The empirical findings of this study contribute valuable policy implications and recommendations. The findings that globalisation had unfavourable effects on energy consumption in Brazil and South Africa and favourable ones in India, China and Russia demonstrate that stronger trade and investment flows, closer social relationships and better political strategies across economies can reduce the demand for energy due mostly to the awareness of energy-efficient technologies. Despite rising economic globalisation, the producers in Brazil and South Africa might not use advanced production techniques; therefore, they consume larger amounts of energy for their production activities. The unfavourable impacts of globalisation on energy use and resulting higher environmental costs via augmented energy
consumption may occur in the form of natural disasters and global warming and, hence, must be given proper attention.
Regarding the effect of capital on energy consumption is concerned, the findings revealed that capitalisation increased energy consumption in the majority of the BRICS countries. These countries should adopt energy conservation policies, invest in innovative energy-saving capital and machineries, and apply clean and ‘green’ technologies for production and consumption purposes. A transfer from inferior to higher-quality energy sources is expected not only to trim total energy consumption; it may also reduce environmental impact of energy use. An obvious example would be a shift from coal use to natural gas use. Natural gas is a cleaner burning energy source and produces less carbon emissions per unit of energy derived. Similarly, hydro and wind energy could also have fewer environmental impacts. The environmental impact of energy use may also change over time due to technological innovations that reduce emissions of various pollutants or other environmental impacts associated with an energy source. Therefore, despite the strong connection between energy consumption and economic growth, the environmental impact of growth can be reduced through several channels. In addition, if there are restrictions to adopting clean energy sources and to technologically transforming old technologies, the potential reduction in environmental intensity of economic production is ultimately limited.
Finally, reducing energy consumption through consumer life style changes, that is, using pricing and taxation to discourage the use of energy-intensive devices and encouraging the use of energy-conserving devices, is highly desirable. To be successful, these strategies must link both suitable supply and end-use technologies. Policy agents must convert these strategies into policies. Complete hardware plus ‘software’— policies, management, financing, training, and institutions-solutions are essential for the deployment of energy as an instrument of sustainable development.
7. Conclusion
Globalisation performs an imperative role as an instrument linking growing economies, while it affects environmental degradation due primarily to the immense use of energy in both production and consumption activities in both advanced and developing economies. According to Shahbaz et al. (2015), “… our effort is hopefully worthy of empirical investigation in a threatening
environment of climate change and global warming”. Given that the environmental costs of globalisation are considered to be greater for a diverse and connected world than for a segmented world, the behaviour of the energy demand function has been investigated by incorporating globalisation (using the index that encompasses three different dimensions of globalisation), economic growth and capital as positional determinants of energy consumption in the BRICS. To this end, the NARDL cointegration approach was applied, which accommodates asymmetries stemming from time series. The robustness of the NARDL analysis was also examined by applying the multiple dynamic adjustment approach.
The results indicate the presence of asymmetric cointegration across energy consumption, globalisation, economic growth and capital. Additionally, the long-run impact of determinants on energy consumption was found to be heterogeneous in the BRICS. Positive (negative) globalisation shocks significantly decreased (increased) energy consumption in Brazil and Russia (Brazil and South Africa). Energy consumption declined with negative globalisation shocks in Russia and China.
In India, globalisation (both negative and positive shocks) reduced energy consumption levels. Comparing both positive and negative shocks, the analysis concluded that globalisation increases (decreases) energy consumption in Brazil and South Africa (Russia, India and China).
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Table-1. Descriptive Statistics
Variable Statistics Brazil Russia India China South Africa
E
tln
Mean 6.9436 8.7118 5.9433 6.8130 7.8573 Median 6.8952 8.5435 5.9116 6.6871 7.8797 Maximum 7.3123 9.3621 6.4572 7.8231 7.9993 Minimum 6.6763 8.2894 5.5927 6.1689 7.6536 Std. Dev. 0.1674 0.3466 0.2591 0.4768 0.0806 Skewness 0.6437 0.5418 0.4320 0.7112 -0.7359 Kurtosis 2.5057 1.8026 2.1037 2.3487 3.0227 Jarqu-Bera 3.4080 4.7812 2.8417 4.4877 3.8827 Probability 0.1819 0.0915 0.2415 0.1060 0.1435
G
tln
Mean 3.9144 3.7569 3.5671 3.6299 3.8711 Median 3.9504 3.8340 3.5521 3.7145 3.7087 Maximum 4.0997 4.2085 3.9442 4.1122 4.1746 Minimum 3.6533 3.0217 3.2082 3.0090 3.6085 Std. Dev. 0.1611 0.4011 0.2847 0.4252 0.2406 Skewness -0.2739 -0.2981 0.0458 -0.1835 0.2260 Kurtosis 1.5093 1.6236 1.2954 1.3340 1.1886 Jarqu-Bera 4.5190 4.1245 5.3420 5.3351 6.2446 Probability 0.1044 0.1271 0.0691 0.0694 0.0440
Y
tln
Mean 8.3711 8.7755 6.1902 6.5378 8.7992 Median 8.3420 8.7868 6.0699 6.4613 8.7892 Maximum 8.6904 9.5164 7.1686 8.3081 8.9371 Minimum 8.0367 8.0964 5.5754 5.0706 8.6719 Std. Dev. 0.1648 0.4133 0.4900 1.0428 0.0814 Skewness 0.4254 0.0857 0.5133 0.1606 0.2580 Kurtosis 2.5930 2.0341 2.0192 1.7256 1.9614 Jarqu-Bera 1.5940 1.7642 3.6959 3.1664 2.4097 Probability 0.4506 0.4139 0.1575 0.2053 0.2997 Mean 6.7812 7.7194 4.7558 5.3872 7.0094
Median 6.7360 7.3565 4.5530 5.4235 7.0448 Maximum 7.1777 9.4003 5.9797 7.5503 7.3999 Minimum 6.4990 6.1201 3.8798 3.1977 6.5894 Std. Dev. 0.1842 1.0617 0.6880 1.3043 0.2438 Skewness 0.6212 0.1615 0.5017 0.0845 -0.0366 Kurtosis 2.6237 1.6125 1.9192 1.7844 1.6642 Jarqu-Bera 3.0200 3.7207 3.9874 2.7610 3.2062 Probability 0.2209 0.1556 0.1361 0.2514 0.2012
Table-2. BDS Non-linearity Tests Variable BDS-Statistic Prob.
Brazil
Table-3. Unit Root Analysis with Structural Breaks Variables
ADF Test at Level ADF Test at 1st diff.
Statistics Break Date Statistics Break Date Brazil
Significance Level CV 1% -4.949133 CV 5% -4.443649 CV 10% -4.193627 Note: *: p≤0.01; **: p≤0.05.
Table-4. NARDL Cointegration Analysis
Dependent Variable: Δln EY
Brazil Russia India China South Africa
Constant 4.4179*** (1.0229) 1.5856*** (1.5982) 2.9571*** (0.9138) 1.7551*** (0.5219) 1.8349** (0.7978)
ln EY" -0.6872*** (0.1591) -1.1246*** (0.1696) -0.5232*** (0.1622) -0.3023*** (0.0842) -0.2309** (0.1039)
ln GY! -0.2234*** (0.0694) -0.1288* (0.0778) -0.0444** (0.0542) 0.0431 (0.0967) -0.0940 (0.0863)
ln GY" 1.0092*** (0.2530) -3.5498*** (0.6466) -1.9137*** (0.5780) -1.7928* (0.9112) 3.1379*** (0.7488)
ln YY! 0.0239* (0.0170) 0.6841* (0.3386) 0.1159 (0.1057) -0.2720*** (0.0806) -0.6598 (0.5619)
ln YY" 0.3151* (0.0049) 0.8221*** (0.1544) 0.0144 (0.1174) 0.9411* (0.5679) 1.2249** (0.5023)
ln KY! 0.3909*** (0.0755) -0.2959 (0.2060) 0.0878* (0.0478) 0.2780*** (0.0532) 0.2003 (0.1999)
ln KY" -0.1341 (0.1413) -0.0235 (0.0236) 0.1855 (0.1837) 0.2733 (0.1842) -0.8258*** (0.2133)
DY 0.0351** (0.0159) -0.0006 (0.0117) 0.0190 (0.0125) 0.0821*** (0.0238) 0.0767**
∆ln EY 0.1944 (0.1298) -0.3327** (0.1566)
∆ln EY" 0.2705** (0.1004) -0.3344* (0.1664)
∆ln EY" 0.3855** (0.1389) -0.3149* (0.1667) 0.1637* (0.0919)
∆ln GY! -0.1814* (0.1136)
∆ln GY"! -0.3169** (0.1370)
∆ln GY"! -1.0431* (0.5912) -1.1556* (0.5646) 1.2829* (0.7545) 2.2381** (0.8201)
∆ln GY" 0.5873 (0.4114) 1.4765** (0.5397) 1.4265* (0.8584) 1.5778** (0.7108)
∆ln GY"" 0.4642** (0.2126) 0.6710*** (0.1860) -0.3548** (0.1677)
∆ln YY! 0.5751** (0.2467)
∆ln YY"! 1.2534** (0.4927)
∆ln YY"! 0.7039*** (0.1622) -0.9153* (0.5028)
∆ln YY" -0.2286** (0.0983) -1.3394* (0.7069) -1.3747** (0.6314)
∆ln YY"" 0.1685* (0.0893) 0.2327*** (0.0778) 0.6100*** (0.1719)
∆ln KY! -0.1940*** (0.0656) -0.2306*** (0.0617)
∆ln KY"! -0.1434** (0.0610) 0.5176** (0.2325)
∆ln KY" 0.4421** (0.1629) -0.6689*** (0.2317) 0.4292* (0.2285)
R2 0.8276 0.9342 0.5944 0.7679 0.7432
Adj-R2 0.7055 0.8977 0.4677 0.7157 0.5614
D-W Test 1.9618 2.163 2.3414 2.1781 2.4908
T=U 3.0483 2.6253 1.7735 2.1367 2.1889
TVU 0.5588 2.2328 0.9335 0.3832 0.7315
T^^ 0.9307 2.0295 2.3841 0.2434 0.2381
_!` -0.3217 [0.0048] -0.1139 [0.0866] -0.1674 [0.1103] 0.6555 [0.0555] -0.4070 [0.3007]
Notes: The superscripts “+” and “-” denote positive and negative variations, respectively. L+and L−are the estimated long-run coefficients associated with positive and negative changes, respectively, defined by β)=−θ /ρ
. 2 χSC, 2
χFF and 2
χHET denote LM tests for serial correlation, normality, functional form and heteroscedasticity, respectively. WLRand WSRrepresent the Wald test for the null of long- and short-run symmetry for the respective variables. FPSS shows the statistic of the Pesaran et al. (2001) bounds test. TBDM denotes the statistic of Banerjee et al. (1998). Figures in brackets show p-values. *: p≤0.01; **: p≤0.05; ***: p≤0.10.
_"` 0.4198 [0.0981] -3.1579 [0.0000] -2.6969 [0.0002] -9.2378 [0.0000] 13.5914 [0.0451]
_!a 0.2434 [0.0396] 0.6073 [0.0569] 0.1739 [0.3708] -0.9405 [0.0314] -2.8578 [0.3451]
_"a 0.9825 [0.0136] 0.7335 [0.0000] 0.0891 [0.6959] 0.7687 [0.6967] 5.3056 [0.0543]
_!b 0.4061 [0.0004] -0.2626 [0.1701] 0.2526 [0.0612] 0.8314 [0.0069] 0.8674 [0.3892]
_"b -0.2770 [0.0556] -0.0215 [0.1796] 0.1094 [0.7209] 1.7580 [0.0290] -3.5770 [0.0684]
cde,` 3.2688*** 42.7059* 17.3330* 32.2349* 4.4340**
cde,a 3.2489*** 10.1456* 0.1201 0.6916 1.9049
cde,b 13.3289* 1.6059 0.1976 4.4453** 2.9044**
c=e,` 4.1265** 15.9713* 0.5045 8.3665* 10.6434*
c=e,a 2.6362*** 20.6383* 0.2998 6.6091* 3.0625***
c=e,b 5.2113** 2.4043 2.5714** 14.8548* 7.8381*
FPSS 9.3640* 7.6616* 6.5665* 8.8337* 6.0740**
TBDM -4.9124* -6.8275* -4.6544* -4.6667* -4.6789*
CUSUMSQ Stable Stable Stable Stable Stable
Source: Enerdata (2015)
Fig. 1. Growth of energy consumption across the major world economies (%/year)
Fig. 2. Cumulative dynamic multipliers for Brazil
-3-2-10
0 5 10 15 20
Time periods
positive change negative change
asymmetry CI for asymmetry
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnG on lnE
-4-3-2-101
0 5 10 15 20
Time periods
positive change negative change
asymmetry CI for asymmetry
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnY on lnE
0.511.5
0 5 10 15 20
Time periods
positive change negative change
asymmetry CI for asymmetry
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnK on lnE
Fig. 3. Cumulative dynamic multipliers for Russia
Fig. 4. Cumulative dynamic multipliers for India
-4-20246
0 5 10 15 20
Time periods
positive change negative change
asymmetry CI for asymmetry
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnG on lnE
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnY on lnE
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnK on lnE
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnG on lnE
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnY on lnE
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnK on lnE
Fig. 5. Cumulative dynamic multipliers for China
Fig. 6. Cumulative dynamic multipliers for South Africa
05101520
0 5 10 15 20
Time periods
positive change negative change
asymmetry CI for asymmetry
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnG on lnE
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnY on lnE
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnK on lnE
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnG on lnE
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnY on lnE
Note: 90% bootstrap CI is based on 100 replications
Cumulative effect of lnK on lnE