Convergence of energy productivity in Australian states and territories: Determinants and forecasts

Journal Pre-proof Convergence of energy productivity in Australian states and territories: Determinants and forecasts Mita Bhattacharya, John N. Inekwe, Perry Sadorsky

PII:

S0140-9883(19)30333-0

DOI:

https://doi.org/10.1016/j.eneco.2019.104538

Reference:

ENEECO 104538

To appear in: Received Date:

23 September 2018

Revised Date:

21 September 2019

Accepted Date:

23 September 2019

Please cite this article as: Bhattacharya M, Inekwe JN, Sadorsky P, Convergence of energy productivity in Australian states and territories: Determinants and forecasts, Energy Economics (2019), doi: https://doi.org/10.1016/j.eneco.2019.104538

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Convergence of energy productivity in Australian states and territories: Determinants and forecasts

Mita Bhattacharyaa, John N Inekweb and Perry Sadorskyc a

Department of Economics, Monash University, Caulfield 3145, Australia, [email protected] (corresponding author) b

Centre for Financial Risk, Macquarie University, Sydney 2109, Australia, [email protected] c

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Schulich School of Business, York University, Toronto, Ontario M3J 1P3, Canada [email protected]

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Analyse the dynamics of energy productivity for Australian states and territories. Australian states and territories converge to two energy productivity clubs. New South Wales and Victoria will be the forerunners in maintaining higher energy productivity in 2030.

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Highlights

Abstract

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The Australian government has recently launched a National Energy Productivity Plan that calls for a 40% increase in energy productivity (economic output divided by energy use) before 2030. Improving energy productivity would help boost economic competitiveness, reduce energy costs, and reduce carbon dioxide emissions in Australia. Understanding energy productivity dynamics at the state level is essential for the success of this program. This research analyses the convergence path of energy productivity in Australian states and territories. Club convergence analysis applied to data over the period 1990 to 2015 reveals two converging energy productivity clubs. Initial energy productivity, industry structure, and automobile fuel prices are important determinants of higher energy productivity. Based on Australian state energy productivity forecasts to 2030, New South Wales and Victoria will be the forerunners in maintaining higher energy productivity in 2030. Australia will not achieve a 40% increase in energy productivity before 2030 without significant changes to its fuel mix and industry structure.

Keywords: Australia; club convergence; energy productivity; forecasting; renewable energy 1

JEL codes: C33, Q43, Q47

1. Introduction Since 1991, Australia has experienced an impressive 25 years of uninterrupted economic growth, and per capita GDP growth during this time is above the OECD average (OECD, 2017).1 Part of this economic growth can be attributed to Australia’s participation in the

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global commodity super cycle. Australia is a small open economy and exports and imports each account for approximately 20% of GDP. Australia’s top two exports are iron ore and coal. Approximately 28% of Australia’s exports are destined for China and half of these

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exports are iron ore (OECD, 2017). Western Australia and Queensland are particularly

dependent upon mining. Mining exports to China helped to cushion Australia from the 2008-

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2009 global financial crises.

In addition to its strong economic growth over the past 25 years, Australia is also

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characterised by per capita energy consumption and per capita carbon dioxide (CO2) emissions that are each above their respective OECD averages. Australia is one of the highest

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energy consumers on a per capita basis in the world (Falk and Settle, 2011). Energy consumption in Australia is primarily sourced from oil (including liquefied petroleum gas and

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refined petroleum products), coal and natural gas. In 2016, coal, oil, and natural gas each

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accounted for 32%, 37%, and 25% of Australia’s energy consumption, respectively (Australian Government Department of the Environment and Energy, 2017). Despite having an abundance of renewable energy sources, renewable energy only accounts for 6% of fuel consumption. This reluctance to move away from fossil fuels is hampering Australia’s ability to transition to a low carbon economy (Kinrade, 2007; Pears, 2007). In

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The last time that Australia recorded two consecutive quarters of negative growth was March and June of 1991.

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response to high energy usage and CO2 emissions, the Australian Government launched the National Energy Productivity Plan (NEPP) (Australian Government, 2017). The main focus of the NEPP is to help Australia increase energy productivity (economic output divided by energy use) by 40% before 2030. According to the NEPP, improving energy productivity can boost economic competitiveness, reduce electricity costs to consumers, and reduce carbon dioxide emissions. The NEPP is ambitious and areas of focus include light vehicle energy efficiency, commercial building efficiency, residential building efficiency, energy market

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efficiency, and new energy efficient products and innovations. The topic of energy productivity or energy intensity in Australia has been investigated by

several authors. Shahiduzzaman and Alam (2013) use the logarithmic mean Divisia index

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(LMDI) approach to study energy intensity in Australia. They find that efficiency and sectoral composition are the two main factors behind Australia’s reduction in energy

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intensity. Voigt et al. (2014) use input-output data to study energy intensity in 40 major

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economies. For most countries, they find that technological change was the main determinant of energy intensity reduction. For Australia, they find that changes in the energy mix are most important. Hu and Liu (2016) use data envelope analysis (DEA) to investigate total factor

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productivity and energy productivity in the Australian construction industry. The state level data set covers the years 1990 to 2010. They find that energy productivity and total factor

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productivity improved over the period of study by 2.8% and 0.7% respectively. These

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improvements resulted from technological advancements. Ma et al. (2018) use panel regression techniques to investigate energy productivity in the Australian construction sector. They find that the construction sector energy productivity in New South Wales, Northern Territory, South Australia, Tasmania, and Victoria converge to stable long-run equilibriums. There is, however, little evidence to show that energy productivity in Queensland and Western Australia converge. Construction energy productivity is significantly determined by

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aggregate technology, local technology, capital-energy ratio, and the labour-energy ratio. Du and Lin (2017) use the stochastic frontier approach to study energy productivity in 123 countries for the period 1990 to 2010. They find that average energy productivity increased by 34.6% and that this increase was mostly driven by technological progress. For Australia, energy productivity increased by 51% and this was above the average for developed countries (38%). Ivanovski et al. (2018) use the Phillips and Sul (2007) approach to study convergence in per capita energy consumption for Australian regions and sectors. Using data from 1990 to

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2016 they find evidence of two convergent clubs and one divergent state (Queensland). The first convergent club consists of the Northern Territory and Western Australia. The second convergent club consists of New South Wales, South Australia, Tasmania, and Victoria.

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Queensland may be divergent because it is experiencing the largest growth in energy

consumption due to increased demand for air conditioning and a growing LNG industry.

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While these studies are helpful in understanding energy productivity or energy intensity in

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Australia, there are still some important areas that need to be studied. For example, how do the dynamics of energy productivity compare across Australian states and territories? Some states are more energy intensive and resource extractive and this has implications for energy

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productivity dynamics and energy policy. If energy productivity in all states and territories is converging to long run equilibrium then a common energy policy would be appropriate. If

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energy productivity in states and territories is converging to different long run equilibria then

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one common energy policy is not optimal. Under the current energy policy, responsibilities for the implementation of energy policies are shared through cooperative action between the Commonwealth and state and territory governments. Understanding the dynamics of energy productivity at the state level is essential to formulating efficient energy productivity policy. This paper makes four important contributions to the literature. First, this research focuses on the dynamics of energy productivity across Australian states and territories. This helps to 4

deepen our knowledge of Australian energy productivity, which is important for the success of the NEPP. Second, this research uses recently-developed club convergence analysis to analyse Australian state-level energy productivity. More specifically, the Phillips and Sul (2007) approach is employed to analyse energy productivity convergence in Australian states and territories. Phillips and Sul (2007, 2009) have developed a regression-based test for convergence that internally selects the states for club membership. This approach groups states into clusters or clubs in a way that allows for individual state heterogeneity. States

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within a group share a common equilibrium, but states between groups do not. Third, the determinants of energy productivity club membership are investigated. In particular, the

impact of energy-mix, industry share of income, capital to labour ratio, and fuel prices on the

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formation of club convergence is studied. Finally, the path of energy productivity for each

Australian state and territory until 2030 is forecast. Club convergence tests are applied to the

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expanded data set to see what Australian state energy productivity convergence would look

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like in the year 2030.

Evidence is presented showing that Australian state-level energy productivity converges to

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two clubs with New South Wales, South Australia, and Victoria in the high energy productivity club. The fact that state-level productivity does not converge to one club

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indicates that a common energy policy would be sub-optimal. Energy policy should be designed considering the differences in club membership.

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Initial energy productivity, industrial structure, and automobile fuel prices are important determinants of higher energy productivity. Forecasting the path of Australian state energy productivity to 2030 under a business-as-usual scenario reveals three convergent clubs and one divergent state. New South Wales and Victoria are members of the high energy productivity club. This analysis shows that Australia needs to make significant changes in

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fuel mix and industry structure in order to achieve a 40% increase in energy productivity before 2030. The rest of the paper is structured as follows. Section 2 briefly discusses the relevant literature focussing on club convergence in energy productivity or intensity. Section 3 describes the data and estimation methods we use in the research, while section 4 analyses the empirical findings. Section 5, identifies some key factors determining the observed convergence pattern. Section 6 covers the forecasting of energy productivity and the patterns

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of converging clubs in the long-run. The final section of the paper concludes with policy implications.

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2. Literature: A brief overview

The convergence of energy intensity or productivity has been investigated using Beta

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convergence, Sigma convergence, or stochastic convergence. These concepts of convergence originated from within the economic growth literature to explain convergence of GDP per

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capita (Islam, 2003). Beta convergence, sometimes referred to as the catch-up effect, occurs when poor countries grow faster than rich countries and the variable of interest for the poor

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country (e.g. GDP per capita) eventually converges to a value similar to that of rich countries. Sigma convergence refers to convergence in variance and occurs when there is a reduction in

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the dispersion of the variable of interest. Stochastic convergence refers to convergence in the

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time series properties of the variable of interest. Quah (1996), in describing the literature on economic convergence, points out that it might be unrealistic to expect that GDP per capita converges to a common value for all countries. Instead, it may be more accurate to think of groups of countries converging to different longrun equilibria. Countries within a group share a common equilibrium but countries between

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groups do not. This idea of twin peaking or multi-modal peaking was what led Phillips and Sul (2007) to develop an algorithm for detecting club convergence. Club convergence of energy productivity or energy intensity has been studied by several authors. In this section, we review the studies that use the Phillips and Sul (2007) approach to investigate club convergence (Table 1). Bhattacharya et al. (2018) provide a more thorough review of the literature on energy intensity/productivity convergence. Herrerias and Liu (2013) investigate the energy intensity convergence of Chinese provinces

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using monthly data covering the period January 2003 to December 2013. Three converging

clubs are found with one divergence club. The first convergent club consists of Zhejiang and Hainan. The second club is the largest and consists of 20 provinces, while the third

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convergent club contains 3 provinces. Three provinces do not belong to converging clubs.

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Kim (2015) investigates the convergence of electricity intensity for 109 countries over the period 1971 to 2010. Evidence is found showing electricity intensity does converge for all

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countries, but electricity consumption per capita does not. Yu et al. (2015) study energy intensity convergence for 109 countries over the period 1971 to 2010. They find four

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convergent clubs and some countries do not belong to a convergent club. In general, North American and European countries have low energy intensity, while Asian and African

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countries have higher values of energy intensity. Using an ordered logit model, evidence is presented showing that initial energy intensity and trade openness are important determinants

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of club formation, while industry share and R&D are not. Apergis and Christou (2016) test energy productivity club convergence for 31 countries

using data from 1972 to 2012. Energy productivity for the 31 countries does not converge. Instead, six converging clubs are found and three countries display divergence. Zhang and Broadstock (2016) study the dynamic path of energy intensity in Chinese provinces for the period 1995 to 2008. They find three energy intensity clubs. The first club has 4 provinces, 7

the second club has 12 provinces, and the third club has 12 provinces. The impact of economic and social factors varies across the clubs.

Parker and Liddle (2017a) investigate energy productivity dynamics in the manufacturing sector of the OECD using data covering the period 1980 to 2009. Evidence is found for club convergence. The first club has four sectors (mostly high technology sectors), the second club has 13 sectors (about half are high technology sectors), the third club has 14 sectors, club four

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has 25 sectors, club 5 has 2 sectors, and club 6 has three sectors. In general, the higher productivity clubs are dominated by high technology sectors while the low productivity clubs are dominated by resource sectors. Ordered logit analysis shows that higher investment rates

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and high technology production are important factors in explaining club membership.

Parker and Liddle (2017b) investigate energy productivity club convergence at the economic

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level and manufacturing level for 33 countries (a mixture of OECD and non-OECD

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countries) using data covering 1971 to 2008. Evidence is found that shows four clubs for economy-wide energy productivity and six clubs for manufacturing energy productivity. The

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results display considerable variation. At the country level, club membership is not closely aligned with geographical proximity. For countries such as Brazil and Indonesia,

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manufacturing energy productivity is worsening while overall country level energy productivity is increasing. Bhattacharya et al. (2018) study energy productivity in Indian

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states and territories using data for the period 1988 to 2016. Four energy productivity clubs are found. Initial energy productivity and industry structure are important determinants of energy productivity clubs. In summary, the literature on energy productivity or energy intensity convergence finds evidence in favor of multiple clubs. This means that energy productivity or energy intensity does not converge to common long run equilibrium. The practical implication of this result is 8

that a common energy policy is suboptimal when there are multiple clubs and that the political economy of negotiating energy policy becomes more complicated as the number of clubs increase. As discussed in the introduction, Shahiduzzaman and Alam (2013) study aggregate Australian energy intensity while Hu and Liu (2016) and Ma et al. (2018) explicitly study energy productivity in the Australian construction sector. Voigt et al. (2014) and Du and Lin (2017) include Australia as one of the countries in their multicounty studies. While each of

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these studies makes an important contribution to understanding energy productivity in

Australia, there is, however, no detailed study of Australian energy productivity convergence

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at the state and territory level.

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3. Data and methods

This section presents the energy productivity data and describes the club convergence

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estimation method.

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3.1 Data on Australian energy productivity

Data on energy productivity is obtained from Australian government sources (see the

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Appendix). The data are at the state and territory level covering the period between 1990 and 2015. Data on the energy productivity of each state ($ million/PJ) is calculated using chain

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weighted gross state product (GSP). The major seven states and territories within the panel are New South Wales (NSW), Northern Territory (NT), South Australia (SA), Tasmania (TAS), Queensland (QLD), Victoria (VIC), and Western Australia (WA).2 Time plots of state

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The data for Australian Capital Territory are combined with NSW. We use terms states or states and territories interchangeably throughout the manuscript.

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and territory energy productivity are shown in Figure 1. All of the states and territories show

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an upward trend in energy productivity but the strength of the trend varies.

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Figure 1. Time plots of state and territory energy productivity.

The energy productivity of New South Wales has the highest mean value ($272 million/PJ)

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over the period of study, as presented in Table 2. Queensland has the lowest mean value ($186 million/PJ) over the period of our study. The standard deviation of energy productivity is lowest in Tasmania and highest in South Australia. The mean value of energy productivity in Australia increases from $179 million/PJ in 1990 to $266 million/PJ in 2015, indicating a 49% improvement in average energy productivity. From 1990 to 2015 the standard deviation of energy productivity increases, which indicates greater variability and is counter to sigma

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convergence. New South Wales, South Australia, and Victoria have the greatest percentage increase in energy productivity over the period 1990 to 2015.

3.2 Estimations: The clustering algorithm The Phillips and Sul (2007, 2009) (PS hereafter) club convergence approach is used to

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estimate convergence of energy productivity across states and territories in Australia. Compared to other methods of identifying convergence in variables of interest, the PS-club convergence approach has several advantages. States within a group share a common

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equilibrium but states between groups do not. The presence of a mixture of stationary and non-stationary series in the panel does not affect the results, and unit root or cointegration

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tests are not required. Instead of convergence towards the absolute level, the relative

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convergence of cross-sectional averages can be measured.

A time-varying common factor representation of energy productivity, EPi,t, can be

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decomposed into a common trend αt and an individual element δit as follows: 𝐸𝑃𝑖𝑡 = 𝛿𝑖𝑡 𝛼𝑡

(1)

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where for state i at time t, EPi,t is energy productivity; αt denotes a single common component, and δit varies over time across all the states in Australia and this helps to capture

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the deviation of each state from the common trend αt. If lim 𝛿𝑖𝑡 = 𝛿 for all I (i = 1,…,N) , all N groups will converge towards steady-state within 𝑡→∞

this framework. The transition paths of individual states can be obtained from 𝛿𝑖,𝑡 . Following the PS-approach to estimate δit, equation (1) is modified as follows:

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ℎ𝑖𝑡 =

𝐸𝑃𝑖𝑡 1 𝑁 ∑ 𝐸𝑃𝑖𝑡 𝑁 𝑖=1

=

𝛿𝑖𝑡 1 𝑁 ∑ 𝛿 𝑁 𝑖=1 𝑖𝑡

(2)

The transition path relative to the common average is gauged using the relative measure hit. The PS approach considers a semi-parametric form of δit in order to formulate an econometric test of convergence and an empirical algorithm for identifying the clubs such that: δit= δi + σi ξ it L(t)-1t-α

(3)

where δi is fixed across the states, σi˃0, t≥0, and 𝜉𝑖,𝑡 is an i.i.d (0,1) across i, but could be

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weakly time-dependent. L(t) is a slowly varying function that tends to infinity as t tends to infinity.3 The null hypothesis of convergence under this specific form for δit is H0: 𝛿𝑖𝑡 =

𝛿(𝑤ℎ𝑒𝑟𝑒 α≥0), as opposed to the alternative hypothesis HA: 𝛿𝑖𝑡 ≠ 𝛿 (𝑓𝑜𝑟 𝑎𝑙𝑙 𝑖 𝑎𝑛𝑑 α<0).

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The parameter α measures the speed of convergence.

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We estimate the following regression by following the PS-approach: 𝐻

𝑙𝑜𝑔 ( 𝐻1 ) − 2 log(log(𝑡)) = µ̂ + 𝑚 ̂ log(t) + 𝜀𝑡

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𝑡

(4) 1

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2 where 𝐻𝑡 =𝑁 ∑𝑁 𝑖=1(ℎ𝑖𝑡 − 1) is the square cross-sectional distance relative to transition

coefficients. The null hypothesis can be constructed as a one-sided test of 𝑚 ̂ ≥ 0 against the

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alternative hypothesis of 𝑚 ̂ < 0 for the case of ̂ 𝑚 = 2µ̂. At the 5% level of significance, the rejection of the null hypothesis occurs when the t-statistic has a value below -1.65.

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The use of log (t) regressions enables the testing for the existence of club convergence or the convergence patterns within states and territories as proposed by the PS-approach. Since there is the possibility of having two separate points of equilibrium, or following separate steady-state growth paths, or converging within clusters in the same or divergent groups, the

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Following the PS-approach, we maintain L(t) = log (t) in order to guarantee convergence.

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rejection of the null hypothesis of convergence does not necessarily depict divergence. This is a unique feature of this approach in identifying convergence among observable series. The PS-approach follows the steps below to identify the clubs in a panel. Step 1: Rank the variables: Order the values of the energy productivity for the Australian states according to the final values of their time series. Step 2: Form the core group: Select the first k highest states in the panel to form a subgroup

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Gk, where k lies between 2 and N and find the t-statistic for the group by running the regression in equation (4) and then use the following cut-off point criterion:

k*=ArgMax𝑘 [𝑡𝑚̂ ], subject to Min𝑘 [𝑡𝑚̂ ] > -1.65 for k= 2, 3, ….., N to identify the core group.

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Step 3: Club membership: Add one state at a time to the core group for the remaining states and for each of the formation, re-estimate equation (4). Based on the sign criterion [𝑚 ̂ ≤ 0],

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the decision on whether a state should join the core group is reached.

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Step 4: Stopping rule: States that do not belong to the first convergence club are then grouped into the second club. The second club is evaluated in the same way as above using the log t

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test. Steps 1-3 are repeated for the remaining states iteratively and the process ceases when no further clubs can be formed. If some states in the last group do not have a convergent path,

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conclude that these states form a divergent club. Following the PS-approach, over-estimated club numbers may be generated when a sign

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criterion in step (2) is used to make a decision. It is recommended that club merging tests are performed after estimating equation (4).

Before implementing the PS-approach on the energy productivity data, we the Hodrick and Prescott (1997) filter is used to extract the trend component and remove business cycle effects. The trend component that minimises the squared changes in trend and deviations is calculated as follows: 13

∗ ∗ ∗ ∗ 2 𝑚𝑖𝑛𝑦𝑡∗ {∑𝑇𝑡=1 (𝑦𝑡 − 𝑦𝑡∗ )2 + 𝜆 ∑𝑇−1 𝑡=2 [(𝑦𝑡+1 − 𝑦𝑡 ) − (𝑦𝑡 − 𝑦𝑡−1 )] }

(5)

The variable yt is the natural log of the energy productivity data and lambda is set at 6, which is the recommended value for annual data. 4. Empirical findings The club convergence test rejects the null hypothesis of full convergence in energy productivity across Australian states (Table 3). Hence, it becomes relevant to examine if there

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are sub-convergent groups. This analysis will enable us to determine the pattern of energy productivity across the states. The results identify two convergence clubs of energy

productivity as the test statistics are greater than -1.65. This result implies that the energy

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productivity across these states can be grouped into clusters as depicted in Table 3. The results show that the first energy productivity club consists of New South Wales, South

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Australia, and Victoria. The second energy productivity club consists of Northern Territory,

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Queensland, Tasmania, and Western Australia.

Northern Territory Queensland

Western Australia South Australia New South Wales Australia Victoria 14

Tasmania

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Figure 2. Club convergence map. Club 1 (NSW, SA, VIC), Club 2 (NT, QLD, TAS, WA).

Club 1 includes New South Wales, South Australia, and Victoria as presented in Figure 2.

These three states are close to each other geographically and had the largest improvement in energy productivity over the period 1990 to 2015 (Table 2). These states are located in the

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south-eastern part and have been very proactive in reducing emissions and changing energy-

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mix. For instance, in 2003, New South Wales introduced the world’s first mandatory emissions trading scheme, while South Australia (in 2004) and Victoria (in 2006) introduced

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state-based renewable energy targets.4 These states generate a lot of electricity from wind and solar sources. South Australia leads the country in wind and solar PV. Over 40% of

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Australia’s wind capacity is located in South Australia. Australia’s largest solar thermal plant is located in New South Wales. Victoria has the largest and third largest wind farms in

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Australia. The rest of the states viz. Northern Territory, Queensland, Tasmania, and Western Australia are in Club 2. Amongst these members in Club 2, TAS has the largest increase in

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energy productivity over the period 1990 to 2015, while QLD has the smallest (Table 2). The PS approach can generate too many clubs when the selection of club clusters is based on a sign criterion. As recommended by PS, an additional test for merging clubs is used to

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The Climate Council of Australia (2014). The Australian renewable energy race: Which States Are Winning or losing? http://www.climatecouncil.org.au/uploads/ee2523dc632c9b01df11ecc6e3dd2184.pdf

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overcome this issue. This will not be an issue in our analysis given that only two clubs are identified. However, to establish the reliability of the identified convergence clubs, we test the convergence between two adjacent clubs. The results, as reported in Table 4, reflect that clubs one and two cannot be merged to form a larger club. The club merging analysis does not support the club merging proposition as expected. In this case, the results are reliable and no further aggregation is needed. The club merging analysis shows that the classification of the states into two convergent clubs is appropriate. Our results indicate that a common

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Australian energy policy across all states and territories is sub-optimal and energy policy should be designed whilst considering the presence of two energy productivity clubs.

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5. The determinants of Australian energy productivity club convergence

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The focus of this section is to determine the underlying factors behind the observed Australian energy productivity convergence clusters. The club convergence approach

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provides an ordinal ranking of the clubs. In the previous section, three Australian states and territories were identified as belonging to the high energy productivity club (Club 1) and the

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remaining four states in the second club (Club 2). A new binary variable is created that takes on the values zero or one. States in the high energy productivity club are coded as one and the

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remaining states coded as zero. The determinants of club convergence can be estimated using

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logit regression with the zero one variable used as the dependent variable. The literature on the determinants of energy productivity or energy intensity identify GDP, industry structure, technology and capital, foreign trade and FDI, energy prices, and energy structure as important variables (Zhang and Broadstock, 2016). In addition, the energy convergence literature identifies the importance of initial conditions in explaining convergence patterns (Bhattacharya et al., 2018; Yu et al., 2015). States that are similar in

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structural characteristics converge to long run equilibrium if they have similar initial conditions5. Consequently, initial energy productivity is included as an explanatory variable. GDP is often included in levels and with a squared term to model scale effects. Increasing GDP reflects greater economic development and higher economic development should lead to more productive use of energy (Zhang and Broadstock, 2016). Industry structure (the share of industrial value added in GDP) captures the composition effect. This variable is relevant for energy productivity (Atalla and Bean, 2017; Bhattacharya et al., 2018; Fisher-Vanden et

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al., 2004; Herrerias et al., 2013; Hübler and Keller, 2010; Yu et al., 2015). The importance of industry effects has been emphasised recently by Ivanovski et al. (2018) in their study of per capita energy consumption in Australian states and territories. A higher industrial share is

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expected to lower energy productivity because more energy products are required (Fisher-

Vanden et al., 2004). The fuel mix may affect energy productivity with a higher proportion of

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energy sourced from renewables increasing energy productivity (Liddle, 2010). An increased

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use of technology or higher capital to labour ratio should increase economic productivity and efficiency and lead to higher energy productivity. Trade openness can affect energy productivity through the scale, composition, and technical effect although a priori it is

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difficult to determine the net effect of these factors (Hübler and Keller, 2010). Higher fossil fuel prices are expected to create an incentive for fuel switching and more efficient use of

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energy (Fisher-Vanden et at., 2004, 2006).

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Following this discussion we include initial energy productivity, GSP per capita, GSP per capita squared, the share of the industrial sector, the capital to labour ratio, the share of renewable energy in electricity generation, and automobile fuel prices. The automobile fuel price component of the CPI is used to measure fossil fuel price movements. Automobile fuel

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The economic growth literature emphasizes the importance of initial conditions. See, for example, Bartkowska and Riedl (2012), Galor (1996), and Quah (1996).

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prices are a reasonably good proxy for fossil fuel price movements because these prices are determined by competitive market conditions and the transport industry accounts for 27.5% of Australian energy consumption and is one of the largest consumers of energy6. At a 72% share the road component is the largest subsector of the transport industry. The transport industry accounted for 4.6% of GDP in 2015-20167. We don’t include a variable for trade openness because Western Australia and the Northern Territory have the highest average values of trade openness at 60% and 50% respectively (due to the mining sector) and average

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energy productivity below the national average. A priori it is expected that increases in initial energy productivity, the share of renewable energy in electricity generation, the capital to

labour ratio and fossil fuel prices increases the likelihood of a state being in a high energy

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productivity club. An increase in the share of the industrial sector is expected to decrease the likelihood of a state being in a high energy productivity club. The variable definitions and

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data sources are provided in the Appendix (Table A1).

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On average, GSP per capita is highest in the Northern Territory and lowest in Tasmania (Table 5). The share of electricity generated from renewable energy sources varies

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considerably from a high of 92% in Tasmania to a low of 0.97% in the Northern Territory. Except for Tasmania, most states use coal as the primary fuel in producing electricity.

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Tasmania is unique in that almost all of its electricity is sourced from hydro power. Western Australia has the highest share of industry (due to mining) while New South Wales has the

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lowest. Western Australia and Tasmania provide an interesting comparison. Western Australia has a high per capita GSP, high share of industry output, and low share of electricity generated from renewable energy. In comparison, Tasmania has a low GSP per

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https://www.energy.gov.au/sites/default/files/australian_energy_update_2018.pdf https://www.abs.gov.au/AUSSTATS/[email protected]/mediareleasesbyReleaseDate/DE8BC8AC9DB69E00CA25833 6000CF536?OpenDocument 7

18

capita, low share of industrial output, and high share of electricity generated from renewable energy. On average, Victoria has the highest capital to labour ratio while Western Australia has the lowest. Queensland has the lowest average automobile fuel prices while Tasmania has the highest.

The logit regression results for the determinants of club convergence are reported in Table 68. The estimated coefficients from the logit regression are expressed as odds ratios. While

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probabilities can take on values between 0 and 1, odds ratios range between 0 (event will never happen) and infinity (event is certain to happen). Odds ratios larger than one indicate the odds of Club = 1 (the high energy productivity club) increase. A value of 1.173, for

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example, indicates that a one-unit increase in initial energy productivity (EP_1) increases the odds of being in the high energy productivity club by 1.173 (or 17.3%). Some of the variables

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are highly correlated so that different model specifications are estimated.

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The estimated odds ratio for initial energy productivity is positive, greater than one, and statistically significant for all specifications, showing that a one unit increase in initial energy productivity increase the odds of a state being in the high energy productivity club by a factor

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between 1.093 and 2.329 (Table 6). This result agrees with the findings of Bhattacharya et al. (2018) and Yu et al. (2015) who find that initial energy productivity is important in

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explaining energy productivity club membership. The estimated odds ratio for IND is

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positive, less than one, and statistically significant, indicating that a one unit increase in the industrial share of GSP reduce the odds of a state being in Club 1 by between 40% and 60%. This results is consistent with the findings of other researchers who find that increases in industry share reduce energy productivity (Atalla and Bean, 2017; Bhattacharya et al., 2018;

8

A reviewer asked about complications arising from using possibly non-stationary variables in a logit model. Park and Phillips (2000) derive the asymptotic theory for time series binary choice models with integrated explanatory variables. They prove that the limit distribution theory of the maximum likelihood estimator is mixed normal and standard methods of inference are valid.

19

Fisher-Vanden et al., 2004; Herrerias et al., 2013; Hübler and Keller, 2010; Yu et al., 2015). The estimated odds ratio for REN is positive, less than or close to one, and statistically significant signifying that increases in REN reduces the odds of a state belonging to Club 1. This result is somewhat surprising given that a fuel mix with a higher proportion of renewable energy should have a positive impact on membership in the high energy productivity club. One contributing factor may be that Tasmania has a high share of renewable energy in electricity production also has a low energy productivity. The estimated

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odds ratio for the capital to labour ratio is larger than one but statistically significant in only one model. In model 4, a one unit increase in the capital to labour ratio increases the odds of being in the high energy productivity club by a factor of 1.071. These results are in agreement

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with Ma et al. (2018) who studied energy productivity in the Australian construction industry. The estimated odds ratio for the automobile fuel price variable shows that a one unit increase

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in automobile fuel prices increases the odds of being in the high energy productivity club by a

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factor of 1.774. Higher energy prices have been found to increase energy productivity by other authors (Atalla and Bean, 2017; Zhang and Broadstock, 2016). The variable GSPpc includes a squared term and this makes interpretation of the odds ratio very difficult. This

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variable is further discussed below in the graphical analysis of the marginal effects.

Average marginal effects provide a more detailed look at the impact of the explanatory

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variables on club memberships (Table 7). Average marginal effects are positive and significant for initial energy productivity, the capital to labour ratio (in model 4), and automobile fuel prices. Average marginal effects are negative for GSPpc, IND and REN. While it is tempting to interpret the values in Table 7 as the impact on the probability of Club 1 resulting from a one-unit increase in an explanatory variable, this may not be accurate because the logit model is nonlinear and for continuous variables, the marginal effects 20

measure the instantaneous rate of change. Rather than thinking in terms of instantaneous rates of changes, it is more informative to see how the probability of belonging to the high energy productivity club varies when an explanatory variable takes on specific values and other explanatory variables are set to their sample mean values. As an example, Figure 3 presents marginal plots based on Model 3 in Table 7. Model 3 is chosen because, based on BIC, it has the best fit.

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For an initial energy productivity value of 170 ($ million/PJ), the probability of a state

belonging to the high energy productivity club (Club 1) is about 0% (Figure 3). Once initial

energy productivity reaches 185, the probability of being in the high energy productivity state

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is close to 100%. This result is consistent with the economic growth literature that stresses the

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importance of initial conditions in determining club convergence. The probability of Club 1 is close to 100% for per capita GSP up to $55,000. Further increases in per capita GSP lower

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the probability of being the high energy productivity club and by $60,000 the probability has decreased down to 0%. For the Australian economy, this result is somewhat troublesome

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because higher per capita income is associated with a lower probability of a state belonging to the high energy productivity club. This result can in part be explained by the fact that some

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of the wealthiest states in Australia are heavily engaged in energy-intensive industries, such as mining. Energy productivity increases are not keeping up with the increases in per capita

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GSP. For low industry shares the probability of being in the high energy productivity club is close to 100%. As industry share increases past 15%, the probability of being in the high energy productivity club decreases towards 0%. The pattern for REN is similar to that of IND and somewhat surprising given that increases in renewable energy should help to increase energy productivity. One plausible explanation for this result is that the high income states and territories are dependent on the mining sector and have low electricity generation from 21

renewable energy sources. For automobile fuel prices below 50, the probability of belonging to the high energy productivity club is essential zero. As automobile fuel prices increase past 50 the probability of belonging to the high energy productivity club increase and after 80 this

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probability is 100%.

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Figure 3. Marginal plots for model 3 in Table 7.

6. Australian energy productivity convergence in 2030 In this section, the path of energy productivity at the state and territory level is forecast to 2030 in order to see what energy productivity convergence may look like in the future. There are several interesting questions to consider. Will there be energy productivity convergence at the national level? Will there be energy productivity club convergence? Will the current 22

energy productivity clubs be temporary? In order to address these questions, a number of assumptions are made. The analysis is conducted assuming there are no significant policy changes or external shocks to the economy over the period of time the forecasts are made. In other words, we assume a business-as-usual (BAU) scenario. Our approach to forecasting energy productivity is similar to the approach used by Zhang and Broadstock (2016) to forecast Chinese energy intensity. First, the state-level energy productivity data are detrended using a linear time trend regression. Forecasts are made from 2016 to 2030 of the trend

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component. Energy productivity is forecast to 2030 to match the NEPP target year. Second, the detrended component is forecasted using simple exponential smoothing. Third, the forecasted trend component is added to the forecasted detrended component to form a

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forecast of energy productivity. Figure 4 shows the forecasted energy productivity values for each state. Energy productivity is predicted to increase up to 2030, but the rate of increase

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varies by state.

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Figure 4. Energy productivity forecasts; 1990-2030.

The club convergence test for the expanded data set, 1990 to 2030, reveals three convergent clubs and one divergent club (Table 8). The first club consists of New South Wales and South Australia (Figure 5). These two states were members of the previous high energy productivity club (Table 3). The second club consists of Victoria and the Northern Territory. The third club consists of Tasmania and Western Australia. Queensland is not a member of a

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converging club. Comparing the results in Table 8 with those in Table 3, we see there is an

increase in clubs and the formation of one non-converging group. Under the BAU scenario,

energy productivity convergence does not occur at the national level. The clubs have different

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transition paths and different equilibrium values of energy productivity (Figure 6). Club 1 has

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further evidence of merging (Table 9).

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the highest energy productivity, followed by Club 2 and Club 3. Club merging tests reveal no

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Western Australia

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Northern Territory Queensland

South Australia New South Wales Australia Victoria

Tasmania

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Figure 5. Club convergence map for expanded data set. Club 1 (NSW, SA), Club 2 (VIC, NT), Club 3 (TAS, WA), NC (QLD).

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It is also interesting to compare forecasted energy productivity in 2030 with actual energy

productivity in 2015. Ranked in order of largest to smallest percentage increases in energy

productivity over the period 2015 to 2030: South Australia (22%), Victoria (22%), Western

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Australia (22%), Queensland (20%), New South Wales (17%), Northern Territory (16%), and Tasmania (14%). Under BAU, none of these states come at all close to achieving the 40%

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increase in energy productivity that the NEPP is calling for. In order to reach the 40%

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increase in energy productivity by 2030, the energy productivity in each state will, on average, need to double over the BAU scenario. This means that if the NEPP is going to

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achieve its target of a 40% increase in energy productivity by the year 2030, it will need to

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introduce policy aimed at doubling current levels of energy productivity.

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Figure 6. Relative energy productivity transition paths. Club 1 (NSW, SA), Club 2 (VIC, NT), Club 3 (TAS, WA).

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7. Concluding remarks and policy implications

The Australian economy has experienced strong economic growth over the past 25 years, but

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energy consumption and CO2 emissions are higher than the OECD average. The Australian government has recently initiated a National Energy Productivity Plan that calls for a 40%

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increase in energy productivity before 2030. Understanding energy productivity dynamics at the state and territory level is essential for the success of this program. This gap in the literature is filled by analysing energy productivity club convergence for the Australian states and territories. Empirical evidence is presented showing that Australian states and territories converge to two energy productivity clubs. The high energy productivity club consists of

27

New South Wales, South Australia, and Victoria. The second club consists of Northern Territory, Queensland, Tasmania, and Western Australia. In analysing the determinants of club convergence, initial energy productivity and automobile fuel prices are important determinants of higher energy productivity. These results are consistent with theoretical expectations. A reduction in the industrial sector of the economy would help increase energy productivity. Reducing the industrial sector is going to be challenging because some states with high per capita income are heavily dependent on

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mining. The relationship between energy productivity and per capita income is complicated by the fact that the highest income per state occurs in mining regions where renewable electricity generation is low.

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The paths of Australian state and territory energy productivity are forecast to 2030. Under a

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business-as-usual scenario, three convergent clubs emerge and one state, Queensland, does not converge. The high energy productivity club in 2030 consists of New South Wales and

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Victoria. Under the BAU scenario, energy productivity in Australia will grow by 20% between 2015 and 2030. Therefore, in order to meet the NEPP target of a 40% increase in

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energy productivity before 2030, Australia will have to find a way to increase energy productivity 20% above the BAU scenario. The analysis finds that automobile fuel prices and

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industry structure are important drivers in achieving high energy productivity. One important energy policy recommendation is for Australia to make significant changes in fuel mix and

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industry structure as a way of meeting the NEPP target. Since the states and territories do not converge to a common equilibrium, a common energy policy will be sub-optimal. At the federal level, Australia’s energy policy is driven by the Prime Minister’s ideology on climate change and there is a lack of bipartisan support for national targets on CO2 emissions reductions and renewable energy adoption (Cheung and Davies, 2017). At the state level, the situation is somewhat better as most of the states have instituted their own renewable energy 28

targets: Victoria (40% by 2025), Queensland and Northern Territory (each 50% by 2030), South Australia (50% by 2025), and New South Wales (net-zero state-wide emissions by 2050). While state-level targets for renewable energy are desirable, bipartisan federal support is greatly needed if Australia is to make significant improvements in energy productivity.

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Acknowledgements: We thank two anonymous reviewers for their helpful comments which led to an improved version of this paper. Mita Bhattacharya acknowledges financial support from the Monash Energy Materials and Systems Institute (MEMSI).

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References

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Apergis N, Christou C.2016. Energy productivity convergence: New evidence from club converging. Appl. Econ. Letters 23 (2), 142-145. Atalla T, Bean P.2017. Determinants of energy productivity in 39 countries: An empirical investigation. Energy Econ. 62 (Supplement C), 217-229. Australian Government.2017. National energy productivity plan 2015–2030: Annual report 2017 http://coagenergycouncil.gov.au/sites/prod.energycouncil/files/publications/documents/NEPP%20Ann ual%20Report%202017-web.pdf. Australian Government Department of the Environment and Energy.2017. Australian energy update 2017 https://www.energy.gov.au/sites/g/files/net3411/f/energy-update-report-2017.pdf. Bartkowska M, Riedl A.2012. Regional convergence clubs in Europe: Identification and conditioning factors. Econ. Modelling 29 (1), 22-31. Bhattacharya M, Inekwe JN, Sadorsky P, Saha A.2018. Convergence of energy productivity across Indian states and territories. Energy Econ. 74, 427-440. Cheung G, Davies PJ.2017. In the transformation of energy systems: What is holding Australia back? Energy Policy 109, 96-108. Du, K., Lin, B. 2017. International comparison of total-factor energy productivity growth: A parametric Malmquist index approach, Energy 118, 481-488. Falk J, Settle D.2011. Australia: Approaching an energy crossroads. Energy Policy 39 (11), 68046813. Fisher-Vanden K, Jefferson GH, Jingkui M, Jianyi X.2006. Technology development and energy productivity in China. Energy Econ. 28 (5-6), 690-705. Fisher-Vanden K, Jefferson GH, Liu H, Tao Q.2004. What is driving China’s decline in energy intensity? Resource Energy Econ. 26 (1), 77-97. Galor O.1996. Convergence? Inferences from theoretical models. The Economic Journal, 1056-1069. Herrerias MJ, Cuadros A, Orts V.2013. Energy intensity and investment ownership across Chinese provinces. Energy Econ. 36 (0), 286-298. Herrerias MJ, Liu G.2013. Electricity intensity across Chinese provinces: New evidence on convergence and threshold effects. Energy Econ. 36, 268-276. Hodrick RJ, Prescott EC.1997. Postwar us business cycles: An empirical investigation. J Money Credit Bank 29 (1), 1-16. Hu, X. & Liu. C. 2016. Energy productivity and total-factor productivity in the Australian construction industry, Architectural Science Review, 59:5, 432-444. Hübler M, Keller A.2010. Energy savings via FDI? Empirical evidence from developing countries. Environ. Devel. Econ. 15 (1), 59-80. Islam N.2003. What have we learnt from the convergence debate? J. Econ. Surveys 17 (3), 309-362. Ivanovski, K., Churchill, S. A., and Smyth, R. (2018). A club convergence analysis of per capita energy consumption across Australian regions and sectors. Energy Economics, 76, 519-531. Kim YS.2015. Electricity consumption and economic development: Are countries converging to a common trend? Energy Econ. 49, 192-202. Kinrade P.2007. Toward a sustainable energy future in Australia. Futures 39 (2), 230-252. Liddle B.2010. Revisiting world energy intensity convergence for regional differences. Applied Energy 87 (10), 3218-3225. Ma, L., Hosseini,M.R., Jiang,W., Martek,I., Mills,A. 2018. Energy productivity convergence within the Australian construction industry: A panel data study, Energy Economics, 72, 313-320. OECD. Oecd economic surveys: Australia. Organisation for Economic Co-operation and Development: OECD Publishing, Paris; 2017. Park, J., Y. and Phillips, P.C.B. 2000. Nonstationary binary choice. Econometrica, 68, 1249-1280. Parker S, Liddle B.2017a. Analysing energy productivity dynamics in the OECD manufacturing sector. Energy Econ. 67, 91-97. Parker S, Liddle B.2017b. Economy-wide and manufacturing energy productivity transition paths and club convergence for OECD and non-OECD countries. Energy Econ. 62, 338-346. Pears AK.2007. Imagining Australia's energy services futures. Futures 39 (2-3), 253-271. 30

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Phillips PCB, Sul D.2007. Transition modeling and econometric convergence tests. Econometrica 75 (6), 1771-1855. Phillips PCB, Sul D.2009. Economic transition and growth. J. Appl. Econometrics 24 (7), 1153-1185. Quah DT.1996. Twin Peaks: Growth and convergence in models of distribution dynamics. The economic journal, 1045-1055. Shahiduzzaman M, Alam K. 2013. Changes in energy efficiency in Australia: A decomposition of aggregate energy intensity using logarithmic mean Divisia approach. Energy Policy 56, 341-351. Voigt S, De Cian E, Schymura M, Verdolini E.2014. Energy intensity developments in 40 major economies: Structural change or technology improvement? Energy Econ. 41, 47-62. Yu Y, Zhang Y, Song F.2015. World energy intensity revisited: A cluster analysis. Appl. Econ. Letters 22 (14), 1158-1169. Zhang D, Broadstock DC.2016. Club convergence in the energy intensity of China. Energy J. 37 (3).

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Table 1 A summary on recent literature focusing on energy productivity/intensity using the club convergence approach. Variable (s) Electricity-intensity

Time period (s) 2003-2009

Electricity consumption/intensity

1971–2009

Yu et al. (2015)

Energy intensity

1971 - 2010

Apergis and Christou (2016) Zhang and Broadstock (2016) Parker and Liddle (2017a)

Energy productivity

1972 to 2012

Findings There is a dominant club and other smaller clubs in Chinese provinces Convergence of electricity intensity among 109 countries, but not for per capita electricity consumption Convergence clubs exist across panel of 109 countries Convergence-clubs across 31 countries

Energy intensity

1995 - 2008

Convergence clubs exist in China

Manufacturing energy productivity

1980 to 2009

Parker and Liddle (2017b)

Energy productivity

1971 to 2008

Bhattacharya et al. (2018)

Energy productivity

1988- 2016

Presence of clubs in OECD which are influenced by investment and technology structure Convergence-clubs for economy-wide and manufacturing energy productivity across 33 countries Club convergence across Indian states and territories. Industry structure and initial energy productivity are relevant for convergence

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Note: Energy intensity is the inverse of energy productivity.

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Author (s) Herrerias and Liu (2013) Kim (2015)

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Table 2 Descriptive statistics for Australian energy productivity (chained $ million/PJ). State

N

Mean

SD

Minimum

Maximum

%Δ

NSW

26

272.308

33.337

229

350

42.420

NT QLD

26

200.077

34.415

159

264

36.770

26

186.885

21.623

162

229

27.370

SA

26

233.923

36.357

187

304

43.050

TAS

26

207.308

19.091

162

242

39.720

VIC

26

205.885

31.503

160

264

47.000

WA

26

189.962

24.637

161

239

34.790

1990

7

179.429

25.566

161

229

2015

7

265.571

47.155

213

350

Year

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182 213.764 40.165 159 350 1990-2015 Note: N is the number of observations, SD=standard deviation. Data are sourced from https://www.energy.gov.au/publications/australian-energy-update-2017, Table B.

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Table 3 The findings of the log-t regression test for Australian states and territories. Clubs All Club 1 Club 2

States

log(t) coefficient -0.900 0.103 1.408

NSW, SA and VIC NT, QLD, TAS and WA

t-statistic -6.349** 0.575 2.366

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Note: ** denotes p < 5% level of significance.

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Table 4 Club merging test.

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log(t) Club1+2 coefficient -0.900 t-statistic -6.349** Note: ** denotes p < 5% level of significance.

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Table 5 Descriptive statistics for explanatory variables mean sd min max

Northern Territory

mean sd min max

72.00 12.08 55.40 94.60

0.97 0.36 0.33 1.92

21.05 3.59 14.43 28.50

60.54 18.36 37.28 98.04

70.11 22.17 44.83 109.95

Queensland

mean sd min max

52.64 9.27 38.51 64.74

2.99 1.43 0.90 6.53

15.44 1.58 12.69 17.37

64.52 17.77 40.99 97.09

64.91 23.37 40.00 103.50

South Australia

mean sd min max

49.18 7.11 38.19 58.39

7.98 12.72 0.33 40.69

13.28 1.61 10.73 15.42

76.58 13.18 56.23 99.91

69.83 21.13 44.83 103.15

Tasmania

mean sd min max

43.01 5.78 34.17 49.88

92.01 5.80 81.08 98.92

13.08 3.14 8.41 18.26

78.01 13.76 59.32 100.84

70.56 21.38 46.23 106.25

Victoria

mean sd min max

53.37 7.78 40.01 61.60

3.26 2.91 0.14 11.63

14.60 3.98 9.25 20.49

81.01 15.69 55.93 100.63

70.00 21.60 46.33 103.98

mean sd min max

69.99 15.35 48.28 98.55

2.70 1.65 0.55 6.76

26.70 1.66 23.41 30.78

56.07 17.95 36.50 96.48

69.39 21.29 45.45 103.98

IND 10.47 1.16 8.27 12.52

KL 80.43 13.58 59.32 98.92

Afuelprice 68.95 22.15 43.95 103.25

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Western Australia

REN 8.39 2.38 5.28 14.19

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New South Wales

GSPpc 58.81 6.74 47.43 67.51

Total

mean 57.00 16.90 16.37 71.02 69.11 sd 13.76 31.33 5.79 18.31 21.59 min 34.17 0.14 8.27 36.50 40.00 max 98.55 98.92 30.78 100.84 109.95 Gross State Product per capita, GSPpc, (chained $1000 per person); renewable electricity production as a share of total electricity production, REN, (%); industry share of GDP, IND, (%); capital labour ratio, KL; automobile fuel prices, Afuelprice. Data sources are listed in the Appendix (Table A1).

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Table 6 The determinants of club convergence (1) (2)

(4)

2.329*** (4.45)

1.199*** (6.54)

EP_1

1.173*** (4.09)

GSPpc

2.404 (1.59)

1132.4*** (3.16)

3.661*** (2.69)

GSPpc * GSPpc

0.990* (-1.92)

0.920*** (-3.32)

0.988*** (-2.85)

IND

0.536*** (-4.81)

0.591*** (-4.25)

REN

0.898*** (-6.62)

0.925*** (-6.31)

Afuelprice

154 45.04*** 97.10

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182 46.72*** 99.33

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1.071*** (4.15)

1.013 (0.75)

N χ2 BIC

0.399*** (-3.03)

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KL

1.093*** (4.53)

(3)

1.774*** (3.16) 182 23.40*** 69.06

154 53.09*** 99.56

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Exponentiated coefficients (odds ratio); t statistics in parentheses The t statistics are calculated using robust standard errors. * p < 0.10, ** p < 0.05, *** p < 0.01 Logit regression results are reported where the dependent variable is 1 (high energy productivity club) and 0 other wise. χ2 is a Wald statistic testing all slope coefficients jointly equal to zero. BIC is the Bayesian Information Criteria.

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Table 7 Average marginal effects for the determinants of club convergence (1) (2) (3) EP_1 0.00990*** 0.00638*** 0.0240*** (0.00229) (0.000989) (0.00437) GSPpc

-0.0121*** (0.00212)

IND

-0.0387*** (0.00427)

-0.0377*** (0.00526)

-0.0261*** (0.00994)

REN

-0.00663*** (0.000576)

-0.00558*** (0.000720)

-0.0176*** (0.00636) 0.00535*** (0.000871)

0.000896 (0.00120)

0.0163*** (0.00142) 182

Afuelprice N

182

154

154

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Marginal effects; Standard errors in parentheses *** p < 0.01

-0.00497*** (0.00192)

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KL

-0.0383*** (0.00504)

(4) 0.0142*** (0.00159)

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Table 8 The log-t regression test for future energy productivity across the states. Clubs All Club 1 Club 2 Club 3 NC

States

log(t) coefficient -1.310 -0.118 1.841 6.356

NSW, SA VIC, NT TAS, WA QLD

t-statistic -27.494** -0.591 2.734 3.049

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Note: ** denotes p < 5% level of significance. NC=Non-convergence

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Table 9 Club merging test. log(t) coefficient t-statistic

Club1+2 -0.550 -2.622**

Club2+3 -1.165 -4.227**

Club3+NC -0.998 -3.845**

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Note: ** denotes p < 5% level of significance.

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