.5)***
0.62(14.5)***
0.31(6.0)***
0.32 (5.4)***
0.93(22.0)***
Non‐ferrous metals
0.30 (5.8)***
0.75(15.0)***
0.30(1.3)
0.20 (0.4)
0.70(14.0)***
Non‐metallic minerals
0.35 (2.6)*
0.39(0.6)
0.38(0.3)
0.32 (9.1)***
0.66(23.4)***
Pulp & paper 0.50 (10.8)***
0.53(17.1)***
0.67(18.1)***
0.35 (11.1)***
0.75(15.4)***
Notes: CD-test statistic in parentheses. Null hypothesis is cross-section independence.
Statistical significance indicated by *** < 0.001 and * < 0.05.