Effects of financial development,economic growth and trade on electricity consumption: Evidence from post-Fukushima Japan

Effects of fi nancial development, economic growth and trade on electricity consumption: Evidence from post-Fukushima Japan

Abdulkadir Abdulrashid Ra fi ndadi

^a,n

, Ilhan Ozturk

^b

aFaculty of Social Sciences, Department of Economics Usmanu Danfodiyo University, Sokoto, Nigeria

bFaculty of Economics and Administrative Sciences Cag University, Adana -Mersin karayolu, 33800 Yenice, Turkey

a r t i c l e i n f o

Article history:

Received 11 May 2015 Received in revised form 12 July 2015

Accepted 19 October 2015

Keywords:

Financial development Growth

Energy consumption Cobb–Douglas

a b s t r a c t

This study examines the long-run and short-run effects offinancial development, economic growth, export, imports and capital on the Japanese energy predicaments as a result of the foregoing energy crisis in the country. To ensure a robust outcome, the study applied the extended Cobb–Douglas production function and used time series data from 1970 to 2012. Following to this, structural break unit root test, ARDL bounds test approach to cointegration and the Johansen cointegration test were applied. In addition, the VECM Granger causality framework was used in determining the causal relationship between the variables. Thefindings of the study establish that, in the long-run a 1% rise infinancial development, economic growth, exports and imports in Japan will exert a significant pressure on the Japanese electricity consumption by 0.2429%; 0.5040%; 0.0921% and 0.2193% respectively. However, capital was found to decline energy consumption in all material respect. In the short-run, the study discovered how a 1% rise in the dynamics of financial development, economic growth, exports and imports to add to the Japanese electricity predicaments by 0.2210%; 0.5840%; 0.0521% and 0.2031%

respectively. The existence of the feedback relationship between most of the variables was discovered, while, economic growth, exports, imports, and trade openness were found to Granger-cause electricity consumption. The study advocates the adoption of massive but competitive renewable energy system in Japan. How it should be done and why it should be done are carefully set by this study.

&2015 Elsevier Ltd. All rights reserved.

Contents

1. Introduction . . . 1073

2. Literature review . . . 1075

3. Data and methodological framework . . . 1076

4. Results and discussions . . . 1077

4.1. Exports model . . . 1080

4.2. The VECM Granger causality analysis . . . 1080

5. Conclusion and policy implications . . . 1081

References . . . 1083

1. Introduction

Multiplicity of energy economics literature have established that electricity consumption is a crucial element to national

productivity. It is also argued that electricity consumption facil- itates sustainable economic growth and ensures the continuity of national prosperity irrespective of the direction of causality. Fol- lowing to this, the fast-growing need among nations for a sig- nificant rise in sustainable economic growth is increasingly becoming a competitive challenge considering the damages cre- ated by the periods of recentfinancial crises. In addition to that, the need to attract and sustain the huge volume of international Contents lists available atScienceDirect

journal homepage:www.elsevier.com/locate/rser

Renewable and Sustainable Energy Reviews

http://dx.doi.org/10.1016/j.rser.2015.10.023 1364-0321/&2015 Elsevier Ltd. All rights reserved.

nCorresponding author.

E-mail addresses:aarafi[email protected](A.A. Rafindadi), [email protected](I. Ozturk).

investment inflow through FDI and other international investment mechanism constitute the cornerstone of every nation. This is because; investment without sufficient, efficient and sustainable energy is of no significant value. Equally important is the fact that the old existing economic literature which emphasized for coun- tries to pursue international trade has now been condemned due to the recurring negative influence of unprecedented financial crisis Tang et al. [40]. Following to this, electricity is seen as a multifaceted development carrier in a modern global economy that has the embodiment and the characteristics of cementing and sustaining human welfare, investment, productivity, exports and imports. These factors in turn accelerate the tides of national economic growth and prosperity. In spite of key economic dama- ges afflicted by the recentfinancial crisis to the Japanese economy which rendered the second global economic giant to drop to the third position, yet, Japan in 2011 was afflicted by a natural disaster.

The 9.0 earthquake in Fukushima Daiichi was reported to have led to the shutdown of 8 nuclear plants. This development resulted in a considerable loss of electricity production, physical and human capital. The combined effect of this phenomenon caused addi- tional stress on the country’s economic growth wherewithal.

According to the Japanese ministry of trade and industry[17], the damages caused by this disaster were approximated to range between USD 195 billion to USD 305 billion. The latter amount (USD 305) was estimated to be equivalent to the quadruple cost of damages caused by Hurricane Katrina’s $81 billion, and almost equivalent to Greece’s GDP, and two times the GDP of the New Zealand Nanto et al.[26]. These are apart from insurance claims estimated at the tune of USD50 billion. While the cost of nuclear pollution and contaminants were to date not confirmed.

Additional substantiation provided by the IHS Global Insight [14]pointed out that economic growth in Japan was envisaged to have a significant boost to an estimated level of 0.5%, after the 2007/2008financial crisis; unfortunately, the Fukushima disaster overturned these expectations to a 0.0% level of economic growth in March 2011. These myriads of economic traumas plunged the Japanese economy into deep recession, making the economy to contract by 3.6%. Following this development, and to show that the level of economic recession had not improved much up to 2014, the Economic Watch[7]reported in the third quarter of the same year, that Japan was expecting 0.4% contraction in its econ- omy, but disappointingly the outcome shows an exceededfigure to the range of 0.5%. In another related development, The Inter- national Business Outlook[41]reported that Japan’s national debt was estimated at 1 quadrillion Yen that is USD 10.46 trillion, in the second quarter of 2014. These figures were reported to be far greater than the German, France, and the United Kingdom economies combined. These massive debts were estimated to be 240% of the Japanese GDP. Parallel to this development and con- sidering the contentious economic doldrums recorded in Japan during the Fukushima energy crisis, the value of the yen was reported to have deteriorated from 83.8 Yen/USD on February 15, 2011, to 122 Yen/USD in 2012, and down to 118.45 in 2015. To reduce the negative consequence of the devaluing position of the Yen on the Japanese trading relationship with the outside world, Nanto et al.[26]pointed out that the Bank of Japan injected $418 billion (i.e. 33 trillion Yen) into thefinancial markets, becoming a far more exceedingfigure of what was injected to salvage the“too big to fail”companies in the US as a result of the 2008financial crisis which is put at USD 300 billion[11].

The implication of a devaluing yen amidst crisis and shrinking economy was observed in the possibility of making the Japanese productive entities and exports weaker and less competitive in the world markets. In addition to that, since China's Yuan has been linked closely to the value of the dollar, the Chinese exporters are likely to gain further price competitiveness relative to those from

Japan and this will ultimately affect the Japanese exports and hence an increased balance of trade problems. To show the implication of this development, the Economic Watch[7]reported that the trade deficit in Japan as at September 2014 deteriorated to JPY 767 billion.

In spite of the above economic adversities, Japan was said to be the second-highest electricity consumer in Asia. In 2012, the country's total electricity generation was put at less than 1000 Terawatts[13]. The IEA [13]continue to assert that the decom- position of that bloc figure shows that 338 TW h was obtained from coal, while 408 TW h from gas while only 14 TW h was generated from nuclear as against 274 TW h that was produced from the same nuclear energy in 2010. In addition to that 161 TW h was also produced from fossil fuel and thisfigure was found to increase by 94 TW h as against what was obtained in 2010. Similar to that line of development, 84 TW h was also found to have been produced from hydro. Surprisingly, the contribution of renewable energy in Japan in 2013 was found to be meager, and it was estimated in that period that solar energy contributed 10 TWh and 5 TW h from wind electricity generation. In addition to that, geothermal was found to contribute 2.6 TW h, biomass and waste 41 TW h. The EIA[8], continue to assert that in May 2012 Japan was found to have lost completely its wherewithal’s of producing nuclear energy power for thefirst time in over forty years and this was due to the devastation of the 2011 earthquake.

In a bid to save the country from massive energy shortages, the government managed to operate two reactors in July, 2012 which produced an estimated nuclear energy of about 2.4 GW, following to other observed complications from the operation of these two reactors, the government decided to halt their operation in Sep- tember, 2013, thus leaving Japan with a complete loss of nuclear energy for the second time in the history of the country. Following to this development, and considering the enormous electricity shortages Japan is suffering from the EIA[8]pointed out that Japan had to resort to massive rolling blackouts and at times the risks of unprecedented blackouts were also observed. This development reduced the outputs of major existing energy intensive companies operating in Japan. There was also a significant tension for the rise in the cost of electricity production and consumption in the country as claimed by the EIA[8].

From the foregoing development, the major contributions of this study is to examine the position of the long-run and short-run effects of the Japanese exports, imports,financial development and economic growth on the current electricity predicaments of the country. To ensure this, the study determines at what degree could the selected variables exert significant pressure on the country’s electricity demand and how could this be responsible to the slow piquing of the country’s economic growth prospects? In addition to that, the study also investigates if the current capital formation processes of the country have additional pressure to the electricity predicaments of the country? From these empirical findings, the study assesses the likely policy implications and offers some recommendations. To make this study unique and apart from its multivariate contributions, the study used the most extensive econometric estimation procedures and also ensured the application of up to date econometric methodology. In this new dimension, the study applied the extended Cobb–Douglas pro- duction function and used time series data for 1970–2012. It is remarkable to evaluate the case of Japan in order to establish if there is any positive outlook for the possibilities of the economic growth prospects for the former No. 2 world economic giant. The findings of this study will equally be a lesson to other countries with similar electricity and macroeconomic challenges. To ensure successful accomplishment of this study, this paper is organized in five sections: apart from the introduction above, section two provides recent empirical literature reviews linking energy

consumption, trade openness, financial development and eco- nomic growth; Section 3 is the methodology section which introduce the data, the model specification, and the model esti- mation procedure;Section 4contains the results and discussion.

Finally,Section 5presents the conclusion and policy implication.

2. Literature review

The existence of overwhelming researches on electricity con- sumption and economic growth nexus in the energy economics literature has to date not determined an ending search or a balanced ground on electricity consumption and economic growth among continents. For instance, the assertions of Ozturk[31]and Omri [29] outlined a comprehensive literature survey on the determinants of energy growth nexus and also on the electricity growth nexus. This captivating development motivated early researchers to underscore the contributions of energy consump- tion to economic growth in advanced economies. For instance, Rafindadi [34] established how the effects of financial develop- ment and trade openness influence the German energy con- sumption. Thefindings of the author further uncovered economic growth to be the cardinal element that piques energy consump- tion in Germany. Other variables of the study used by the same author discovered how financial development, capital use, and trade openness tend to have a negative influence on the German energy demand. In contrast to thefindings of[34,35], the author identified how the expanding economic growth prospects of the United Kingdom could pose a threat to the country’s existing energy predicaments. Although the results of the author indicated that economic growth is negatively linked with energy demand in the United Kingdom. In contrast to that, the study discovered how the negative trade balances of the United Kingdom to be the major additive factor to the country’s electricity predicaments. The comparative influence of these studies indicates that electricity consumption and economic growth have no single unified direc- tion in which it contributes to a country’s economic growth pro- spects. These developments warrants endless searches in thefield of energy economics in both tranquil and in energy crisis periods.

Kwon et al.[21] analyzed the effects of induced reduction of electricity consumption through raising electricity tariffs in the short-run due to the escalation in electricity demand by most countries. Thefindings established that a reduction in electricity use affects economic activity, thereby, impacting negatively on the profitability, employment prospects, national output, and subse- quently economic growth. Sun and Anwar[39]applied the context of trivariate vector autoregressive framework to study the rela- tionship between entrepreneurship, electricity demand and industrial production outputs with respect to Singapore's manu- facturing entities. Thefindings reveal the existence of a feasible long-run relationship between electricity demand and entrepre- neurial output in Singapore. According to the results, the growth hypothesis concerning energy consumption and economic growth are validated in the case of Singapore. In another related devel- opment, using panel of 160 countries from 1980 to 2010, Karanfil and Li[18]concluded that high electricity consumption was found to be sensitive to regional differences, continental income varia- bility, degree and level of urbanization attained and supply risks.

Mouton[25]in his research contributions examined the impacts of electricity sector reforms in the Philippines, which took place in the year 2000. The aim of the author is to assess whether the impacts of the Philippines electricity sector reform could have an effect on the country’s electricity supply and tariffs. Thefindings of the study suggested the need for ensuring efficient electricity supply and clearer electricity regulatory framework which should

be accompanied in the reform models if the impacts of the reform exercise are to permeate in all sectors of Philippines economy.

Fukushige and Yamawaki[12] studied the factors that neces- sitate electricity supply constraint, electricity generation capacity and the factors accounting for electricity demand in Taiwan. The findings of the authors established that electricity consumption in Taiwan is fraught with key supply constraints in the early periods of 1959–1972. In 1973 electricity generation capacity was found to have attained an efficient level in Taiwan. Following this investi- gation, the authors established that the economic growth prospect of Taiwan came to be more robust during that period. The con- clusion of the authors unanimously agreed that ordinary Granger causality approach are not in any way efficient and effective in revealing the relationship that may exist between electricity con- sumption and economic growth of a country. They further argued that in most parts of the developing world of today, electricity supply constraint sometimes plays a significant role when inves- tigating the relationship between energy consumption and eco- nomic growth. Marques and Fuinhas[24]studied the comparative impacts of renewable sources of energy which they termed as special regime and the conventional sources of electricity which they termed as the ordinary regime using the Portuguese elec- tricity generation network. In the first instance, the authors appraised the relationships that exist between the two regimes and the respective economic activities that ensued within them.

The findings of their study established the existence of a com- plementary relationship between the two regimes. While key economic activities were found to cause significant rise in elec- tricity consumption, however, in a more close analysis it was discovered that it is the special regime that causes more economic boom and contrary was found to be the case with respect to the ordinary regime.

Using a panel of 224 cities in China from 2002 to 2007, Elliott et al.[9]assessed the possibilities of whether declining economic growth in China could be attributable to poor energy distribution network. To ensure robust outcome from this study, the authors applied the leading electricity network distribution theory. While the propositional law of power distribution was used as the main gauging factor in the study. The propositional law established that the size of an economy and its prospects of energy consumption could be measured on the basis of capital by capita and this should be anchored on the basis of the electricity consumption per capita.

According to the authors, the law of efficient electricity distribu- tion is based on the direction that if an exponential bound of½ and ¾is discovered in the study then the existence of distribu- tional efficiency in China is quite certain. Following this estab- lishment, thefindings of the authors discovered a result that is a bit higher than 2/3 and these estimates were compared with similar finding of a US-based study conducted in 2011. This development compelled the authors to succumb to the fact that their study is more robust than thefindings obtained with respect to that of the US. In conclusion the authors, however, noted the currency of the period in which they conducted their study and this made them draw an observation that by implication of their findings there exist significant drawbacks on the overall energy distributional efficiency network particularly at the tail end of the study period.

Kim[19]conducted a study that aimed at developing an elec- tricity convergence parameter with respect to the level of devel- opment attained by 109 continents. To ensure this, the modeling pattern of the case study areas were allowed to exhibit apparent heterogeneous properties using the logttest convergence meth- odology. From this analysis, the study reported that, the 109 samples converged at a point indicating significant electricity intensity but the finding did not explain the level of per capita electricity consumption of the continents in comparison with the

level of development that has been achieved in those continents.

In this respect, the study proceeds to apply the multi-component model that decomposed the position of the selected continents. In that second stage analysis, the study discovered how 24 developed economies exhibited strong convergence with respect to all indi- cators. The study concluded that electricity consumption is a sig- nificant indicator to the rise in per capita income in those con- tinents which in turn leads to economic development in the selected sample.

On the aspect of electricity efficiency and competitiveness, the study of Nazemi and Mashayekhi[28]identified how the recently restructured Iranian electricity market impacted positively on the country’s energy production and distribution. In their study, the authors used 2006 and 2012 as the lead study periods. Thefind- ings indicated an insignificant contributions of the restructured electricity market in enhancing electricity production and com- petitiveness in the early periods. The study then proceeds to investigate the dynamics of electricity demand in the after restructuring periods i.e. 2012 and the effects generated by pro- duction efficiencies. The two periods were then compared to determine a common benchmark. The findings of the authors established a non-efficient tendency in both periods. This situation were found to be attributable to electricity marketing distortions commonly traceable to the learning curve effects in the post- restructuring periods, and this have greatly impacted on the newly restructured electricity market in Iran.

Al-Mulali and Che Sab [1] examined the impact of energy consumption on the economic and financial development of 19 countries by taking the periods of 1980 to 2008. The results show that energy consumption enables these countries to achieve high economic and financial development. However, the high devel- opment that these countries have achieved in the late three dec- ades increased the CO2 emission of the respective continents.

Kyophilavong et al.,[20]explored the relationship between energy consumption, trade openness and economic growth in case of Thailand. The findings of the authors showed how energy con- sumption could stimulate economic growth and how Trade openness could add to economic growth. Also, thefindings of the authors established that the causal relationship between the variables in the case of Thailand showed energy consumption is the Granger-cause of economic growth in that country. Omri et al.

[30]applied simultaneous equation modeling approach in a panel of 12 MENA countries from 1990–2011. The author aims to investigate the impacts of the relationship that may subsist between financial development, CO2 emissions, trade and eco- nomic growth. The results showed the existence of bidirectional causality between CO2emissions and economic growth. In addi- tion to that, the study further identified the existence of bidirec- tional causality suggesting the interrelationship between eco- nomic growth and trade openness. Moreover, the feedback hypothesis was discovered. These discoveries validated the exis- tence of a perfect relationship to exist between trade openness andfinancial development. The causality relationship, on the other hand, reveals the existence of unidirectional causal relationship fromfinancial development to economic growth and from trade openness to CO2emissions. The authors concluded with assertion that the environmental Kuznets curve does exist in all continents and that policy makers have significant policy challenges of bal- ancing the impacts and implications of the findings in order to ensure a balanced environmental benefits and efficient energy use in the respective MENA countries studied.

From the perspective of the above reviews, there are very limited studies on the relationship between electricity consump- tion and economic growth in the case of Japan. To the best of our knowledge, there are only 5 studies in which electricity consumption-economic growth nexus has been examined for

Japan. In the four of these studies[23,3,37], discovered the exis- tence of causality running from electricity consumption to eco- nomic growth and no causal relationship is found in the study of Narayan and Prasad[27]. In addition, Cheng[5]found the exis- tence of a causal relationship from GDP to energy consumption, Erol and Yu[10]found bidirectional causality, and Soytas and Sari [38]found causality from energy consumption to GDP for Japan.

Most of these studies were using only two variables (energy consumption and growth). In other words, they employed bivari- ate models that cause an omitted variable problem. To avoid this, the current study employed multivariate modeling approach and taking the post-Fukushima energy crisis in Japan which plunged the country into series of electricity and macroeconomic chal- lenges. The aim of this study apart from its multivariate modeling approach is to figure out the long-run and short-run macro- economic repercussion of electricity consumption on the Japanese economic growth prospects.

3. Data and methodological framework

The study applied time series data from 1970 to 2012. The data sets were obtained from the World Bank Development Indicators [42] (CD-ROM). The variables used in this study are real GDP, energy consumption (kg of oil equivalent), real domestic credit to private sector, real exports, real imports and real capital stock;

each in per capita terms. Fig. 1 below shows the trend of the selected variables in Japan.

To examine the long-run effects between energy consumption and economic growth, the following Cobb–Douglas production function is employed in this study:

G¼AE^α1K^α2L^α3e^u ð1Þ

where,Gis real domestic output;E,KandLdenote, energy, capital and labor respectively. The term refers to technology ande the error term assumedN(iid).The output is elasticity with respect to energy consumption, capital and labor is and

α

³ respectively.

Following to the direction of the Cobb–Douglas model, it is certain that when technology is restricted to (

α

¹þ

α

²þ

α

³¼1) the result will be constant returns to scale. In the model developed by this study, technology was allowed to be endogenously determined by the level offinancial development and international trade within an extended Cobb–Douglas production function. This is because, financial development promotes economic growth via capital formation that in turn makes capital more efficient in usage, in addition to thatfinancial development, encourages FDI inflow and transfer of superior technology and managerial skills. International trade, on the other hand, helps technological advancements and

6 8 10 12 14 16 18

65 70 75 80 85 90 95 00 05 10

LEC LFD LY

LK LEX LIM

LTR

Year

Fig. 1.Trends of variables in Japan.

its diffusion. The model can be written as:

AðtÞ ¼

φ

UTRðtÞ^αFðtÞ^δ ð2Þ where

φ

is time-invariant constant, TR is an indicator of trade openness and F isfinancial development. Substituting Eq. 2into Eq.1:

GðtÞ ¼

φ

:EðtÞ^δ1FðtÞ^δ2TRðtÞ^δ3KðtÞ^βLðtÞ^1β ð3Þ Following, Lean and Smyth[22], Rafindadi and Ozturk[36]and also Rafindadi[34,35], the study divide both sides by population and obtained each series in per capita terms. However, the impact of labor was left constant. By taking log, the linearized Cobb– Douglas production function is:

lnGt ¼ β1þβ2lnEtþβ3lnFtþβ4lnTRtþβ5lnKtþβ6lnLtþμt

ð4Þ where lnGt, lnEt, lnFt, lnTRtand lnLt, lnKtrepresent real GDP, energy consumption, real domestic credit to private sector as a proxy forfinancial development, real trade openness labor and real capital use respectively, each is transformed into logarithm and expressed in per capita terms. In this paper the study used three different indicators of trade openness in per capita terms;

real exports, real imports, and real trade (exports plus imports as share of GDP ), which are then, estimated as separate equations.

The term

μ

tis a random error term. The specification also captures the relationship between energy use and economic growth where technology takes effect through the financial development and international trade. Prior to testing for cointegration, the statio- narity of each series was checked using the ADF and the PP test with trend and intercept. The study noted that this test cannot capture the presence of structural breaks in the series. Following to this shortcomings and after the accomplishment of the ADF and the PP test, the study proceed to apply the Zivot and Andrew[43], and the Clemente et al.[6]unit root tests to identify the possibility of an existing structural break within the series. When these tests are accomplished, the Pesaran et al. [32] ARDL bounds testing approach to cointegration is applied in the determination of the long-run and the short-run dynamics of the variables. This test according to Inder[15]is found to have serial advantages over the Johansen cointegration techniques. These include, the provision of consistent results irrespective of the order of the variables in so far they are within the mix order ofI(0) andI(1) or where there is mutual integration. This is in contrast with the Engle and Granger and the Johansen and Juslius[16]approaches. Inder[15]continues to maintain that the ARDL bounds test could effectively deal with the issue of endogeneity problem and is also best at reporting an unbiased test statistics even if the sample is small. In addition to that, the ARDL model can efficiently correct for omitted lag vari- able bias. In order to implement the ARDL bounds testing proce- dure in this study, Eq.(1) is transformed into the unconditional error correction model (UECM) as indicated below:

Δ

^lnECt¼c0þX^p

i¼1

ci

Δ

^lnECtiþX^p

i¼1

di

Δ

^lnFtiþX^p

i¼1

di

Δ

^lnCti

þX^p

i¼1

di

Δ

^lnYtiþX^p

i¼1

di

Δ

^lnTRti

þ

π

^1lnECt1þ

π

^2lnFt1þ

π

^3lnCt1þ

π

^4lnYt1

þ

π

^5lnTRt1þ

π

^DDUMtþu1t ð5Þ

Δ

^lnCt¼c0þX^p

i¼1

ci

Δ

^lnCtiþX^p

i¼1

di

Δ

^lnECtiþX^p

i¼1

di

Δ

^lnFti

þX^p

i¼1

di

Δ

^lnYtiþX^p

i¼1

di

Δ

^lnTRti

þ

π

^1lnCt1þ

π

^2lnECt1þ

π

^3lnFt1þ

π

^4lnYt1

þ

π

^5lnTRt1þ

π

^DDUMtþu2t ð6Þ where

Δ

denotes thefirst different operator, thec0andd0are the drift components, DUM is dummy variable to capture the struc- tural break date,pis the maximum lag length andut is the usual white noise residuals. The procedure of the ARDL bounds testing approach has two steps. Thefirst step isF-test for the joint sig- nificance of the lagged level variables. The second step is the estimation of long-run and short-run parameters by using the error correction model (ECM). To ensure the convergence of the dynamics to the long-run equilibrium, the sign of the coefficient for the lagged error correction term (ECM_t–1) must be negative and statistically significant. Further, the diagnostic tests comprise the testing for the serial correlation, functional form, normality, and the heteroscedasticity[33]. Once the variables are cointegrated for the long-run relation, then long-run and short-run causality can be investigated. The existence of a long-run relationship between financial development, economic growth, export, imports, capital and energy consumption requires us to detect the direction of causality between the variables by applying the VECM (vector error correction method) Granger causality framework. The vector error correction method (VECM) is as follows:

Δ

^lnECt

Δ

^lnCt

Δ

^lnFt

Δ

^lnYt

Δ

^lnTR

2 66 66 66 4

3 77 77 77 5

¼ b1

b2

b3

b4

b5

2 66 66 66 4

3 77 77 77 5 þ

B11;1 B12;1 B13;1 B14;1 B15;1

B_21;1 B_22;1 B_23;1 B_24;1 B_25;1 B_31;1 B_32;1 B_33;1 B_34;1 B_35;1 B_41;1 B_42;1 B_43;1 B_44;1 B_45;1 B_51;1 B_52;1 B_53;1 B_54;1 B_55;1 2

66 66 66 4

3 77 77 77 5

Δ

^lnECt1

Δ

^lnCt1

Δ

^lnFt1

Δ

^lnYt1

Δ

^lnTRt1

2 66 66 66 4

3 77 77 77 5

þ:::þ

B_11;m B_12;m B_13;m B_14;m B_15;m B_21;m B_22;m B_23;m B_24;m B_25;m B_31;m B_32;m B_33;m B_34;m B_35;m B_41;m B_42;m B_43;m B_44;m B_45;m B51;m B52;m B53;m B54;m B55;m

2 66 66 66 4

3 77 77 77 5

Δ

^lnECt1

Δ

^lnCt1

Δ

^lnFt1

Δ

^lnYt1

Δ

^lnTRt1

2 66 66 66 4

3 77 77 77 5 þ

ζ

1

ζ

3

ζ

3

ζ

4

ζ

5

2 66 66 66 4

3 77 77 77 5

ðECMt1Þþ

μ

1t

μ

2t

μ

3t

μ

4t

μ

5t

2 66 66 66 4

3 77 77 77 5

ð7Þ

where the difference in operator is ð1LÞ, and the ECM_t1 is generated from the long-run relation. The long-run causality is indicated by the significance of the coefficient for theECMt1by using the t-test statistic. The F statistic for thefirst-differenced lagged independent variables is used to test the direction of short- run causality between the variables.

4. Results and discussions

To make our investigation robust, the study starts with the assessment of the unit root test. This is in order to examine the stationarity properties of the variables. To ensure this, the study applied the ADF and PP unit root tests. The results of ADF and PP are presented inTable 1. The results show that all the variables are not stationary at a level. This development suggests the existence of a unit root problem within the series thereby, making it impossible to reject the null hypothesis of the unit root problem.

However, after taking the first difference of all the variables, the series were found to be stationary with intercept and trend. This leads us to reject the null hypothesis of the unit root problem. At the end, the study found all variables to be stationary at thefirst difference and 1% level of significance. However, it is only the variable of trade openness that is found to be significant at 5%.

The major problem with ADF and PP unit root tests is that they do not provide information on the structural breaks position of the

series. This development could in actual sense provide an ambiguous result if no action is taken. To solve this problem, the study applied the Zivot and Andrew[43]unit root test. This test is best at accommodating single unknown structural break in the series. The selection of the break date is base onT-statistic and the break date is be selected where the evidence is favorable to the null hypothesis. In this test, the critical values of ADF unit root test are used. The results of the test are shown inTable 2, thefinding from that Table shows how all the variables are non-stationary at a level in the presence of structural breaks. The test indicated the

structural breaks dates to be 1999, 1987, 1988, 1990, 1986, 1988 and 1988. The structural break problems are found within the confines of the series of electricity consumption, economic growth,finan- cial development, capital, exports, imports and trade openness respectively. However, at the first difference, the variables are found to be stationary. Following this development we conclude that the variables are integrated atI(1).

The Zivot and Andrew unit root test accommodates informa- tion on a single unknown structural break in the series but ignore the role of other structural breaks that may exist within the series.

To further investigate and solve this problem of two recurring structural breaks in the series, the Clemente–Montanes–Reyes (1998) test of dual structural breaks is used. The result of this test is indicated inTable 3. Thefindings from that table shows that in the presence of two structural breaks at the level, all variables are non-stationary, and there is a problem of unit root in all the variables in the case of Japan. However, in the presence of two structural breaks, all the variables of the model were found to be stationary atfirst difference. For this reason, we may conclude that our series have the same order of integration, and that isI(1).

In investigating the existence of cointegration among the variables in the presence of structural breaks, this study applied the ARDL bounds testing approach to cointegration. In addition to that, the AIC is also used in the lag selection exercise. In this analysis, the study found the maximum lag length to be 2. Fol- lowing to this ascertainment, the study proceeds to estimate theF- statistic, which will confirm the existence of cointegration among the variables or otherwise. The commonest rule here is, if the calculated F-statistic is found to be greater than the critical bounds, then we may reject the hypothesis of no cointegration.

The result of this analysis is reported inTable 4. Thefindings of the Table shows how the model; i.e. the calculatedF-statistics to have exceeded the upper critical bounds, at 1% and 5% levels respec- tively. This development indicates that we had three co- integrating vectors when electricity consumption, financial development, and capital were used as dependent variables. The same inference is found in imports and trade openness models.

Following to this, the study concludes for the existence of coin- tegration relationship between the variables in the presence of structural breaks in the series.

The robustness of the long-run relationships is investigated by applying Johansen cointegration approach, and the results are reported in Table 5. The results show that both the Maximum Eigenvalue and Trace Statistics are significant. The null hypothesis of no cointegration is rejected in this respect, suggesting the existence of cointegrating vectors in three models. This confirms the presence of a long-run relationship between the variables. This finding is an attestation to the fact that our earlierfinding using the ARDL model on the long run results are robust.

Table 1

Unit Root Analysis.

Variable ADF unit root test P–P unit root test

T. statistic Prob. value T. statistic Prob. value lnEt 1.8304 (1) 0.6752 1.7843 (3) 0.6997 lnYt 2.3142 (3) 0.4189 2.2995 (3) 0.4265 lnFt 0.7294 (2) 0.9651 1.4760 (3) 0.8250 lnKt 1.8024 (2) 0.6884 1.3119 (6) 0.8737 lnEXt 2.5126 (0) 0.3211 2.6962 (3) 0.2425 lnIMt 2.7341 (1) 0.2881 1.9758 (3) 0.6002 lnTRt 2.2186 (2) 0.4693 2.2549 (3) 0.4500 ΔlnYt 4.7836 (1)^a 0.0017 5.7041 (3)^a 0.0058 ΔlnEt 4.3590 (1)^a 0.0058 5.8956 (3)^a 0.0001 ΔlnFt 4.4072 (1)^a 0.0051 6.0785 (3)^a 0.0000 ΔlnKt 4.6636 (1)^a 0.0024 4.3917 (3)^a 0.0052 ΔlnEXt 6.5521 (2)^a 0.0000 9.0342 (3)^a 0.0000 ΔlnIMt 4.9639 (1)^a 0.0010 6.3812 (3)^a 0.0000 ΔlnTRt 3.6784 (1)^b 0.0336 7.2991 (3)^a 0.0000

aIndicates significant at 1% levels of significance. Lag length of variables is shown in small parentheses.

bIndicates significant at 5% levels of significance. Lag length of variables is shown in small parentheses.

Table 2

Zivot–Andrews structural break trended unit root test.

Variable At level At 1st difference

T-statistic Time break T-statistic Time break lnEt 2.335 (1) 1999 6.800 (2)^a 1983 lnYt 4.485 (1) 1987 5.471 (1)^b 2000 lnFt 3.967 (1) 1988 6.684 (1)^a 1976 lnKt 4.599 (1) 1990 5.229 (1)^b 1992 lnEXt 4.706 (1) 1986 7.166 (2)^a 2004 lnIMt 5.002 (1) 1988 5.886 (1)^a 1984 lnTRt 4.939 (1) 1988 6.352 (1)^a 2008

aRepresents significance at 1%, levels respectively. The lag order is shown in parenthesis.

bRepresents significance at 5% levels respectively. The lag order is shown in parenthesis.

Table 3

Clemente–Montanes–Reyes detrended structural break unit root test.

Variable Innovative outliers Additive outlier

T-statistic TB1 TB2 Decision T-statistic TB1 TB2 Decision

lnECt 4.188 (2) 1974 1985 Unit Root Exists 6.490 (2)^a 1981 1996 Stationary lnYt 3.076 (3) 1983 2000 Unit Root Exists 6.110 (3)^a 1990 2000 Stationary lnFt 4.352 (3) 1975 1983 Unit Root Exists 6.834 (3)^a 1986 1990 Stationary lnKt 3.401 (1) 1974 1982 Unit Root Exists 6.026 (3)^a 1984 2000 Stationary lnEXt 4.175 (1) 1986 1998 Unit Root Exists 6.546 (2)^a 1986 1989 Stationary lnIMt 3.080 (3) 1986 2000 Unit Root Exists 7.271 (5)^a 1986 1991 Stationary lnTRt 4.089 (2) 1976 1998 Unit Root Exists 8.401 (2)^a 1991 1995 Stationary

aIndicates significant at 1% level of significance. The lag length of variables is shown in small parentheses.

The long-run results are presented inTable 6, and thefindings of the study established thatfinancial development has positive and significant impact on electricity consumption in Japan. To support the direction of this claim the study discovered that a 1%

increase in financial development will lead to a corresponding increase of 0.2429% in electricity demand in Japan keeping other things constant. In another related development that compliments the earlierfinding, the study discovered how economic growth in Japan to be totally reliant on electricity consumption. Specifically, the study found the Japanese economic growth prospects to be positively and statistically related with electricity consumption.

Thefindings of this study discovered how a 1% increase in eco- nomic growth will lead to a corresponding increase of 0.5040% in electricity consumption in Japan. Surprisingly, the impact of capital on electricity consumption is found to be negative, and it is statistically significant at 1% level. The result further indicates that a 1% increase in physical capital decreases electricity demand by 0.2142% if all other things remain the same. The Japanese exports were, on the other hand, found to be positively related with electricity demand. The result of the long-run analysis indicates that any 1% increase in the Japanese exports will have a significant impact on electricity demand by a cumulative rise of 0.0921%.

Similar to this line of development, the study further discovered the existence of positive and significant relationship between imports in Japan and electricity demand in Japan. This finding reveals that a 1% increase in imports will lead to 0.2193% rise in electricity demand. Similar to that, Trade openness was equally discovered to influence the Japanese electricity consumption positively, and it is statistically significant at 1%.

The results of the short-run analysis are also reported in the lower level of Table 6. The results indicate that financial devel- opment has positive and significant relationship with electricity demand. As a result a 1% increase in financial development increases electricity consumption by 0.2210 in the short-run.

Similar to the long-runfinding, the study further discovered eco- nomic growth to exhibit a persistent long-run and short-run

positive and significant relationship with electricity consump- tion, but capital is still found to be negatively linked with elec- tricity consumption as the case in the long-runfinding. The impact of exports on electricity consumption is positive, but it is statisti- cally insignificant in the short-run. This development is in contrast to the long-runfinding. The study also discovered the existence of a positive and significant relationship between imports and elec- tricity demand in Japan. The association between trade openness and electricity consumption is positive, and it is statistically sig- nificant. The value of the ECM is found to be negative and statis- tically significant. The estimates of exports, imports and trade models are 0.1650, 0.4279 and 0.2279 respectively. This suggests that short-run deviations toward long-run would be corrected by 16.50%, 42.79% and 22.79% in the models of exports, Table 4

The results of ARDL cointegration test.

Bounds testing to cointegration Diagnostic tests

Estimated models Optimal lag length Structural break F-statistics χ²_NORMAL χ²_ARCH χ²_RESET χ²_SERIAL FEðE=Y;F;K;EXÞ 2, 2, 2, 2, 2 1999 7.478^a 0.8383 [1]: 0.0567 [1]: 0.8237 [1]: 1.6304 FYðY=E;F;K;EXÞ 2, 2, 2, 2, 2 1987 1.652 0.6504 [1]: 0.0090 [2]: 0.0267 [2]: 0.0359 FFðF=Y;E;K;EXÞ 2, 2, 2, 1, 2 1988 6.991^b 7.4470 [1]: 2.0034 [2]: 0.3787 [1]: 0.3966 FKðK=Y;E;F;EXÞ 2, 2, 2, 2, 2 1990 5.571^b 3.5465 [1]: 0.1571 [2]: 0.4171 [1]: 1.5246 FEXðEX=Y;E;F;KÞ 2, 1, 2, 2, 2 1986 1.925 5.0645 [1]: 1.5098 [1]: 0.0010 [1]: 6.0886 FEðE=Y;F;K;IMÞ 2, 1, 2, 2, 2, 1999 5.756^b 2.6653 [1]: 1.4913 [1]: 0.4437 [1]: 0.4838 FYðY=E;F;K;IMÞ 2, 2, 1, 2, 2 1987 1.3682 0.0332 [1]: 0.7103 [1]: 1.8734 [1]: 0.1816 FFðF=Y;E;K;IMÞ 2, 1, 2, 2, 2 1988 13.7594^a 1.2541 [1]: 5.5309 [1]: 2.9821 [4]: 0.5487 FKðK=Y;E;F;IMÞ 2, 2, 2, 2, 2 1990 5.7221^a 0.3980 [4]: 0.8680 [3]: 2.5997 [1]: 0.4327 FIMðIM=Y;E;F;KÞ 2, 1, 2, 2, 2, 1988 1.9948^a 0.6014 [1]: 0.0632 [1]: 0.0759 [1]: 0.5663 FEðE=Y;F;K;TRÞ 2, 1, 2, 2, 2 1999 5.4261^a 0.5313 [1]: 0.5672 [1]: 1.1742 [1]: 0.5304 FYðY=E;F;K;TRÞ 2, 2, 2, 2, 2 1987 1.9409 0.2153 [1]: 0.9201 [1]: 0.5040 [1]: 0.7601 FFðF=Y;E;K;TRÞ 2, 1, 2, 2, 2 1988 7.9616^a 1.65404 [1]: 3.5300 [4]: 1.7404 [1]: 1.6573 FKðK=Y;E;F;TRÞ 2, 2, 2, 2, 2 1990 5.4261^c 3.0124 [1]: 0.4815 [1]: 5.8061 [1]: 0.2616 FTRðTR=Y;E;F;KÞ 2, 1, 2, 2, 2 1988 2.5015 0.5385 [2]: 0.2304 [4]: 1.6662 [1]: 0.7830 Significant level Critical values (T¼43)^#

Lower boundsI(0) Upper boundsI(1)

1% level 6.053 7.458

5% level 4.450 5.560

10% level 3.740 4.780

aDenotes the significant at 1% levels, respectively. The optimal lag length is determined by AIC. [ ] is the order of diagnostic tests.#Critical values are collected from Narayan (2005).

bDenotes the significant at 5% levels, respectively. The optimal lag length is determined by AIC. [ ] is the order of diagnostic tests.#Critical values are collected from Narayan (2005).

cDenotes the significant at 10% levels, respectively. The optimal lag length is determined by AIC. [ ] is the order of diagnostic tests.#Critical values are collected from Narayan (2005).

Table 5

Results of Johansen cointegration test.

Hypothesis Trace Statistic Maximum Eigen Value

Yt¼fðEt;Ft;Kt;EXtÞ

R¼0 106.1547^a 39.4866^b

Rr1 66.6680^a 25.2575

Rr2 41.4104 19.5260

Rr3 21.8843 15.1901

Rr4 6.6941 6.6941

Yt¼fðEt;Ft;Kt;IMtÞ

R¼0 131.1673^a 48.3739^b

Rr1 82.7933^a 36.5483^b

Rr2 46.2450^b 18.7134

Rr3 27.5315^b 16.1356

Rr4 11.3959 11.3959

Yt¼fðEt;Ft;Kt;TtÞ

R¼0 113.6845^a 39.2150^b

Rr1 74.4694^a 29.5157

Rr2 44.9533^b 23.1358

Rr3 21.8179 16.6111

Rr4 5.2067 5.2067

aShows significant at 1% levels of significance.

bShows significant at 5% levels of significance.

imports and trade openness respectively. The results of diagnostic tests indicate that the error terms of the short-run model are normally distributed in all models. There is no heteroskedasticity, serial correlation, and also no ARCH problem. The value of the Ramsey reset test shows that the functional form for the short-run models is well specified.

4.1. Exports model

In finding the stability of the long-run and short-run para- meters of exports model, the cumulative sum (CUSUM) and the cumulative sum of squares (CUSUMsq) are used as proposed by Brown et al.[4]. The plot of the CUSUM is found to be within the line and significant at 5%. However, the plot of the CUSUMsq did not lie within the line after 2001 and is not significant at 5% level of significance. This indicates the presence of a structural break in 2001. The imports and trade models show consistent and efficient parameters of the long-and-short-run as confirmed by the CUSUM and CUSUMsq inFigs. 2and3for the export model, same stability test for CUSUM and CUSUMsq was conducted with respect to the import and trade models and are presented inFigs. 4and5, and Figs. 6and7respectively.

To confirm the stability, we have applied Chow breakpoint test that confirmed the absence of no break point over selected period (Table 7). This shows that our estimated model is a goodfit.

4.2. The VECM Granger causality analysis

The VECM Granger causality test is applied in this study in order to establish the direction of causality between the variables

of the model. The direction of the causal relationship between the variables is helpful in designing comprehensive economic,finan- cial and trade policies to control energy demand for sustainable economic growth. The results are reported inTable 8. Thefindings in that Table reveal the existence of both long and short run causal relationships. In the long-run, the study discovered the existence of bidirectional causality between financial development and electricity consumption, electricity consumption and capital. Fol- lowing to this, the feedback effect was found to exist between financial development and capital. In another development, the study found the existence of unidirectional causality running from economic growth, exports, imports and trade to electricity con- sumption. These respectivefindings confirm the existence of an economic growth led electricity consumption, exports-led elec- tricity consumption, imports-led electricity consumption, and trade-led electricity consumption in Japan.

In the short-run, the relationship between economic growth and electricity consumption is bidirectional. Financial Table 6

Long and short runs results.

Dependent variable¼lnECt

Long Run Analysis

Variables Coefficient T-statistic Coefficient T-statistic Coefficient T-statistic

Constant 0.7904 0.5307 1.0630 1.1416 0.7463 0.6172

lnFt 0.2429^a 2.9832 0.2145^a 5.2765 0.2112^a 4.2586

lnYt 0.5040^a 3.2792 0.2007^b 1.8243 0.2833^b 1.8580

lnKt 0.2142^a 2.7787 0.1018^c 2.0300 0.1272^b 1.9275

lnEXt 0.0921^c 2.1352 … … …

lnIMt … … 0.2193^a 5.1538 … …

lnTRt … … … … 0.1687^a 2.8002

Short-Run Analysis

Variables Coefficient T-statistic Coefficient T-statistic Coefficient T-statistic

Constant 0.0017 0.2986 0.0071 1.1983 0.0071 1.1983

lnFt 0.2210^c 2.2522 0.2469^a 2.8944 0.2469^a 2.8944

lnYt 0.5840^c 2.6074 0.7053^a 2.8433 0.7053^a 2.8433

lnKt 0.1468^b 1.8038 0.2685^c 2.3547 0.2685^c 2.3547

lnEXt 0.0521 0.8924 … … … …

lnIMt … … 0.2031^a 2.7555 … …

lnTRt … … … … 0.2031^a 2.7555

ECMt1 0.1650^c 2.1150 0.4279^c 2.0929 0.2279^c 2.0731

R² 0.57 0.6485 0.6485

F-statistic 9.6648^a 13.2854^a 13.2854^a

D. W 1.5208 1.5412 1.7489

Short Run Diagnostic Tests

Test F-statistic Prob. value F-statistic Prob. value F-statistic Prob. value

χ²NORMAL 3.9608 0.0907 0.8459 0.1680 0.1143 0.3282

χ²SERIAL 1.9900 0.1523 2.3153 2.8339 2.1153 0.1362

χ²ARCH 1.6083 0.3522 0.6510 5.4234 4.0156 0.0483

χ²WHITE 0.9394 0.5129 1.2267 1.1495 2.2789 0.0339

χ²REMSAY 0.9691 0.3391 2.1242 0.1346 1.2721 0.2117

aShows significant at 1% level of significance.

bShows significant at 10% level of significance.

cShows significant at 5% level of significance.

-15 -10 -5 0 5 10 15

92 94 96 98 00 02 04 06 08 10 12

CUSUM 5% Significance

Fig. 2.(Export model) Plot of cumulative sum of recursive residuals.