35

에너지경제연구 Korean Energy Economic ReviewVolume 14, Number 1, March 2015 : pp. 35~64

Energy Consumption, GDP and Trade

in East Asian Countries:

A cointegrated panel analysis

36

1. Introduction

Whether energy saving policies could hinder economic growth has been at

the center of controversy, and various empirical studies on this issue have been

conducted since the 1970s. In recent years, the causal relationships between

energy consumption and economic growth and between energy consumption and

international trade, exports, and imports have been studied.

Studies on the relationship between energy consumption and economic growth

have been performed using various time-series analysis methods on a variety of

aspects. In particular, these studies were performed mainly after the 1970s oil

shock (Ozturk, 2010; Payne, 2010). In the past, analysis on a particular country

was common; recently, panel analyses on multiple countries have been

conducted. Thus, an analysis on the relationship between energy consumption and

economic growth may provide information regarding whether an energy-saving

policy negatively affects economic growth, and these studies provide important

implications for the energy policy of a particular country or region.

According to Ozturk (2010), the relationship between energy consumption and

economic growth affects energy policy according to the following four

hypotheses. First, the growth hypothesis suggests a uni-directional causality

running from energy consumption to economic growth. This hypothesis suggests

that for economic growth, energy consumption plays an important role in the

production process as a complement to labor and capital. Second, the

conservation hypothesis proposes a uni-directional causality running from economic

growth to energy consumption, suggesting that the policy of conserving energy

37

consumption may be implemented with no adverse effect on economic growth.

Third, the feedback hypothesis is a bi-directional causality between energy

consumption and economic growth, suggesting that energy consumption and

economic growth are jointly determined and concurrently affected. Fourth, the

neutrality hypothesis proposes no causality between energy consumption and

economic growth, claiming that neither conservative nor expansive policies on

energy consumption affect economic growth.

Recently, the causal relationship between energy consumption and trade has

been studied. Specially, if energy consumption is caused by exports, energy

saving and greenhouse gas mitigation policies may adversely affect exports.

Therefore, energy consumption may ultimately affect economic growth through

exports, and, hence, the regions or countries in which exports lead economic

growth may experience negative economic effects because of energy

conservation policies. Specifically, the causal relationship between exports and

energy consumption exists because energy, in addition to capital and labor, is

an important input into the export function. Therefore, whether an increase in

exports can cause an increase in energy consumption is deeply related to the

ability to address climate change for countries where exports lead economic

growth.

If the empirical results indicate a uni-directional causality running from

energy consumption to exports, countries where exports lead economic growth

do not need to actively promote an energy conservation policy. In the current

UN climate change regime, exporting countries are responsible for GHG

emissions caused by the input energy into the export goods production.

However, some countries have raised opinions internationally that this

responsibility is not fair in terms of equity for addressing climate change. Six

East Asian countries have achieved export-led economic growth; therefore, these

38

countries should consider the export policy in addressing climate change. The

analysis on the causal relationship between energy consumption and trade in

these countries is thus different from the previous analysis, which focused on

countries that do not have export-led economic growth. In particular, one of the

important controversial issues in addressing climate change is whether energy

conservation policies can negatively affect exports. These issues are addressed

under “the adverse economic effect” in the United Nations Framework Convention

on Climate Change (UNFCCC) climate change negotiations.

Therefore, this study analyzes the cross-causal relationship among energy

consumption, economic growth and trade for six East Asian countries with high

trade dependence in their economy. The economic growth of these countries is

oriented more by exports than by any other area. Their share in the world

exports has also continuously increased. Furthermore, the Free Trade Agreement

(FTA) between Korea, Japan and China has been recently discussed. The Asia

Newly Industrialized Countries (Korea, Singapore, Taiwan, and Hong Kong)

conduct a significant amount of trade with Japan and China, and these

countries are closely linked in terms of their economy. In particular, Singapore,

Taiwan, and Hong Kong are very highly interdependent with China

economically. Thus, using the Vector Error Correction Models as the main

methodology (as in Sadorsky (2011, 2012)), this study analyzes the causal

relationships between energy consumption, economic growth and trade on these

six countries, namely, China, Korea, Japan, Hong Kong, Singapore, and Taiwan.

The long-run and short-run causal relationships are analyzed for the 30 years

from 1980 to 2010, where the economies of these countries changed steadily

into export-led economic growth structures in this periods.

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2. Literature Review and Empirical Models

The recent multi country studies on the causal relationship between on

energy consumption and economic growth includes Apergis and Payne (2009a,

2009b, 2009c), Lee and Chang (2008), Narayan and Smyth (2008), Huang et

al. (2008), Lee et al (2009), Chiou-Wei et al. (2008) and Akinlo (2008) etc.

Each paper analyzed using different econometric methods for different countries.

A general conclusion from these studies is that contradictory results were

reported. Apergis and Payne (2009a) and Narayan and Smyth (2008) found

that the causality runs from energy consumption to economic growth and

Apergis and Payne (2009c) found the bi-directional causality between energy

consumption and economic growth. Huang et al. (2008) found that the causality

from economic growth to energy consumption.

The causal relationship between energy consumption and trade (i.e., exports

and imports) has been analyzed in studies such as those by Lean and Smyth

(2010a, 2010b), Narayan and Smyth (2009), and Sardorsky (2011). Lean and

Smyth (2010a, 2010b) found short-run Granger causality from electricity

consumption to real GDP and from income to exports in six Middle Eastern

countries (Iran, Israel, Kuwait, Oman, Saudi Arabia, and Syria), and they also

found long-run Granger causality from exports to real GDP, from electricity

consumption to real GDP, from exports to electricity consumption, and from

real GDP to electricity consumption. Lean and Smyth (2010a) found Granger

causality from power generation to electricity consumption, but Lean and Smyth

(2010b) found this causality. Narayan and Smyth (2009) analyzed the causality

40

between exports and electricity consumption, and Sardorsky (2011) analyzed

more general indicators than those in Narayan and Smyth (2009), such as

energy consumption and trade. Sardorsky (2011) analyzed the short-run

dynamics for eight Middle Eastern countries (Bahrain, Iran, Jordan, Oman,

Qatar, Saudi Arabia, Syria, and UAE) and found Granger causality from energy

consumption to exports and from imports to energy consumption.

Meanwhile, the recent studies have begun considering energy consumption,

economic growth, and trade comprehensively (Sadorsky 2011, 2012). Sadorsky

(2011) and Sadorsky (2012) similarly analyzed six Middle Eastern countries and

seven South American countries, respectively. However, these previous studies

on the causal relationship between energy consumption, economic growth and

trade were biased to South American and Middle Eastern countries, where the

trade dependence on economy is relatively small. The results of the cross-

causality of energy consumption, economic growth, and trade can be derived

depending on the economic conditions of the countries. This study analyzes the

causal relationships between energy consumption, economic growth and trade on

these six countries, namely, China, Korea, Japan, Hong Kong, Singapore, and

Taiwan.

In this paper, the relationship between energy consumption, trade, and output

is modeled using the production function following Lean and Smyth (2010b)

and Sadorsky (2012). The model in this paper considers both exports and

imports, similar to Sadorsky (2012). That is, output, , can be written as a

function of capital, , labor, , energy, , trade openness, , and a country

specific variable, .

(1)

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This equation can be parameterized as follows.

(2)

Taking the natural logarithms of Eq. (2) and adding a random error term,

the following empirical model can be derived.

(3)

Where denotes countries and denotes the

period. Individual fixed country effects are denoted by and denotes the

stochastic error term. The coefficients of is expected to have a positive

signs.

First, we tests whether these time series have unit roots. If so, I use panel

cointegration techniques to investigate the relationship between energy

consumption and trade. Panel cointegration tests have recently been used by a

number of authors to investigate the relationship between energy consumption

and output (Apergis and Payne, 2009, 2010; Chen et al., 2007; Lee, 2005; Lee

and Chang, 2008; Lee et al., 2008; Mahadevan and Asafu-Adjaye, 2007;

Mehhara, 2007; Narayan and Smyth, 2008, 2009; Narayan et al., 2007;

Sadorsky, 2009a, 2009b, 2011, 2012).

If the time series are I (1) and these variables are cointegrated, a panel

vector error correction model (VECM) can be used to estimate causality, as in

Engel and Granger (1987). Finding cointegration between groups of variables is

very important because it ensures that an error correction mechanism exists

according to which changes in the dependent variable are modeled as a

function of the level of the equilibrium in the cointegration relationship and

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changes in other explanatory variables. Eq. 3 can be written as the following

VECM model.

(4)

Where is the first difference operator, is the lag length, is the

natural log of real output, is the natural log of real capital formation, is

the natural log of the labor force, is the natural log of trade openness (real

43

exports or real imports ), is the error correction term, and is the

random error term. The VECM is estimated using a seemingly unrelated

regression (SUR) technique that allow for cross-sectional specific coefficient

vectors and cross-sectional correlations in the residuals.

3. Data

The East Asian economies included in this research are China, Hong Kong,

Japan, Korea (i.e., Republic of Korea), Singapore, and Taiwan. Energy

consumption (energy) is measured by energy use in kt of oil equivalent. Data

on the energy consumption of these countries except Taiwan are from the

World Bank (2013), World Development Indicators online database. The data

on the energy consumption of Taiwan is from the “Statistical Yearbook of the

Republic of China” for the years 1995, 2000, and 2011. Output is measured

using real GDP (constant 2005 US dollars). Capital stock is measured using

gross fixed capital formation (constant 2005 US dollars), similar to papers such

as Apergis and Payne (2009, 2010), Sari and Soytas (2007), and Sadorsky

(2012). Data on the output and gross fixed capital formation of these countries

except Taiwan are from the World Bank (2013) World Development Indicators

online database. Data for Taiwan are from the National Statistics, Republic of

China database. Labor is measured using the number of people in the total

labor force. Data on labor from 1980 to 2008 are from the International Labor

Organization (ILO), and data on labor in 2009 and 2010 are estimated from

labor force data based on the World Bank data. Data on exports and imports

for the countries except Taiwan are measured using constant 2005 US dollars

44

and from the World Bank (2013) dataset. Data on the exports and imports of

Taiwan are from the National Statistics, Republic of China database and

converted to constant 2005 US dollars.

Time series plots of the natural logs of energy consumption for each of the

countries are shown in Fig. 1. Energy consumption has been increasing over

time, although the strength of this trend varies by country. In the case of

Japan, energy consumption has been fluctuating since 2000, showing a

reduction trend in recent years. China is the biggest energy consumer, followed

by Japan. Energy consumption in China, Korea, and Taiwan has increased

steadily over time whereas that in Singapore and Hong Kong has been

fluctuating since the mid-1990s. Fig. 2 shows time series plots of natural logs

of the real GDP for each country, and overall, GDP has been increasing over

time. In the case of Hong Kong, Japan, Korea, and Singapore, the GDPs

dropped temporarily in the mid-1990s because of the Asian financial crisis and

once again temporarily at the end of the 2000s by the global financial crisis.

However, overall GDP has shown an increasing trend. In the case of Japan,

GDP growth has slowed rapidly since 1990.

The time series plots of the natural logs of real exports for each country are

shown in Fig. 3. Despite that the exports may have been reduced temporarily

by foreign economic conditions, the trends have been generally upward sloping.

China is the largest exporter, followed by Japan and Korea. Fig. 4 shows time

series plots of the natural logs of the real imports of the countries. Exports and

imports show a similar pattern, but import fluctuates more than exports do;

furthermore, they are highly correlated, with a correlation coefficient of 0.98.

China is the largest importer, followed by Japan and Korea.

Table 1 shows the average annual growth rates of each variable from 1980

to 2010. The average growth rates of energy consumption vary among the

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countries, ranging from a low of 1.2 (Japan) to a high of 6.4 (Singapore).

Except for China, energy consumption and GDP growth rates show similar

trends, but the GDP growth rate was generally higher than the energy

consumption growth rate. In China, despite a high GDP growth rate (in the

10% range), the energy consumption growth rate was only 4.8%. In most

countries, exports and imports have been growing much faster than GDP or

energy consumption. In China, Hong Kong, and South Korea, the average

annual growth rates in both imports and exports exceeded 10% and showed

double-digit growth rates.

Tables 2 and 3 show the share of exports and imports of goods and services

of each country’s GDP. The share of exports increased year over year. Since

1990, the share of exports has increased rapidly in China and Hong Kong,

whereas it has increased steadily in the other countries. In 2010, the share of

exports of GDP was 30.6% in China, 52.3% in South Korea, and 63.9% in

Taiwan. In particular, the share of exports of GDP exceeded 200% in Hong

Kong and Singapore. Except for Japan, exports as a percentage of GDP in

these countries were higher than the world average of 29% and the OECD

average of 26.2%. Although some differences exist between the shares of

exports and imports as a portion of GDP, they show a similar pattern.

Moreover, exports of East Asian countries have an important share of the

world export. As of 2010 (constant 2005 US dollars), China was the world's

second largest exporter and Japan was the world's fourth largest exporter. South

Korea was the world's eighth place, Hong Kong, Singapore and Taiwan,

respectively tenth, eleventh and twenty-fourth places in the world ntries. These

facts show that trade causes economic growth in East Asian countries.

Therefore, analyzing the causal relationship between trade (exports and imports)

and energy consumption in these countries is very useful.

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Table 1. Average Annual Growth Rates 1980–2010 (percent)

CountryEnergy

ConsumptionGDP

Gross capital formation

Labor Exports Imports

China 4.764 10.061 12.275 2.078 12.018 12.170

Hong Kong 3.707 4.909 3.836 1.503 11.670 11.485

Japan 1.228 2.160 1.005 0.477 4.843 3.824

Korea 6.194 6.304 6.434 1.883 11.845 9.733

Singapore 6.375 6.840 6.002 2.162 9.841 9.375

Taiwan 5.431 7.565 6.327 1.683 8.670 8.370

Table 2. Exports of goods and services (% of GDP)

1980 1990 2000 2010

China 10.648 16.074 23.326 30.615

Hong Kong 88.933 130.657 141.764 218.977

Japan 13.423 10.288 10.877 15.171

Korea 32.065 27.954 38.564 52.278

Singapore 202.609 177.449 192.338 203.585

Taiwan 47.016 40.822 46.423 63.878

OECD 18.108 18.159 23.607 26.158

World 19.551 19.856 26.039 29.040

Table 3. Imports of goods and services (% of GDP)

1980 1990 2000 2010

China 11.013 13.085 20.917 26.612

Hong Kong 89.450 122.119 137.355 213.547

Japan 14.372 9.379 9.429 13.976

Korea 39.965 29.029 35.707 49.703

Singapore 209.554 167.381 179.487 174.098

Taiwan 46.871 33.266 43.105 63.878

OECD 19.506 18.776 24.397 26.752

World 20.610 20.177 25.834 28.909

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Table 4. Correlations for the panel data set (variables in growth rates)

1.000000 0.746315 0.339998 0.258646 0.575988 0.670054

0.746315 1.000000 0.305813 0.211243 0.324666 0.597807

0.339998 0.305813 1.000000 0.152652 0.103004 0.118231

0.258646 0.211243 0.152652 1.000000 0.151503 0.200070

0.575988 0.324666 0.103004 0.151503 1.000000 0.660501

0.670054 0.597807 0.118231 0.200070 0.660501 1.000000

Table 4 shows the correlations among the panel data growth rate variables.

Most of correlations are positive. The growth rate of energy consumption is

correlated with the growth rate of GDP by 0.259, followed respectively by

correlations with the growth rates of capital, imports, labor and exports. The

growth rate of energy consumption is more highly correlated with the growth

rate of imports than with that of exports. The growth rate of GDP is highly

correlated with the growth rate of capital. One of the reasons is that countries

such as Japan and Korea are exporting relatively more the capital intensive

goods than any other countries. The growth rate of exports has the lowest

correlation with the growth rate of labor.

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Fig. 1. Natural Log of Energy Consumption

49

Fig. 2. Natural Log of Real Output

26.0

26.5

27.0

27.5

28.0

28.5

29.0

1980 1985 1990 1995 2000 2005 2010

_CHN

24.4

24.8

25.2

25.6

26.0

26.4

1980 1985 1990 1995 2000 2005 2010

_HKG

28.5

28.6

28.7

28.8

28.9

29.0

29.1

29.2

1980 1985 1990 1995 2000 2005 2010

_JPN

25.6

26.0

26.4

26.8

27.2

27.6

28.0

1980 1985 1990 1995 2000 2005 2010

_KOR

23.5

24.0

24.5

25.0

25.5

26.0

1980 1985 1990 1995 2000 2005 2010

_SGP

25.0

25.5

26.0

26.5

27.0

1980 1985 1990 1995 2000 2005 2010

_TAI

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Fig. 3. Natural Log of Real Exports

24

25

26

27

28

29

1980 1985 1990 1995 2000 2005 2010

_CHN

23

24

25

26

27

1980 1985 1990 1995 2000 2005 2010

_HKG

25.6

26.0

26.4

26.8

27.2

27.6

1980 1985 1990 1995 2000 2005 2010

_JPN

23

24

25

26

27

1980 1985 1990 1995 2000 2005 2010

_KOR

23.5

24.0

24.5

25.0

25.5

26.0

26.5

27.0

1980 1985 1990 1995 2000 2005 2010

_SGP

23.5

24.0

24.5

25.0

25.5

26.0

26.5

1980 1985 1990 1995 2000 2005 2010

_TAI

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Fig. 4. Natural Log of Real Imports

24

25

26

27

28

1980 1985 1990 1995 2000 2005 2010

_CHN

23

24

25

26

27

1980 1985 1990 1995 2000 2005 2010

_HKG

25.8

26.0

26.2

26.4

26.6

26.8

27.0

27.2

1980 1985 1990 1995 2000 2005 2010

_JPN

23.5

24.0

24.5

25.0

25.5

26.0

26.5

27.0

1980 1985 1990 1995 2000 2005 2010

_KOR

23.5

24.0

24.5

25.0

25.5

26.0

26.5

27.0

1980 1985 1990 1995 2000 2005 2010

_SGP

23.5

24.0

24.5

25.0

25.5

26.0

26.5

1980 1985 1990 1995 2000 2005 2010

_TAI

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Method

statistic prob. Statistic prob. Statistic prob. Statistic prob. Statistic prob. Statistic prob.

Null: Unit root (assumes common unit root process)

Levin, Lin &Chu t* -2.571 0.005 -6.873 0.000 -1.761 0.039 -6.141 0.000 -5.384 0.000 -4.596 0.000

Null: Unit root (assumes individual unit root process)

Im, Pesaran and Shin W-stat -1.495 0.068 -7.110 0.000 0.705 0.760 -6.166 0.000 -2.145 0.016 -6.131 0.000

ADF Fisher Chi-square 26.061 0.011 68.820 0.000 8.427 0.751 59.060 0.000 23.063 0.027 59.289 0.000

PP Fisher Chi-square 36.025 0.000 66.546 0.000 7.660 0.811 52.833 0.000 23.210 0.026 70.595 0.000

4. Empirical Results

4.1 Unit root tests

In this paper, we conducted four types of panel unit root tests that assume

cross-sectional independence (Levin et al., 2002; Im et al., 2003; Dickey and

Fuller, 1979; Phillips and Perron, 1988). For these tests, the null hypothesis is

that there is a unit root while the alternative hypothesis is that there is no unit

root. The result of these tests is that for each series in levels except GDP (),

labor (), the null hypothesis cannot be rejected at the 5% level. In case of

GDP, the three tests except that of Im et al. (2003) indicate that we can reject

the null hypothesis at the 5% level. For each series in the first differences, the

null hypothesis that there is a unit root can be rejected at the 1% level.

Table 5. Panel unit root tests

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Method

statistic prob. Statistic prob. Statistic prob. Statistic prob. Statistic prob. Statistic prob.

Null: Unit root (assumes common unit root process)

Levin, Lin &Chu t* -2.023 0.022 -8.606 0.000 -1.593 0.056 -9.675 0.000 -1.829 0.034 -9.077 0.000

Null: Unit root (assumes individual unit root process)

Im, Pesaran and Shin W-stat 0.423 0.664 -9.206 0.000 1.904 0.972 -9.412 0.000 1.615 0.947 -7.852 0.000

ADF Fisher Chi-square 16.333 0.177 91.568 0.000 11.607 0.478 93.834 0.000 11.221 0.510 78.320 0.000

PP Fisher Chi-square 23.341 0.025 92.964 0.000 14.618 0.263 106.684 0.000 12.743 0.388 86.576 0.000

All unit root test regressions were run with constant.** Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume

asymptotic normality.

4.2. Cointegration tests

We tested whether these I (1) variables are cointegrated using the tests of

Pedroni (1999, 2004). The Pedroni panel cointegration tests are to test the

residuals from the following equation for the unit root variables.

In total, Pedroni (1999, 2004) provides seven statistics for tests of the null

hypothesis of no cointegration in heterogeneous panels. These tests can be

classified as either within-dimension (panel tests) or between-dimension (group

tests). For the within-dimension approach, the null of no cointegration (

for all i) is tested against the alternative of ( for all i). The group

means approach is less restrictive because it does not require a common value

of under the alternative hypothesis ( for all i).

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The results of these tests for a model with exports are mixed. Three of the

within-dimension statistics indicate cointegration at the 5% level, and one of

them indicates cointegration at the 10% level. In the between-dimension case,

one of the statistics indicates cointegration at the 5% level. The results of these

tests for the model with imports are also mixed. Three of the within-dimension

statistics indicate cointegration at the 5% level and one of them indicates

cointegration at the 10% level. In the between-dimension case, one of the

statistics indicates cointegration at the 10% level.

Table 6. Panel cointegration tests for a model with exports

Alternative hypothesis: common AR coefs. (within-dimension) 　　 　 Statistic Prob.

Weighted Statistic

Prob.

Panel v-Statistic 3.488*** 0.000 2.032** 0.021

Panel rho-Statistic 1.434 0.924 1.315 0.906

Panel PP-Statistic -1.023 0.153 -0.965 0.167

Panel ADF-Statistic -1.920** 0.028 -1.542* 0.062

Alternative hypothesis: individual AR coefs. (between-dimension) 　　 　 　 Statistic 　 Prob.

Group rho-Statistic 2.335 0.990

Group PP-Statistic -0.559 0.288

Group ADF-Statistic 　 -2.381*** 　 0.009

Null Hypothesis: No cointegrationTrend assumption: Deterministic intercept and TrendAutomatic lag length selection based on SIC with a max lag of 5Newey-West automatic bandwidth selection and Bartlett kernel

55

Table 7. Panel cointegration tests for model with imports

Alternative hypothesis: common AR coefs. (within-dimension) 　　 　 Statistic Prob.

Weighted Statistic

Prob.

Panel v-Statistic 5.046*** 0.000 3.462*** 0.000

Panel rho-Statistic 1.563 0.941 1.501 0.933

Panel PP-Statistic -0.635 0.263 -0.509 0.305

Panel ADF-Statistic -1.722** 0.043 -1.505* 0.066

Alternative hypothesis: individual AR coefs. (between-dimension) 　　 　 　 Statistic Prob.

Group rho-Statistic 2.249 0.988

Group PP-Statistic -0.667 0.253

Group ADF-Statistic 　 -1.539* 0.062

Null Hypothesis: No cointegrationTrend assumption: Deterministic intercept and TrendAutomatic lag length selection based on SIC with a max lag of 5Newey-West automatic bandwidth selection and Bartlett kernel

4.3 VECM Tests

4.3.1 Short run dynamics

Short-run dynamics for equations with exports are estimated by the same

method of Engle and Granger (1987). The vector auto regression lag length

is set at 4, which was determined using the Schwarz Information Criterion

(SIC). The results of this causality test are shown in Table 4. A 5% level of

significance is used to interpret the results.

The results of the short-run Granger causality test are shown in Tables 8 and

9. The main interest of this paper is a feedback relationship among output,

56

energy consumption, and trade (exports and imports). For the causal relationship

between output and energy consumption, there is some evidence of short-run

causality from energy consumption to output. Table 8 shows that there is

short-run causality from energy consumption to output at the 5% significance

level, from output to energy consumption at the 10% significance level. In

addition, there is short-run causality from energy consumption to exports at the

10% significance level but there is no short-run causality from exports to

energy consumption.1) This is because these countries have achieved export-led

economic growth and these export industries needed the stable energy

consumption. The stable energy supply of these countries may contribute the

promotion of exports and economic growth. Therefore, the energy conservative

policies may affect negatively the exports of these regions.

Table 9 shows that there is no short-run causality from energy consumption

to output but there is short-run causality from output to energy consumption at

the 10% significance level. But, there is no short-run causality from energy

consumption to imports and from imports to energy consumption. These results

show that there is no direct mutual causal relationship between imports and

energy consumption. One of the reasons is that the imports of energy by

amount depend on the price of energy. Even though the increase of energy

consumption cause an increase of the quantity of energy imports, the energy

imports by amount may not increase by the decrease of energy price.

1) The results of Sadorsky (2012) showed that there was a feedback relationship between 1) energy consumption and exports, and 2) output and exports. But there was no evidence of short-run causality running from energy to output or output to energy. The difference of results between this paper and Sadorsky (2012) is due to the differences in countries sampled and the economic structures between South America countries and East Asia countries.

57

Table 8. Short-run Granger causality results for VECM with exports

FromTo

12.499** 12.670** 8.654* 18.03***(0.014) (0.013) (0.070) (0.001)

10.820** 13.864*** 4.410 4.760(0.028) (0.007) (0.353) (0.313)

7.615 3.405 24.566*** 4.445

(0.107) (0.492) (<0.01) (0.349)

9.803** 11.069** 13.750*** 8.932*(0.044) (0.023) (0.008) (0.063)

9.578** 4.598 6.999 4.548(0.048) (0.331) (0.136) (0.337)

The table reports chi-sq statistics with values in parenthesis.The chi-sq tests for short-run Granger causality have 4 degrees of freedom.The system of equation is estimated using OLS with SUR technique.*: means 10% significance level, ** means 5% significance level, *** means 1% significance

level.

Table 9. Short-run Granger causality results for VECM with imports

FromTo

26.192*** 8.506* 10.055* 11.803**

(<0.1) (0.075) (0.039) (0.019)

12.763*** 10.430** 4.779 14.510***

(0.013) (0.034) (0.311) (0.006)

6.977 2.221 25.437*** 1.444

(0.137) (0.695) (<0.1) (0.837)

5.547 9.649** 13.703*** 3.931

(0.233) (0.047) (0.008) (0.415)

2.220 4.427 8.892* 6.246

(0.695) (0.351) (0.064) (0.182)

The table reports chi-sq statistics with values in parenthesis.The chi-sq tests for short-run Granger causality have 4 degrees of freedom.The system of equation is estimated using OLS with SUR technique.*: means 10% significance level, ** means 5% significance level, *** means 1% significance

level.

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4.3.2 Long-run equilibrium

Long-run output elasticities are estimated using ordinary least squares (OLS),

generalized least squares (GLS) or fully modified OLS (FMOLS; Pedroni, 2001).

Each estimation provide very similar results. The estimated coefficients are

elasticities because the variables are measured in natural logarithms. The equations

with exports shown in Table 10 tell the following facts. According to the results of

FMOLS, the long-run elasticity of capital to GDP is 0.458, which means that a 1%

increase in capital output increases GDP by 0.458%. And a 1% increase in labor

and energy increase output by 0.641% and by 0.093%, respectively. A 1% increase

in exports increases output by 0.178%. In the long run, labor and capital had a

major role in economic growth. Energy and exports also contributed to economic

growth. All these coefficients except energy consumption are significant at the 1%

level. The coefficient of energy consumption is significant at 10% level.

Table 10. Long-run equilibrium for equations

Equations with exports Equations with imports

GLS OLS FMOLS GLS OLS FMOLS

k 0.464***(0.032)

0.442***(0.023)

0.458***(0.041)

0.487***(0.042)

0.410***(0.027)

0.434***(0.047)

l 0.123***(0.036)

0.644***(0.110)

0.641***(0.196)

0.091**(0.041)

0.629***(0.117)

0.562***(0.206)

e 0.119***(0.034)

0.093***(0.033)

0.093*(0.057)

0.156***(0.039)

0.114***(0.035)

0.104*(0.060)

x 0.221***(0.022)

0.187***(0.017)

0.178***(0.030)

m 0.173***(0.029)

0.202***(0.022)

0.204***(0.038)

0.998 0.998 0.998 0.997

Wald chi2(4) 2068.53*** 1656.04***Wooldridge test for

autocorrelation 25.931*** 34.737***

Modified wald test for groupwise

heteroskedasticity160.49*** 72.36***

*: means 10% significance level, ** means 5% significance level, *** means 1% significance level.( ) is standard error.

59

The results of the equations for imports are also shown in Table 10. For the

FMOLS results, a 1% increase in capital increases output by 0.434%. Therefore,

a 1% increase in labor and energy, increase output by 0.562% and by 0.104%,

respectively. A 1% increase in imports increases output by 0.204%. The

coefficient of energy consumption is significant at 10% level. In the long run,

this model shows that labor and capital has been a major role in economic

growth. Energy also contributed to economic growth. All these coefficients

except energy consumption are significant at the 1% level. The estimated

coefficients for capital, labor, and energy from Tables 10 have the same sign

as the ones found by Apergis and Payne (1010) and Sadorsky (2012) in their

model related to output, capital, labor, exports, and imports.

5. Conclusion

This study examines the causal relationship between energy consumption,

output and trade in East Asian countries from 1980 to 2010 using panel

cointegration regression methods. The short-run causality and long-run

equilibrium for the six East Asian countries were analyzed. First, the feedback

hypothesis, one of the four hypotheses previously indicated, was supported for

the relationship between energy consumption and GDP particularly in the

equations with exports (see table 8). This hypothesis supports both the growth

hypothesis and the conservation hypothesis, and the growth of GDP affects the

increase of energy consumption, which in turn affects GDP growth. This means

that energy policies designed to reduce energy consumption may be causing

negative effects on economic growth. Some energy policies reducing energy

60

consumption of this region may negatively affect the economic growth of this

region.

In the long-run equilibrium, energy consumption deeply affects economic

growth. Therefore, the environmental and energy policies that support stable

energy consumption in this region positively affect the economic growth.

There is short-run Granger Causality from energy consumption to exports.

This implies that energy consumption can affect exports of this highly trade

intensive region. The energy or environmental policies designed to restrict

energy consumption may cause negative impacts on exports in this region, and

may provide an unintended indirect effect on the economic growth of this

region. Therefore environmental and climate change policies will have to get a

policy to ensure a reliable a low carbon energy.

In the long run, the energy consumption has been increasing with economic

growth in East Asian countries and the stable energy consumption has been

supported economic growth and exports. Therefore the greenhouse gas

mitigation policies that restrict energy consumption may affects the exports of

this region, and may slow economic growth in this region as a result.

Therefore, greenhouse gas mitigation policies should be promoted in the way

that encourages the low carbon energy sources such as the non-fossil fuel energy

(new and renewable energy, nuclear energy etc) and in the way that provide

the stable energy supply.

접수일(2014년 8월 14일), 수정일(2014년 10월 8일), 게재확정일(2015년 2월 2일)

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