Embed Size (px)
Citation preview
Energy consumption, pollutionand economic developmentin 16 emerging countries
Usama Al-mulaliDepartment of Real Estate, Faculty of Geoinformation & Real Estate,
Centre for Real Estate Studies, Universiti Teknologi Malaysia,Johor Bahru, Malaysia, and
Che Normee Che SabEconomics Section, University Sains Malaysia, Pulau Penang, Malaysia
Abstract
Purpose – This study aims to investigate the impact of total primary energy consumption andCO2 emissions on the economic development in 16 emerging countries.
Design/methodology/approach – The panel model was used taking the period 1980-2008.
Findings – The results showed that a long-run relationship is present between total primary energyconsumption, CO2 emission, and economic development in the countries under investigation. It wasalso found that both total primary energy consumption have a positive causal relationship with theeconomic development and other economic aspects playing an important role in achieving higheconomic performance with the consequence of higher pollution.
Practical implications – The main recommendation of this study is to increase their investmentand government spending on green energy projects to increase the share of green energy out of theirtotal energy consumption. This can be considered a good solution for their energy woes.
Originality/value – Different from the previous studies, it was also found that total primary energyconsumption have a positive causal relationship with the economic development and other economicaspects playing an important role in achieving high economic performance with the consequence ofhigher pollution. In addition, there are a number of countries that had not investigated before.
Keywords CO2 emission, Economic development, Total primary energy consumption
Paper type Research paper
1. IntroductionEnergy consumption plays an important role in achieving high economic growth anddevelopment in different countries. Thus, in the last three decades the world energyconsumption rose more than 45 per cent increasing CO2 emission more than 40 per cent(Energy Information Administration (EIA)). This caused the increase of globalwarming and climate change which have become major issues of concern in the last30 years. Therefore, energy consumption, CO2 emission and economic growth havebecome an important subject that attracts a great deal of research over the years.16 emerging countries, namely Brazil, Chile, China, Egypt, India, Indonesia, Jordan,Malaysia, Mauritius, Mexico, Morocco, Pakistan, Peru, Philippines, South Africa, andThailand have been experiencing high increase in both energy consumption andpollution. Based on the EIA, these countries basically consume more than 33 per centof the world primary energy consumption and produce 40 per cent of the worldCO2 emission. The main goal of this study is to examine the impact of total primary
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0144-3585.htm
Journal of Economic StudiesVol. 40 No. 5, 2013pp. 686-698q Emerald Group Publishing Limited0144-3585DOI 10.1108/JES-05-2012-0055
JES40,5
686
energy consumption and CO2 emission on the economic development in theinvestigated countries. The relationship between energy consumption, CO2 emission,and economic growth has been widely studied, however most of the previous studiessuffered from omitted variables which might cause biased results especially the studiesthat conducted the Granger causality test (Stern, 1993). Thus, a complete GDP model willbe presented in this study. In addition, different from the previous studies; this studywill investigated the impact of energy consumption on total trade, governmentconsumption, and gross fixed capital formation. Also, this study will include a number ofcountries had not investigated before. Table I shows that these countries have managedto increase their economic growth in the last three decades. However, the total primaryenergy consumption and CO2 emission have also increased more than 50 per cent.
2. Evidence on the relationship between energy consumption, CO2 emissionand growthA large number of studies have examined the relationship between energyconsumption, CO2 emission and economic growth in different countries. As anexample, Chang (2010) have found out that the high economic growth increased bothenergy consumption and CO2 emission in the short run and the long run in China.The Chinese high growth has an adverse impact regarding global climate change.Fallahi (2011) however found a strong bi-directional causal relationship betweenenergy consumption and GDP growth in the USA. Similar results were reached byZhixin and Xin (2011) in Shandong in the eastern coast of China. A long relationshipbetween energy consumption and growth in the investigated area was also found.In MENA countries Farhani and Ben Rejeb (2012) found a long run unidirectionalcausal relationship running from GDP growth and CO2 emission to energyconsumption. Soytas and Sari (2009) found a one way causal relationship from
GDP (%) GDPPP (%) EC (%) CO2 (%)
Brazil 50 21 62 56Chile 73 60 64 54China 93 90 79 78Egypt 73 51 77 76India 80 0.67 79 80Indonesia 76 63 80 78Jordan 71 22 74 73Malaysia 81 62 82 83Mauritius 75 67 78 81Mexico 51 22 48 46Morocco 63 40 60 55Pakistan 74 49 74 75Peru 53 22 42 36Philippines 56 19 55 54South Africa 48 9 52 51Thailand 79 70 87 86
Notes: GDP is the gross domestic product; GDPPP is the GDP per capita based on the purchasingpower parity; EC is the total primary energy consumption (quadrillion Btu); CO2 is the total carbondioxide emissions from the consumption of energy (million metric tons)
Table I.1980-2008 average
growth rate
Energyconsumption and
pollution
687
CO2 emission to economic growth in the European countries. A lack of long run causalrelationship between both total income and CO2 emission to reduce CO2 emission wasalso found. In the ASEAN countries, a long run one way causal relationship fromelectricity consumption to economic growth was found. Moreover, a short run one waycausal relationship from CO2 emission to electricity consumption was found (Lean andSmyth, 2010). In Turkey neither energy consumption per capita nor per capita CO2
emission has a causal relationship with GDP per capita. This shows that energyconservation polices such as rationing energy consumption and controlling CO2
emission have no negative impact on Turkey’s real output growth (Ozturk andAcaravci, 2010). In France, Ang (2007) found a long run relationship between theeconomic growth, energy use, and CO2 emission. A one way causal relationship fromenergy use to economic growth was also found. In Central America, Apergis and Payne(2009a, b, c) found a short run one directional causal relationship from energyconsumption and output growth to CO2 emission and bi-directional causal relationshipwas present between energy consumption and output growth. Besides, bi-directionalcausal relationship between energy consumption and CO2 emission was present. In theBRIC countries, a long run positive relationships as well as short run causalrelationship between energy consumption and CO2 emissions were found. Moreover,a long run causal relationship between energy consumption and output was present inthe investigated countries (Pao and Tsai, 2010). Wang et al (2011) found similar resultsin China where a bi-directional causal relationship was present between energyconsumption, CO2 emissions and growth. In Canada, Haggar (2011) found a one wayshort run causal relationship from energy consumption and CO2 emission, economicgrowth to CO2 emission, and a weak causal relationship from CO2 emission to energyconsumption. In the long run, weak causal relationships from energy consumption andeconomic growth to CO2 emission were found. In Brazil, while the increase in itseconomic activities is a major source of the increase in CO2 emission, the reduction inthe carbon intensity and the increase in the country’s focus on cleaner energy is a mainfactor contributing to emission mitigation (Freitas and Kaneko, 2011). Alam et al (2011)found a bi-directional causal relationship between energy consumption andCO2 emission in India. In addition, neither energy consumption nor CO2 emissionaffect real income in the short run and the long run. According to the writer, that mighthelp India to employ the energy conservation and efficiency improvement policies toreduce pollution without affecting its economic growth. In Malaysia, it was found thatenergy consumption had a positive long run relationship with its economicdevelopment. A bi-directional causal relationship between energy consumption andeconomic development was also present (Ang, 2008). In Iran, a one way causalrelationship from economic growth and energy consumption (petroleum products andnatural gas consumption) to CO2 emission was found. However, there was no causalrelationship running from fossil fuels consumption to CO2 emission. Moreover, noevidence that CO2 emission, petroleum products, fossil fuel consumption led toeconomic growth (Lotfalipour et al., 2010). Niu et al (2011) found a long run relationshipbetween energy consumption and growth in the eight Asian Pacific countries.In addition, energy consumption per capita in the developing countries was lower thanthe developed countries; however, CO2 emission produced in the developed countrieswas much lower than in the developing countries. Apergis and Payne (2009a, b, c)found out a long run relationships between energy consumption and economic growth
JES40,5
688
in 11 commonwealth countries. One way causal relationship from energy consumptionand growth and long run bi-directional causal relationship between energyconsumption and growth were also found. The same results were found by the sameresearchers in a different study in Central America. In Argentina, Brazil, Colombia,Mexico and Venezuela, it was found that energy consumption plays an important rolein increasing its economic growth; however, the efforts made by these countries toreduce its energy intensity and CO2 emission were insignificant due to the increase in itsindependence on fossil fuels for their energy consumption (Sheinbaum et al., 2011).In China, Zhang and Cheng (2009) found one way causal relationship from economicgrowth to energy consumption and from energy consumption to CO2 emission.However, there was no evidence of a causal relationship from energy consumption andCO2 to economic growth; thus, the conservative energy policy and carbon emissionsreduction policy can be employed without affecting China’s economic growth in thelong run. In the USA, Soytas et al (2007) found that income had no causal relationshipwith CO2 emission but energy consumption did; thus, income itself may not become asolution for environmental problems. In South Africa, a long run relationship betweenenergy consumption, CO2 emission and economic growth and a unidirectional causalrelationship from CO2 emissions to economic growth, from energy consumption toeconomic growth, and energy consumption to CO2 emission were found. It wassuggested that South Africa had to sacrifice its economic growth to reduce energyconsumption and pollution (Menyah and Rufael, 2010). Li et al (2011) found that therewas a positive long run relationship between energy consumption, CO2 emission andGDP per capita in east and west China. However, this relationship was stronger in theeast rather than in the west. Warr and Ayres (2010) found one way causal relationshipfrom energy consumption to economic growth in the USA, while no evidence of causalrelationship from economic growth to energy consumption was found. Therefore, tosustain long run growth, it is important to increase either energy suppliers or theefficiency of energy usage. Kahrl and Holst (2009) found that the incipient structuralchanges in the Chinese energy economy and the sustainable economic and energydemand growth would create important and different challenges for the Chinesepolicymakers. Zhang (2011) found that the energy consumption was cointegrated witheconomic growth and bi-directional causal relationship between energy consumptionand economic growth in Russia was also found. Pao and Tsai (2011a, b) found similarresults in Brazil. Pao et al (2011) also found similar results in Russia. Zhang et al. (2011)found that the energy consumption of primary, secondary and tertiary industrydemonstrated and unversed a sort of relationship with the gross domestic productin China. A linear relationship was also present between energy consumption and GDP.In the BRIC countries, it was found that the CO2 emission appeared to be energyconsumption elastic and FDI inelastic. Furthermore, a bi-directional causal relationshipbetween FDI and CO2 emission was found. It was suggested that these countries shouldstrictly examine the qualification for foreign investment to prevent any environmentaldamage. A bi-directional causality between output energy consumption and outputCO2 emissions was also found, while unidirectional causal relationship from energyconsumption to CO2 emission was present (Pao and Tsai, 2011a, b). Apergis et al. (2010)found a long run negative relationship between nuclear energy consumption andCO2 emission and a long run positive relationship between renewableenergy consumption and economic growth in 18 developed and developing countries.
Energyconsumption and
pollution
689
It was also found that nuclear energy consumption had a negative causal relationshipwith CO2 emission while a renewable energy had no effect on CO2 emission. Similarresults were found by Acaravci and Ozturk (2010) in the European countries. Lee andChang (2007) found a strong positive relationship between energy consumption andeconomic growth in Taiwan.
3. Methodology and resultsThe panel model will be used in this study taking the period 1980-2008. It is specifiedas follows:
GDPPit ¼ aþ b1 GFCit þ b2 GOVit þ b4 TDit þ b5 EMit þ b6 ECit þ 1it ð1Þ
where GDPP is the gross domestic product per capita based on the purchasing powerparty as an indicator of economic development measured in 2000 constant US dollar.GFC is the gross fixed capital formation as an indicator of investment measured inmillions of 2000 constant US dollars. GOV is the government consumption expendituremeasured in millions of 2000 constant US dollars. TD is the total trade of goods andservices measured in 2000 constant US dollars. EM is the total carbon dioxideemissions from the consumption of energy measured in million metric tons. EC is thetotal primary energy consumption measured in quadrillion Btu. b1, b2, b3, b4 and b5 isthe slope coefficients of the model, t is time, i is the cross-section unit (ith country), a isa scalar. The variables namely GDPP, GFC, GOV, TD are taken from the WorldDevelopment Indicators (WDI) while EM and EC are taken from the EIA.
This study will use the panel unit root test to examine whether the variables containa panel unit root. If the variables have a panel unit root, this study will proceed to usethe cointegration test to find out if the variables are cointegrated. If the variables arecointegrated, the Granger causality based on the error correction term (VECM) will beimplemented to examine whether the short run and the long run causal relationshipexists between the variables. The explanation of the tests mentioned below was takenfrom Baltagi (2005).
3.1 Panel unit root testThe Im, Pesaran, and Shin, and the Fisher-ADF and PP tests will be applied in thisstudy since they allow for individual unit root processes, so ri may vary across thecross-sections. These tests are basically characterized by a combination of individualunit root tests to derive a panel specific result. These unit root tests specify a separateADF regression for each cross-section:
Dyit ¼ ayit21
Xpij¼1
bij Dyit2j þ X0
itdþ 1it ð2Þ
where y is the dependent variable, X is the independent variable, a and d are individualentity and time effects, respectively, t is time, i is the cross section, and 1 is the residual.
The null hypothesis can be written as follows:
H0: a ¼ 0; for all i
JES40,5
690
The alternative hypothesis can be written as follows:
H1:ai ¼ 0 for i ¼ 1; 2; 3; . . . ;N 1
ai , 0 for i ¼ N þ 1;N þ 2; . . . ;N
(
where the i may be reordered as necessary. This may be interpreted as a stationarynon-zero fraction of the individual processes.
Table II reviews the panel unit root test results and clearly shows that all thevariables are not stationary at the levels. However, all the variables are stationary atthe first deference rejecting the null hypothesis indicating that all the variables containa panel unit root.
3.2 Pedroni (Engle-Granger based) cointegration testsSince the variables contain a panel unit root, the Pedroni test is applied to examinethe long run relationship between the variables. Pedroni proposes several tests forcointegration that allow for heterogeneous intercepts and trend coefficients acrosscross-sections. Consider the following regression:
yit ¼ ai þ dit þ b1x1it þ b2x2it þ · · · þ bkixkit þ 1it ð3Þ
where t ¼ 1, . . . , T; i ¼ 1, . . . , N; j ¼ 1, . . . , k; and y and x are assumed to be integratedof order 1, i.e. I(1). The parameters ai and di are individual entity and time effects,
Level First differenceVariable Intercept Intercept and trend Intercept Intercept and trend
Im, Pesaran and Shin W-stat.GDPPP 9.28567 3.18354 5.63354 * * * 5.93054 * * *
GFC 8.74256 3.61313 4.93001 * * * 4.85467 * * *
GOV 12.0542 4.89436 4.39001 * * * 4.39173 * * *
TD 13.1232 7.54629 3.68153 * * * 4.29413 * * *
EM 7.98143 1.17578 8.18660 * * * 6.40211 * * *
EC 9.10320 0.67523 8.62929 * * * 6.89381 * * *
ADF-Fisher x 2
GDPPP 2.63369 33.0173 103.264 * * * 100.897 * * *
GFC 6.17024 22.8139 98.5156 * * * 86.1427 * * *
GOV 2.49495 18.4389 86.2450 * * * 81.2736 * * *
TD 3.60611 27.7774 86.8167 * * * 86.3998 * * *
EM 3.90774 31.8981 138.267 * * * 107.328 * * *
EC 2.33118 36.2931 146.518 * * * 115.135 * * *
PP-Fisher x 2
GDPPP 1.15792 16.5848 169.787 * * * 204.804 * * *
GFC 3.61119 11.5035 140.274 * * * 133.149 * * *
GOV 4.43782 18.1240 198.708 * * * 211.823 * * *
TD 1.34559 15.7555 140.034 * * * 197.613 * * *
EM 2.83378 31.8485 257.386 * * * 255.647 * * *
EC 1.86639 42.1820 289.397 * * * 408.044 * * *
Notes: Significant at: * *5 and * * *1 per cent levels; the optimal lag length was selected automaticallyusing the Schwarz information criteria
Table II.Panel unit root
test results
Energyconsumption and
pollution
691
respectively, which may be set to zero if desired, k is the number of regresses, t is thenumber of observations. And b1, b2 and bK are slope coefficients. Under the nullhypothesis of no cointegration, the residuals 1it will be I(1). The existence ofcointegration in the model will be determined by the significance of the Pedronicointegration statistics. These cointegration statistics are the panel v-statistics,panel p-statistics, panel t-statistic (non-parametric), panel t-statistic (parametric), groupr-statistics, group t-statistics (non-parametric) and the group statistics (parametric).
Table III shows the Pedroni cointegration test results and shows that nine statisticsreject the null hypothesis of no cointegration indicating that the long run relationship ispresent between economic development, gross fixed capital formation, government
Alternative hypothesis: common AR coefficients (within-dimension)Individual intercept
Statistic Prob. Weighted statistic Prob.Panel v-statistic 20.611663 0.3309 20.658859 0.3211Panel r-statistic 2.311888 * * 0.0276 2.437657 * * 0.0204Panel PP-statistic 20.328295 0.3780 20.145873 0.3947Panel ADF-statistic 21.047290 0.2305 21.448462 * 0.1397
Alternative hypothesis: individual AR coefficients (between-dimension)Statistic Prob.
Group r-statistic 4.055930 * * * 0.0001Group PP-statistic 20.056316 0.3983Group ADF-statistic 21.005863 0.2406
Alternative hypothesis: common AR coefficients (within-dimension)Individual intercept and individual trend
Statistic Prob. Weighted statistic Prob.Panel v-statistic 2.747059 * * 0.0092 1.396731 * 0.1504Panel r-statistic 2.539659 * * 0.0159 2.881738 * * 0.0063Panel PP-statistic 21.206355 * 0.1927 20.704437 0.3113Panel ADF-statistic 21.774956 * * 0.0826 21.087950 0.2207
Alternative hypothesis: individual AR coefficients (between-dimension)Statistic Prob.
Group r-statistic 4.370881 * * * 0.0000Group PP-statistic 21.816128 * * 0.0767Group ADF-statistic 21.237817 * 0.1854
Alternative hypothesis: common AR coefficients (within-dimension)No intercept or trend
Statistic Prob. Weighted statistic Prob.Panel v-statistic 23.977459 * * * 0.0001 24.677601 * * * 0.0000Panel r-statistic 2.738527 * * 0.0094 2.681522 * * 0.0110Panel PP-statistic 1.119588 0.2132 1.291918 * 0.1732Panel ADF-statistic 20.008325 0.3989 0.107539 0.3966
Alternative hypothesis: individual AR coefficients (between-dimension)Statistic Prob.
Group r-statistic 3.900551 * * * 0.0002Group PP-statistic 0.991176 0.2441Group ADF-statistic 0.035073 0.3987
Notes: Significant at: *10, * *5 and * * *1 per cent levels; we use the automatic selection based on theSchwarz to choose the optimal lag length
Table III.Pedroni cointegrationtest results
JES40,5
692
consumption expenditure, total trade of goods and services, total primary energyconsumption, and CO2 emission.
3.3 The fully modified OLSAfter having found that existence of a general cointegration in the model of this studythe fully modified OLS will be utilized. The panel FMOLS was made by Pedroni (2000).It eliminates the long run correlation problem in between the cointegrating equation.Lastly, the FMOLS is basically unbiased and has fully efficient mixture normalasymptotics allowing for standard Wald tests using asymptotic x 2 statisticalinference. The FMOLS estimator is presented as follows:
u ¼b
y1
� �¼
XTt¼1
zit z0it
!21 XTt¼1
zityþit 2 T
lþ
102
0
" # !ð4Þ
where Zt is the deterministic trend and stochastic regressors, the estimation of theFMOLS is the construction of long run covariance matrix estimators.
Table IV shows the FMOL test results and it is clear that all the variables namelygross fixed capital formation, government consumption expenditure, total trade, CO2
emission, and total primary energy consumption have a long run positive relationshipwith the economic development in the investigated countries.
1 per cent increase in gross fixed capital formation, government consumptionexpenditure, total trade, CO2 emission, and total primary energy consumptionwill increase the economic development by 0.596859 per cent, 0.441138 per cent,1.548560 per cent, 2.007567 per cent, and 4.3743353 per cent, respectively.
GFC GOV TD EM EC
Brazil 0.584325 * * * 2.033943 * * 3.544721 * * 1.296143 * 4.727245 * *
Chile 1.857023 * 0.440272 * * * 0.044678 * 0.114594 6.336022 * *
China 0.184738 * * * 1.019848 * * 0.143647 * * 0.011294 12.14258 * * *
Egypt 1.732401 * * 0.387956 * * * 0.091868 * * 2.347912 * * * 3.520878 * * *
India 1.629128 * 21.033116 0.861372 * * 0.029646 * * 0.014634 * *
Indonesia 20.862305 0.844163 * * 5.016718 * * 1.958481 * 2.991737 * *
Jordan 0.593945 * 20.248693 * * * 21.500271 3.412137 * * 0.007812 * *
Malaysia 1.208105 * * * 1.673444 * * * 0.012411 * * 1.553642 * * 8.693762 * *
Mauritius 2.374853 * * 0.566031 * * * 20.434815 11.47987 * * * 3.774369 * * *
Mexico 0.309325 * * * 0.331543 * * * 10.71537 * * 22.166904 3.267104 * * *
Morocco 21.2586741 20.015538 22.501892 20.001811 0.002810 *
Pakistan 0.594832 1.022240 * * * 0.038639 * * 1.661106 * * * 16.31157 * * *
Peru 21.928427 * 21.980235 * 6.965945 * * * 5.018680 * * * 1.298215 * * *
Philippines 0.724867 * * * 1.387871 * * 0.711306 * * 4.427821 * * * 4.171595 * * *
South Africa 0.9483203 * * * 0.913960 * * 20.017970 0.967619 * * 0.944223 * *
Thailand 0.857302 * * 20.2854759 * 1.085243 * 0.010850 * * 1.784809 *
Panel 0.596859 * * * 0.441138 * * 1.548560 * * * 2.007567 * * * 4.3743353 * * *
Notes: Significant at: *10, * *5 and * * *1 per cent levels; the numbers presented in the table are theslop coefficient
Table IV.Fully modified OLS test
results GDP as thedependent variable
Energyconsumption and
pollution
693
3.4 Panel Granger causalityEngle and Granger show that non-stationary variables are cointegrated in the model,so the vector error-correction model will be used to investigate the temporal short-runcausality between the variables. Short-run Granger causality which is based on theF-test and x 2-test can be established by conducting a joint test of the coefficients.The long-run causal relationship, on the other hand, is applied through the significanceof the lagged error correction term in the VECM, based on the t-test. The followingequation introduces the Granger causality model with uniform lag length:
DDEPit ¼ ait þ bitectit21 þXli¼1
jitDDEPit21 þXli¼1
witDlogðINDPÞit21 þ mit ð5Þ
where DEP is the dependent variable, INDP is the independent variable, D is the firstdifference operator, ait is the constant term, bit, jit and wit are the parameters, ectit21 isthe lagged error correction term obtained from the cointegrating equation and mit is thewhite noise error.
Table V shows the Granger causality test results, based on the error correction term.While a bi-directional causal relationship between all the variables was found in thelong run, a positive bi-directional causal relationship was found in the short runbetween gross fixed capital formation and economic development, CO2 emissions andeconomic development, and total primary energy consumption and economicdevelopment. A positive short run causal relationship is also present betweengovernment consumption expenditure and the gross fixed capital formation, total tradeof goods and services and the gross fixed capital formation, CO2 emissions and thegross fixed capital formation, and total primary energy consumption and the grossfixed capital formation. Moreover, a positive bi-directional short run relationshipbetween CO2 emissions and government consumption expenditure, total primaryenergy consumption and government consumption expenditure, and between CO2
emissions and total primary energy consumption was found. However, a one way shortrun causal relationship exists from total trade of goods and services and governmentconsumption expenditure to economic development. The most important finding inthis test is that both total primary energy consumption and CO2 emissions havepositive causal relationship with the economic development. Moreover, different fromthe previous studies it was found that total primary energy consumption and CO2
emissions have also a positive causal relationship with total trade of goods andservices, gross fixed capital formation as a measure of investment, and governmentconsumption expenditure. These economic aspects play an important role in achievinghigh economic growth and development in the investigated countries.
4. ConclusionThis study investigated the influence of total primary energy consumption and CO2
emissions in 16 emerging countries, namely Brazil, Chile, China, Egypt, India,Indonesia, Jordan, Malaysia, Mauritius, Mexico, Morocco, Pakistan, Peru, Philippines,South Africa, and Thailand. The panel model was used in this study taking the period1980-2008. The Pedroni cointegration test results showed the existence of long runrelationship between total primary energy consumption, CO2 emissions and economicdevelopment in the investigated countries. The Granger causality test results also
JES40,5
694
DG
DP
PP
DG
FC
DG
OV
DT
DD
EM
DE
Cec
t(2
1)
DG
DP
PP
–23
.086
58*
**
1.76
1377
**
1.72
8553
**
1.97
2923
**
2.32
3699
**
22.
2240
83*
*
DG
FC
26.4
0915
**
*–
4.80
1221
**
*2.
4701
65*
*3.
0590
94*
**
2.37
5576
**
22.
0851
81*
*
DG
OV
0.87
5694
6.98
9815
**
*–
1.36
5940
10.9
9668
**
*7.
1965
19*
**
21.
2997
75*
DT
D1.
2239
772.
0432
24*
**
0.49
6945
–4.
0902
30*
**
2.62
5557
**
21.
3334
73*
DE
M3.
4072
99*
**
3.68
4908
**
*18
.164
81*
**
3.75
4992
**
*–
50.3
541
**
*2
1.55
9718
*
DE
C2.
8251
18*
*8.
3209
11*
**
21.2
8139
**
*1.
8294
41*
**
58.5
753
**
*–
21.
4194
94*
Notes:
Sig
nifi
can
tat
:* 1
0,*
* 5an
d*
** 1
per
cen
tle
vel
s;th
en
ull
hy
pot
hes
isis
that
ther
eis
no
cau
sal
rela
tion
ship
bet
wee
nv
aria
ble
s;v
alu
esin
par
enth
eses
arep-
val
ues
for
Wal
dte
sts
wit
hax
2d
istr
ibu
tion
;D
isth
efi
rst
dif
fere
nce
oper
ator
;ec
t(2
1)re
pre
sen
tsth
eer
ror
corr
ecti
onte
rmla
gg
edon
ep
erio
d Table V.Granger causality
test results
Energyconsumption and
pollution
695
showed that both total primary energy consumption and CO2 emissions have a longrun and a positive short run causal relationship with the economic development, totaltrade, government consumption, and the gross fixed capital formation. So, it is clearthat the total primary energy consumption did not only increase the economicdevelopment in these countries but also affected other economic aspects which play anessential role in achieving high economic performance in the countries underinvestigation but with the consequence of high CO2 emissions. These countries sufferfrom high levels of pollution which have increased more than double in the last threedecades. Therefore, it is very important that these countries reduce their CO2
emissions. Since it was found that both gross fixed capital formation (investment) andgovernment consumption expenditure have a significant causal relationship with CO2
emissions, it is very important that these countries increase their investment andgovernment spending on green energy projects to increase the role of green energy inachieving their economic development. Also, it is important to adopt trade-relatedmeasures and policies to increase environmental protection after having found thattotal trade increases CO2 emission. Moreover, the increase in energy productivity byincreasing energy efficiency, implementation of energy savings projects, conservation,and energy infrastructure outsourcing. This motivates the writer to recommend theabove mentioned policy for the countries under investigation to help them overcome allproblems of pollution. However, these polices should be implemented without affectingtheir economic growth negatively.
References
Acaravci, A. and Ozturk, I. (2010), “On the relationship between energy consumption,CO2 emissions and economic growth in Europe”, Energy, Vol. 35, pp. 5412-5420.
Alam, M., Begum, I., Buysse, J. and Rahman, S. (2011), “Dynamic modeling of causal relationshipbetween energy consumption, CO2 emissions and economic growth in India”, Renewableand Sustainable Energy Reviews, Vol. 15, pp. 3243-3251.
Ang, J. (2007), “CO2 emissions, energy consumption, and output in France”, Energy Policy, Vol. 35,pp. 4772-4778.
Ang, J. (2008), “Economic development, pollutant emissions and energy consumption inMalaysia”, Journal of Policy Modeling, Vol. 30, pp. 271-278.
Apergis, N. and Payne, J. (2009a), “CO2 emissions, energy usage, and output in Central America”,Energy Policy, Vol. 37, pp. 3282-3286.
Apergis, N. and Payne, J. (2009b), “Energy consumption and economic growth: evidence fromthe commonwealth of independent states”, Energy Economics, Vol. 31, pp. 641-647.
Apergis, N. and Payne, J. (2009c), “Energy consumption and economic growth in CentralAmerica: evidence from a panel cointegration and error correction model”, EnergyEconomics, Vol. 31, pp. 211-216.
Apergis, N., Payne, J., Menyah, K. and Rufael, Y. (2010), “On the causal dynamics betweenemissions, nuclear energy, renewable energy, and economic growth”, EcologicalEconomics, Vol. 69, pp. 2255-2260.
Baltagi, B. (2005), Econometric Analysis of Panel Data, Wiley, Chichester.
Chang, C. (2010), “A multivariate causality test of carbon dioxide emissions, energy consumptionand economic growth in China”, Applied Energy, Vol. 87, pp. 3533-3537.
JES40,5
696
Fallahi, F. (2011), “Causal relationship between energy consumption (EC) and GDP:a Markov-switching (MS) causality”, Energy, Vol. 36, pp. 4165-4170.
Farhani, S. and Ben Rejeb, J. (2012), “Energy consumption, economic growth and CO2 emissions:evidence from panel data for MENA region”, International Journal of Energy Economicsand Policy, Vol. 2, pp. 71-81.
Freitas, L. and Kaneko, S. (2011), “Decomposition of CO2 emissions change from energy consumptionin Brazil: challenges and policy implications”, Energy Policy, Vol. 39, pp. 1495-1504.
Haggar, M. (2011), “Greenhouse gas emissions, energy consumption and economic growth:a panel cointegration analysis from Canadian industrial sector perspective”, EnergyEconomics, Vol. 34, pp. 358-364.
Kahrl, F. and Holst, D. (2009), “Growth and structural change in China’s energy economy”,Energy, Vol. 34, pp. 894-903.
Lean, H. and Smyth, R. (2010), “CO2 emissions, electricity consumption and output in ASEAN”,Applied Energy, Vol. 87, pp. 1858-1864.
Lee, C. and Chang, C. (2007), “The impact of energy consumption on economic growth: evidencefrom linear and nonlinear models in Taiwan”, Energy, Vol. 32, pp. 2282-2294.
Li, F., Dong, S., Li, X., Liang, Q. and Yang, W. (2011), “Energy consumption-economic growthrelationship and carbon dioxide emissions in China”, Energy Policy, Vol. 39, pp. 568-574.
Lotfalipour, M., Falahi, M. and Ashena, M. (2010), “Economic growth, CO2 emissions, and fossilfuels consumption in Iran”, Energy, Vol. 35, pp. 5115-5120.
Menyah, K. and Rufael, Y. (2010), “Energy consumption, pollutant emissions and economicgrowth in South Africa”, Energy Economics, Vol. 32, pp. 1374-1382.
Niu, S., Ding, Y., Niu, Y., Li, Y. and Luo, G. (2011), “Economic growth, energy conservation andemissions reduction: a comparative analysis based on panel data for 8 Asian-Pacificcountries”, Energy Policy, Vol. 39, pp. 2121-2131.
Ozturk, I. and Acaravci, A. (2010), “CO2 emissions, energy consumption and economic growth inTurkey”, Renewable and Sustainable Energy Reviews, Vol. 14, pp. 3220-3225.
Pao, H. and Tsai, C. (2010), “CO2 emissions, energy consumption and economic growth in BRICcountries”, Energy Policy, Vol. 38, pp. 7850-7860.
Pao, H. and Tsai, C. (2011a), “Modeling and forecasting the CO2 emissions, energy consumption,and economic growth in Brazil”, Energy, Vol. 36, pp. 2450-2458.
Pao, H. and Tsai, C. (2011b), “Multivariate Granger causality between CO2 emissions, energyconsumption, FDI (foreign direct investment) and GDP (gross domestic product): evidencefrom a panel of BRIC (Brazil, Russian Federation, India, and China) countries”, Energy,Vol. 36, pp. 685-693.
Pao, H., Yu, H. and Yang, Y. (2011), “Modeling the CO2 emissions, energy use, and economicgrowth in Russia”, Energy, Vol. 36, pp. 50094-55100.
Pedroni, P. (2000), “Fully modified OLS for heterogeneous cointegrated panels”, AdvancedEconometrics, Vol. 15, pp. 93-130.
Sheinbaum, C., Ruız, B. and Ozawa, L. (2011), “Energy consumption and related CO2 emissions infive Latin American countries: changes from 1990 to 2006 and perspectives”, Energy,Vol. 36, pp. 3629-3638.
Soytas, U. and Sari, R. (2009), “Energy consumption, economic growth, and carbon emissions:challenges faced by an EU candidate member”, Ecological Economics, Vol. 68,pp. 1667-1675.
Energyconsumption and
pollution
697
Soytas, U., Sari, R. and Ewing, B. (2007), “Energy consumption, income, and carbon emissions inthe United States”, Ecological Economics, Vol. 62, pp. 482-489.
Stern, D.I. (1993), “Energy growth in the USA: a multivariate approach”, Energy Economics,Vol. 15, pp. 137-150.
Wang, S.S., Zhou, D.Q., Zhou, P. and Wang, Q.W. (2011), “CO2 emissions, energy consumptionand economic growth in China: a panel data analysis”, Energy Policy, Vol. 39,pp. 4870-4875.
Warr, B.S. and Ayres, R.U. (2010), “Evidence of causality between the quantity and quality ofenergy consumption and economic growth”, Energy, Vol. 35, pp. 1688-1693.
Zhang, J., Deng, S., Shen, F., Yang, X., Liu, G., Guo, H., Li, Y., Hong, X., Zhang, Y., Peng, H.,Zhang, X., Li, L. and Wang, Y. (2011), “Modeling the relationship between energyconsumption and economy development in China”, Energy, Vol. 36, pp. 4227-4234.
Zhang, X. and Cheng, X. (2009), “Energy consumption, carbon emissions, and economic growthin China”, Ecological Economics, Vol. 68, pp. 2706-2712.
Zhang, Y. (2011), “Interpreting the dynamic nexus between energy consumption and economicgrowth: empirical evidence from Russia”, Energy Policy, Vol. 39, pp. 2265-2272.
Zhixin, Z. and Xin, R. (2011), “Causal relationships between energy consumption and economicgrowth”, Energy Procedia, Vol. 5, pp. 2065-2071.
Further reading
Bownden, N. and Payne, J.E. (2010), “Sectoral analysis of the causal relationship betweenrenewable and non-renewable energy consumption and real output in the US”,Energy Sources, Part B: Economics, Planning, and Policy, Vol. 5, pp. 400-408.
Lu, C., Zhang, X. and He, J. (2010), “A CGE analysis to study the impacts of energy investment oneconomic growth and carbon dioxide emission: a case of Shaanxi province in westernChina”, Energy, Vol. 35, pp. 4319-4327.
Ozturk, I. (2010), “A literature survey on energy-growth nexus”, Energy Policy, Vol. 38,pp. 340-349.
Payne, J.E. (2010), “On biomass energy consumption and real output in the US”, Energy Sources,Part B: Economics, Planning, and Policy, Vol. 6, pp. 47-52.
Yoo, S. (2006), “Oil consumption and economic growth: evidence from Korea”, Energy Sources,Part B: Economics, Planning, and Policy, Vol. 1, pp. 235-243.
Corresponding authorUsama Al-mulali can be contacted at: [email protected]
JES40,5
698
To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints
