A club convergence analysis of per capita energy consumption across Australian regions and sectors

Accepted Manuscript A club convergence analysis of per capita energy consumption across Australian regions and sectors

Kris Ivanovski, Sefa Awaworyi Churchill, Russell Smyth PII: DOI: Reference:

S0140-9883(18)30437-7 https://doi.org/10.1016/j.eneco.2018.10.035 ENEECO 4206

To appear in:

Energy Economics

Received date: Revised date: Accepted date:

18 July 2018 29 September 2018 25 October 2018

Please cite this article as: Kris Ivanovski, Sefa Awaworyi Churchill, Russell Smyth , A club convergence analysis of per capita energy consumption across Australian regions and sectors. Eneeco (2018), https://doi.org/10.1016/j.eneco.2018.10.035

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ACCEPTED MANUSCRIPT

A club convergence analysis of per capita energy consumption across Australian regions and sectors

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Kris Ivanovski Department of Economics Monash University, VIC 3800, Australia Email: [email protected]

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Sefa Awaworyi Churchill School of Economics, Finance & Marketing RMIT University, VIC 3000 Australia Email: [email protected]

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Russell Smyth Department of Economics Monash University, VIC 3800, Australia Email: [email protected] Abstract

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We examine convergence in per capita energy consumption across nine sectors and seven states and territories in Australia over the period 1990-2016 using the Phillips-Sul club

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convergence approach. We find evidence of multiple convergence clubs for aggregate energy consumption per capita and for energy consumption per capita in eight of the nine sectors with full club convergence in the case of electricity. The presence of multiple equilibria for

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most sectors reflect that states with similar features in terms of climate, economic activities and population size, among others, tend to exhibit similar energy consumption patterns. Our

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results highlight the need to tailor policies designed to reduce energy consumption in specific sectors to the consumption convergence paths unique to particular clusters of states. Keywords: Energy consumption, club convergence/clustering, Australia. JEL Codes: C50, Q40

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ACCEPTED MANUSCRIPT 1. Introduction Australia is considered to be one of the most developed countries in the world and, according to the United Nation’s recent Human Development Index, it ranks second in the world for human development (UNDP, 2016). Australia’s path to economic development has been characterized by strong growth in the manufacturing sector since World War II with an

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emphasis on energy intensive activities. As such, it has been argued that the energy sector is

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one of Australia’s major industries and an important contributor to economic development

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(see, e.g., Narayan & Smyth, 2005a). However, since the 1990s there has been significant overhauls to the energy sector, which has included the introduction of a national wholesale

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electricity market, the separation of vertically integrated companies and establishment of retail competition in the electricity market (see e.g., Abbott, 2002; Apergis et al., 2017a;

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Apergis & Lau, 2015; Narayan & Smyth, 2005b; Valadkhani et al., 2018). While energy consumption in Australia has traditionally been high, it has remained

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relatively stable in recent years. To be concrete, total energy consumption in Australia in

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1990 was 87 Million Tonnes of Oil Equivalent (Mtoe) and this figure rose to 127 Mtoe in 2008, but it has remained relatively stable at this rate since with 129 Mtoe consumed in 2017

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(Enerdata, 2018). Energy consumption in the Australian residential sector in 1990 was about 299 petajoules, which had increased to 402 petajoules by 2008, but there is only a slight

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projected increase to about 467 petajoules by 2020 (DEWHA, 2016). We seek to understand the convergence patterns in Australia’s energy per capita consumption given these trends in energy consumption. The concept of convergence was originally tested in the context of the Solow (1956, 1957) model, which predicts that permanent economic growth is attained only through technological progress and that economies tend to converge to steady state equilibrium in the long run. Many studies have tested for per capita income convergence across different groups of countries and regions

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ACCEPTED MANUSCRIPT (see, e.g., Ben-David, 1993; Quah, 1996; Slaughter, 1997). This idea of convergence has more recently been explored in the context of the energy and environmental economics literature. This literature includes studies on convergence of pollutants (see, e.g., Aldy, 2006; Ezcurra, 2007b; Herrerias, 2013; Nguyen Van, 2005; Romero-Ávila, 2008; Strazicich & List, 2003; Westerlund & Basher, 2008) and energy consumption (see, e.g., Fallahi, 2017; Fallahi

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& Voia, 2015; Mohammadi & Ram, 2012; Payne et al., 2017a, Solarin et al., 2018).

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Much of the existing literature that has examined energy convergence has done so in a

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cross-country context or using data on groups of countries (see, e.g., Anoruo & DiPietro, 2014; Fallahi, 2017; Meng et al., 2013; Mishra & Smyth, 2014). These studies have further

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focussed mostly on unit root tests to examine stochastic convergence, finding evidence in support of convergence in energy consumption. Recently, there have been several calls for

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sub-national evidence on convergence for specific countries focussing on states within countries and disaggregated energy across various sectors (see Mishra & Smyth, 2014; Smyth

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& Narayan, 2015). Payne et al. (2017b) respond to this call and apply unit root tests to

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examine stochastic conditional convergence in energy consumption across states in the US. Similarly, in the context of Australia, Mishra and Smyth (2017) respond to the call and

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examine stochastic convergence in energy consumption at the sectorial level. Nonetheless, as Mishra and Smyth (2017, p. 402) suggest, “future research could examine

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energy convergence at the province or state level within countries for countries other than the US”. We take up this suggestion by examining club convergence in per capita energy consumption for nine sectors across seven Australian states and territories over the period 1990-2016. Australia represents an important case study given its patterns in energy consumption and the series of energy reforms that have occurred since the 1990s. On a per capita basis, Australia is the eighth largest emitter of carbon dioxide in the world (World Resources Institute, 2014) with just under three quarters of carbon emissions due to energy

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ACCEPTED MANUSCRIPT consumption (Department of Climate Change, 2010). Despite reaching a tipping point, consistent with the environmental Kuznet’s Curve (EKC) hypothesis (Shahbaz et al., 2017), carbon emissions are still on the rise (Slezak, 2017). This underpins the need to understand differences in regional patterns of convergence in energy consumption across sectors. The only study of which we are aware that examines energy consumption convergence in

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Australia is Mishra and Smyth (2017). Our study differs from Mishra and Smyth (2017) in

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several important aspects. First, Mishra and Smyth (2017) examine convergence across

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sectors in Australia. Our dataset, on the other hand, is further disaggregated, thus allowing us to investigate energy convergence not only at the sectorial levels but across regions as well.

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The use of further disaggregated data provides the advantage of allowing us to pinpoint convergence in energy consumption by sector and region. Second, while Mishra and Smyth

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(2017) employ the Residual Augmented Least Squares-Lagrange Multiplier (RALS-LM) methodology to examine stochastic convergence, we examine club convergence using the

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Phillips and Sul (2007) log 𝑡 test. Using cluster analysis to test for divergence or convergence

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in energy consumption across regions and sectors, allows us to examine the transition paths of energy consumption as well as how they evolve over time.

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The log 𝑡 test approach offers a number of potential benefits over other approaches to study convergence, including that it is based on a general time-varying nonlinear factor

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model that takes into account possible transitional heterogeneity (Panopoulou & Pantelidis, 2009). In the presence of heterogeneity, standard cointegration and unit root tests are not suitable for testing convergence (Phillips & Sul, 2007). Thus, given the significant structural changes associated with Australian energy reforms over the years, the log t test is well-suited to take into account the heterogeneity in our dataset across regions and sectors as well as over time. Our approach is robust to heterogeneity and also to the stationarity properties of the series, and, as discussed by Panopoulou and Pantelidis (2009), the test can be interpreted as

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ACCEPTED MANUSCRIPT an asymptotic cointegration test which does not suffer from the small sample properties of conventional unit root and cointegration tests. Lastly, the log t test allows us to endogenously determine the number of groups of states, as well as sectors, that belong to each convergence club, which is important for targeting policies to reduce energy consumption. Our results indicate the existence of multiple convergence clubs of per capita energy

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consumption for eight of the nine sectors across states. This finding carries important

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implications for policy given that the existence of clubs suggests that a common or single

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energy policy may not be effective across Australian states and sectors. As different clubs of states and sectors exhibit different energy convergence patterns, it is important to design

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relevant policies to influence energy consumption identified for groups in each club. We contribute to the literature on convergence in energy consumption in several important

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ways. One is that we examine convergence in energy consumption disaggregated by state and sector. This is important because convergence in aggregate energy consumption across

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countries can mask subnational regional and sector differences. A second is that we

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contribute to the small literature that has examined convergence in energy consumption at the sub-national level outside of the US. This is important because energy consumption patterns

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in the US are not necessarily typical of other countries. A third is that we utilise the methodological approach developed by Phillips and Sul (2007) which has several advantages

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over previous convergence tests such as 𝛽- and 𝜎-convergence. Specifically, it allows for strict testing of convergence clubs and, thus, estimation of convergence transition paths in relation to common trends of the identified group(s). Convergence tests are applied to data sets on sub-sector energy consumption comprising of all states. Moreover, application of the Phillips and Sul (2007) methodology in the energy literature is relatively new and does not rely on strong assumptions on trend or stochastic stationarity in the data generating process.

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ACCEPTED MANUSCRIPT To this point, the only study that has used the Phillips and Sul (2007) method to test for convergence in some form of energy consumption is Herrerias et al. (2017), who test for club convergence on residential energy consumption across Chinese provinces. In using the Phillips and Sul (2017) approach, however, we contribute to a broader literature that has tested for club convergence in other energy-related variables across countries and sub-

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national regions. This literature includes Ulucack and Apergis (2018) (environmental

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degredation), Apergis and Payne (2017) (carbon dioxide emissions), Apergis and Christou

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(2016), Bhattacharya et al. (2018), Parker and Liddle (2017a, 2017b) (energy productivity), Kim (2015), Yu et al. (2015) and Zhang and Broadstock (2016) (energy intensity).

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The remainder of our study is structured as follows. The next section provides an overview of the related literature. Sections 3 and 4 describe the methodology and data, respectively,

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while Section 5 presents the empirical results. Section 6 concludes. 2. Related Literature

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The energy-related literature on convergence has produced several strands. One of these

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deals with environmental convergence and has mostly focused on convergence in carbondioxide emissions. List (1999) pioneers this field with an examination of convergence in

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various pollutants across US regions while Strazicich and List (2003) was the first to examine convergence in carbon-dioxide emissions using a sample of OECD countries. Since then,

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several studies have emerged focusing on various groups of countries, which have produced mixed results. For instance, studies such as Romero-Ávila (2008), Westerlund and Basher (2008) and Awaworyi Churchill et al. (2018) are among those that report convergence across different groups of countries, while studies such as Aldy (2006), Nguyen Van (2005) and Panopoulou and Pantelidis (2009), among others, find evidence of divergence. Apergis and Payne (2017) report evidence of multiple convergence clubs in carbon dioxide emissions.

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ACCEPTED MANUSCRIPT A second strand of studies in energy economics has explored convergence in energy intensity, defined as energy consumption or usage per unit output. Markandya et al. (2006) examine the distributional patterns of energy intensity between 12 Eastern European transition countries and the EU15 countries. They present evidence which suggests that the energy intensity of transition countries show significant convergence towards the levels in

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EU15 countries. Ezcurra (2007a) and Le Pen and Sévi (2010) examine convergence in a

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broader cross-country context focussing on 98 and 97 countries, respectively, over a similar time span. However, while Ezcurra (2007a) presents evidence in support of 𝜎-convergence,

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Le Pen and Sévi (2010) adopt Pesaran’s pairwise comparison method to examine stochastic

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convergence and do not find evidence to support convergence in the full sample. Other studies including Liddle (2010), Duro and Padilla (2011), Kim (2015), Yu et al.

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(2015) and Zhang and Broadstock (2016) have also focussed on different samples as well as methods and reported mixed evidence on the convergence on energy intensities across

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countries and, within countries, across regions. A related strand of literature focuses on

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energy productivity, broadly defined as the inverse of energy intensity (see, e.g., Apergis & Christou, 2016; Bhattacharya et al., 2018; Ma et al., 2018; Miketa & Mulder, 2005; Mulder &

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De Groot, 2007; Parker & Liddle, 2017a, 2017b). This literature, which has also used a divergent range of empirical methods and samples, has produced mixed results.1

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A third strand of literature, which is more closely related to our study, examines convergence in energy consumption including disaggregated components of energy such as electricity, fossil fuels and renewable energy among others. Maza Villaverde (2008) examine convergence in per capita electricity consumption using cross-country data on 98 countries from 1980 to 2007. They adopt multiple approaches to examine convergence and conclude that there is weak convergence in electricity consumption. Mohammadi and Ram (2012)

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Bhattacharya et al. (2018) Table 1 contains a recent review of this literature

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ACCEPTED MANUSCRIPT examine cross-country convergence in electricity consumption and report strong evidence of convergence in most cases. Solarin et al. (2018) uses fractional integration tests to examine stochastic convergence in renewable energy consumption in 27 OECD countries, finding evidence of stochastic convergence for just under half the sample. As discussed in the introduction, most studies that examine energy convergence tend to do

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so in a cross-country context (see Anoruo & DiPietro, 2014; Fallahi, 2017; Fallahi & Voia,

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2015; Le et al., 2017; Meng et al., 2013; Mishra & Smyth, 2014). However, a few studies

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have pointed to the importance of focussing on single countries and thus used disaggregated datasets for single countries (see, e.g., Herrerias et al., 2017; Mishra & Smyth, 2017;

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Mohammadi & Ram, 2017; Payne et al., 2017a, 2017b).

Most of these studies focus on the US. Motivated by state and federal policies to curb the

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usage of fossil fuel in the US, Payne et al. (2017a) adopts multiple approaches, including 𝛽and 𝜎-convergence tests as well as LM and RALS-LM unit root tests, to examine

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convergence in per capita renewable energy consumption across US states. They find

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evidence of both 𝛽- and 𝜎-convergence, as well as evidence of stochastic convergence for the majority of US states. In a related study, Apergis et al. (2017b) focus on CO2 intensity across

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US states and find evidence of 𝛽- and 𝜎- convergence, but no evidence of stochastic convergence. Payne et al. (2017b), using LM and RALS-LM unit root tests, examine

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stochastic convergence of per capita fossil fuel consumption across US states, and find evidence in support of stochastic convergence. Mohammadi and Ram (2017) test for convergence in per capita energy consumption across US states using various parametric and non-parametric approaches and report mixed results. Lean et al (2016) test for stochastic conditional convergence in aggregated and disaggregated petroleum consumption at the sector level in the US using a GARCH unit root test and find evidence of convergence for about half of the series considered. Burnett and Madariaga (2017) augment a neoclassical

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ACCEPTED MANUSCRIPT growth model to include the accumulation of physical capital and energy consumption. Applying this model to a panel of 50 US states, they find that both renewable and nonrenewable energy use has a positive impact on per capita economic growth and is an important determinant in the convergence process. Karimu et al. (2017) study energy intensity in 14 Swedish industrial sectors over the period 1990-2008 using non-parametric

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techniques. These authors find that energy prices play a significant role in determining

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convergence in energy intensity across Swedish industrial sectors.

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To summarize, despite calls for studies to examine convergence in energy consumption using disaggregated data for single countries, there is a paucity of such studies. These studies

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use either disaggregated data by region (province or state) or sector. There are no studies that use disaggregated data by region and sector. Most of the few studies that do exist examine

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convergence across US states, with Herrerias et al. (2017) and Mishra and Smyth (2017) being exceptions. Hence, there is a lack of evidence for other countries. Finally, most of these

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studies have used either 𝛽- and 𝜎-convergence tests, LM and RALS-LM unit root tests or

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fractional integration tests. There is very little research that has tested for club convergence in energy consumption. Herrerias et al. (2017) is the exception. We contribute to the literature

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by testing for club convergence in per capita energy consumption across Australian states and sectors. We adopt the Phillips-Sul club convergence approach that has several advantages

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compared to approaches used in most of the extant literature. 3. Methodology

To test the hypothesis of club convergence in energy consumption across Australia sectors and regions we utilise the approach developed by Phillips and Sul (2007). It detects groups of individuals in the panel that share similar convergence patterns, even if full panel convergence is present. The process may reveal the existance of clusters, while, at the same time, allowing some individuals to diverge. In addition, the group clustering algorithm is

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ACCEPTED MANUSCRIPT based on the properties of the data as opposed to a priori assumptions, as well as allowing for heterogeneity among the time series contained in the panel. The Phillips and Sul (2007) methodology is robust irrespective of whether the series are trend stationary. It has the advantage that it provides a framework for modelling the transitional dynamics as well as long-run behaviour via a nonlinear time-varying factor model.

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The Phillips and Sul (2007) methodology makes use of the following time-varying

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common factor representation for the set of observable series, 𝑦𝑖𝑡

(1)

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𝑦𝑖𝑡 = 𝛿𝑖𝑡 𝜇𝑡 .

where 𝜇𝑡 is a single common trend and 𝛿𝑖𝑡 is a time-varying idiosyncratic element that

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captures the deviation of state 𝑖 from the common trend path. Within this context, all 𝑁 states (either the entire sample or within the cluster) will converge to a steady state (at some point

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in the future) if lim𝑘→∞ 𝛿𝑖𝑡+𝑘 = 𝛿 for all 𝑖 = 1,2, … , 𝑁 regardless of whether energy consumption in sectors across regions are close to the steady state or in transition. This is of

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importance given that the paths to steady state(s) in energy consumption across sectors and

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regions may vary substantially. In estimating 𝛿𝑖𝑡 , Phillips and Sul (2007) modify Eq. (1) to eliminate the trend component through rescaling the panel average as follows: 𝑦𝑖𝑡 𝛿𝑖𝑡 = . (2) 𝑁 (1/𝑁) ∑𝑖=1 𝑦𝑖𝑡 (1/𝑁) ∑𝑁 𝑖=1 𝛿𝑖𝑡 captures the transition path with respect to the panel average. This approach

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where ℎ𝑖𝑡

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ℎ𝑖𝑡 =

considers the following semi-parametric form of 𝛿𝑖𝑡 which provides an empirical algorithm for identifying clubs and thus an econometric test of convergence given by: 𝛿𝑖𝑡 = 𝛿𝑖 + 𝜎𝑖𝑡 𝜉𝑖𝑡

(3)

𝜎

where 𝜎𝑖𝑡 = 𝐿(𝑡)𝑡𝑖 𝛼, 𝜎𝑖 > 0, 𝑡 ≥ 0, and 𝜉𝑖𝑡 is weakly dependant over 𝑡, but 𝑖𝑖𝑑(0,1) across 𝑖. The function 𝐿(𝑡), which is equal to log⁡(𝑡), is increasing in 𝑡 and divergent as 𝑡 tends to

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ACCEPTED MANUSCRIPT infinity.2 The null hypothesis of convergence for 𝛿𝑖𝑡 is: 𝐻0 :⁡𝛿𝑖 = 𝛿, 𝛼 ≥ 0, against the alternative hypothesis for non-convergence for some 𝑖: 𝐻𝐴 :⁡𝛿𝑖 ≠ 𝛿, 𝛼 < 0.3 The following regression is estimated to test the null hypothesis: log(𝐻1 ⁄𝐻𝑡 ) − 2 log[log(𝑡)] = 𝑐̂ + 𝑏̂ log(𝑡) + 𝑢̂𝑡

(4)

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

coefficients. Phillips and Sul (2007) suggest Eq. (4) is estimated for 𝑡 = [𝑟𝑡], [𝑟𝑇] + 1, … , 𝑇

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where 𝑟 > 0 is set on the [0.2, 0.3] interval. Also note that for 𝑏̂ = 2𝛼̂, the null hypothesis

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can be constructed as a one-sided test of 𝑏̂ ≥ 0 against the alternative of 𝑏̂ < 0. A rejection of

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the null hypothesis at the 5% level of significance occurs when 𝑡𝑏̂ < −1.65. 4 The idenfitifcation of clubs in a panel utilises the robust clustering algorithm procedure

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proposed by Phillips and Sul (2007) and implemented as follows: (i) Order the N states according to their last observation;

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(ii) Beginning from the highest-order state, we add adjacent states from our ordered lists. For

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each formation, we estimate the log(𝑡) regression in Eq. (4). Then we select a core group using the following cut-off point criterion: k ∗ = ArgMaxk {t b̂k } subject to Mink {t b̂k } >

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⁡−1.65 for 𝑘 = 2,3, . . , 𝑁.

(iii) We add one state at a time to the core group and re-estimate the log(𝑡) regression in Eq.

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(4). The decision as to whether a state/territory should join the core group is based on the sign criterion 𝑏̂ ≤ 0; and (iv) For the remaining sectors/states, we repeat steps (ii)-(iii) until we can no longer form clubs, and each club will have its own convergence path. If the last group from the algorithm does not converge, then these states/territories form a divergent club. 2

Like Phillips and Sul (2007), we adopt 𝐿(𝑡) = log(𝑡) to guarantee convergence. Note, that a rejection of the null hypothesis of convergence in the panel does not exclude the potential that subconvergence clubs may occur since multiple equilibria may be present. 4 Phillips and Sul (2007) also recommend estimating 𝑏̂ with robust standard errors since Eq. (1) may be weakly time-dependent. 3

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ACCEPTED MANUSCRIPT Phillips and Sul (2007) indicate that using a sign criterion in step (ii) may lead to an overestimation of the number of clubs. As a result, Phillips and Sul (2007) suggest performing club merging tests after running the algorithm in Eq. (4). Finally, implementing the club convergence approach requires the extraction of the trend component of a series. We use the Hodrick and Prescott (1997) filter to estimate the trend

𝑇

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that minimizes the squared changes in trend and deviations: 𝑇−1

∗ ∗ + 𝜆 ∑[(𝑦𝑡+1 − 𝑦𝑡∗ ) − (𝑦𝑡∗ − 𝑦𝑡−1 )]2 }. 𝑡=2

(5)

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𝑡=1

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min𝑦𝑡∗ {∑(𝑦𝑡 −

𝑦𝑡∗ )2

This filtering technique eliminates short-run erratic behaviour, while extracting long-run

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trends from the data. 4. Data

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We utilise annual data from the Department of Industry, Innovation and Science (DIIS, 2016a) on final energy demand in Australia by state and sector (Table E) over the period

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1990-2016. Our panel of seven Australian states/territories include New South Wales (NSW),

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Northern Territory (NT), Queensland (QLD), South Australia (SA), Tasmania (TAS), Victoria (VIC), and Western Australia (WA)5. Final energy demand is divided into the

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following nine sectors (measured in GJ): agriculture, mining, manufacturing, electricity generation, construction, transport, commercial, residential, and ‘other’. We also include total

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final energy demand. We convert energy consumption into per capita figures by using annual data on Australia states/territory population from the Australian Bureau of Statistics (ABS)6. Figure 1 illustrates energy consumption per capita (GJ) by sectors across the six Australian states and the NT that constitute our panel. Total energy consumption (bottom right hand corner) increased through the 1990s and early 2000s, before dropping from the mid-2000s. Energy consumption in agriculture, manufacturing, residential and transport as well as 5

Due to data availability, the Australian Capital Territory (ACT) is not included. For Australian state population statistics, we use Australian Demographic Statistics (Table 4) (URL: http://www.abs.gov.au/Population). 6

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ACCEPTED MANUSCRIPT electricity, generally conforms to this pattern. The decline in electricity consumption reflects the combined effects of energy efficiency programs, structural changes in the economy away from electricity intensive industries and, since 2010, the response of electricity consumers, especially residential consumers, to higher electricity prices (Sadler, 2013). The decline in energy consumption in the manufacturing sector reflects a fall in energy use

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in the chemicals and rubber manufacturing sub-sector. Several fertiliser, pesticide and

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chemical plants closed in 2014 and 2015. Reduced demand from Australian motor vehicle manufacturers for rubber also contributed to the fall in energy use in manufacturing

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(Department of Environment and Energy, 2017). Energy consumption in the commercial and

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mining sector does not decline, but flattens out from the mid-2000s, while energy consumption in the construction sector declined throughout the period studied. The observed

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pattern in construction likely reflects improvements in energy productivity in the construction sector in the larger states, in which there are economies of scale (Ma et al., 2018).

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5. Results

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This section presents the results for the club convergence tests for aggregate energy consumption (Table 1), and, at the sector level, energy consumption for agriculture, mining,

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manufacturing, electricity supply, construction, transport, commercial, residential and ‘other’,

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across each of the states and the NT (Tables 2-10). The results are summarised in Table 11.

5.1 Aggregate energy consumption Table 1 reports the club convergence results for aggregate energy consumption per capita across Australian states. The first row in Panel A of Table 1 reports the results for full sample convergence (i.e., logt test for the six states and the NT), while rows 2 to 4 display the results for the club clustering algorithm. The first row of Panel A in Table 1 indicates rejection of the null hypothesis of full panel convergence since 𝑡𝑏̂ = −27.745 < −1.65. Based on this result

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ACCEPTED MANUSCRIPT we proceed with the club clustering procedure to determine if potential club clusters exist. The results show two distinctive clubs plus one divergent state (rows 2 to 4). The first sub-group (1st Club) consists of NT and WA, implying sub-group convergence since 𝑡𝑏̂ = −1.534 > −1.65. The second sub-group (2nd Club) consists of NSW, SA, TAS, and VIC which also shows evidence of sub-group convergence given that 𝑡𝑏̂ = 3.418 > −1.65. Therefore, in the

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case of aggregate energy consumption per capita, there are two convergence clubs, along with

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one state, QLD, categorised as divergent. As discussed in Section 3, the convergence of clubs

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tends to overestimate the true number of clubs. As a result, we evaluate whether the merging of adjacent numbered clubs into larger clubs is feasible. Based on the results reported in Panel

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B of Table 1, the merger of the identified clubs into larger clubs is not possible. These results suggest that the variation across convergence clubs maybe attributed to

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changes in energy sources consumed across states. The first club (NT and WA) is located in the northern and western part of Australia in which agriculture and mining is important. The

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second club (NSW, SA, TAS, VIC) is located in the south-eastern part of Australia, which

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includes a high proportion of the commerce and manufacturing sectors. Formation of clubs may depend on the amount of energy consumed and growth in energy

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consumption over time (cf Bhattacharya et al., 2018; Mishra & Smyth 2014, 2017; Parker & Liddle 2017a). The three most populous states – NSW, QLD and VIC – account for almost

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three-quarters of Australia’s energy consumption, with QLD exhibiting the largest growth in energy consumption over the last few years (DIIS, 2016b). One reason for the strong growth in energy consumption in QLD is recent abnormally hot weather and resultant energy use for residential cooling. In 2017, 76% of homes in South East QLD had air conditioning compared with just 45% in 2004 (Watts, 2018). Another reason is QLD’s growing LNG industry, which has driven higher demand for gas and electricity (Department of Energy and Environment, 2017). Apergis and Payne (2017) found that, in club analysis, extreme states tend to be

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ACCEPTED MANUSCRIPT divergent. Strong growth in energy consumption sets QLD apart from the other states and the NT and may potentially explain why QLD shows no evidence of convergence. The transition paths for aggregate energy consumption are reported in Figure 2. In the first club (NT and WA), aggregate energy consumption in WA increased, before plateauing, reflecting the fortunes of the mining sector, while in the NT, it decreases before undertaking a

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slight upturn after 2005. In the second club (NSW, SA, TAS, VIC), NSW, SA and WA

5.2 Agricultural sector energy consumption

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exhibit an inverted U-shape, while TAS transitions up over the course of the period studied.

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Table 2 report the results for energy consumption per capita in the agriculture sector. The null hypothesis of full panel convergence is rejected since 𝑡𝑏̂ = −48.576 < −1.65. As listed

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in Panel A of Table 2, two convergence clubs are identified. NSW, TAS, and WA make up the first club, while NT, QLD, and SA make up the 2nd club. The results reported in Panel B of

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Table 2 suggest that the merger of the two clubs into a larger cluster is possible. The null

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hypothesis of club convergence cannot be rejected since 𝑡𝑏̂ = −1.320 > −1.65, thus indicating that initial Clubs 1 and 2 from a larger cluster (i.e., New Club I). This result is

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expected given that agriculture is a mainstay of the Australian economy and is important across most states and the NT. Due to expansive arable land in NSW, SA, WA, QLD, NT and

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TAS, large-scale farming activities thrive, consisting of some of the largest beef and sheep properties in the world as well as huge wheat and barley fields. The third largest farm in the world, and largest in Australia, is Anna Creek in SA. Anna Creek consists of 6,000,000 acres, a land size that is larger than Israel. The fourth largest farm in the world is Clifton Hills, which is also in SA and consists of 4,200,000 acres. The fifth largest farm in the world is Alexandria, in the NT, with 4,000,000 acres. Other farms in Australia that are considered among the biggest in the world include: Davenport Downs in

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ACCEPTED MANUSCRIPT QLD (3,700,000 acres), Home Valley in WA (3,500,000 acres), Innamincka in SA (3,340,000 acres), Wave Hill in the NT (3,330,000 acres), and Marion Downs in QLD (3,070,000 acres). 7 VIC is the only divergent state. VIC is a manufacturing and commercial hub and, as such, a state not as heavily invested in the agricultural industry or grazing. While the NT has the highest percentage of agricultural land used for grazing (96%), followed by Queensland

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(94%), VIC has the lowest percentage (54%) in Australia (ABS, 2012). Moreover, much of

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the agricultural land located on the outskirts of Melbourne has been subdivided and turned

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into housing as part of urban growth over the last decade. This is particularly true of the south eastern and north western growth corridors (see e.g., Millar & Fyfe, 2012).

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The transition paths for energy consumption in agriculture are given in Figure 3a. In club 1 (NSW, TAS, WA), energy consumption in NSW transitions up, while energy consumption

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in the other two states transitions down. In club 2 (QLD, WA, NT), NT and QLD generally

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transition down, while SA transitions up until the mid-2000s, before declining again.

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5.3 Mining sector energy consumption

Table 3 reports the club convergence results for energy consumption per capita in the

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mining sector. Given that 𝑡𝑏̂ = −33.458 < −1.65 in Panel A, the null hypothesis of full panel convergence is rejected. The results indicate three convergence clubs, which reflect the

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observed growth in the mining sector since the early 2000s –reflected in increases in capital stock driven by large-scale investment geared towards expanding the sector's productive capacity. Club 1 consists of NT and WA, which are Australia’s largest mining states. WA alone represents about 32 percent of the world’s top 50, and 63 percent of the top 10 global mining projects (DMIRS, 2017). In addition, LNG is a significant industry for WA. In 20162017 WA produced 28.7 billion tonnes of LNG, generating sales of $12.7 billion. LNG

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see https://www.worldatlas.com/articles/biggest-farms-in-the-world.html.

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ACCEPTED MANUSCRIPT accounts for 66 per cent of all Western Australian petroleum sales. LNG is currently produced from four projects in WA: the North West Shelf Joint Venture project, Woodside’s Pluto project (first LNG in mid-2012), the Gorgon Gas project (first LNG in 2016), and the Wheatstone project, which shipped its first LNG in October of 2017. WA is also home to Shell’s Prelude Floating LNG Project (Government of WA, 2018). The majority of mineral

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commodities produced in the NT are metallic, including gold dore (a mixture of gold and

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uranium (NT Department of Treasury and Finance, 2018).

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silver), manganese, zinc/lead concentrate (including individual concentrates), bauxite, and

Club 1 exhibits the highest speed of convergence amongst any club in the empirical

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analysis, with a value of 𝑏̂ = 4.772. Club 2 consists of NSW, QLD and SA, states which also have a number of mining projects, while Club 3 consists of TAS and VIC, which are not as

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heavily invested in mining compared to the other Australian states. No divergent clubs exist and Panel B of Table 3 indicates the merging of clubs into larger clubs is not possible.

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The transition paths for energy consumption in mining are given in Figure 3b. In the first

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club, NT and WA converge from high and low bases, respectively with similar trends observed for TAS and VIC in club 3. In club 2 (NSW, QLD, SA), energy consumption in

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NSW and QLD transitions up, while energy consumption in SA transitions down. 5.4 Manufacturing sector energy consumption

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Panel A of Table 4 finds that for per capita energy consumption in the manufacturing industry, the null hypothesis of full panel convergence is rejected since 𝑡𝑏̂ = −37.744 < −1.65. The results also indicate two convergence clubs with Club 1 consisting of TAS and WA and Club 2 consisting of NSW, NT, QLD, SA, and VIC. As with mining, no divergent clubs exist and the merging of clubs into larger clubs is not possible. The states in the second club predominantly consist of the larger manufacturing states in the south east and eastern seaboard. Traditionally these large manufacturing states have been

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ACCEPTED MANUSCRIPT energy intensive. Australia’s economy has been transitioning away from goods-producing industries for almost a decade in these states (DIIS, 2016c). Increasingly, policymakers have turned their attention to the redesign of new businesses and future industries that can promote export, transfer skills and innovation across other Australian sectors and industries (DIIS, 2016c). For example, the former Holden car plant in the northern suburbs of Adelaide in SA is

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in the process of being converted into a high-tech business park (ABC, 2017), while the

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Victorian government is redeveloping Ford’s former car plant in Fisherman’s Bend in

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Melbourne as an education and innovation hub (Johanson, 2016). The de-coupling of goods production in the manufacturing sector in NSW, QLD, SA and VIC has been characterised by

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a general shift towards the adoption of cleaner technologies, consistent with convergence to a lower equilibrium level of energy consumption in manufacturing in these states.

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The transition paths for energy consumption in manufacturing are given in Figure 3c. In club 1 the two states are converging from different directions. WA exhibits an inverted U

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shape transition path, while the transition path in TAS resembles a U shape. The transition

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paths in club 2 also divide into two subsets with NT and QLD converging from a high base, while VIC, NSW and SA have followed a low transition path over much of the period.

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5.5 Electricity sector energy consumption Panel A of Table 5 indicates that for energy consumption per capita in the electricity

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generation sector, the null hypothesis of full panel convergence cannot be rejected since 𝑡𝑏̂ = 0.444 > −1.65. This finding is consistent with Mishra and Smyth’s (2017) results for the electricity sector at the national level. This result reflects that Australia’s electricity market is fully integrated across state and territory borders. Australia has the world’s most extensive interconnected power system, in the form of the National Electricity Market (NEM) (see eg AEMO, 2017). The NEM operates over 5000 kilometers from Queensland to South Australia and across the Bass Strait to Tasmania and supplies over $10 billion worth of electricity per

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ACCEPTED MANUSCRIPT year to meet the demand of the Australian population (excluding energy exports). It is usually credited with reducing electricity consumption since the mid-2000s across the board. Full panel convergence highlights the importance of regional integration of electricity markets and, thus, prices in Australia. Apergis et al. (2017a) argue that short-term and longterm effects drive electricity price convergence across Australian states and territories. In the

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short-term, arbitrage opportunities exist (depending on respective energy demand and supply),

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giving rise to short-run profit maximisation. In the long-term, however, price convergence of

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electricity depends on the structure and evolution of power markets. Australia has the world’s largest interconnection capacity. Indeed, Apergis et al. (2017a) identified long-run common

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price growth patterns across Australian states and territories. Between July 2012 and July 2014, Australia introduced the carbon tax which affected energy production of fossil fuels,

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and thus the electricity market. Apergis et al. (2017a) found that controlling for the carbon tax period in Australia did not alter the price convergence process across Australian states (except

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for South Australia which heavily invested in renewable energy generation).

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5.6 Construction sector energy consumption Table 6 displays the club convergence results for energy consumption per capita in the

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construction sector. Full panel convergence is rejected since 𝑡𝑏̂ = −25.291 < −1.65. The results identify two clubs. Club 1 consists of QLD and WA while Club 2 consists of NSW,

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SA, and VIC. The states in the first club have, on average, made up a larger share of the construction industry. While these states have witnessed significant booms in residential construction, their biggest growth has been in non-residential engineering construction associated with the mining boom (Ma et al., 2018). As Ma et al. (2018) discuss, in QLD and WA, the value of engineering projects increased with the mining boom, encouraging construction firms to invest in new technologies and adopt energy productivity enhancing technologies. Meanwhile in the second club, and in NSW and VIC in particular, the

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ACCEPTED MANUSCRIPT construction boom has focused largely just on the residential sector. The residential boom in VIC and NSW, which has driven up housing prices in those states, has been fuelled by investment from Asia, generating returns to scale in energy saving technologies. The nonconvergent states, NT and TAS, have much smaller construction industries. The results in Panel B of Table 6 do not support the merger of Clubs 1, 2 and non-convergent group.

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The transition paths for energy consumption in construction are given in Figure 3d. Both

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clubs display downward transition paths, consistent with the overall decline in per capita

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energy consumption in construction in Figure 1. The only difference is that the decline in energy consumption in the first club is much smoother from around 2000, while the

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downward transition in the second club begins earlier and is more jagged. 5.7 Transportation sector energy consumption

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The results for energy consumption per capita in the transport sector in Panel A of Table 7 indicate that the null hypothesis of full panel convergence is rejected since 𝑡𝑏̂ = −1.905 <

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−1.65. One convergent group is identified which consists of all the states and the NT, except

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for TAS which in the only non-convergent state. The results for convergence in the transport sector across all mainland states and the NT are similar to the findings reported in Mishra and

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Smyth (2017). This result may reflect the introduction of several initiatives reduce transport energy consumption, including fuel economy standards, vehicle and fuel taxation, subsidies

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for energy efficient and electric vehicles and making public transport more accessible and inexpensive (Lo, 2014). These initiatives have been pursued across all mainland states and the NT under the national land transport network. The geographical size of Australia makes integrated transport networks very important, which contributes to convergence in energy use. In Australia, around 30 percent of freight is transported by road and 50 per cent of freight is transported by rail (NTC, 2016) and these networks are integrated on the mainland.

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ACCEPTED MANUSCRIPT The transition paths for energy consumption in the single identified club in transport are given in Figure 3e. NT transitions up sharply from a low base. WA shows a more gradual upward transition from a relatively high base and the other states are in the middle. 5.8 Commercial sector energy consumption Concerning energy consumption per capita in the commercial sector, the results in Panel A

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of Table 8 indicate a rejection of the null hypothesis of full panel convergence since 𝑡𝑏̂ =

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−29.393 < −1.65. The results show two convergence clubs and one divergent group. Club 1

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consists of TAS and VIC while Club 2 consists of QLD, SA, and WA. The commercial sector in the states in the second club have, on average, had higher energy consumption per capita

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than states in the first club. This likely reflects the warmer weather in QLD and the northern parts of SA and WA, which increases energy use for office and shop cooling. NSW and NT

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make up the non-convergent group. The reason for this could be that both states are extremes in terms of the commercial sector. NSW, with Sydney as its capital, is the commercial and

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financial hub of Australia, while the commercial sector is smallest in NT. The results in Panel

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B of Table 8 suggest the merging of clubs into larger clubs is not possible. The transition paths for energy consumption in the commercial sector are given in Figure

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3f. In the first club, VIC and TAS converge from high and low bases respectively. In the second club, the transition paths are more uniform. All three states transition upwards, before

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peaking just prior to the Global Financial Crisis and declining slightly thereafter. 5.9 Residential sector energy consumption Panel A of Table 9 illustrates the club convergence results for energy consumption per capita in the residential sector. As with previous findings, the null hypothesis of full panel convergence is rejected since 𝑡𝑏̂ = −29.393 > −1.65. The results indicate one convergence club consisting of SA, VIC, and WA. Herrerias et al. (2017) emphasise that convergence in residential energy consumption across regions is heavily influenced by differences in the

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ACCEPTED MANUSCRIPT energy types employed by households in different locales. Convergence in SA, VIC and WA likely reflects that the residential sector in these states relies heavily on gas as a common fuel type, which is readily accessible across VIC, SA and WA, at least in the capital cities (DEWHA, 2016). Gas is a much less common energy source in NSW, QLD, TAS and NT, which make up the core of the non-convergent group. QLD and NT also have generally low

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significant proportion of households with low demand for gas.

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heating requirements, given that both have tropical climates and both of these regions have a

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The transition paths for energy consumption in the residential sector are given in Figure 3g. VIC transitions downwards from a high base, while SA and WA transition up. SA comes

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off a much lower base and shows a much steeper transition path compared with WA. 5.9 Other sector energy consumption

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Finally, Table 10 illustrates the club convergence results in terms of energy consumption per capita for ‘other’, which includes consumption of lubricants and greases, bitumen and

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solvents, energy consumption in the gas production and distribution industries and energy that

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is unable to be classified. The null hypothesis of full panel convergence is rejected since 𝑡𝑏̂ = −16.018 > −1.65. The results identify one convergence club consisting of NSW, SA,

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VIC, and WA and one non-convergent group consisting of NT, QLD, and TAS. The transition paths for energy consumption in ‘other’ are given in Figure 3h. Each of the

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four states exhibit an inverted U transition path, with energy consumption in SA and WA bottoming out in the second half of the 1990s, which is earlier than the other two states. 6. Summary and concluding remarks There have been several calls to examine convergence in energy consumption per capita at the sub-national level. The reason is that focusing on energy consumption at the national level masks differences across regions and sectors. To this point, studies that have responded to these calls have tended to examine convergence in energy consumption disaggregated at

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ACCEPTED MANUSCRIPT the region or sector level. We have examined energy consumption per capita across regions at the sector level. Our ‘2x2’ approach is similar in spirit to Apergis and Payne (2017) and Herrerias et al. (2017). We differ from these studies, though, in that Apergis and Payne (2017) focus on convergence in carbon dioxide emissions and Herrerias et al. (2017) focuses on convergence in energy consumption in just the residential sector.

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To realize our objective, we applied the methodology proposed by Phillips and Sul (2007)

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to investgiate club convergence for nine sectors across seven Australain regions over the

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period 1990-2016. To this point, most studies that have tested for stochastic conditional convergence in energy consumption have used stationarity and unit root tests, but these tests

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cannot detect potential multiple equilibria associated with groups of regions following different convergence paths. The advatange of the Phillips and Sul (2007) methodology is

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that it is based on a general non-linear time-varying factor model to assess club convergence that move from disequilibria positions to club-specific steady state postions.

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The summary of our findings across regions and sectors are presented in Table 11. Our

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results are consistent with previous studies that have tested for club convergence in energy variables in that we find evidence of multiple equilibria and a mixture of shifting convergent

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and divergent clubs (see e.g,. Bhattacharya et al., 2018; Herrerias et al., 2017). Typically, some combination of the south-eastern and eastern seaboard mainland states

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(VIC, NSW, SA, QLD) form a club for energy consumption in most sectors. This reflects that these are the most populous states with similar economic bases, suggesting similar energy consumption patterns. Climate is also important in explaining club formation across regions with respect to energy consumption and carbon dioxide emissions (Apergis & Payne, 2017). VIC, NSW and SA also have similar temperate climates. As a separate grouping, NT, QLD and WA are also similar in that each of these regions have large mining interests, large cattle properties throughout northern Australia and the tropical climate is similar in QLD, NT and

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ACCEPTED MANUSCRIPT the northern part of WA. The similarities for these states are reflected in clubs for agricultural, construction, mining and aggregate energy consumption. Club divergence could suggest that differences in energy consumption per capita across states and territories reflect each region’s resource endowments, geographic orientation and state economic policy, as well as transition to renewable energy sources. In previous

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applications of the Phillips and Sul (2007) methodology, extreme regions have been found to

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be more likely to be divergent (Apergis & Payne, 2017). This is reflected in the results for

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aggregate energy consumption in this study with QLD exhibiting the fastest growth rate in energy consumption. There is also evidence of this in the non-convergent states in the

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commercial sector. More generally, at the sector level, the two regions that are nonconverging the most are the two smallest regions in terms of energy consumption. TAS is

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non-converging for four of nine energy types and NT is non-converging a third of the time. At the United Nations Framework Convention Conference of the Parties (COP) in Paris,

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Australia committed to reducing greenhouse gas emissions by 26-28% by 2030 compared

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with 2005 levels. Whether this commitment will be realized depends on whether policies to reduce energy consumption, which is primarily from fossil fuels, is effective. Evidence of

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convergence, coupled with lower levels of consumption, is suggestive of a lower energy consumption equilibrium. Previous research for Australia by Mishra and Smyth (2017),

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employing unit root tests, for energy consumption at the sector level, found evidence of convergence in energy consumption in all sectors except transport. Our results, however, suggest that once one allows for multiple equilibria and disaggregation at the state level, as well as sector level, the policy implications that emerge are more complicated. One sector for which our results are completely consistent with Mishra and Smyth (2017) is electricity. The non-rejection of full panel convergence in the electricity sector is suggestive that the NEM and other policies to reduce electricity consumption are working.

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ACCEPTED MANUSCRIPT This result is important in terms of Australia meeting its commitment under COP, given that electricity is the most important energy source in Australia and a major consumer of coal. Overall, about 42% of Australia’s electricity generating capacity is coal-fired, while 63% of the actual electricity produced in Australia is produced from coal (OCE, 2015). The club convergence results emphasise the need to tailor policies designed to reduce

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energy consumption in specific sectors to the consumption convergence paths unique to

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clusters of states. More generally, recognition of differences in convergence patterns across

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states is important for the design and implementation of policies to curtail energy consumption across most sectors because it is important that policy makers take into account

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differences in economic structures across regions and the effect that such policies may have on the local region. One avenue for future research would be to examine the factors

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responsible for the formation of clusters in particular sectors as well as why some regions diverge in a more systematic way (cf Apergis & Payne, 2017, p. 371). Another avenue for

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future research would be to disaggregate energy consumption by type. Future research may

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be able to examine club convergence for different energy types by sector and region.

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Quah, D. T. (1996). Empirics for economic growth and convergence. European Economic Review, 40(6), 1353-1375. Romero-Ávila, D. (2008). Convergence in carbon dioxide emissions among industrialised countries revisited. Energy Economics, 30(5), 2265-2282. Sadler, H. (2013). Why is electricity consumption decreasing? The Australia Institute, Institute Paper No. 14 Shahbaz, M., Bhattacharya, M. & Ahmed, K. (2017) CO2 emissions in Australia: economic and non- economic drivers in the long-run. Applied Economics, 49(13), 1273-1286. Slaughter, M. J. (1997). Per Capita Income Convergence and the Role of International Trade. American Economic Review, 87(2), 194-199. Slezak, M. (2017) Australia's greenhouse gas emissions highest on record. The Guardian (Australia) December 11 https://www.theguardian.com/environment/2017/dec/11/australias-transportemissions-in-past-year-the-highest-on-record Smyth, R., & Narayan, P. K. (2015). Applied econometrics and implications for energy economics research. Energy Economics, 50, 351-358. Solarin, S.A., Gil-Alana, L.A. & Al-Mulali, U. (2018). Stochastic convergence of renewable energy consumption in OECD countries: A fractional integration approach. Environmental Science and Pollution Research 25(18): 17829-17299, Solow, R. (1956). A contribution to the theory of economic growth. Quarterly Journal of Economics, 70(1), 65–94. Solow, R. M. (1957). Technical Change and the Aggregate Production Function. The Review of Economics and Statistics, 39(3), 312-320. Strazicich, M. C., & List, J. A. (2003). Are CO2 emission levels converging among industrial countries? Environmental and Resource Economics, 24(3), 263-271. UNDP (2016) Human Development Report 2016. UNDP: Geneva. Valadkhani, A., Nguyen, J. & Smyth, R. (2018). Consumer electricity and gas prices across Australian capital cities: Structural breaks, effects of policy reforms and interstate differences. Energy Economics 72, 365-375. Watts, E, (2018). QLD sets demand record again …. and again …. and again, March 1, https://www.energynetworks.com.au/news/energy-insider/qld-sets-demand-recordagainand-againand-again Westerlund, J., & Basher, S. A. (2008). Testing for convergence in carbon dioxide emissions using a century of panel data. Environmental and Resource Economics, 40(1), 109120. World Resources Institute (2014) Climate Analysis Indicators Tool (CAIT) Version 2.0. Washington, DC: World Resources Institute. Yu, Y., Zhang, Y. & Song, F. (2015) World energy intensity revisited: A cluster analysis. Applied Economics Letters 22(14), 1158-1169. Zhang, D. & Broadstock, D. (2016) Club convergence in energy intensity in China. Energy Journal 37(3).

29

ACCEPTED MANUSCRIPT Table 1 Club convergence of energy consumption per capita: Aggregate 𝑡𝑏̂

Panel A: Club convergence tests Full sample convergence: NSW, NT, QLD, SA, TAS, VIC, WA 1st Club: NT, WA 2nd Club: NSW, SA, TAS, VIC Non-convergent group: QLD

-1.389 -1.038 0.238 -

-27.745* -1.534 3.418 -

Panel B: Club merging analysis New Club I: Merging Clubs 1 + 2 New Club II: Clubs 2 + Non-convergent group

-1.389 -0.857

PT

𝑏̂ coef.

-26.660* -30.857*

AC

CE

PT E

D

MA

NU

SC

RI

Notes: for testing the one-sided null hypothesis: 𝑏 ≥ 0 against 𝑏 < 0, we use the critical value: 𝑡0.05 = −1.651 in all cases. Statistical significance at the 5% level is denoted by *, rejecting the null hypothesis of convergence

30

ACCEPTED MANUSCRIPT Table 2 Club convergence of energy consumption per capita: Agriculture 𝑡𝑏̂

Panel A: Club convergence tests Full sample convergence: NSW, NT, QLD, SA, TAS, VIC, WA 1st Club: NSW, TAS, WA 2nd Club: NT, QLD, SA Non-convergent group: VIC

-0.681 0.283 0.566 -

-48.576* 3.930 2.604 -

Panel B: Club merging analysis New Club I: Merging Clubs 1 + 2 New Club II: Club 2 + Non-convergent group

-0.153 -0.588

PT

𝑏̂ coef.

-1.320 -8.822*

AC

CE

PT E

D

MA

NU

SC

RI

Notes: for testing the one-sided null hypothesis: 𝑏 ≥ 0 against 𝑏 < 0, we use the critical value: 𝑡0.05 = −1.651 in all cases. Statistical significance at the 5% level is denoted by *, rejecting the null hypothesis of convergence

31

ACCEPTED MANUSCRIPT Table 3 Club convergence of energy consumption per capita: Mining 𝑡𝑏̂

-0.923 4.772 0.777 0.037

-33.458* 3.120 11.529 0.770

PT

Panel A: Club convergence tests Full sample convergence: NSW, NT, QLD, SA, TAS, VIC, WA 1st Club: NT, WA 2nd Club: NSW, QLD, SA 3rd Club: TAS, VIC Non-convergent group: -

𝑏̂ coef.

RI

Panel B: Club merging analysis New Club I: Merging Clubs 1 + 2 New Club II: Merging Clubs 2 + 3

-0.299 -0.517

-11.711* -73.443*

AC

CE

PT E

D

MA

NU

SC

Notes: for testing the one-sided null hypothesis: 𝑏 ≥ 0 against 𝑏 < 0, we use the critical value: 𝑡0.05 = −1.651 in all cases. Statistical significance at the 5% level is denoted by *, rejecting the null hypothesis of convergence

32

ACCEPTED MANUSCRIPT Table 4 Club convergence of energy consumption per capita: Manufacturing 𝑏̂ coef. Panel A: Club convergence tests Full sample convergence: NSW, NT, QLD, SA, TAS, VIC, WA 1st Club: TAS, WA 2nd Club: NSW, NT, QLD, SA, VIC Non-convergent group: -

-37.744* 0.830 -1.518

PT

Panel B: Club merging analysis No clubs can be merged

-1.187 0.237 -0.177

𝑡𝑏̂

AC

CE

PT E

D

MA

NU

SC

RI

Notes: for testing the one-sided null hypothesis: 𝑏 ≥ 0 against 𝑏 < 0, we use the critical value: 𝑡0.05 = −1.651 in all cases. Statistical significance at the 5% level is denoted by *, rejecting the null hypothesis of convergence

33

ACCEPTED MANUSCRIPT Table 5 Club convergence of energy consumption per capita: Electricity supply 𝑏̂ coef. Panel A: Club convergence tests Full sample convergence: NSW, NT, QLD, SA, TAS, VIC, WA

0.015

𝑡𝑏̂ 0.444

Panel B: Club merging analysis No clubs can be merged

AC

CE

PT E

D

MA

NU

SC

RI

PT

Notes: for testing the one-sided null hypothesis: 𝑏 ≥ 0 against 𝑏 < 0, we use the critical value: 𝑡0.05 = −1.651 in all cases. Statistical significance at the 5% level is denoted by *, rejecting the null hypothesis of convergence

34

ACCEPTED MANUSCRIPT Table 6 Club convergence of energy consumption per capita: Construction 𝑡𝑏̂

Panel A: Club convergence tests Full sample convergence: NSW, NT, QLD, SA, TAS, VIC, WA 1st Club: QLD, WA 2nd Club: NSW, SA, VIC Non-convergent group: NT, TAS

-2.776 -1.067 1.577 -3.459

-25.291* -0.397 0.669 -1.690*

Panel B: Club merging analysis New Club I: Merging Clubs 1 + 2 New Club II: Club 2 + Non-convergent group

-4.335 -3.466

PT

𝑏̂ coef.

-17.595* -22.597*

AC

CE

PT E

D

MA

NU

SC

RI

Notes: for testing the one-sided null hypothesis: 𝑏 ≥ 0 against 𝑏 < 0, we use the critical value: 𝑡0.05 = −1.651 in all cases. Statistical significance at the 5% level is denoted by *, rejecting the null hypothesis of convergence

35

ACCEPTED MANUSCRIPT Table 7 Club convergence of energy consumption per capita: Transport Panel A: Club convergence tests Full sample convergence: NSW, NT, QLD, SA, TAS, VIC, WA 1st Club: NSW, NT, QLD, SA, VIC, WA Non-convergent group: TAS

𝑡𝑏̂

-0.195 0.142 -

-1.905* 1.577 -

PT

Panel B: Club merging analysis No clubs can be merged

𝑏̂ coef.

AC

CE

PT E

D

MA

NU

SC

RI

Notes: for testing the one-sided null hypothesis: 𝑏 ≥ 0 against 𝑏 < 0, we use the critical value: 𝑡0.05 = −1.651 in all cases. Statistical significance at the 5% level is denoted by *, rejecting the null hypothesis of convergence

36

ACCEPTED MANUSCRIPT Table 8 Club convergence of energy consumption per capita: Commercial 𝑡𝑏̂

Panel A: Club convergence tests Full sample convergence: NSW, NT, QLD, SA, TAS, VIC, WA 1st Club: TAS, VIC 2nd Club: QLD, SA, WA Non-convergent group: NSW, NT

-0.753 -1.275 0.644 -0.936

-37.103* -0.913 1.497 -36.398*

Panel B: Club merging analysis New Club I: Merging Clubs 1 + 2 New Club II: Clubs 2 + Non-convergent group

-0.674 -0.751

PT

𝑏̂ coef.

-23.343* -34.920*

AC

CE

PT E

D

MA

NU

SC

RI

Notes: for testing the one-sided null hypothesis: 𝑏 ≥ 0 against 𝑏 < 0, we use the critical value: 𝑡0.05 = −1.651 in all cases. Statistical significance at the 5% level is denoted by *, rejecting the null hypothesis of convergence

37

ACCEPTED MANUSCRIPT Table 9 Club convergence of energy consumption per capita: Residential Panel A: Club convergence tests Full sample convergence: NSW, NT, QLD, SA, TAS, VIC, WA 1st Club: SA, VIC, WA Non-convergent group: NSW, NT, QLD, TAS

𝑡𝑏̂

-0.982 0.433 1.661

-29.393* 5.842 -17.179*

PT

Panel B: Club merging analysis No clubs can be merged

𝑏̂ coef.

AC

CE

PT E

D

MA

NU

SC

RI

Notes: for testing the one-sided null hypothesis: 𝑏 ≥ 0 against 𝑏 < 0, we use the critical value: 𝑡0.05 = −1.651 in all cases. Statistical significance at the 5% level is denoted by *, rejecting the null hypothesis of convergence

38

ACCEPTED MANUSCRIPT Table 10 Club convergence of energy consumption per capita: Other C Panel A: Club convergence tests Full sample convergence: NSW, NT, QLD, SA, TAS, VIC, WA 1st Club: NSW, SA, VIC, WA Non-convergent group: NT, QLD, TAS

𝑡𝑏̂

-3.706 0.838 -4.449

-16.018* 1.746 -14.818*

PT

Panel B: Club merging analysis No clubs can be merged

𝑏̂ coef.

AC

CE

PT E

D

MA

NU

SC

RI

Notes: for testing the one-sided null hypothesis: 𝑏 ≥ 0 against 𝑏 < 0, we use the critical value: 𝑡0.05 = −1.651 in all cases. Statistical significance at the 5% level is denoted by *, rejecting the null hypothesis of convergence

39

ACCEPTED MANUSCRIPT Table 11 Summary of club convergence results Energy consumption Clubs: per capita (by sector): 1st Club

Agriculture Mining Manufacturing

NSW, TAS, WA NT, WA TAS, WA

Electricity supply Construction Transport

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

Commercial Residential Other C Aggregate

NSW, SA, VIC, WA NT, WA

Speed of convergence (𝑏̂) 0.283 4.772 0.237

C C

-1.038

3rd Club

Speed of convergence (𝑏̂) 0.037

Nonconverging

1.577 -

-

-

NT, TAS TAS

QLD, SA, WA -

0.644 -

-

-

-

-

-

-

NSW, SA, TAS, VIC

0.238

-

-

NT, QLD, SA NSW, QLD, SA NSW, NT, QLD, SA, VIC NSW, SA, VIC -

D E

T P E 0.838

T P

Speed of convergence (𝑏̂) 0.566 0.777 -0.177

M

I R

C S U

N A

-1.067 0.142 -1.275 0.433

2nd Club

TAS, VIC -

-

VIC -

NSW, NT NSW, NT, QLD, TAS NT, QLD, TAS QLD

A

40

ACCEPTED MANUSCRIPT Agriculture

300

Com m ercial

18 17

Energy use per capita (GJ)

Energy use per capita (GJ)

290

280

270

260

16 15 14 13 12

250 11 240

10 1995

1998

2001

2004

2007

2010

2013

1992

Construction

3.2

1995

1998

2001

1.6

70 65 60 55

1.2

50 1992

1995

1998

2001

2004

2007

2010

2013

1992

Manufacturing

40

1998

2001

2004

2007

2010

2013

2007

2010

2013

2007

2010

2013

2007

2010

2013

Mining

Energy use per capita (GJ)

30

MA

68

64

60

25

20

15

52 1992

1995

1998

2001

2004

2007

2010

2013

PT E

Residential

21.2

20.8

20.4

CE

20.0

19.6

18.8 1992

AC

19.2

1995

1998

2001

2004

10

D

56

Energy use per capita (GJ)

2013

35

1992

2001

2004

Transport

70

68

66

64

62 2007

2010

2013

1992

1995

1998

2001

2004

Total (all sectors)

6.4

6.0

Energy use per capita (GJ)

5

1998

72

Other

6

1995

74

Energy use per capita (GJ)

Energy use per capita (GJ)

72

Energy use per capita (GJ)

1995

NU

76

2010

RI

2.0

75

SC

Energy use per capita (GJ)

Energy use per capita (GJ)

80

2.4

2007

Electricity Generation

85

2.8

2004

PT

1992

4

3

2

5.6

5.2

4.8

4.4

1

4.0 1992

1995

1998

2001

2004

2007

2010

2013

1992

1995

1998

2001

2004

Fig. 1. Energy consumption per capita (GJ) by sectors in all Australian states (panel mean)

41

ACCEPTED MANUSCRIPT Club 1 1.01 1.01 1.00 1.00

PT

1.00 1.00

RI

0.99

0.99 1990

2000

2005

2010

2015

2010

2015

NU

1995

SC

0.99

NT

WA

MA

Club 2

D

1.03

PT E

1.02

CE

1.01

0.99

AC

1.00

0.98 1990

1995

2000 NSW

2005 SA

TAS

VIC

Fig. 2. Relative transition paths of energy consumption per capita (ℎ𝑘,𝑖𝑡 ) for member states: Aggregate. Notes: The relative transition curves for convergence club 𝑗 are identified as ℎ𝑘,𝑖𝑡 = 𝑦𝑘,𝑖𝑡 ⁄(

1 𝑁𝑘

𝑁

𝑘 ∑𝑖=1 𝑦𝑘,𝑖𝑡 ),

where 𝑦𝑘,𝑖𝑡 is the Hodrick-Prescott trend of energy consumption for state i in club k at time t.⁡𝑁𝑘 is the number of sates in club k.

42

ACCEPTED MANUSCRIPT Club 1 1.6

1.4

1.2

PT

1.0

RI

0.8

1995

2000 NSW

2005

NU

0.4 1990

SC

0.6

TAS

2010

2015

2010

2015

WA

MA

Club 2

1.10

PT E

D

1.05

1.00

CE

0.95

0.85

AC

0.90

0.80 1990

1995

2000 NT

2005 QLD

SA

Fig. 3a. Relative transition paths of energy consumption per capita (ℎ𝑘,𝑖𝑡 ) for member states: Agriculture. Notes: see notes in Fig. 2

43

ACCEPTED MANUSCRIPT Club 1 1.3

1.2

1.1

1.0

0.9

1995

2000

2005 NT

2010

WA

SC

Club 2

2015

RI

0.7 1990

PT

0.8

1.6

NU

1.5 1.4 1.3

MA

1.2 1.1 1.0 0.9

2000

2005

NSW

QLD

2010

2015

2010

2015

SA

Club 3

CE

1.05

1995

PT E

0.7 1990

D

0.8

1.04

AC

1.03 1.02 1.01 1.00 0.99 0.98

0.97 1990

1995

2000

2005 TAS

VIC

Fig. 3b. Relative transition paths of energy consumption per capita (ℎ𝑘,𝑖𝑡 ) for member states: Mining. Notes: see notes in Fig. 2

44

ACCEPTED MANUSCRIPT

Club 1 1.04 1.03 1.02

PT

1.01 1.00

RI

0.99

SC

0.98

0.96 1990

1995

2000

NU

0.97

2005

2015

2010

2015

WA

MA

TAS

2010

Club 2

PT E

D

1.16

1.12

CE

1.08

1.00

0.96

AC

1.04

0.92 1990

1995 NSW

2000 NT

2005 QLD

SA

VIC

Fig. 3c. Relative transition paths of energy consumption per capita (ℎ𝑘,𝑖𝑡 ) for member states: Manufacturing. Notes: see notes in Fig. 2

45

ACCEPTED MANUSCRIPT

Club 1 1.2

1.1

PT

1.0

RI

0.9

0.7 1990

1995

2000

NU

SC

0.8

2005

2015

2010

2015

WA

MA

QLD

2010

Club 2

PT E

D

2.0

1.6

CE

1.2

0.4

AC

0.8

0.0 1990

1995

2000 NSW

2005 SA

VIC

Fig. 3d. Relative transition paths of energy consumption per capita (ℎ𝑘,𝑖𝑡 ) for member states: Construction. Notes: see notes in Fig. 2

46

ACCEPTED MANUSCRIPT

Club 1 1.06 1.04 1.02

PT

1.00 0.98

RI

0.96

SC

0.94

0.90 1990

1995

2000

2005

NT WA

QLD

2010

2015

SA

MA

NSW VIC

NU

0.92

Fig. 3e. Relative transition paths of energy consumption per capita (ℎ𝑘,𝑖𝑡 ) for member states: Transport.

AC

CE

PT E

D

Notes: see notes in Fig. 2

47

ACCEPTED MANUSCRIPT

Club 1 1.04 1.03 1.02

PT

1.01 1.00

RI

0.99

SC

0.98

0.96 1990

1995

2000

NU

0.97

2005

2015

2010

2015

VIC

MA

TAS

2010

Club 2

D

1.01

PT E

1.00 0.99

CE

0.98

0.96 0.95

AC

0.97

0.94 1990

1995

2000 QLD

2005 SA

WA

Fig. 3f. Relative transition paths of energy consumption per capita (ℎ𝑘,𝑖𝑡 ) for member states: Commercial. Notes: see notes in Fig. 2

48

ACCEPTED MANUSCRIPT Club 1 1.04 1.03 1.02 1.01 1.00

PT

0.99 0.98

RI

0.97

1995

2000 SA

2005

VIC

NU

0.95 1990

SC

0.96

2010

2015

WA

MA

Fig. 3g. Relative transition paths of energy consumption per capita (ℎ𝑘,𝑖𝑡 ) for member states: Residential. Notes: see notes in Fig. 2

D

Club 1

PT E

1.16 1.12 1.08

1.00

AC

0.96

CE

1.04

0.92 0.88 0.84

0.80 1990

1995

2000 NSW

2005 SA

VIC

2010

2015

WA

Fig. 3h. Relative transition paths of energy consumption per capita (ℎ𝑘,𝑖𝑡 ) for member states: Other. Notes: see notes in Fig. 2

49

AC

CE

PT E

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

50

ACCEPTED MANUSCRIPT A club convergence analysis of per capita energy consumption across Australian regions and sectors

AC

CE

PT E

D

MA

NU

SC

RI

PT

Highlights We examine convergence in per capita energy consumption across Australian regions and sectors We use the Phillips-Sul club convergence approach We find evidence of multiple convergence clubs for aggregate energy consumption per capita We also find evidence of multiple convergence clubs in eight of the nine Australian sectors States with similar features tend to exhibit similar energy consumption patterns

51