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Fourier quantile unit root test for the integrational properties of clean energy consumption in emerging economies
Accepted Manuscript Fourier Quantile Unit Root Test for the Integrational Properties of Clean Energy Consumption in Emerging Economies
Yifei Cai, Angeliki N. Menegaki PII: DOI: Reference:
S0140-9883(18)30452-3 https://doi.org/10.1016/j.eneco.2018.11.012 ENEECO 4221
To appear in:
Energy Economics
Received date: Revised date: Accepted date:
7 May 2018 15 September 2018 18 November 2018
Please cite this article as: Yifei Cai, Angeliki N. Menegaki , Fourier Quantile Unit Root Test for the Integrational Properties of Clean Energy Consumption in Emerging Economies. Eneeco (2018), https://doi.org/10.1016/j.eneco.2018.11.012
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Fourier Quantile Unit Root Test for the Integrational Properties of Clean
T
Energy Consumption in Emerging Economies
IP
Yifei Cai1
Economics, Business School, University of Western Australia,
CR
Perth, Australia
[email protected]
US
Angeliki N.Menegaki
TEI STEREAS ELLADAS, UNIVERSITY OF APPLIED SCIENCES Economics and Management of Tourist and Culture Units
AN
Amfissa, Nea Poli, Greece
AC
CE
PT
ED
M
[email protected]
1
Corresponding author, Yifei Cai acknowledges that this paper was carried out while the author
was in receipt of a “University Postgraduate Award (UPA) and an Australian Government Research Training Program Scholarship (RTP)” at The University of Western Australia. [1]
ACCEPTED MANUSCRIPT
Fourier Quantile Unit Root Test for the Integrational Properties of Clean Energy Consumption in Emerging Economies
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Abstract
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Since the adoption of the Kyoto protocol in 1997 and its entry into force in 2005, as well its aftermath such as the
CR
Doha amendment and Paris agreement, national policies have become more conscious of the usage of clean energy, mostly the different forms of renewable energy and nuclear energy. Ratifying countries and signatories had
US
commited themselves to binding targets for the reduction of greenhouse emissions by 8% with respect to 1990 levels until 2012, also based on the particular contribution to global emissions from each country. This paper
AN
examines the integrational properties of clean energy consumption from eight emerging economies which are also high greenhouse gas emitters. The empirical results show that the clean energy consumption is stationarity for
M
Brazil and Philippines by using a quantile unit root test without smooth breaks (Koenker and Xiao, 2004).
ED
However, after capturing the smooth breaks (Bahmani-Oskooee et al., 2014), we find the clean energy consumption of China, Pakistan and Thailand are stationary. The time-varying deterministic trend with smooth
PT
breaks are more fitted to the path of clean energy consumption in comparison to the deterministic trend without smooth breaks. The paper suggests economic insights useful for policy making.
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Keywords: Clean energy; Emerging economies; Fourier quantile unit root; integration;
[2]
Introduction
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1.
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ACCEPTED MANUSCRIPT
Within a framework of soaring global temperature, developing clean energy, namely without carbon
ED
emissions, has been viewed as the main policy to curb the CO2 emissions in the long-run. Previous studies have contributed much to investigate the role of foreign direct investment on clean energy use
PT
(Lee, 2013 and Paramati et al., 2016) and the nexus between clean energy consumption and economic
CE
growth (Cai et al., 2018). However, no studies focus on the integrational properties of clean energy use, although numerous papers test the integration of energy consumption (including primary energy
AC
consumption, coal consumption, natural gas consumption and renewable energy consumption) and CO2 emissions (Narayan and Smyth, 2007; Chen and Lee, 2007; Liddle, 2009; Hasanov and Telatar, 2011; Meng et al., 2013; Gozgor, 2016; Payne et al, 2017). The changes of industrial structure always affect the energy consumption structure. In other words, the heavy industries emit more carbon emissions in comparison to agriculture and service sectors because these industries consume a large amount of fossil fuel to support their long-run growth. With the rising of emerging market countries, these countries are experiencing a significant industrial structure change. The energy consumption structure should, without doubt, be altered with the polluting [3]
ACCEPTED MANUSCRIPT sectors becoming reduced or eliminated. In other words, clean energy will be paid increasingly more attention by countries, worldwide, due to their lower carbon emissions. However, the clean energy consumption always contains many structural breaks which significantly affect their stochastic convergence. This occurs because of the investment and the sunk costs taking place for renewable energy investments at some time and the subsidies they are usually accompanied with. This in turn,
T
entails changes (usually increases) in clean energy production which follows the new investments and
IP
integration into the electricity systems. In fact, many scholars (Lee and Chang, 2008 and Payne et al.,
CR
2014) have shown that the structural breaks in the energy consumption are the main threat against robust estimation. Many empirical papers use dummies to approximate the sharp structural breakpoints
US
in the series. However, Enders and Lee (2012) suggest smooth breaks which could be approximated by the Fourier function. Moreover, the stochastic convergence might perform differently at different
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quantiles.
Thus, this paper utilizes a newly proposed Quantile Unit Root test with Fourier Function of
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Bahmani Oskooee et al. (2018) to examine the integrational properties of clean energy consumption for
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8 Emerging Market (EM) countries. This paper provides not only integration over the whole quantiles, but also the integration at each quantile. The merits of the Fourier quantile unit root test could be
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summarized as follows. First, the Fourier quantile unit root test could provide asymmetric dynamics by allowing different mean-reverting speed at different quantiles of clean energy consumption. Second, the
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asymmetries revealed by the quantile regression do not rely upon a special assumption about the functional form of nonlinearities and asymmetries. Third, the conventional unit root tests and even
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some which consider structural breaks, all focus on testing on the conditional central tendency. However, through the quantile regression, we could test on clean energy consumption at different quantiles, otherwise we would investigate the whole distribution of clean energy consumption. Specifically, the central tendency is broken down based on the quantile regression. Fourth, many shocks such as wars and political unrest or instability and regulatory policies would make the clean energy consumptions of many countries experience structural breaks. Fifth, because of the structural breaks occurring to clean energy consumption, it is reasonable to test the integrational properties in a framework taking structural breaks into account. Using the Fourier function in the trend function, those [4]
ACCEPTED MANUSCRIPT structural breaks could be approximated as a smooth process. Sixth, the clean energy consumption for some countries such as China, India, Indonesia, Malaysia and Thailand is not subject to normal distribution. As Koenker and Xiao (2004) noted, the non-normality of the series would result to the loss of power by traditional unit root tests2. Overall the originality of our paper is three-fold: First the clean energy (per se)
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integration properties have not been examined so far. Literature has provided abundant
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examples on energy use in general and very few examples of renewable energy, but the clean
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energy has not been investigated as such. Second, the sample of emerging economies has not been investigated in relation to this concept either. The investigation of emerging economies is very
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important, because they have a high economic growth which is fueled mostly by conventional energy, while clean energy is still under a development process and has not stabilized to a certain
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amount. Last, the employed econometric method is novel and recently developed. Thus, the paper
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combines a unique contribution with all these three novel aspects: topic, sample, method. The remainder of the paper is organized as follows. Section 2 reviews the existing studies
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related to the test of convergence of energy consumption. Section 3 presents datasets and descriptive statistics. Section 4 introduces the econometric models. Section 5 shows the empirical results. The last
2.
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section concludes the paper.
Literature Review
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The literature of energy consumption convergence has started more than twenty years ago with the work by Nilsson (1993) and Goldemberg (Goldemberg 1996). These studies found evidence that countries tended to converge to a common pattern of energy use. Afterwards, a number of studies has proliferated both in energy economics and in the broader field of environmental and resource economics with the study of carbon emissions convergence.
2 Meng et al. (2013) utilize the new LM tests based on residual augmented least square regression to solve the problems of non-normal errors when implementing nonlinear analysis.
[5]
ACCEPTED MANUSCRIPT Although convergence per se has been studied in many ways and with different methods, for example with the distribution approaches such as beta convergence (catch up process from high to low convergence), gamma convergence (intra generation mobility) or sigma convergence (spread of distribution). Another strand of energy convergence studies which we will not handle in this paper is the papers
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which study convergence by means of decomposition analysis. Theil decomposition analysis, Divisia
Decomposition methods separate among various effects such as within and between group
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reported.
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Decomposition analysis, Laspeyers or Fisher Index decomposition analysis are the most frequently
formation and an account of within group inequalities.
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decomposition, contribution of energy transformation indexes, contribution of energy to GDP
The interest on whether energy consumption per capita is stationary is motivated by the need to
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know about the effects shocks have on it. If a unit root is present, a permanent effect is due after the
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shock occurence. There are numerous papers that examine the stationarity or non-stationarity of energy consumption and other variables typically perused in the energy-growth nexus. Actually, the
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investigation of stationarity of energy consumption is the first step in every energy-growth nexus study (e.g. Magazzino 2014; 2016; 2017). Hence, practically, stationarity studies would be at least as many
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in number, as the number of energy-growth studies.
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However, the current literature review will not include pure energy-growth nexus studies per se. It includes only studies whose main and exclusive focus is to investigate stochastic convergence of
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energy consumption (through unit root investigation) and does not include studies that fall within the energy-growth nexus field whose focus is mainly on the causality analysis. A succinct literature review for this type of studies up to 2012, is provided by Smyth (2013). To make our search yardstick in the current literature review more concrete, we have employed Scopus bibliographic database (www.scopus.com) with keywords: “energy” plus “structural breaks” or “nonlinearity”, “unit root” and “fractional integration” in their titles, keywords and abstracts and have identified 27 studies on the investigation of the convergence of energy variables which appear to be mostly energy consumption per capita, total energy consumption (in aggregate or disaggregate form). [6]
ACCEPTED MANUSCRIPT None of the studies is concerned with the study of the convergence of clean energy consumption as such. The collected studies are shown in Tables 1 and 2. We provide separately studies with aggregate magnitudes (Table 1) and studies with disaggregate magnitudes. (This is also advised in Menegaki
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and Tsani (2018); Tsani and Menegaki (2018))
[7]
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Table 1. Energy consumption convergence studies Study
Period
Variable
Test
Countries
[1]
[2]
[3]
[4]
[5]
Narayan & Smith (2005)
1979–2000
Energy consumption per capita
T P
182 countries
ADF
Result
Agg/Disagg
[6]
[7]
I R
1
Univariate tests (with one or 2 breaks): No stationarity
1
Univariate test with break(s): Rejects H0 of unit root for 56 countries namely 31% of the sample
C S
Panel data unit root test: Stationarity (yes)
Ozcan (2013)
Hsu et (2008)
al.
1980-2009
Energy consumption per capita
1971-2003
Energy use
17 Middle East countries
LM (LS and IPS)
Panel SURADF cross-sectional and identifies countries contain root)
D E
M
A
U N
(hosts effects which a unit
LM tests: Stationarity (yes)
84 countries
Conventional unit root tests reveal stationarity even if only one series in the panel is strongly stationary.
1
Chen & Lee (2007)
1971-2002
Energy consumption per capita
CBL panel unit root
104 countries (7 regional panels)
Non stationarity (even with proper consideration of cross sectional correlations)
1
Mishra et al. (2009)
1980-2005
Energy consumption per capita
CBL panel unit root
13 Pacific island countries
60% of individual countries: Stationarity
1
Ozturk & Aslan (2011)
1970-2006
Energy consumption per capita
LM (IPS)
Turkey
LM univariate test without break Non-stationarity except for the residential sector
1
Narayan et al. (2008)
1973-2007
Energy consumption (sectoral)
LM (LS)
Stationarity except for South Australia
1
T P
C A
E C
(7 sectors) Australia states in industries)
[8]
(6 9
Whole sample: Stationarity
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Study
Period
Variable
Test
Countries
Result
[1]
[2]
[3]
[4]
[5]
[6]
Aslan & Kum (2011)
1970-2006
Energy consumption
LM (LS)
Turkey sectors)
(7
T P
Linear Unit root is rejected in 4/7 sectors (transport, non-energy use, final energy consumption, cycle & energy).
I R
Agg/Disagg [7] 1
LM test reveals stationarity with two structural breaks
Hasanov & Telatar (2011)
1980-2006
Primary consumption capita
energy per
Kum (2012)
1971-2007
Energy consumption per capita
LM
LM (FLM)
Yilanci & Tunali (2014)
1960-2011
Energy consumption per capita
Zhu & Guo (2016)
1993-2013
Nuclear consumption capita
Lean &Smyth (2014)
1978-2010
Kula al.(2012)
1960-2005
Kula (2014)
et
1923-2008
D E
T P
energy per
E C
C A
ADF, LM (KSS, S)
Simultaneous use of structural breaks and nonlinear unit root tests contribute to the higher probability of evidencing stationarity
1
15 East Asia & Pacific countries
No break
1
109 countries
FLM Stationarity for 24 countries after acknowledging the unknown nature of breaks. For the remaining countries, the standard LM test is use
1
27 countries
If no breaks Non-stationarity(low, upper-middle,developing countries)
0
U N
A
M
C S
178 countries
CBL panel unit root, Longitudinal clustering
One break
Nonstationarity Stationarity
If breaks Stationarity(total, lower-middle,high, developed countries)
Final energy demand (10 fuel types in 4 sectors)
LM (SP, LS)
Malaysia
Stationarity for 50%-70% of energy types in 25%-50% of sectors (no break, one break and two breaks)
0
Electricity consumption capita
LM (LS)
23 high income countries
Stationarity for 21/23 countries
0
LM (LS)
Turkey
Stationarity
0
Electricity
per
[9]
ACCEPTED MANUSCRIPT
Study
Period
[1]
[2]
Lean & Smyth (2009)
Variable
1973m1- 2008m7
[3] consumption capita
Test
Countries
Result
[4]
[5]
[6]
Petroleum consumption (24 types in 5 sectors)
LM tests
(FI)
USA sectors)
U N
1982-2007
Coal consumption
LM (IPS, CBL, DBC, W)
Apergis al.(2010b)
1980-2007
Total Natural gas consumption in billions of cubic feet (per state)
Panel unit roots (various)
Apergis & Payne (2010)
1960-2007
Petroleum consumption
Aslan (2011)
1960-2008
Natural consumption
C A
E C
Pestana Barros et al. (2012)
1981m1- 2010m10
Pestana Barros et al. (2013)
1973m1- 2011m10
gas
I R
Univariate unit roots: Non-stationarity for less than 50% of sectors and products for 9/24 series
0
Multivariate unit roots: Non-stationarity for the commercial and industrial sectors, Stationarity for the residential sector. Evidence for fractorial integration (FI)
USA states)
(50
Stationarity with structural breaks
0
USA states)
(50
Panel unit roots:
0
LM (LS, NP)
USA states)
(50
Stationarity; Endogenously structural breaks
LM (KSS)
USA states)
(50
60% of states follow non-linear behavior
A
D E
T P
(5
[7]
C S
Apergis et al. (2010a)a et
T P
per
Agg/Disagg
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Nonstationarity
Panel unit roots with endogenously determined structural breaks: Stationarity determined
0 0
For 27/50 states: Nonstationarity For 23/50 states: Stationarity
Renewable consumption
energy
FI
USA
Nonstationarity
0
Nuclear consumption
energy
FI
USA
Nonstationarity
0
[10]
ACCEPTED MANUSCRIPT
Study
Period
Variable
Test
Countries
Result
[1]
[2]
[3]
[4]
[5]
[6]
Gil- Alana et al. (2010)
1973m1- 2009m5
Electricity consumption
Apergis Tsoumas (2012)
&
1989m1-2009m12
Fossil, coal electricity consumption
Apergis Tsoumas (2011)
&
Narayan al.(2008)
et
Borozan Borozan (2015)
&
T P
FI
USA
Nonstationarity
and
FI
USA (different sectors)
Stationarity
1989m1-2009m12
Solar, geothermal and biomass energy consumption
FI
USA (different sectors)
Stationarity
1971-2003
Crude oil production
LM
60 countries
No breaks
2001-2013
Electricity consumption capita
Wang et al. (2016)
1965-2011 (nuclear)
Herrerias al. (2017)
et
1995-2011
Mishra and Smyth (2017)
1973-2014
1990-2011 (RES)
D E
Non-fossil consumption
energy
Electricity, consumption
Coal
T P
E C
C A
LM per
Energy consumption per capita
I R
C S
U N
A
M
LM (LS, IPS)
Breaks
[7] 0 0
0
Nonstationarity
0
Stationarity
Croatia
Nonstationarity
0
Japan
Nonstationarity (nuclear)
0
FLM
Club modeling
Agg/Disagg
Stationarity (RES) convergence
LM and RALS-LM
Chinese regions
Different convergence between rural and urban areas
0
Australia (sector level)
Convergence is confirmed for 6 out of 7 sectors
1
Note: Column [4], ADF: Augmented Dickey-Fuller, LS: Lee and Strazicich 2003-one break: SP:Schmidt and Phillips (1992)-no break, IPS: Im, Pesaran et al. 2003, SURADF: Panel Seemingly unrelated regressions (Breuer et al. 2001), CBL: Carrion-i-Silvestre et al. (2005), KSS: Kapetanios and Shin(2003) and Sollis (2004), F.I. fractorial integration ((Nielsen 2005), (DBC: Del Barrio-Castro et al. 2005; W: Westerlund 2005); NP: Narayan and Popp (2010), FLM: Fourier type Lagrange multiplier (Enders and Lee 2012), S: Sollis (2004) with simultaneous structural change and nonlinearity
[11]
ACCEPTED MANUSCRIPT
1: Aggregate, 0: Disaggregate
T P
I R
C S
A
U N
D E
M
T P
E C
C A
[12]
ACCEPTED MANUSCRIPT Studies either focus on whole economies or group of economies or even sectors of a particular economy such as is done in Ozturk and Aslan (2011) or Aslan and Kum (2011). Typically studies employ tests that have not been used before in a particular country or set of countries and thus they corroborate their results and prove their robustness to make sound policy recommendations. They also demonstrate the difference of results when breaks are inserted (Ozturk and Aslan, 2011) and linear tests vs non-linear unit root tests are employed (Aslan and Kum, 2011). Apparently only four three studies in Table 1, focus on the convergence of clean energy
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consumption. These are Zhu & Guo (2016), Pestana Barros et al. (2012) and Pestana Barros et al. (2013) for nuclear energy and renewable energy consumption and Apergis & Tsoumas (2011) for solar,
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geothermal and biomass energy consumption. Particular focus on these eight emerging economies has
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not taken place so far. Thus our study is novel both in the topic and the employed sample. Apparently, none of the studies in Table 1 deals with the investigation of clean energy convergence. Most of the studies deal with total energy or various disaggregate forms of energy
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consumption. Most of the studies find evidence of convergence which is improved after the employment of structural breaks. Breaks are some crucial time moments after which energy consumption behavior stabilizes towards a certain amount or pattern. These events may be new
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policy generation occurrences or economic and political events.
Furthermore, when one observes the results of previous studies on the convergence of renewable energy, or nuclear energy or natural gas or other clean form, then one sees that
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non-convergence is the main result. This reveals that the penetration of renewable energy sources is still under full development in most economies around the world and that the relevant studies
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are still so few that do not allow us to reach a firm conclusion. This justifies the need for the research carried out in the current paper.
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Throughout the time span of the current analysis, namely from 1965 to 2016, all the emerging economies have experienced a high average GDP annual growth rate. Brazil has grown at a yearly 4.04%, China at 6.50%, India at 7.17%, Indonesia at 4.43%, Malaysia at 5.89%, Philippines at 4.63%
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and Thailand at 5.09% (WDI, 2017). The corresponding figure for high income countries has been 2.76% and for low middle income countries 4.56%. This shows the high GDP growth experienced in the
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emerging economies. Particularly Brazil, since 2015 has had a negative growth, but the rest of the sampled countries have pertained a positive economic growth despite the world economic crisis. That been said, it is more than likely that for this economic growth to be maintained, a considerable amount of energy consumption is necessary. This generates greenhouse gas emissions. Thus, emerging economies are facing the issue of energy security (Sadorsky, 2009), given that most of fossil energy proven reserves are found in other countries such as the Middle East, Russia etc. Energy security combined with environmental considerations, brings forward the development of clean energy sources in emerging countries. Two of them, namely China and India are among the top five renewable energy producers. According to Financial Times (2017), emerging economies are close to overtaking the highly developed countries in the renewable energy production with an installed capacity of wind and solar to 307GW and 272GW respectively, fulfilling a 51% and 53% respectively. Evidence for convergence is an issue of huge importance for international environmental organizations since the [13]
ACCEPTED MANUSCRIPT increase of renewable and generally of clean energy is a priority in environmental policy agendas and a crucial result for emerging economies which are to blame for their significant amount of emissions.
3.
Datasets and Descriptive Statistics
To test the stochastic integrational properties of clean energy consumption, we should first define the concept of clean energy. Based on the previous studies of Lee (2013), Paramati et al. (2016) and Cai et
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al. (2018), the term clean energy is regarded as the energy which does not produce CO 2 emissions. That been said, clean energy is regarded as the sum of the energy consumption from nuclear energy and
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renewable energy3. All the data used in this paper has been selected from BP Statistical Review of
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World Energy covering the period from 1965 to 2016 expressed in million tonnes of oil equivalent. The countries selected for this paper include Brazil, China, India, Indonesia, Malaysia, Pakistan, Philippines and Thailand, based upon data availability. The descriptive statistics are listed in Table 2. Moreover,
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these countries have signed and ratified the Kyoto protocol. As reported in Table 2, the maximum of clean energy consumption is in China with 483.5916 million tonnes of oil equivalent. However, the minimum of clean energy consumption is in Malaysia with 4.8633 million tonnes of oil equivalent.
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Besides, the standard deviation of China is 112.6459 which is the highest among the countries. For skewness, only Philippines negatively skewed. In terms of the kurtosis, China, India, Malaysia and Thailand is over 3. For the normality test, we find that null is rejected for China, India, Indonesia,
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Malaysia and Thailand suggesting non-normality of the series.
Econometric Models
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4.
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These conventional unit root tests are notorious for leading to lower testing power if no structural breaks are considered. Perron (1989) has suggested that the unit root tests without considering structural breaks would result to the bias in the empirical results. Given that suggestion, many studies further extend the conventional unit root tests by pouring dummies into The 𝑄𝐾𝑆 statistics are lower than 90% significance level indicating non-stationarity for clean energy consumption for 6 countries, i.e., China, India, Indonesia, Malaysia, Pakistan and Thailand, over the whole quantiles. This means that the shocks on clean energy consumption would make permanent effects on the path for these 6 countries. In other words, through results from the quantile unit root test without smooth breaks, these countries should mandatorily implement energy encouragement policy
Yifei Cai, Angeliki N. Menegaki PII: DOI: Reference:
S0140-9883(18)30452-3 https://doi.org/10.1016/j.eneco.2018.11.012 ENEECO 4221
To appear in:
Energy Economics
Received date: Revised date: Accepted date:
7 May 2018 15 September 2018 18 November 2018
Please cite this article as: Yifei Cai, Angeliki N. Menegaki , Fourier Quantile Unit Root Test for the Integrational Properties of Clean Energy Consumption in Emerging Economies. Eneeco (2018), https://doi.org/10.1016/j.eneco.2018.11.012
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Fourier Quantile Unit Root Test for the Integrational Properties of Clean
T
Energy Consumption in Emerging Economies
IP
Yifei Cai1
Economics, Business School, University of Western Australia,
CR
Perth, Australia
[email protected]
US
Angeliki N.Menegaki
TEI STEREAS ELLADAS, UNIVERSITY OF APPLIED SCIENCES Economics and Management of Tourist and Culture Units
AN
Amfissa, Nea Poli, Greece
AC
CE
PT
ED
M
[email protected]
1
Corresponding author, Yifei Cai acknowledges that this paper was carried out while the author
was in receipt of a “University Postgraduate Award (UPA) and an Australian Government Research Training Program Scholarship (RTP)” at The University of Western Australia. [1]
ACCEPTED MANUSCRIPT
Fourier Quantile Unit Root Test for the Integrational Properties of Clean Energy Consumption in Emerging Economies
T
Abstract
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Since the adoption of the Kyoto protocol in 1997 and its entry into force in 2005, as well its aftermath such as the
CR
Doha amendment and Paris agreement, national policies have become more conscious of the usage of clean energy, mostly the different forms of renewable energy and nuclear energy. Ratifying countries and signatories had
US
commited themselves to binding targets for the reduction of greenhouse emissions by 8% with respect to 1990 levels until 2012, also based on the particular contribution to global emissions from each country. This paper
AN
examines the integrational properties of clean energy consumption from eight emerging economies which are also high greenhouse gas emitters. The empirical results show that the clean energy consumption is stationarity for
M
Brazil and Philippines by using a quantile unit root test without smooth breaks (Koenker and Xiao, 2004).
ED
However, after capturing the smooth breaks (Bahmani-Oskooee et al., 2014), we find the clean energy consumption of China, Pakistan and Thailand are stationary. The time-varying deterministic trend with smooth
PT
breaks are more fitted to the path of clean energy consumption in comparison to the deterministic trend without smooth breaks. The paper suggests economic insights useful for policy making.
AC
CE
Keywords: Clean energy; Emerging economies; Fourier quantile unit root; integration;
[2]
Introduction
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1.
AN
US
CR
IP
T
ACCEPTED MANUSCRIPT
Within a framework of soaring global temperature, developing clean energy, namely without carbon
ED
emissions, has been viewed as the main policy to curb the CO2 emissions in the long-run. Previous studies have contributed much to investigate the role of foreign direct investment on clean energy use
PT
(Lee, 2013 and Paramati et al., 2016) and the nexus between clean energy consumption and economic
CE
growth (Cai et al., 2018). However, no studies focus on the integrational properties of clean energy use, although numerous papers test the integration of energy consumption (including primary energy
AC
consumption, coal consumption, natural gas consumption and renewable energy consumption) and CO2 emissions (Narayan and Smyth, 2007; Chen and Lee, 2007; Liddle, 2009; Hasanov and Telatar, 2011; Meng et al., 2013; Gozgor, 2016; Payne et al, 2017). The changes of industrial structure always affect the energy consumption structure. In other words, the heavy industries emit more carbon emissions in comparison to agriculture and service sectors because these industries consume a large amount of fossil fuel to support their long-run growth. With the rising of emerging market countries, these countries are experiencing a significant industrial structure change. The energy consumption structure should, without doubt, be altered with the polluting [3]
ACCEPTED MANUSCRIPT sectors becoming reduced or eliminated. In other words, clean energy will be paid increasingly more attention by countries, worldwide, due to their lower carbon emissions. However, the clean energy consumption always contains many structural breaks which significantly affect their stochastic convergence. This occurs because of the investment and the sunk costs taking place for renewable energy investments at some time and the subsidies they are usually accompanied with. This in turn,
T
entails changes (usually increases) in clean energy production which follows the new investments and
IP
integration into the electricity systems. In fact, many scholars (Lee and Chang, 2008 and Payne et al.,
CR
2014) have shown that the structural breaks in the energy consumption are the main threat against robust estimation. Many empirical papers use dummies to approximate the sharp structural breakpoints
US
in the series. However, Enders and Lee (2012) suggest smooth breaks which could be approximated by the Fourier function. Moreover, the stochastic convergence might perform differently at different
AN
quantiles.
Thus, this paper utilizes a newly proposed Quantile Unit Root test with Fourier Function of
M
Bahmani Oskooee et al. (2018) to examine the integrational properties of clean energy consumption for
ED
8 Emerging Market (EM) countries. This paper provides not only integration over the whole quantiles, but also the integration at each quantile. The merits of the Fourier quantile unit root test could be
PT
summarized as follows. First, the Fourier quantile unit root test could provide asymmetric dynamics by allowing different mean-reverting speed at different quantiles of clean energy consumption. Second, the
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asymmetries revealed by the quantile regression do not rely upon a special assumption about the functional form of nonlinearities and asymmetries. Third, the conventional unit root tests and even
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some which consider structural breaks, all focus on testing on the conditional central tendency. However, through the quantile regression, we could test on clean energy consumption at different quantiles, otherwise we would investigate the whole distribution of clean energy consumption. Specifically, the central tendency is broken down based on the quantile regression. Fourth, many shocks such as wars and political unrest or instability and regulatory policies would make the clean energy consumptions of many countries experience structural breaks. Fifth, because of the structural breaks occurring to clean energy consumption, it is reasonable to test the integrational properties in a framework taking structural breaks into account. Using the Fourier function in the trend function, those [4]
ACCEPTED MANUSCRIPT structural breaks could be approximated as a smooth process. Sixth, the clean energy consumption for some countries such as China, India, Indonesia, Malaysia and Thailand is not subject to normal distribution. As Koenker and Xiao (2004) noted, the non-normality of the series would result to the loss of power by traditional unit root tests2. Overall the originality of our paper is three-fold: First the clean energy (per se)
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integration properties have not been examined so far. Literature has provided abundant
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examples on energy use in general and very few examples of renewable energy, but the clean
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energy has not been investigated as such. Second, the sample of emerging economies has not been investigated in relation to this concept either. The investigation of emerging economies is very
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important, because they have a high economic growth which is fueled mostly by conventional energy, while clean energy is still under a development process and has not stabilized to a certain
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amount. Last, the employed econometric method is novel and recently developed. Thus, the paper
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combines a unique contribution with all these three novel aspects: topic, sample, method. The remainder of the paper is organized as follows. Section 2 reviews the existing studies
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related to the test of convergence of energy consumption. Section 3 presents datasets and descriptive statistics. Section 4 introduces the econometric models. Section 5 shows the empirical results. The last
2.
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section concludes the paper.
Literature Review
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The literature of energy consumption convergence has started more than twenty years ago with the work by Nilsson (1993) and Goldemberg (Goldemberg 1996). These studies found evidence that countries tended to converge to a common pattern of energy use. Afterwards, a number of studies has proliferated both in energy economics and in the broader field of environmental and resource economics with the study of carbon emissions convergence.
2 Meng et al. (2013) utilize the new LM tests based on residual augmented least square regression to solve the problems of non-normal errors when implementing nonlinear analysis.
[5]
ACCEPTED MANUSCRIPT Although convergence per se has been studied in many ways and with different methods, for example with the distribution approaches such as beta convergence (catch up process from high to low convergence), gamma convergence (intra generation mobility) or sigma convergence (spread of distribution). Another strand of energy convergence studies which we will not handle in this paper is the papers
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which study convergence by means of decomposition analysis. Theil decomposition analysis, Divisia
Decomposition methods separate among various effects such as within and between group
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reported.
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Decomposition analysis, Laspeyers or Fisher Index decomposition analysis are the most frequently
formation and an account of within group inequalities.
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decomposition, contribution of energy transformation indexes, contribution of energy to GDP
The interest on whether energy consumption per capita is stationary is motivated by the need to
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know about the effects shocks have on it. If a unit root is present, a permanent effect is due after the
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shock occurence. There are numerous papers that examine the stationarity or non-stationarity of energy consumption and other variables typically perused in the energy-growth nexus. Actually, the
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investigation of stationarity of energy consumption is the first step in every energy-growth nexus study (e.g. Magazzino 2014; 2016; 2017). Hence, practically, stationarity studies would be at least as many
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in number, as the number of energy-growth studies.
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However, the current literature review will not include pure energy-growth nexus studies per se. It includes only studies whose main and exclusive focus is to investigate stochastic convergence of
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energy consumption (through unit root investigation) and does not include studies that fall within the energy-growth nexus field whose focus is mainly on the causality analysis. A succinct literature review for this type of studies up to 2012, is provided by Smyth (2013). To make our search yardstick in the current literature review more concrete, we have employed Scopus bibliographic database (www.scopus.com) with keywords: “energy” plus “structural breaks” or “nonlinearity”, “unit root” and “fractional integration” in their titles, keywords and abstracts and have identified 27 studies on the investigation of the convergence of energy variables which appear to be mostly energy consumption per capita, total energy consumption (in aggregate or disaggregate form). [6]
ACCEPTED MANUSCRIPT None of the studies is concerned with the study of the convergence of clean energy consumption as such. The collected studies are shown in Tables 1 and 2. We provide separately studies with aggregate magnitudes (Table 1) and studies with disaggregate magnitudes. (This is also advised in Menegaki
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and Tsani (2018); Tsani and Menegaki (2018))
[7]
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Table 1. Energy consumption convergence studies Study
Period
Variable
Test
Countries
[1]
[2]
[3]
[4]
[5]
Narayan & Smith (2005)
1979–2000
Energy consumption per capita
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182 countries
ADF
Result
Agg/Disagg
[6]
[7]
I R
1
Univariate tests (with one or 2 breaks): No stationarity
1
Univariate test with break(s): Rejects H0 of unit root for 56 countries namely 31% of the sample
C S
Panel data unit root test: Stationarity (yes)
Ozcan (2013)
Hsu et (2008)
al.
1980-2009
Energy consumption per capita
1971-2003
Energy use
17 Middle East countries
LM (LS and IPS)
Panel SURADF cross-sectional and identifies countries contain root)
D E
M
A
U N
(hosts effects which a unit
LM tests: Stationarity (yes)
84 countries
Conventional unit root tests reveal stationarity even if only one series in the panel is strongly stationary.
1
Chen & Lee (2007)
1971-2002
Energy consumption per capita
CBL panel unit root
104 countries (7 regional panels)
Non stationarity (even with proper consideration of cross sectional correlations)
1
Mishra et al. (2009)
1980-2005
Energy consumption per capita
CBL panel unit root
13 Pacific island countries
60% of individual countries: Stationarity
1
Ozturk & Aslan (2011)
1970-2006
Energy consumption per capita
LM (IPS)
Turkey
LM univariate test without break Non-stationarity except for the residential sector
1
Narayan et al. (2008)
1973-2007
Energy consumption (sectoral)
LM (LS)
Stationarity except for South Australia
1
T P
C A
E C
(7 sectors) Australia states in industries)
[8]
(6 9
Whole sample: Stationarity
ACCEPTED MANUSCRIPT
Study
Period
Variable
Test
Countries
Result
[1]
[2]
[3]
[4]
[5]
[6]
Aslan & Kum (2011)
1970-2006
Energy consumption
LM (LS)
Turkey sectors)
(7
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Linear Unit root is rejected in 4/7 sectors (transport, non-energy use, final energy consumption, cycle & energy).
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Agg/Disagg [7] 1
LM test reveals stationarity with two structural breaks
Hasanov & Telatar (2011)
1980-2006
Primary consumption capita
energy per
Kum (2012)
1971-2007
Energy consumption per capita
LM
LM (FLM)
Yilanci & Tunali (2014)
1960-2011
Energy consumption per capita
Zhu & Guo (2016)
1993-2013
Nuclear consumption capita
Lean &Smyth (2014)
1978-2010
Kula al.(2012)
1960-2005
Kula (2014)
et
1923-2008
D E
T P
energy per
E C
C A
ADF, LM (KSS, S)
Simultaneous use of structural breaks and nonlinear unit root tests contribute to the higher probability of evidencing stationarity
1
15 East Asia & Pacific countries
No break
1
109 countries
FLM Stationarity for 24 countries after acknowledging the unknown nature of breaks. For the remaining countries, the standard LM test is use
1
27 countries
If no breaks Non-stationarity(low, upper-middle,developing countries)
0
U N
A
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C S
178 countries
CBL panel unit root, Longitudinal clustering
One break
Nonstationarity Stationarity
If breaks Stationarity(total, lower-middle,high, developed countries)
Final energy demand (10 fuel types in 4 sectors)
LM (SP, LS)
Malaysia
Stationarity for 50%-70% of energy types in 25%-50% of sectors (no break, one break and two breaks)
0
Electricity consumption capita
LM (LS)
23 high income countries
Stationarity for 21/23 countries
0
LM (LS)
Turkey
Stationarity
0
Electricity
per
[9]
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Study
Period
[1]
[2]
Lean & Smyth (2009)
Variable
1973m1- 2008m7
[3] consumption capita
Test
Countries
Result
[4]
[5]
[6]
Petroleum consumption (24 types in 5 sectors)
LM tests
(FI)
USA sectors)
U N
1982-2007
Coal consumption
LM (IPS, CBL, DBC, W)
Apergis al.(2010b)
1980-2007
Total Natural gas consumption in billions of cubic feet (per state)
Panel unit roots (various)
Apergis & Payne (2010)
1960-2007
Petroleum consumption
Aslan (2011)
1960-2008
Natural consumption
C A
E C
Pestana Barros et al. (2012)
1981m1- 2010m10
Pestana Barros et al. (2013)
1973m1- 2011m10
gas
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Univariate unit roots: Non-stationarity for less than 50% of sectors and products for 9/24 series
0
Multivariate unit roots: Non-stationarity for the commercial and industrial sectors, Stationarity for the residential sector. Evidence for fractorial integration (FI)
USA states)
(50
Stationarity with structural breaks
0
USA states)
(50
Panel unit roots:
0
LM (LS, NP)
USA states)
(50
Stationarity; Endogenously structural breaks
LM (KSS)
USA states)
(50
60% of states follow non-linear behavior
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D E
T P
(5
[7]
C S
Apergis et al. (2010a)a et
T P
per
Agg/Disagg
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Nonstationarity
Panel unit roots with endogenously determined structural breaks: Stationarity determined
0 0
For 27/50 states: Nonstationarity For 23/50 states: Stationarity
Renewable consumption
energy
FI
USA
Nonstationarity
0
Nuclear consumption
energy
FI
USA
Nonstationarity
0
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Study
Period
Variable
Test
Countries
Result
[1]
[2]
[3]
[4]
[5]
[6]
Gil- Alana et al. (2010)
1973m1- 2009m5
Electricity consumption
Apergis Tsoumas (2012)
&
1989m1-2009m12
Fossil, coal electricity consumption
Apergis Tsoumas (2011)
&
Narayan al.(2008)
et
Borozan Borozan (2015)
&
T P
FI
USA
Nonstationarity
and
FI
USA (different sectors)
Stationarity
1989m1-2009m12
Solar, geothermal and biomass energy consumption
FI
USA (different sectors)
Stationarity
1971-2003
Crude oil production
LM
60 countries
No breaks
2001-2013
Electricity consumption capita
Wang et al. (2016)
1965-2011 (nuclear)
Herrerias al. (2017)
et
1995-2011
Mishra and Smyth (2017)
1973-2014
1990-2011 (RES)
D E
Non-fossil consumption
energy
Electricity, consumption
Coal
T P
E C
C A
LM per
Energy consumption per capita
I R
C S
U N
A
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LM (LS, IPS)
Breaks
[7] 0 0
0
Nonstationarity
0
Stationarity
Croatia
Nonstationarity
0
Japan
Nonstationarity (nuclear)
0
FLM
Club modeling
Agg/Disagg
Stationarity (RES) convergence
LM and RALS-LM
Chinese regions
Different convergence between rural and urban areas
0
Australia (sector level)
Convergence is confirmed for 6 out of 7 sectors
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Note: Column [4], ADF: Augmented Dickey-Fuller, LS: Lee and Strazicich 2003-one break: SP:Schmidt and Phillips (1992)-no break, IPS: Im, Pesaran et al. 2003, SURADF: Panel Seemingly unrelated regressions (Breuer et al. 2001), CBL: Carrion-i-Silvestre et al. (2005), KSS: Kapetanios and Shin(2003) and Sollis (2004), F.I. fractorial integration ((Nielsen 2005), (DBC: Del Barrio-Castro et al. 2005; W: Westerlund 2005); NP: Narayan and Popp (2010), FLM: Fourier type Lagrange multiplier (Enders and Lee 2012), S: Sollis (2004) with simultaneous structural change and nonlinearity
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1: Aggregate, 0: Disaggregate
T P
I R
C S
A
U N
D E
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T P
E C
C A
[12]
ACCEPTED MANUSCRIPT Studies either focus on whole economies or group of economies or even sectors of a particular economy such as is done in Ozturk and Aslan (2011) or Aslan and Kum (2011). Typically studies employ tests that have not been used before in a particular country or set of countries and thus they corroborate their results and prove their robustness to make sound policy recommendations. They also demonstrate the difference of results when breaks are inserted (Ozturk and Aslan, 2011) and linear tests vs non-linear unit root tests are employed (Aslan and Kum, 2011). Apparently only four three studies in Table 1, focus on the convergence of clean energy
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consumption. These are Zhu & Guo (2016), Pestana Barros et al. (2012) and Pestana Barros et al. (2013) for nuclear energy and renewable energy consumption and Apergis & Tsoumas (2011) for solar,
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geothermal and biomass energy consumption. Particular focus on these eight emerging economies has
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not taken place so far. Thus our study is novel both in the topic and the employed sample. Apparently, none of the studies in Table 1 deals with the investigation of clean energy convergence. Most of the studies deal with total energy or various disaggregate forms of energy
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consumption. Most of the studies find evidence of convergence which is improved after the employment of structural breaks. Breaks are some crucial time moments after which energy consumption behavior stabilizes towards a certain amount or pattern. These events may be new
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policy generation occurrences or economic and political events.
Furthermore, when one observes the results of previous studies on the convergence of renewable energy, or nuclear energy or natural gas or other clean form, then one sees that
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non-convergence is the main result. This reveals that the penetration of renewable energy sources is still under full development in most economies around the world and that the relevant studies
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are still so few that do not allow us to reach a firm conclusion. This justifies the need for the research carried out in the current paper.
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Throughout the time span of the current analysis, namely from 1965 to 2016, all the emerging economies have experienced a high average GDP annual growth rate. Brazil has grown at a yearly 4.04%, China at 6.50%, India at 7.17%, Indonesia at 4.43%, Malaysia at 5.89%, Philippines at 4.63%
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and Thailand at 5.09% (WDI, 2017). The corresponding figure for high income countries has been 2.76% and for low middle income countries 4.56%. This shows the high GDP growth experienced in the
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emerging economies. Particularly Brazil, since 2015 has had a negative growth, but the rest of the sampled countries have pertained a positive economic growth despite the world economic crisis. That been said, it is more than likely that for this economic growth to be maintained, a considerable amount of energy consumption is necessary. This generates greenhouse gas emissions. Thus, emerging economies are facing the issue of energy security (Sadorsky, 2009), given that most of fossil energy proven reserves are found in other countries such as the Middle East, Russia etc. Energy security combined with environmental considerations, brings forward the development of clean energy sources in emerging countries. Two of them, namely China and India are among the top five renewable energy producers. According to Financial Times (2017), emerging economies are close to overtaking the highly developed countries in the renewable energy production with an installed capacity of wind and solar to 307GW and 272GW respectively, fulfilling a 51% and 53% respectively. Evidence for convergence is an issue of huge importance for international environmental organizations since the [13]
ACCEPTED MANUSCRIPT increase of renewable and generally of clean energy is a priority in environmental policy agendas and a crucial result for emerging economies which are to blame for their significant amount of emissions.
3.
Datasets and Descriptive Statistics
To test the stochastic integrational properties of clean energy consumption, we should first define the concept of clean energy. Based on the previous studies of Lee (2013), Paramati et al. (2016) and Cai et
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al. (2018), the term clean energy is regarded as the energy which does not produce CO 2 emissions. That been said, clean energy is regarded as the sum of the energy consumption from nuclear energy and
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renewable energy3. All the data used in this paper has been selected from BP Statistical Review of
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World Energy covering the period from 1965 to 2016 expressed in million tonnes of oil equivalent. The countries selected for this paper include Brazil, China, India, Indonesia, Malaysia, Pakistan, Philippines and Thailand, based upon data availability. The descriptive statistics are listed in Table 2. Moreover,
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these countries have signed and ratified the Kyoto protocol. As reported in Table 2, the maximum of clean energy consumption is in China with 483.5916 million tonnes of oil equivalent. However, the minimum of clean energy consumption is in Malaysia with 4.8633 million tonnes of oil equivalent.
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Besides, the standard deviation of China is 112.6459 which is the highest among the countries. For skewness, only Philippines negatively skewed. In terms of the kurtosis, China, India, Malaysia and Thailand is over 3. For the normality test, we find that null is rejected for China, India, Indonesia,
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Malaysia and Thailand suggesting non-normality of the series.
Econometric Models
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4.
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