Sources of energy productivity change in Australian sub-industries

Economic Analysis and Policy 65 (2020) 1–10

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Sources of energy productivity change in Australian sub-industries✩ Gan-Ochir Doojav a , Kaliappa Kalirajan b , a b

∗

Bank of Mongolia, Baga toiruu-3, 15160, Ulaanbaatar 46, Mongolia Crawford School of Public Policy, Australian National University, 132 Lennox Crossing, ACT 2601, Australia

article

info

Article history: Received 5 August 2019 Received in revised form 8 November 2019 Accepted 8 November 2019 Available online 13 November 2019 JEL classification: C14 D24 Q48 Keywords: Energy productivity Data envelopment analysis Malmquist productivity change index Australian sub-industries

a b s t r a c t This paper examines the causes for the sluggishness or deterioration in energy productivity in some key sub-industries in Australia during the recent years by analysing the sources of energy productivity change in those sub-industries over the period 2003– 2015. Energy productivity change is decomposed into three components attributable to technical efficiency change, technological change and changes in factor inputs (i.e., labour-energy and capital-energy ratios). The data envelopment analysis (DEA) is implemented to examine the relative contributions of the components. Empirical results show that (1) the technical efficiency change positively contributed to energy productivity change in the sub-industries; (2) decreases in technological change played the most important role in the process of energy productivity drop in the selected 8 sub-industries over the years 2003–2015; and (3) falling capital-energy and labour-energy ratios played the most important role in the process of the drop in the selected 10 sub-industries over the years 2007–2015. © 2019 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.

1. Introduction Sustained energy productivity will boost a country’s competitiveness, help consumers manage their energy costs and reduce the country’s greenhouse gas emissions. Though in 2007, Australia had the second lowest electricity prices in the world (Simshauser et al., 2011), between 2007 and 2013, tariffs doubled due to policy and regulation changes, which increased Network Costs including taxes/levies (Simshauser, 2014). In this context, it is worth noting that the Energy Efficiency Opportunities (EEO) Program was introduced in 2006 by the Coalition Government for the purpose of encouraging the implementation of cost-effective energy efficiencies by large energy using businesses in Australia.1 As a consequence between 2006 and 2011, the EEO Program was responsible for approximately 40 per cent of the energy efficiency improvements in the Australian industrial sector. Nevertheless, the Coalition Government repelled the EEO Program in 2014 with the argument that it was no longer needed because the economic and regulatory context had changed since it was first introduced. Subsequently, the government submitted the Intended Nationally determined Contributions (INDC) to the United Nations Framework Convention on Climate Change (UNFCCC) at the 21st Conference of the Parties (COP21) in Paris in ✩ Comments and suggestions by three anonymous referees of this Journal and Professor Clevo Wilson are gratefully acknowledged. ∗ Corresponding author. E-mail addresses: [email protected] (G.-O. Doojav), [email protected] (K. Kalirajan). 1 The Australian Government introduced The National Greenhouse and Energy Reporting (NGER) Scheme in 2007 as a regulatory instrument to provide data and accounting in relation to greenhouse gas emissions and energy consumption and production. Energy efficiency was not given sufficient prominence in this scheme. https://doi.org/10.1016/j.eap.2019.11.001 0313-5926/© 2019 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.

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December 2015 to reduce emissions by 26–28 per cent below 2005 levels by 2030. On 4 December 2015, the Council of Australian Governments (COAG) Energy Council launched a new National Energy Productivity Plan (NEPP) to contribute more than 25% of the savings required to meet Australia’s 2030 greenhouse gas emissions reduction target. Such a contribution by the NEPP required a commitment to an energy productivity target of 40 per cent improvement between 2015 and 2030. The NEPP was expected to avoid placing additional burdens on businesses and to get more value from Australia’s energy investments. Two stylised facts are observed and analysed by earlier studies in the Australian energy studies literature. First, Australia’s energy productivity is far below than that in many G-20 countries. Australia’s energy productivity, measured in real GDP per unit of energy, has been around 14 per cent lower than the G-20 average. Second, Australia’s average rate of improvement in energy productivity was 1.1 per cent per annum during the 1995–2012 periods, which was the third lowest among all developed G-20 and BRIC countries (Stadler et al., 2015). In particular, some key industries have been experiencing sluggishness or deterioration in energy productivity over the recent years. It is important to explain the contributors to the changes in energy productivity, especially for Australia, which has committed to achieve big improvements in energy productivity. Therefore, this paper examines the sources of energy productivity change in the Australian sub-industries, which have been experiencing sluggishness or deterioration in energy productivity. As far as we are aware, it is the only Australian economic study to examine sources of energy productivity change in the selected 10 sub-industries by ANZIC sub-categories/category combinations. These subdivisions that cover the majority of energy intensive industries in Australia were selected on the basis of data availability and of strategic interest. The non-parametric approach of the Data Envelope Analysis (DEA), which uses linear programming to attach optimal weights to a set of inputs and outputs that firms’ use in their production process, is employed in estimating and decomposing the change in EP into explanatory components, attributable to technical efficiency change, technological change and changes in factor inputs.2 Such a decomposition of EP will provide answers to the question whether changes in EP are inputs-driven or technology-driven, which bear policy implications. Policy recommendations generally differ for each component of EP. For example, in the case of improving technical efficiency, on-the-job training programmes to accelerate implementation of the best practice of the chosen technology may be preferred. In the case of enhancing technological progress, government incentives to increase technological progress need to be emphasised. The remainder of the paper is structured as follows. Section 2 discusses the basic framework for decomposing energy productivity change to provide the foundation for our empirical analysis. Section 3 describes data and discusses the decomposition results. Finally, Section 4 concludes the paper with policy recommendations to achieve the energy productivity target suggested in terms of improving the contributions of technology and technical efficiency. 2. A decomposition framework The decomposition analysis of energy productivity (EP) change facilitates identifying the sources of change, which bear policy implications. Policy recommendations generally differ for each component of EP. For example, in the case of improving technical efficiency, on-the-job training programmes to accelerate implementation of the best practice of the chosen technology may be preferred. In the case of enhancing technological progress, government incentives to increase technological progress need to be emphasised. The approach used in decomposing the EP change is based on the literature of the decomposition analysis in energy efficiency. The approach to decomposing productivity growth was popularised by Färe et al. (1994), and has been extended by several others (see in the context of energy, among others, Wang, 2007, 2011) to study energy productivity growth. The approach is based on Shepard output distance functions, which can be computed using the data envelopment analysis (DEA) technique. According to the approach, the EP change in the specified sectors is decomposed into explanatory factors as shown in Fig. 1. Theoretical background of the decomposition is discussed in details in the following section. 2.1. The Malmquist index of EP change To define the output-based Malmquist index of EP change, we assume that for each time period t = 1, 2, . . . , T , the production technology, S t , is defined as the transformation of inputs, Kt , Lt , Et ∈ RN+ , into outputs, Yt ∈ RM +, S t = {(Kt , Lt , Et , Yt ) : (Kt , Lt , Et ) can produce Yt }

(1)

where Kt , Lt and Et are capital, labour and energy, respectively, and Yt is output. It is assumed that the set S satisfies certain conditions which suffice to define output distance functions. The output distance function at period t is defined as t

Dt0 (Kt , Lt , Et , Yt ) = inf θ : (Kt , Lt , Et , Yt /θ) ∈ S t

{

}

(2)

This distance function (2) measures the maximum feasible expansion of the observed output, Yt , given the input vector (Kt , Lt , Et ), and technology, S t . It is important to note that Dt0 (Kt , Lt , Et , Yt ) ≤ 1 always holds, and Dt0 (Kt , Lt , Et , Yt ) = 1 if 2 For a comprehensive discussion on production theory, see Färe (1988). Detailed discussion about the DEA methodology and estimation is given in Coelli et al. (2005).

G.-O. Doojav and K. Kalirajan / Economic Analysis and Policy 65 (2020) 1–10

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Fig. 1. Decomposition of EP change.

Fig. 2. Output distance functions.

and only if the observation (Kt , Lt , Et , Yt ) is on the boundary or frontier of the technology, S t (i.e., production is technically efficient). This is illustrated in Fig. 1, in which scalar input is used to produce scalar output. The distance function seeks the reciprocal of the greatest proportional increase in output(s), given input(s), such that output is still feasible. In the diagram, maximum feasible production, given (Kt , Lt , Et ), is at (yt /θ ∗ ). The value of the distance function, Dt0 (Kt , Lt , Et , Yt ), (in terms of distances on the y-axis) is 0a/0b, which is less than one.3 It is the reciprocal of Farrell’s (1957) measure of output technical efficiency, which calculates ‘‘how far’’ an observation is from the frontier of technology: In Fig. 2, Farrel output technical efficiency of (Kt , Lt , Et , Yt ) is 0b/0a. To define the Malmquist index, we need to define output distance functions with respect to different time periods, for instance, Dt0 (Kτ , Lτ , Eτ , Yτ ) = inf θ : (Kτ , Lτ , Eτ , Yτ /θ ) ∈ S t

{

}

(3)

This distance function (3) measures the maximum proportional change in outputs required to make (Kτ , Lτ , Eτ , Yτ ) feasible in relation to the technology at time period t. This is illustrated in Fig. 2. For instance, production (Kτ , Lτ , Eτ , Yτ ) occurs outside the set of feasible production in period t, implying that technical progress has occurred. The value of distance function, Dt0 (Kτ , Lτ , Eτ , Yτ ), evaluating (Kτ , Lτ , Eτ , Yτ ) relative to technology in period t is 0d/0e, which is greater than one.4 The output distance functions are homogeneous of degree +1 in outputs, which implies, Dτ0 (Kt , Lt , Et , α Yt ) = α Dτ0 (Kt , Lt , Et , Yt ) where α is positive scalar. 3 Similarly, the value of the distance function, Dτ (K , L , E , Y ), is 0d/0f , which is less than one. τ τ τ τ 0 4 Similarly, the value of the distance function, Dτ (K , L , E , Y ), is 0a/0c, which is less than one. t t t t 0

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Using the production technology in time period t as a reference, the output-based index for EP change between time periods t and τ can be defined as Dτ0 (Kτ , Lτ , Eτ , Yτ )

} (Yτ /Eτ ) /Dt0 (Kτ , Lτ , Eτ , Yτ ) Yt /Et Dt0 (Kt , Lt , Et , Yt ) Dτ0 (Kτ , Lτ , Eτ , Yτ ) (Yt /Et ) /Dt0 (Kt , Lt , Et , Yt ) τ t t D (Kτ , Lτ , Eτ , Yτ ) D (Kτ , Lτ , Eτ , Yτ ) D (Kt , Lt , Et , Yt ) × Et = 0t × τ0 (4) × t0 D0 (Kt , Lt , Et , Yt ) D0 (Kτ , Lτ , Eτ , Yτ ) D0 (Kτ , Lτ , Eτ , Yτ ) × Eτ ] [ According to Eq. (2), Yt /Dt0 (Kτ , Lτ , Eτ , Yτ ) = 1/Dt0 (Kτ , Lτ , Eτ , Yτ ) 5 represents the maximum potential output when the production technology) is( S t and the input bundle )] line of Eq. (4), ( t ) [ (is (Kt , Lt , Et ). Thus, the )third ( term in the bottom D0 (Kt , Lt , Et , Yt ) × Et / Dt0 (Kτ , Lτ , Eτ , Yτ ) × Eτ = 1/Dt0 (Kτ , Lτ , Eτ , Yτ ) /Eτ / 1/Dt0 (Kt , Lt , Et , Yt ) /Et , measures the EPCH =

Yτ /Eτ

=

×

Dt0 (Kτ , Lτ , Eτ , Yτ )

{

×

change in maximum potential energy productivity during the two periods by using technology S t as a reference. To further decompose it, drawing on Färe (1988), it is assumed that the production technology is constant-return-toscale (CRS), implying that the output distance function is homogeneous of degree −1 in inputs: Dτ0 (β Kt , β Lt , β Et , Yt ) = β −1 Dτ0 (Kt , Lt , Et , Yt ) where β is a positive scalar. Thus, the following identity holds: Dt0 (Kt , Lt , Et , Yt ) × Et / Dt0 (Kτ , Lτ , Eτ , Yτ ) × Eτ = Dt0 (kt , lt , 1, yt ) /Dt0 (kτ , lτ , 1, yτ ) ,

(

) (

)

where kt = Kt /Et , lt = Lt /Et and yt = Yt /Et respectively denote capita-energy ratio, labour-energy ratio and output-energy ratio. Therefore, Eq. (4) can be rewritten as EPCH =

Dτ0 (Kτ , Lτ , Eτ , Yτ ) Dt0 (Kt , Lt , Et , Yt )

×

Dt0 (Kτ , Lτ , Eτ , Yτ )

Dτ0 (Kτ , Lτ , Eτ , Yτ )

×

Dt0 (kt , lt , 1, yt ) Dt0 (kτ , lτ , 1, yτ )

≡ EFFCH × TECH (τ ) × PEPCH t

(5)

Eq. (5) suggests that EP change is the product of three components: technical efficiency change, EFFCH; technological change measured by using the inputs and outputs in period τ , TECH (τ ); and the change in the maximum potential energy productivity, PEPCH t , which is measured by using the time period t technology as a reference. Using the production technology in time period τ instead of the technology in time period t as a reference, analogs to (4)–(5) that can be easily obtained. For instance, the analog to Eq. (5), measuring EP change between time periods t and τ by using the production technology in period τ as a reference, will be EPCH = EFFCH × TECH (t ) × PEPCH τ

(6)

As suggested by Färe et al. (1994), to avoid the ambiguity of choosing one of the decompositions in Eqs. (5) and (6), the geometric mean of the two decompositions can be used. Therefore, the EP change is given by EPCH = EFFCH × [TECH (τ ) TECH (t )]1/2 × PEPCH τ PEPCH t

[

≡ EFFCH × TECH × PEPCH

]1/2 (7)

The EP change is decomposed using Eq. (7) in this paper. It shows that the EP change can be decomposed into 3 components. The first component, EFFCH, measures technical efficiency change (i.e., change in how far the observed production is from the maximum potential production) between the two time periods. Values greater than one (EFFCH > 1) imply improvements in technical efficiency. The second term, TECH, measures technological change, which is the shift in the technology or production frontier between the two time periods. The third component, PEPCH, measures the effects on EP change from the changes in capital-energy ratio and labour-energy ratio between the two time periods. The residual factor input component, PEPCH, is calculated using Eq. (7) as we have values for EPCH, EFFCH and TECH. It is important to note that the product of the first two components in Eq. (7) is the Malmquist total factor productivity (TFP) change index (i.e., TFPCH = EFFCH × TECH),6 which is widely studied in the literature on productivity and efficiency analysis. There are a number of different methods (i.e., Balk, 1993) that can be used to calculate the output distance functions that are used in constructing the Malmquist TFP index. The most popular and widely used method in the Malmquist TFP analysis has been the data envelopment analysis (DEA), which is used in this paper.7 5 This equation holds as output distance function is homogeneous of degree +1 in outputs. 6 The decomposition of TFP change is illustrated in Fig. 2 for CRS technology, and in terms of the distances along the y-axis, the index for TFP change becomes TFPC

= (0d/0f ) (0b/0a) [((0d/0e) /(0d/0f )) ((0a/0b) /(0a/0c ))]1/2 = (0d/0f ) (0b/0a) [(0f /0e) /(0c /0b)]1/2 . 7 Though the DEA approach is widely used, the estimates include the influence of the statistical noise. The bootstrap procedures developed by Simar and Wilson (1999) facilitate measuring the Malmquist TFP indices free from the influence of the statistical noise. Bootstrapping depends on the bootstrap principle that sampling with replacement behaves on the original sample the way the original sample behaves on a population. There are examples in the literature where this principle fails (Doss and Chiang, 1994). Nevertheless, it is a powerful technique for making statistical inferences, which may require substantial computing resources in both time and memory (Tziogkidis, 2012). Recently, Defung et al. (2017) have used this approach to examine the efficiency of the banking sector in Indonesia.

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2.2. Data envelopment analysis (DEA) The DEA is a non-parametric linear programming methodology used in economic analyses for the estimation of production frontiers popularised by Charnes et al. (1978). However, the DEA model proposed by Charnes et al. was not that much flexible in the sense that it assumed constant returns to scale (CRS) in its description of the production process. Banker et al. (1984) developed an alternative DEA model structured by both constant and varying returns to scale (VRS). The deterministic DEA approach was also developed as an alternative to Stochastic Frontier Analysis (SFA) in measuring technical and economic efficiencies. The main advantages of the DEA over SFA are that the former does not require any particular pre-determined parametric functional form representing the underlying technology and an explicit distributional assumption for the inefficiency terms. In addition, the DEA method is capable of handling multiple inputs and outputs, and does not require the panel data with long-time periods. In order to calculate the TFP change index, TFPCH, of industry k′ between t and τ , we need to solve four different linearprogramming problems under the assumption of a CRS technology8 : Dt0 (Kt , Lt , Et , Yt ), Dτ0 (Kτ , Lτ , Eτ , Yτ ), Dt0 (Kτ , Lτ , Eτ , Yτ ) and Dτ0 (Kt , Lt , Et , Yt ).9 It is assumed that each observation of inputs and outputs in data set is strictly positive, and the number of observations remains constant over all the years. Moreover, we assume that there are k = 1, . . . , K industries, k k which use n = 1, . . . , N inputs Xnt to produce m = 1, . . . , M outputs Ymt at each time period, t = 1, . . . , T . Following Färe et al. (1994), the reference (or frontier) technology in period t is assumed as

{ S = (Kt , Lt , Et , Yt ) : Ymt ≤ t

K ∑

m = 1, . . . , M ;

k zk,t Ymt

(8)

k=1 K ∑

n = 1, . . . , N ;

k zk,t Xnt ≤ Xnt

k=1

} k = 1, . . . , K

zk,t ≥ 0

which exhibits CRS and strong disposability of inputs and outputs; where X ∈ {L, K , E }, Ymt and Xnt are respectively any given output m and input n in the data set and zk,t is an intensity variable representing at what intensity a particular activity (in our case, each industry is an activity) may be employed in production. Based on the fact that the output distance function (i.e., 0a/0b) is the reciprocal to the output-based Farrel measure of technical efficiency (i.e., 0b/0a), the linear-programming problem for computing Dt0 (Kt , Lt , Et , Yt ) for each k′ = 1, . . . , K is as follows: The objective function is defined as to maximise the technical efficiencies (Farrel measure): Dt0 Kk′ ,t , Lk′ ,t , Ek′ ,t , Yk′ ,t

(

(

))−1

′

= max ϕ k ,

(9)

′

ϕ k ,zk,t

which is subject to the technology constraints in period t: ′

′

k ϕ k Ymt ≤ ∑K

∑K

k k=1 zk,t Ymt ′ k k Xnt k=1 zk,t Xnt

≤

m = 1, . . . , M n = 1, . . . , N k = 1, . . . , K

zk,t ≥ 0 ′

′

where 1 ≤ ϕ k < ∞ is the Farrel measure of technical efficiencies in period t, and ϕ k − 1 is the proportional increase ′ k′ in outputs ( ) that could be achieved by the k th industry, with input quantities held constant. From Fig. 2, ϕ = (0b/0a) = ′

′

1/θ k , where θ k is the output-oriented technical efficiency score that varies between zero and one (and reported by

the DEAP programme, which is used for estimation in this study). The computation of Dτ0 (Kτ , Lτ , Eτ , Yτ ) is exactly like (8), where τ is replaced for t. The remaining two linear programming problems are mixed period problems. The linear-programming problem for computing Dt0 (Kτ , Lτ , Eτ , Yτ ) is computed for observation k′ as follows: Dt0 Kk′ ,τ , Lk′ ,τ , Ek′ ,τ , Yk′ ,τ

(

(

))−1

′

= max ϕ k ′

(10)

ϕ k ,zk,t

8 This assumption ensures that resulting TFP change measures satisfy the fundamental property that if all inputs are multiplied by the (positive) scaler δ and all outputs are multiplied by the (non-negative) scaler α , then the resulting TFP change index will equal α/δ . 9 To make the full decomposition, including the scale-change component, needs calculation of an additional two programming problems: Dt0v (Kt , Lt , Et , Yt ) and Dτ0v (Kτ , Lτ , Eτ , Yτ ).

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subject to ′

′

ϕ k Ymk τ ≤ ∑K

∑K

k k=1 zk,t Ymt ′ k Xnkτ k=1 zk,t Xnt

≤

zk,t ≥ 0

m = 1, . . . , M n = 1, . . . , N k = 1, . . . , K

This linear-programming (10) involves observations from both periods t and τ). In (10), Dt0 Kk′ ,t , Lk′ ,t , Ek′ ,t , Yk′ ,t ≤ 1 ) ( ( t t ′ ′ ′ ′ ′ in (1), D0 Kk ,τ , Lk′ ,τ , Ek′ ,τ , Yk′ ,τ may take values greater than 1 since (because of Kk ,t , Lk ,t), Ek ,t , Yk ,t ∈ S ; however, Kk′ ,τ , Lk′ ,τ , Ek′ ,τ , Yk′ ,τ need not belong to S t . The computation of Dτ0 (Kt , Lt , Et , Yt ) is like in (10), but the t and τ superscripts are transposed. Both (9) and (10) exhibit CRS and strong disposability of inputs and outputs.10 It is important ′ to note that the ϕ k s and zk,t s are likely to take different values in the above four linear-programming for each period. Moreover, the above four linear programming must be solved for each industry in the sample. If there are T time periods, then (3T − 2) linear programming must be solved for each industry in the sample. To compute changes in scale efficiency, SCH, we also calculate distance functions under VRS, Dτ0v (Kτ , Lτ , Eτ , Yτ ) and Dt0v (Kt , Lt , Et , Yt ), by adding the following restrictions:

(

K ∑

zk,t = 1

)

(11)

k=1

In such case, scale efficiency in each period is constructed as the ratio of the distance function satisfying CRS to distance function restricted to satisfy VRS. The pure efficiency change component, PEFFCH, is calculated as the ratio of the own-period distance functions in each period satisfying VRS. Technical change is calculated relative to the CRS technology. 3. Data and decomposition results 3.1. Data The energy productivity growth and its components were calculated for a sample of the Australian 10 sub-industries11 over the period 2003–2015 using data12 from the Australian Bureau of Statistics (ABS) and the Department of Industry and Science (DIS). These subdivisions and the period of analysis were selected on the basis of strategic interest and consistent data availability respectively. The categories cover the majority of energy intensive industries in Australia but not the service-based industries as, for example, occurring within the commercial office buildings. The measure of output was gross value added (GVA); energy was measured by total energy consumption; and labour and capital stock were respectively approximated by employment and gross fixed capital formation (GFCF). GVA and GFCF were converted into 2011–12 domestic prices using the industry-specific producer price index. Energy productivity was measured as the ratio of real GVA to energy consumption. Nominal GVA, employment and nominal GFCF by sub-divisions of Australian industry were taken from ABS Cat No. 8155.0. Energy consumption data was from Table F of the Australian Energy Statistics, DIS. The industry-specific producer price index (2011–12 = 100) was obtained from ABS Cat No. 6427.0. 3.2. Decomposition results The production frontiers in the years 2003 and 2015 were constructed by using the Data Envelopment Analysis applying the software DEAP Version 2.1 (Coelli, 1996). With the frontiers, each index in the components of decomposition (7) for each industry in each time period can be calculated. As the data for the paper manufacturing and printing (15– 16), Chemical, polymer and rubber product (18–19) were not available, these two sub-industries were not included in the decomposition analysis for energy productivity changes between 2003 and 2015, though those were used in the decomposition analysis between 2007 and 2015. Table 1 reports the contributions to the energy productivity change from technical efficiency change, technological change, and the effects from the changes in capital-energy and labour-energy ratios. As shown in Column (4), the technical efficiency change contributed positively to energy productivity growth in sub-industries except for coal mining (06) and road transport (46). The decomposition analysis shows that the average contribution of the efficiency change is 11.0 per cent. Technical efficiencies for non-metallic mineral product manufacturing (20) and space & air transport have improved by 51 per cent and 128 per cent, respectively over this period. These 10 Following Afriat (1972), some studies relax the assumption of CRS as allowing non-increasing returns to scale by adding following restriction: k=1 zk,t ≤ 1. 11 The selected sub-industries include Agriculture 01, Coal and mining 06, Oil and Gas extraction 07, Other mining 08–10, Food, beverage and tobacco 11–12, Paper manufacturing and printing 15–16, Chemical, polymer and rubber product 18–19, Non-metallic mineral product manufacturing 20, Road transport 46, Air and space transport 49. The numbers are industry number by Australian New Zealand Standard Industrial Classification (ANZSIC). 12 The data for Paper manufacturing and printing 15–16, Chemical, polymer and rubber product 18–19 industries are only available from 2007.

∑K

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Table 1 Energy productivity growth and its sources in 8 sub-industries: 2003–2015. Sub-industries

EP 2003 (1)

EP 2015 (2)

EP 2015 /EP 2003 (3)

EFFCH (4)

TECH (5)

PECH (6)

Agriculture (01)

161.2

207.2

1.28

1.00

0.75

1.71

Coal mining (06)

493.8

235.4

0.48

0.78

0.83

0.74

Oil & gas extraction (07)

310.8

192.5

0.62

1.00

0.38

1.64

Mining (08–10)

454.7

475.3

1.05

1.09

0.97

0.99

Food, beverage & tobacco (11–12)

231.1

138.4

0.60

1.00

0.78

0.77

Non-metallic mineral product manufacturing (20)

49.2

54.2

1.10

1.51

0.72

1.01

Road transport (46)

18.6

19.5

1.05

0.80

0.75

1.75

Space & air transport (49)

32.9

24.5

0.74

2.28

0.64

0.51

Geometric mean

129.3

106.1

0.82

1.11

0.71

1.05

Notes: EP2003 and EP2015 denote energy productivity in 2003 and 2015, respectively. Energy productivity measured in millions 2011–12 Australian dollar per petajoule (PJ) energy. EFFCH refers to Technical efficiency change. TECH refers to Technological change. PECH refers to the combined change of capital-energy ratio and labour-energy ratio.

results indicate that except the sub-industries of coal mining, and road transport, the rest of the sub-industries appear to be adopting the ‘best practice techniques’ of their respective chosen technologies. Drawing on the ‘learning-by-doing’ principle, the interesting question is: whether those sub-industries appearing to be following the ‘best practice techniques’ had been using their selected technologies over a long period without introducing any relevant new technologies. Answer to this question can be obtained by examining the contribution of technological change to energy productivity change in those sub-industries. The effect of technological change on the declining energy productivity, as shown in Column (5), is significant at the average level of the selected 8 sub-industries. The technological change accounted for most of the decrease in energy productivity growth with the contribution of −29.0 per cent on average, and contributed negatively to energy productivity change in all sub-industries. The implication is that those sub-industries following the ‘best practice techniques’ of the chosen technology had been using their technologies without any upgrading. These results emphasise the urgent need for promoting more productive energy services through innovation and competition that can be achieved by reducing barriers to entry in the market for new technologies and service options. The geometric mean joint effect of changes in capital-energy ratio and labour-energy ratio has indicated positive contribution to the energy productivity change. Specifically, the increase in energy productivity improved in agriculture, oil & gas extraction, non-metallic mineral product manufacturing, and road transport sub-industries over this period. Note that the decomposition results might be affected by the choice of the beginning and the concluding years. Hence, the year-to-year energy productivity changes in all the 8 sub-industries taken together for the years 2004–2015 were decomposed. Table 2 reports the geometric means in each of 12 two-year pairs (year-to-year change) for each of the three components over the sub-industries. The results presented in Table 2 are in conformity with the overall findings shown in Table 1 in the sense that technological change did not contribute to the growth of energy productivity change in majority of the period of analysis. It is interesting to note that there was a negative relationship between the components of technical efficiency change and technological change for all years except for 2011, when both indexes increased. For instance, technological change decreased the energy productivity growth in 2004, 2007, 2008, 2010, 2012, and 2014, while technical efficiency change increased the energy productivity growth during those periods. Changes in capital-energy and labour-energy ratios indicated no pattern in their contributions to energy productivity changes. Furthermore, the choice of the base year and sub-industries in the data for the empirical analysis might also affect the decomposition results. To check the robustness of the above results, energy productivity changes between 2007 and 2015 were decomposed. The base year for the analysis was chosen as 2007 as the data for the paper manufacturing and printing (15–16), Chemical, polymer and rubber product (18–19) sub-industries are available from 2007 only. Table 3 reports the decomposition of energy productivity changes in the 10 sub-industries between 2007 and 2015. The results are similar to those in Table 1 in the sense that technical efficiency relatively more positively contributed than the technological change did to the energy productivity growth. However, the decrease in capital-energy and labour-energy ratios played a major role in reducing the energy use over the years 2007–2015. For instance, the energy productivity of Chemical, polymer and rubber product (18–19) had declined by 22.0 per cent, though both technical and technological changes increased by 10.0 and 12.0 per cent, respectively. However, the Oil & gas extraction industry is an exceptional case as the energy productivity growth is mainly driven by increases in capital-energy and labour-energy ratios, and the technical change decreased by 49.0 per cent. Agriculture (01) industry is another exceptional case because its energy productivity had increased by 28.0 per cent as a result of higher growths in both technical efficiency and

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G.-O. Doojav and K. Kalirajan / Economic Analysis and Policy 65 (2020) 1–10 Table 2 Energy productivity growth and its sources in 8 sub-industriesa (Geometric means). End year

EP in end year (1)

EPCH (2)

EFFCH (3)

TECH (4)

PECH (5)

2004

102.5

1.02

1.04

0.94

1.06

2005

96.5

0.94

0.99

1.04

0.91

2006

89.8

0.93

0.98

1.01

0.94

2007

102.9

1.15

1.11

0.77

1.34

2008

92.8

0.90

1.03

0.91

0.96

2009

100.5

1.08

0.84

1.18

1.09

2010

85.7

0.85

1.18

0.86

0.84

2011

94.3

1.10

1.04

1.06

1.00

2012

97.2

1.03

1.02

0.91

1.19

2013

90.0

0.93

1.01

0.94

0.97

2014

96.2

1.07

1.04

0.96

1.06

2015

99.8

1.04

0.98

1.05

1.01

EPCH refers to Energy productivity change. EFFCH refers to Technical efficiency change. TECH refers to Technological change. PECH refers to the combined change of capital-energy ratio and labour-energy ratio. a Notes: The decomposition analysis is conducted using the data for 8 sub-industries. Column (2) is the ratio of column (1) to energy productivity in previous year (see Table 1 for energy productivity for 2003). Table 3 Energy productivity growth and its sources in 10 sub-industries: 2007–2015. Sub-industries

EP 2007 (1)

EP 2015 (2)

EP 2015 /EP 2007 (3)

EFFCH (4)

TECH (5)

PECH (6)

Agriculture (01)

161.3

207.2

1.28

1.48

0.95

0.83

Coal mining (06)

455.6

235.4

0.52

0.76

1.03

0.66

Oil & gas extraction (07)

172.7

192.5

1.11

1.00

0.51

2.17

Mining (08–10)

551.5

475.3

0.86

1.00

1.00

0.86

Food, beverage & tobacco (11–12)

235.8

138.4

0.59

0.96

0.99

0.61

Paper manufacturing and printing (15–16)

148.5

156.8

1.06

1.13

1.37

0.69

Chemical, polymer and rubber product (18–19)

73.5

57.5

0.78

1.10

1.12

0.63

Non-metallic mineral product manufacturing (20)

49.2

54.2

1.10

1.02

0.82

0.98

Road transport (46)

19.3

19.5

1.01

1.21

0.85

0.76

Space & air transport (49)

28.9

24.5

0.85

1.11

0.81

0.95

Geometric mean

117.3

103.7

0.88

1.03

1.01

0.85

Notes: EP2007 and EP2015 denote energy productivity in 2007 and 2015, respectively. Energy productivity measured in millions 2011–12 Australian dollar per petajoule (PJ) energy. EFFCH refers to Technical efficiency change. TECH refers to Technological change. PECH refers to the combined change of capital-energy ratio and labour-energy ratio.

technological changes. For Food, beverage & tobacco industry (11–12), all factors negatively contributed to its energy productivity fall. The technological change was the only positive contributor for Coal mining (06) industries. As the decomposition results might be affected by the choice of the beginning and the concluding years, the year-toyear energy productivity changes in all the 10 sub-industries taken together for the years 2007–2015 were decomposed. Table 4 reports the geometric means in each of the 8 two-year pairs (year-to-year change) for each of the three components over the 10 sub-industries. The results shown in Table 4 indicate that technical efficiency rather than technological change and changes in capitalenergy and labour-energy ratios was the main source of energy productivity changes. This is consistent with the findings presented in Table 2. Nevertheless, the technological change was the main contributor of energy productivity changes in 2015. Thus, the year-by-year analysis of energy productivity growth in the sub-industries taken together shown in Tables 2 and 4 indicates that the growth is around 1 per cent per annum. However, the NEPP’s proposed 40 per cent by 2030 energy productivity target would require overall improvement of minimum 1.5 per cent per annum. Hence, there is an urgent need to implement financially attractive end-use energy efficiency initiatives at the industry levels.

G.-O. Doojav and K. Kalirajan / Economic Analysis and Policy 65 (2020) 1–10

9

Table 4 Energy productivity growth and its sources in 10 sub-industriesa (Geometric means). End year

EP in end year (1)

EPCH (2)

EFFCH (3)

TECH (4)

PECH (5)

2008

92.44

0.98

1.00

0.94

1.04

2009

99.00

1.07

0.86

1.18

1.06

2010

85.09

0.86

1.17

0.87

0.84

2011

92.77

1.09

1.05

1.04

1.00

2012

94.46

1.02

1.00

0.93

1.13

2013

88.71

0.94

1.01

0.98

0.98

2014

92.81

1.05

1.06

0.96

1.03

2015

97.15

1.05

1.00

1.09

0.96

EPCH refers to Energy productivity change. EFFCH refers to Technical efficiency change. TECH refers to Technological change. PECH refers to the combined change of capital-energy ratio and labour-energy ratio. a Notes: The decomposition analysis is conducted using the data for 10 sub-industries. Column (2) is the ratio of column (1) to energy productivity in previous year (see Table 3 for energy productivity for 2007).

4. Conclusions and policy implications This paper decomposed energy productivity changes in the Australian sub-industries between the years 2003 and 2015 (and 2007–2015) into three components using the DEA based on output distance functions to identify the causes for the observed sluggishness in energy productivity. Empirical results show that the technical efficiency change positively contributed to energy productivity change in the sub-industries during the period of analysis. Decreases in technological change played the most important role in the process of energy productivity drop in the selected 8 sub-industries over the years 2003–2015. In addition to technological change, falling capital-energy and labour-energy ratios played important role in the process of the drop in energy productivity in the selected 10 sub-industries over the years 2007–2015. The technical efficiency, technological change, and capital-energy and labour-energy ratios in coal mining sub-industry, which is strategically important for the Australian economy, continuously decreased over time, as a result, the energy productivity in the industry has dropped by 52.0 per cent between 2003 and 2015, and 48.0 per cent between 2007 and 2015. The low contribution of the technological change and the declining contribution from the technical efficiency suggest that the related policy should emphasise on promoting technological growth and improving technical efficiency, particularly in those Australian sub-industries. It is possible that relatively less-efficient sub-industries may survive by inefficiently using different inputs and demanding more government subsidies. It is interesting from the policy perspective to find out what factors would contribute to sub-industry-level energy efficiency and what factors prevent the convergence of energy efficiency indices across the sub-industries. For lack of consistent data, such identification could not be done in this study. To improve technical efficiency, Australia needs to learn from the G-20 countries to follow the best practices of the existing technology, which may require effective on-the-job training. It is imperative to creating an environment that facilitates the diffusion of better technologies for improving energy productivity. In this context, government incentive measures need to be taken to enhance the technological progress in the sub-industries by reducing barriers to entry in the market for new technologies. The findings of this study suggest that the NEPP’s target of 40 per cent improvement in Australia’s energy productivity by 2030 can be achieved by implementing and educating technologically advanced end-use energy efficiency initiatives across the industries. The government may think of reviving the EEO Program that was responsible for approximately 40 per cent of the energy efficiency improvements in the Australian industrial sector between 2006 and 2011 to complement and strengthen the NEPP. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Afriat, S., 1972. Efficiency estimation of production functions. Internat. Econom. Rev. 13, 568–598. Balk, B., 1993. Malmquist productivity indexes and Fisher ideal indexes: Comment. Econom. J. 103, 680–682. Banker, R.D., Charnes, A., Cooper, W.W., et al., 1984. Some models for estimating technical and scale inefficiencies in Data Envelopment Analysis. Manage. Sci. 30 (9), 1078–1092. Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European J. Oper. Res. 2 (6), 429–444. Coelli, T., 1996. A Guide to Version 21: A Data Envelopment Analysis (Computer) Program. Centre for Efficiency and Productivity Analysis, Department of Econometrics, University of New England, Armidale. Coelli, T.J., Rao, D.S.P., O’Donnell, C.J., Battese, G.E., 2005. An Introduction to Efficiency and Productivity Analysis, second ed. Springer, Singapore.

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