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The interdependent relationship between sectoral productivity and disaggregated energy consumption in Malaysia: Markov Switching approach
Renewable and Sustainable Energy Reviews 67 (2017) 752–759
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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser
The interdependent relationship between sectoral productivity and disaggregated energy consumption in Malaysia: Markov Switching approach M.S. Rahman a,n, Farihana Shahari b, Mahfuzur Rahman c, Abu Hanifa Md Noman d a
Department of Finance and Banking, Curtin Business School, Curtin University, Sarawak, Malaysia Department of Finance, International Islamic University Malaysia, Malaysia c Department of Finance and Banking, Faculty of Business and Accountancy, University of Malaya, Malaysia d Department of Business Administration, Faculty of Business Studies, International Islamic University Chittagong, Bangladesh b
art ic l e i nf o
a b s t r a c t
Article history: Received 30 June 2015 Received in revised form 20 November 2015 Accepted 7 September 2016
This study examines the interdependent relationship between sectoral productivity and disaggregated energy consumption in Malaysia by employing the Markov Switching technique. Several outcomes are reached. Firstly, the regime movement of GDP during regime-2 is influenced by disaggregated energy consumption. Secondly, industrial and manufacturing productivity of regime-1 and regime-2 responded with disaggregated energy consumption. Finally, the regimes' carbon emissions depend on both of manufacturing and industrial productivity. This study recommends efficient consumption of energy and the use of green technology in order to minimise energy consumptions and environment pollution. & 2016 Elsevier Ltd. All rights reserved.
Keywords: Manufacturing and industrial productivity Disaggregated energy consumption Markov Switching technique Malaysian economy
Contents 1. 2.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Data and variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Estimation technique. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Analysis of the findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Future study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Concluding remarks and policy implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction This paper examines the interdependent relationship between sectoral productivity (GDP growth, manufacturing and industrial sectors) and disaggregated energy consumption in the Malaysian economy. The Malaysian economy has emerged as one of the fastestgrowing economies by transforming its concentration on agriculture to production sectors, especially industrial and manufacturing sectors. n Correspondence to: Department of Finance, Curtin Business School, Curtin University, Sarawak, Malaysia. Tel.: +60149317712. E-mail address: [email protected] (M.S. Rahman).
http://dx.doi.org/10.1016/j.rser.2016.09.016 1364-0321/& 2016 Elsevier Ltd. All rights reserved.
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Currently, Malaysia is the third leading energy consumer among the ASEAN countries and consumes approximately 40% of primary energy and 55% of the electricity inputs of total energy demand. The transformation process dramatically improves the production sectors and overall GDP. It contributes in growing the domestic economy through exporting products, particularly electronics, oil and gas, palm oil, and rubber. The process requires minimising its energy consumption to an efficient level in order to accelerate the economy. The minimisation of production costs requires identifying energy inputs at disaggregated levels to justify which is interdependent with sectoral production and causes high production costs. Once the concerned energy inputs are identified, the production costs can be minimised through efficient
M.S. Rahman et al. / Renewable and Sustainable Energy Reviews 67 (2017) 752–759
allocation of disaggregated energy consumption. Many studies focus on the efficient allocation of energy consumption. They show that efficient allocation of energy inputs improves economic growth. Previous studies in the area of energy consumption can be segmented into three groups. The first group shows the impact of energy consumption on economic growth. This group concluded the long-run relationship between energy consumption and economic growth [10,15,19,21,27,3,49,54]. They pointed out that efficient level of energy consumption develops with economic growth. The second group indicates that energy is one of the basic elements in improving productivity. They found a positive causal relationship between production growth and disaggregated energy consumption ([34,47,48,7]). The third group investigates whether energy consumption maintains any relationship with income level ([1,52,11,24,5,39,2,47,40]) and concluded that energy consumption does not maintain any relationship with income level, but directly depends on economic prosperity. Although there exists studies on the Malaysian energy markets, very few have examined the relationship between energy consumption and sectoral productivity at disaggregated levels. Studies have focused on a specific sector of energy input. For example, Ong et al. [28] examined energy demand in the Malaysian economy to indicate that Malaysia is currently demanding non-renewable energy consumption such as natural gas, crude oil, coal and hydropower. The statistics shows that the demand for natural gas is 198 million meter cubic per day and increases 22% per year; but the demand for crude oil is around 690 thousand barrels per day and declines over the year; while, the demand for coal is 15 million tons in 2008 and rise 9.5 million per year and the demand of hydropower increases over the period. These findings comply with that of Park and Yoo [30] who showed that oil consumption improves the economic growth in Malaysia. Tang [42] and Chandran et al. [8] focus on electricity usage in Malaysian economy. Using autoregressive distributed lag (ARDL), they showed that electricity demand is increasing with Malaysian economic growth, while the findings of Tang and Tan Shaari and Hussain [35] and [36] show the significant contribution of foreign direct investment and energy consumption in Malaysian economic growth using the Johansen-Juselius Cointegration test. Earlier studies in Malaysia focused on specific energy units in the energy-economic growth nexus. None have investigated the contribution of disaggregated energy consumption on sectoral productivity which considers GDP growth, manufacturing and industrial output productivity. Examining disaggregated energy consumption and sectoral productivity indicates how specific energy units contribute to economic growth, manufacturing, and industrial output productivity. It accurately indicates the degree of dependency in the sectoral productivity of each energy sector. This objective can be investigated by the Markov Switching (MS) approach as an econometric tool employed in this area of research. The MS technique shows the outcome based on switching coefficient and transitional probabilities in both regime 1 (booming) and regime 2 (recession) economic phases [31]. This technique captures the degree of dependency in the sectoral productivity on energy consumption in both booming and recessional phases of an economy. The earlier findings show the impact of a single energy sector on economic growth as a whole regardless of economic phases which does not accurately justify how much energy consumption is used for economic growth in either booming or recessional economic periods. This study bridges the gap in dimensional angles by investigating the interdependent relationship between disaggregate energy and sectoral productivity through the use of the MS technique. This study contributes to existing literature in several ways. Earlier studies showed the causal relationship of energy
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consumption with GDP, income and industrial output growth but failed to show whether GDP growth, manufacturing and industrial productivity varies due to the regime movement of disaggregate energy consumption. The majority of studies use Cointegration tests such as Tota-Yamamoto, ARDL or Johansen in showing the causal relationship but none identifies the relationship between economic productivity and disaggregate energy consumption during both booming and recessional periods which have been examined in this study through the use of the Markov switching mechanism. Finally, this study focuses on Malaysia in order to provide an accurate and promising justification of regime-based relationships between sectoral productivity and disaggregated energy consumption in an emerging and newly industrialised economy. This paper is organized as follows. Section 2 describes the methodology following the analysis of findings in Section 3. While, Section 4 discusses the concluding remarks along with the policy implications.
2. Methodology 2.1. Data and variables In order to examine the interdependent relationship between sectoral productivity and disaggregated energy consumption, this study uses GDP growth, manufacturing and industrial sectors as proxies of sectoral productivity, while total energy, electricity, fossil, natural gas, coal, and mineral are used as disaggregated energy units. Carbon emission is used to justify whether the energy consumption increases environmental pollution as a result of manufacturing and industrial production. Carbon emission refers to the greenhouse gas emission released into the atmosphere. The emission is caused by the recent industrial revolution which uses heavy energy inputs such as fossil fuel and increases the degree of carbon dioxide. All the data are collected from the World Bank spanning the period from 1971 to 2011 on an annual basis and transformed into logs before they are used in TY. Fig. 1 indicates the probable interdependent relationship between sectoral productivity and disaggregated energy consumptions. 2.2. Estimation technique The Markov Switching model developed by Hamilton (1989) is used in the estimation process based on bivariate regime switching techniques. Each of the indicators of sectoral production (observed variables) is used as a dependent variable to be regressed against every disaggregated energy indicators (observed variables). The observed variables vary due to the existence of unobserved regimes, St, and white noise elements, εt . The values of unobserved variables depend on economic expansion or recession. The bivariate model is formed as follows:
y1, t = β1(1 − St ) + β2St + ω(y1, t − 1 − η1(1 − St − 1) − η2St − 1 + Φ(y2, t − δ1(1 − St ) + δ2St ) + ε1, t Assume, St = 1, 2; and t = 1, 2, ....... , T
(1)
Where, y1, t and y2, t in Eq. (1) refer to indicators of sectoral productivity and disaggregated energy consumption respectively. ω and Φ are the coefficients of first-order autoregression and disaggregated energy consumption y2 respectively. Changes in sectoral productivity during regime changes of, y1 is influenced by the response of own lagged value and disaggregated energy consumption, y2 at time t respectively. According to Nelewaik (2007), the noise elements of those two indicators are correlated;
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Inderdependent relationship
Fig. 1. Disaggregated energy consumption and sectoral productivity.
Table 1 Descriptive statistics and cross-correlation.
MEAN SD SKEWNESS KURTOSIS JAR-BERA Cross-correlation GDP MANU IND EMIS ENERGY FOSSIL ELEC COAL MINERAL NATURE
GDP
MANU
IND
EMIS
ENERGY
FOSSIL
ELEC
COAL
MINERAL
NATURE
0.737 0.353 1.798 5.004 29.670
10.055 0.558 0.480 2.329 2.400
10.299 0.525 0.434 2.395 1.955
4.821 0.380 0.102 1.539 3.810
4.390 0.359 0.211 1.738 3.100
1.941 0.035 0.706 2.342 4.248
10.392 0.482 0.162 1.693 3.172
1.414 1.321 0.121 1.374 4.730
0.855 0.514 0.174 2.573 0.532
0.164 0.707 1.384 3.957 15.009
1.000 0.103 0.116 0.128 0.150 0.120 0.161 0.140 0.232 0.198
1.000 0.998 0.977 0.986 0.981 0.984 0.640 0.608 0.876
1.000 0.974 0.985 0.978 0.981 0.602 0.589 0.881
1.000 0.994 0.960 0.993 0.659 0.703 0.806
1.000 0.976 0.996 0.646 0.687 0.853
1.000 0.967 0.672 0.630 0.918
1.000 0.655 0.692 0.830
1.000 0.689 0.548
1.000 0.506
1.000
The Correlation (ε1, t ε2, t ) = ρ12 and ε1, t ~ N(0, σ12) and ε2, t ~ N(0, σ22). constant transition probabilities (P11 and P22) of unobserved regimes can be shown through the following technique:
Prob(St = 1 St − 1 = 1) = p11 and Prob(St = 2 St − 1 = 2) = p22
(2)
The filtered probabilities during expansion and recession are denoted by Pt and 1-Pt respectively, where, Prob(St = 1|Ωt ) = Pt .
3. Analysis of the findings The mean values of GDP growth, manufacturing, and industrial productivity are positive meaning that the sectoral productivity increases over the periods due to energy consumption. This finding is supported by cross-correlations, where the relationship between sectoral productivity and disaggregated energy consumption is negative, as indicated in Table 1. This implies that the increase of productivity reduces the amount of energy units. The findings from switching coefficients in Table 2 indicate the degree of dependency of sectoral productivity and carbon emission on disaggregated energy consumption. The degree of dependency is expressed through a non-linear relationship between economic boom and recession. The interdependent relationship of disaggregated energy consumption is shown with GDP growth, manufacturing productivity, and industrial productivity.
Furthermore, an attempt has been made to investigate whether energy-based manufacturing and industrial productivity influence carbon emissions. The mean regime of GDP growth is found in switching coefficients to maintain the least interdependent relationship with energy consumption in both regimes except disaggregated energy units such as total energy, electricity, fossil fuel, and natural gas in regime 2. The finding implies that some of the energy units in the disaggregate level do not contribute toward improving Malaysian GDP growth, rather, Malaysian GDP heavily depends on electricity, natural gas, and fossil fuel consumption during recession periods. During this period, electricity, fossil fuel, and natural gas are subsidized by both government and PETRONAS (the biggest company that provides oil, gas, fuel and other energy units). Thus, the subsidized energy inputs play a significant role in increasing GDP growth. The Malaysian government implements an expansionary policy in order to recover the recession-effect and to enhance productivity to an efficient level. This is supported by local companies such as PETRONAS. This finding is further supported by the transition probability used to capture the regime-movement of GDP productivity caused by disaggregate energy inputs indicated by probability. The finding of transition probability in Table 3 indicates that the probability of regime-movement of GDP growth by electricity, natural gas, and total energy consumption in regime 2 are higher compared to regime 1. This implies that the Malaysian economy uses heavy energy consumption during economic
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Table 2 Switching coefficients. Indep var
Total energy
Depen var
Reg-1
GDP
0.115 0.131 (0.407) (0.013) 1.446 1.545 (0.053) (0.021) 1.666 1.428 (0.000) (0.000) 1.124 1.119 (0.000) (0.000) Manufacturing Reg 1 0.0809 (0.000)
Manufacturing Industry Emission
Emission
Electricity Reg-2
Coal
Fossil
Mineral
Natural gas
Reg-1
Reg-2
Reg-1
Reg-2
Reg-1
Reg-2
Reg-1
Reg-2
Reg-1
Reg-2
0.083 (0.404) 1.291 (0.001) 1.042 (0.000) 0.777 (0.000)
0.095 (0.017) 1.107 (0.005) 1.158 (0.000) 0.775 (0.000)
0.011 (0.476) 0.213 (0.000) 0.216 (0.002) 0.126 (0.000)
0.001 (0.982) 0.193 (0.000) 0.192 (0.000) 0.134 (0.000)
0.508 (0.725) 21.178 (0.320) 14.717 (0.000) 10.647 (0.000) Industry Reg 1 0.752 (0.004)
1.197 (0.033) 15.826 (0.251) 14.342 (0.000) 10.893 (0.000)
0.041 (0.291) 0.539 (0.000) 0.478 (0.000) 0.043 (0.764)
0.172 (0.185) 0.495 (0.072) 0.471 (0.078) 0.109 (0.388)
0.010 (0.899) 0.416 (0.008) 0.439 (0.000) 0.196 (0.000)
0.060 (0.027) 0.449 (0.000) 0.454 (0.000) 0.385 (0.000)
Reg 2 0.613 (0.000)
Reg 2 0.649 (0.017)
Table 3 Transition probabilities. Total energy GDP
Manufacturing
Industry
Emission
Reg-1 Reg-2 Duration Reg-1 Reg-2 Duration Reg-1 Reg-2 Duration Reg-1 Reg-2 Duration
Emission Reg-1 Reg-2 Duration
Electricity Reg-1 Reg-2 0.162 0.837 0.139 0.861 1.194 7.176 0.978 0.021 0.127 0.873 46.608 7.898 0.881 0.119 0.058 0.941 8.408 17.089 0.946 0.053 0.148 0.851 18.721 0.851 Manufacturing Reg-1 0.936 0.054 15.672
Reg-1 0.163 0.139 1.194 0.842 0.072 6.349 0.943 0.149 17.690 0.839 0.126 6.222
Coal Reg-2 0.837 0.861 7.176 0.157 0.927 13.866 0.056 0.851 6.709 0.161 0.874 7.906
Reg-1 0.861 0.837 7.176 0.977 0.0263 42.848 0.973 0.023 37.447 0.976 0.025 42.393
Reg-2 0.064 0.946 18.624
recession in order to recover economic growth, because the cost of energy inputs falls during this period. This finding is consistent with earlier studies that show electricity consumption is a significant energy component in the Malaysian economy that positivity affects GDP growth [25,32]. This finding is further supported by Shahbaz and Lean [38] and Tang and Shahbaz [43] who found a positive linkage between electricity consumption and economic growth in Pakistan. Soytas and Sari [40] found electricity consumption to positively contribute to the economy in Turkey. Natural gas and fossil fuel are found to have significantly improved GDP growth in Malaysia. This finding is justified by the huge supply of fuel and gas facilitated by PETRONAS which subsidises the gas and fuel resources to promote Malaysian economic growth [32]. The findings that show the linkage between economic growth and energy consumption are presented in Table 4 in which most studies indicate that energy consumption contribute to increased economic growth. Moreover, the mean regimes of manufacturing productivity are found to be interdependent with energy consumption. Interestingly, all of the disaggregate energy inputs except fossil fuel are significant in both regimes in improving manufacturing productivity. These findings are consistent with that of Rahman et al. [32] who showed that the majority of energy sectors in both aggregate and disaggregate levels are used in manufacturing productivity to develop local income levels to meet the Malaysian 2020-vision (high income nation). The findings imply that Malaysia's emerging economy is moving from the agricultural sector
Fossil Reg-2 0.139 0.163 1.194 0.023 0.974 38.059 0.027 0.977 43.117 0.024 0.975 39.363
Reg-1 0.163 0.139 1.134 0.041 0.001 1.042 0.944 0.118 17.786 0.922 0.112 12.850 Industry Reg-1 0.912 0.035 11.401
Mineral Reg-2 0.837 0.861 7.176 0.959 0.999 10574.09 0.056 0.882 8.457 0.077 0.887 8.901
Reg-1 0.861 0.837 7.176 0.980 0.043 49.054 0.979 0.043 49.129 0.976 0.025 41.227
Natural gas Reg-2 0.139 0.163 1.194 0.020 0.957 23.273 0.020 0.957 23.331 0.024 0.975 40.717
Reg-1 0.163 0.139 1.194 0.976 0.024 41.023 0.976 0.024 41.246 0.973 0.025 37.492
Reg-2 0.837 0.861 7.176 0.024 0.976 40.964 0.024 0.975 40.613 0.027 0.975 40.416
Reg-2 0.088 0.965 28.765
to manufacturing and industrial sectors in order to achieve its 2020-vision to be a high-income nation. Furthermore, all of the energy inputs are significant in both regimes indicating that disaggregate energy units play a noteworthy role in improving the industrial productivity of the Malaysian economy during booming and recession periods. This finding complies with transition probabilities that indicate that both manufacturing and industrial productivity maintain the transmission of regime movement. The productivity of these two sectors in both booming and recession periods are contributed to by disaggregate energy consumption in both periods, except manufacturing productivity by fossil fuel. The finding highlights that both manufacturing and industrial productivity depend on the available energy inputs and exhaust them in the same regime. This finding is consistent with previous studies (Table 4) that indicate that disaggregate energy inputs improve sectoral productivity. Furthermore, the fossil fuel that contains radioactive materials is the least consumed energy input in the manufacturing sectors. It implies that the manufacturing sector avoids consuming this fossil fuel due to the serious environmental concern. Fossil fuels such as kerosene and propane causes heavy carbon emission. The burning of this energy inputs produces around 21.3 billion tonnes of carbon dioxide (CO2) every year [16]. The awareness of fossil fuel usage has increased globally, where the United States Environmental Protection Agency (USEPA) imposes regulations to reduce the airborne emission and enforce to reduce the emission by 70% by 2018. Inversely, it is motivated to use the coal that generates fly
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Table 4 Existing studies and their findings on the relationship between growth, energy and emission. Author
Period
Model
Sample
Nexus
Kraft and Kraft [20] Akarca and Long [1] Yu and Hwang [52] [53]
1947–1974 1950–1970 1947–1979 1950–1979
Granger causality Sims' technique Sims' technique Sims' and Granger causality
USA USA USA 5 countries
Erol and Yu [12]
1950–1982
Sims' and Granger causality
6 countries
Nachane et al., [26]
1950–1985
16 countries
Tang and Shahbaz [43] Masih and Masih [24]
1972–2010
Engle-Granger cointegration Sims' and Granger Causality Johansen Cointegration and Toda-Yamamoto Test Johansen Cointegration and Granger causality
GNP-EC GNP—EC GNP—EC GDP—EC (UK, Poland, USA) GDP-EC(Korea) EC-GDP (Philippines) EC2GNP (Japan) GDP-EC (Germany, Italy) EC-GDP (Canada) EC—GDP (France, UK) GDP2EC (except Colombia and Venezuela) Electricity-GDP Electricity2Manufacturing Growth Energy-GDP (India) GDP-Energy (Indonesia) GDP2Energy (Pakistan)
Chandran, Sharma (2010) [41]
1955–1990 (India, Pakistan and Malaysia); 1960–1990 (Indonesia and Singapore); 1955–1991 (Philippines) 1971–2003
Pakistan 6 countries
ARDL and Granger causality
Malaysia
Electricity-GDP
1950–1992
Johansen cointegration and Granger causality
G-7 countries
Yoo and Jung [50]
1977–2002
Korea
Masih and Masih [22]
1955–1991
Johansen cointegration and Granger causality Johansen cointegration and Granger causality
EC2GDP (Argentina) GDP-EC (Italy, Korea) EC-GDP (Turkey, France, Japan, Germany) Energy-GDP
Masih and Masih [23]
Johansen cointegration and Granger causality Johansen Cointegration test and VECM
Korea and Taiwan
Rafiq
1955–1991(Korea); 1952–1992(Taiwan) 1965–2006
Wang et al., [46] Tang and Tan [45]
1972–2006 1972–2009
China Malaysia
Ghali and El-Sakka [13] Soytas and Sari [40] Chang [9]
1961–1997
ARDL and Granger causality Johansen cointegration, ARDL and Granger causality Johansen cointegration and VEC
Energy-GDP(China) GDP-Energy(India) GDP2Energy (Thailand) Energy—GDP (Malaysia, Indonesia, Philippines) Energy-GDP EC2GDP
Canada
EC2GDP
1968–2002 1981–2006
Johansen cointegration and VEC Johansen cointegration and VEC
Turkey China
1968–2005
ARDL and Granger causality
Turkey
1971–2011 1971–2002
ARDL and Granger causality Engle–Granger cointegration and Hsiao's Granger causality test
China Indonesia, Malaysia, Singapore, and Thailand
Tang [42] 1970–2005 Ang [4] 1971–1999 Shaari, Hussain (2014) 1980–2010
ARDL and Granger causality Johansen cointegration and ECM Johansen cointegration and Granger causality
Malaysia Malaysia Malaysia
Park and Yoo [30] Gross [14]
1965–2011 1970–2007
Johansen cointegration and ECM ARDL and Granger causality
Malaysia USA
Liu a, Wang [49]
1992–2014
DEA model
China
Electricity2Value added GDP2Emission GDP2Energy (coal, crude oil and electricity) Energy—GDP Emission—GDP Energy-GDP GDP2 Electricity (Malaysia, Singapore); Electricity-GDP (Indonesia, Thailand) GDP2 Electricity Energy-GDP Energy—GDP (oil, coal) GDP- Electricity Gas-GDP GDP2 Oil Commercial Growth -energy Transport Growth2 energy Industrial output2Energy consumption
Ozturk and Acaravci [29] Shahbaz et al., [37] Yoo [51]
Thailand and Sri Lanka
6 countries
GDP—Energy (Thailand) Energy-GDP (Sri Lanka) EC2GDP
Note: “-” stands for “unidirectional Granger cause”, “—“ stands for “does not Granger cause” and “2” stands for “bidirectional Granger cause”, “ARDL” stands for Autoregressive Distributed Lags, “ECM” stands for error-correction model.
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ash and bottom ash. These materials are environmental friendly and are used in different productive applications. Therefore, the Malaysian manufacturing sector perhaps reduces the fossil fuel consumption in the domestic production, while focuses more on other energy inputs such as coal, electricity, mineral and natural gas in responding to environmental issues. Finally, carbon emission is used to show whether it depends on energy consumption. The finding shows that all of the disaggregate energy inputs except minerals cause the carbon emission, environmental pollution, and greenhouse effect [17]. Although energy consumption improves the sectoral productivity, it causes negative externalities to the society. The manufacturing and industrial productivity are found in both switching coefficients and transition probability to positively relate with that carbon emissions. This implies that the higher the productivity of these two sectors, the greater the environmental pollution. This means that carbon emissions increase with increases in manufacturing and industrial production, implying that the Malaysian manufacturing and industrial sectors use high amounts of energy that ultimately harm the natural environmental. This finding is consistent with Rahman et al. [32] who indicated that manufacturing growth causes carbon emissions in Malaysia. Saboori and Sulaiman [33] concluded the positive relationship between manufacturing growth and emissions in Malaysia, Indonesia and Philippines. The same finding is also observed in the study of Bastola and Sapkota [6] for Nepal, Kivyiro and Arminen [18] for Sub-Saharan Africa, and Tang and Tan [44] for Vietnam. Table 4 presents a summary of the causal relationships between sectoral productivity and carbon emission found for comparative purposes. Both carbon emission and sectoral productivity increase in the same regime when high-energy inputs are consumed. Therefore, carbon emission directly results in excessive energy consumption producing negative externalities to the society. The details of the interdependent relationship between sectoral productivity and energy consumption, and between carbon emission and sectoral productivity (manufacturing and industrial sectors) are summarised in Table 5.
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Productivity growth is maintained to meet the vision 2020 and achieve the status of being a developed nation. This has led policy makers to concentrate on energy inputs that directly help increase sectoral productivity while minimising emissions. Unlike other studies, this study presents the interdependency of energy consumption with sectoral productivity in both booming and recession periods. We found that electricity, natural gas, and fossil fuel help GDP growth during recessional periods. The production sectors become more competitive during this period in consuming electricity, natural gas, and fossil fuel energy inputs so that they can enjoy subsidies from the government and PETRONAS in order to recover from the economic recession. The GDP growth during economic expansion is mostly contributed to by other elements such as FDI, non-production sectors (agro-based firms), and so forth. For this reason, most of the disaggregate energy units do not maintain an interdependent relationship with GDP growth during a booming economy. Moreover, the manufacturing and industrial productivity in both expansion and contraction periods depend on the disaggregate energy consumption to maintain continuous productivity. Finally, the interdependent relationship between sectoral productivity and carbon emission causes negative externalities that harm the society. Rather than threatening the health of the society due to carbon emissions, there is a need to maintain a balanced productivity system in which sectoral productivity would increase while ensuring public safety. To this end, the production sectors are required to prioritise the energy inputs that do not cause carbon emissions. Minerals are found in this study not to cause the carbon emission as it is more environmental friendly. Therefore, energy inputs should be consumed more from mineral resources. Furthermore, other sources of energy inputs such as green technology could be used to mitigate the emission problems along with renewal and non-renewal energy inputs so that the Malaysian economy can continue maintaining high productivity, while achieving its 2020-vision.
5. Future study 4. Discussion Malaysia is an emerging and energy-dependent economy. Numerous energy inputs are consumed in different sectoral levels to improve economic, manufacturing, and industrial growth.
This study employs the Markov Switching approach to examine the interdependent relationship between disaggregate energy consumption and sectoral productivity on the basis of regimemovement. Since this estimation technique is still new in this area of research, future studies could accommodate this technique but
Table 5 direction of interdependent relationship between sectoral productivity and disaggregate energy inputs. Disaggregate energy vs GDP growth
Disaggregate energy vs Manufacturing productivity
Disaggregate energy vs Industrial productivity
Disaggregate energy vs Carbon emission
Total energy (Regime 1)–GDP growth Total energy (Regime 2)-GDP growth
Total energy (Regime 1)-Manufacturing Total energy (Regime 2)-Manufacturing
Total energy (Regime 1)-Industry Total energy (Regime 2)-Industry
Electricity (Regime 1)–GDP growth Electricity (Regime 2)-GDP growth Coal (Regime 1)–GDP growth coal (Regime 2)–GDP growth Fossil (Regime 1)–GDP growth Fossil (Regime 2)-GDP growth Mineral (Regime 1)–GDP growth Mineral (Regime 2)–GDP growth Natural gas (Regime 1)–GDP growth Natural gas (Regime 2)-GDP growth Sectoral productivity vs carbon emission Manufacturing (Regime 1)-Emission Manufacturing (Regime 2)-Emission
Electricity (Regime 1)-Manufacturing Electricity (Regime 2)-Manufacturing Coal (Regime 1)-Manufacturing coal (Regime 2)-Manufacturing Fossil (Regime 1)–Manufacturing Fossil (Regime 2)–Manufacturing Mineral (Regime 1)-Manufacturing Mineral (Regime 2)-Manufacturing Natural gas (Regime 1)-Manufacturing Natural gas (Regime 2)-Manufacturing
Electricity (Regime 1)-Industry Electricity (Regime 2)-Industry Coal (Regime 1)-Industry coal (Regime 2)-Industry Fossil (Regime 1)-Industry Fossil (Regime 2)-Industry Mineral (Regime 1)-Industry Mineral (Regime 2)-Industry Natural gas (Regime 1)-Industry Natural gas (Regime 2)-Industry
Total energy (Regime 1)-Emission Total energy (Regime 2)Emission Electricity (Regime 1)-Emission Electricity (Regime 2)-Emission Coal (Regime 1)-Emission coal (Regime 2)-Emission Fossil (Regime 1)-Emission Fossil (Regime 2)-Emission Mineral (Regime 1)–Emission Mineral (Regime 2)–Emission Natural gas (Regime 1)-Emission Natural gas (Regime 2)-Emission
Note: - is used to show the interdependent relationship, – indicates non-relationship
Industry (Regime 1)-Emission Industry (Regime 2)-Emission
758
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use disaggregate energy components other than the one used in this study. Furthermore, this technique can be applied in other emerging economies to determine whether use of fossil fuel is reduced to protect the negative externality and environmental pollution in both booming and recession economic periods. 5.1. Concluding remarks and policy implications This study explores the regime-based effect between disaggregated energy consumption and sectoral productivity by employing the Markov Switching technique. The productivity in all sectors (GDP growth, manufacturing and industrial sectors) has shown significant improvement demonstrated by both statistical and empirical findings. The objective of this study is satisfied by statistical and empirical findings presented through an in-depth analysis. The mean values and correlation of coefficients imply that the sectoral productivity has increased over the period. The empirical findings of this study present several outcomes. Firstly, the contribution of majority disaggregated energy inputs is very low during a booming economy due to higher cost price. During a booming economy, the local government and PETRONAS cut the subsidy. This increases the production cost which reduces production and affects the GDP growth. Secondly, the economic policy during the recession policy is changed in order to recover GDP growth. The implementation of an expansionary policy reduces the cost of disaggregated energy inputs especially for electricity, fossil fuel, and natural gas. Hence, consumption of these energy inputs significantly influences the GDP growth in Malaysia during regime-2. On the other hand, manufacturing and industrial productivity depends on the majority of the energy inputs in both regimes in order to satisfy both local and foreign-export demand. The fossil fuel energy input is not used in the manufacturing production as a response to global awareness to reduce the carbon emission and greenhouse effect. Finally, carbon emission is seriously affected by manufacturing and industrial production. The higher the manufacturing and industrial production, the greater the carbon emission aggravating environmental pollution. Furthermore, the findings present significant insights in order to implement the further policy implications that would continuously improve the GDP growth and productivity in both manufacturing and industrial sectors, while reducing the carbon emission and environmental pollution. The manufacturing and industrial sectors can better contribute to the economy and society if the energy consumption is reduced to an efficient level. The excessive amount of energy consumption provides inefficient production and negative externalities. The excessive consumption of energy units leads to both carbon emission and shortage of aggregated energy inputs causing a decline in overall productivity. Proper initiatives in the form of green technology have to be taken to minimise the energy consumption to an efficient level to increase the marginal productivity and save the social environment, while also maintaining high sectoral productivity.
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Contents lists available at ScienceDirect
Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser
The interdependent relationship between sectoral productivity and disaggregated energy consumption in Malaysia: Markov Switching approach M.S. Rahman a,n, Farihana Shahari b, Mahfuzur Rahman c, Abu Hanifa Md Noman d a
Department of Finance and Banking, Curtin Business School, Curtin University, Sarawak, Malaysia Department of Finance, International Islamic University Malaysia, Malaysia c Department of Finance and Banking, Faculty of Business and Accountancy, University of Malaya, Malaysia d Department of Business Administration, Faculty of Business Studies, International Islamic University Chittagong, Bangladesh b
art ic l e i nf o
a b s t r a c t
Article history: Received 30 June 2015 Received in revised form 20 November 2015 Accepted 7 September 2016
This study examines the interdependent relationship between sectoral productivity and disaggregated energy consumption in Malaysia by employing the Markov Switching technique. Several outcomes are reached. Firstly, the regime movement of GDP during regime-2 is influenced by disaggregated energy consumption. Secondly, industrial and manufacturing productivity of regime-1 and regime-2 responded with disaggregated energy consumption. Finally, the regimes' carbon emissions depend on both of manufacturing and industrial productivity. This study recommends efficient consumption of energy and the use of green technology in order to minimise energy consumptions and environment pollution. & 2016 Elsevier Ltd. All rights reserved.
Keywords: Manufacturing and industrial productivity Disaggregated energy consumption Markov Switching technique Malaysian economy
Contents 1. 2.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Data and variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Estimation technique. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Analysis of the findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Future study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Concluding remarks and policy implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction This paper examines the interdependent relationship between sectoral productivity (GDP growth, manufacturing and industrial sectors) and disaggregated energy consumption in the Malaysian economy. The Malaysian economy has emerged as one of the fastestgrowing economies by transforming its concentration on agriculture to production sectors, especially industrial and manufacturing sectors. n Correspondence to: Department of Finance, Curtin Business School, Curtin University, Sarawak, Malaysia. Tel.: +60149317712. E-mail address: [email protected] (M.S. Rahman).
http://dx.doi.org/10.1016/j.rser.2016.09.016 1364-0321/& 2016 Elsevier Ltd. All rights reserved.
752 753 753 753 754 757 757 758 758
Currently, Malaysia is the third leading energy consumer among the ASEAN countries and consumes approximately 40% of primary energy and 55% of the electricity inputs of total energy demand. The transformation process dramatically improves the production sectors and overall GDP. It contributes in growing the domestic economy through exporting products, particularly electronics, oil and gas, palm oil, and rubber. The process requires minimising its energy consumption to an efficient level in order to accelerate the economy. The minimisation of production costs requires identifying energy inputs at disaggregated levels to justify which is interdependent with sectoral production and causes high production costs. Once the concerned energy inputs are identified, the production costs can be minimised through efficient
M.S. Rahman et al. / Renewable and Sustainable Energy Reviews 67 (2017) 752–759
allocation of disaggregated energy consumption. Many studies focus on the efficient allocation of energy consumption. They show that efficient allocation of energy inputs improves economic growth. Previous studies in the area of energy consumption can be segmented into three groups. The first group shows the impact of energy consumption on economic growth. This group concluded the long-run relationship between energy consumption and economic growth [10,15,19,21,27,3,49,54]. They pointed out that efficient level of energy consumption develops with economic growth. The second group indicates that energy is one of the basic elements in improving productivity. They found a positive causal relationship between production growth and disaggregated energy consumption ([34,47,48,7]). The third group investigates whether energy consumption maintains any relationship with income level ([1,52,11,24,5,39,2,47,40]) and concluded that energy consumption does not maintain any relationship with income level, but directly depends on economic prosperity. Although there exists studies on the Malaysian energy markets, very few have examined the relationship between energy consumption and sectoral productivity at disaggregated levels. Studies have focused on a specific sector of energy input. For example, Ong et al. [28] examined energy demand in the Malaysian economy to indicate that Malaysia is currently demanding non-renewable energy consumption such as natural gas, crude oil, coal and hydropower. The statistics shows that the demand for natural gas is 198 million meter cubic per day and increases 22% per year; but the demand for crude oil is around 690 thousand barrels per day and declines over the year; while, the demand for coal is 15 million tons in 2008 and rise 9.5 million per year and the demand of hydropower increases over the period. These findings comply with that of Park and Yoo [30] who showed that oil consumption improves the economic growth in Malaysia. Tang [42] and Chandran et al. [8] focus on electricity usage in Malaysian economy. Using autoregressive distributed lag (ARDL), they showed that electricity demand is increasing with Malaysian economic growth, while the findings of Tang and Tan Shaari and Hussain [35] and [36] show the significant contribution of foreign direct investment and energy consumption in Malaysian economic growth using the Johansen-Juselius Cointegration test. Earlier studies in Malaysia focused on specific energy units in the energy-economic growth nexus. None have investigated the contribution of disaggregated energy consumption on sectoral productivity which considers GDP growth, manufacturing and industrial output productivity. Examining disaggregated energy consumption and sectoral productivity indicates how specific energy units contribute to economic growth, manufacturing, and industrial output productivity. It accurately indicates the degree of dependency in the sectoral productivity of each energy sector. This objective can be investigated by the Markov Switching (MS) approach as an econometric tool employed in this area of research. The MS technique shows the outcome based on switching coefficient and transitional probabilities in both regime 1 (booming) and regime 2 (recession) economic phases [31]. This technique captures the degree of dependency in the sectoral productivity on energy consumption in both booming and recessional phases of an economy. The earlier findings show the impact of a single energy sector on economic growth as a whole regardless of economic phases which does not accurately justify how much energy consumption is used for economic growth in either booming or recessional economic periods. This study bridges the gap in dimensional angles by investigating the interdependent relationship between disaggregate energy and sectoral productivity through the use of the MS technique. This study contributes to existing literature in several ways. Earlier studies showed the causal relationship of energy
753
consumption with GDP, income and industrial output growth but failed to show whether GDP growth, manufacturing and industrial productivity varies due to the regime movement of disaggregate energy consumption. The majority of studies use Cointegration tests such as Tota-Yamamoto, ARDL or Johansen in showing the causal relationship but none identifies the relationship between economic productivity and disaggregate energy consumption during both booming and recessional periods which have been examined in this study through the use of the Markov switching mechanism. Finally, this study focuses on Malaysia in order to provide an accurate and promising justification of regime-based relationships between sectoral productivity and disaggregated energy consumption in an emerging and newly industrialised economy. This paper is organized as follows. Section 2 describes the methodology following the analysis of findings in Section 3. While, Section 4 discusses the concluding remarks along with the policy implications.
2. Methodology 2.1. Data and variables In order to examine the interdependent relationship between sectoral productivity and disaggregated energy consumption, this study uses GDP growth, manufacturing and industrial sectors as proxies of sectoral productivity, while total energy, electricity, fossil, natural gas, coal, and mineral are used as disaggregated energy units. Carbon emission is used to justify whether the energy consumption increases environmental pollution as a result of manufacturing and industrial production. Carbon emission refers to the greenhouse gas emission released into the atmosphere. The emission is caused by the recent industrial revolution which uses heavy energy inputs such as fossil fuel and increases the degree of carbon dioxide. All the data are collected from the World Bank spanning the period from 1971 to 2011 on an annual basis and transformed into logs before they are used in TY. Fig. 1 indicates the probable interdependent relationship between sectoral productivity and disaggregated energy consumptions. 2.2. Estimation technique The Markov Switching model developed by Hamilton (1989) is used in the estimation process based on bivariate regime switching techniques. Each of the indicators of sectoral production (observed variables) is used as a dependent variable to be regressed against every disaggregated energy indicators (observed variables). The observed variables vary due to the existence of unobserved regimes, St, and white noise elements, εt . The values of unobserved variables depend on economic expansion or recession. The bivariate model is formed as follows:
y1, t = β1(1 − St ) + β2St + ω(y1, t − 1 − η1(1 − St − 1) − η2St − 1 + Φ(y2, t − δ1(1 − St ) + δ2St ) + ε1, t Assume, St = 1, 2; and t = 1, 2, ....... , T
(1)
Where, y1, t and y2, t in Eq. (1) refer to indicators of sectoral productivity and disaggregated energy consumption respectively. ω and Φ are the coefficients of first-order autoregression and disaggregated energy consumption y2 respectively. Changes in sectoral productivity during regime changes of, y1 is influenced by the response of own lagged value and disaggregated energy consumption, y2 at time t respectively. According to Nelewaik (2007), the noise elements of those two indicators are correlated;
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Inderdependent relationship
Fig. 1. Disaggregated energy consumption and sectoral productivity.
Table 1 Descriptive statistics and cross-correlation.
MEAN SD SKEWNESS KURTOSIS JAR-BERA Cross-correlation GDP MANU IND EMIS ENERGY FOSSIL ELEC COAL MINERAL NATURE
GDP
MANU
IND
EMIS
ENERGY
FOSSIL
ELEC
COAL
MINERAL
NATURE
0.737 0.353 1.798 5.004 29.670
10.055 0.558 0.480 2.329 2.400
10.299 0.525 0.434 2.395 1.955
4.821 0.380 0.102 1.539 3.810
4.390 0.359 0.211 1.738 3.100
1.941 0.035 0.706 2.342 4.248
10.392 0.482 0.162 1.693 3.172
1.414 1.321 0.121 1.374 4.730
0.855 0.514 0.174 2.573 0.532
0.164 0.707 1.384 3.957 15.009
1.000 0.103 0.116 0.128 0.150 0.120 0.161 0.140 0.232 0.198
1.000 0.998 0.977 0.986 0.981 0.984 0.640 0.608 0.876
1.000 0.974 0.985 0.978 0.981 0.602 0.589 0.881
1.000 0.994 0.960 0.993 0.659 0.703 0.806
1.000 0.976 0.996 0.646 0.687 0.853
1.000 0.967 0.672 0.630 0.918
1.000 0.655 0.692 0.830
1.000 0.689 0.548
1.000 0.506
1.000
The Correlation (ε1, t ε2, t ) = ρ12 and ε1, t ~ N(0, σ12) and ε2, t ~ N(0, σ22). constant transition probabilities (P11 and P22) of unobserved regimes can be shown through the following technique:
Prob(St = 1 St − 1 = 1) = p11 and Prob(St = 2 St − 1 = 2) = p22
(2)
The filtered probabilities during expansion and recession are denoted by Pt and 1-Pt respectively, where, Prob(St = 1|Ωt ) = Pt .
3. Analysis of the findings The mean values of GDP growth, manufacturing, and industrial productivity are positive meaning that the sectoral productivity increases over the periods due to energy consumption. This finding is supported by cross-correlations, where the relationship between sectoral productivity and disaggregated energy consumption is negative, as indicated in Table 1. This implies that the increase of productivity reduces the amount of energy units. The findings from switching coefficients in Table 2 indicate the degree of dependency of sectoral productivity and carbon emission on disaggregated energy consumption. The degree of dependency is expressed through a non-linear relationship between economic boom and recession. The interdependent relationship of disaggregated energy consumption is shown with GDP growth, manufacturing productivity, and industrial productivity.
Furthermore, an attempt has been made to investigate whether energy-based manufacturing and industrial productivity influence carbon emissions. The mean regime of GDP growth is found in switching coefficients to maintain the least interdependent relationship with energy consumption in both regimes except disaggregated energy units such as total energy, electricity, fossil fuel, and natural gas in regime 2. The finding implies that some of the energy units in the disaggregate level do not contribute toward improving Malaysian GDP growth, rather, Malaysian GDP heavily depends on electricity, natural gas, and fossil fuel consumption during recession periods. During this period, electricity, fossil fuel, and natural gas are subsidized by both government and PETRONAS (the biggest company that provides oil, gas, fuel and other energy units). Thus, the subsidized energy inputs play a significant role in increasing GDP growth. The Malaysian government implements an expansionary policy in order to recover the recession-effect and to enhance productivity to an efficient level. This is supported by local companies such as PETRONAS. This finding is further supported by the transition probability used to capture the regime-movement of GDP productivity caused by disaggregate energy inputs indicated by probability. The finding of transition probability in Table 3 indicates that the probability of regime-movement of GDP growth by electricity, natural gas, and total energy consumption in regime 2 are higher compared to regime 1. This implies that the Malaysian economy uses heavy energy consumption during economic
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Table 2 Switching coefficients. Indep var
Total energy
Depen var
Reg-1
GDP
0.115 0.131 (0.407) (0.013) 1.446 1.545 (0.053) (0.021) 1.666 1.428 (0.000) (0.000) 1.124 1.119 (0.000) (0.000) Manufacturing Reg 1 0.0809 (0.000)
Manufacturing Industry Emission
Emission
Electricity Reg-2
Coal
Fossil
Mineral
Natural gas
Reg-1
Reg-2
Reg-1
Reg-2
Reg-1
Reg-2
Reg-1
Reg-2
Reg-1
Reg-2
0.083 (0.404) 1.291 (0.001) 1.042 (0.000) 0.777 (0.000)
0.095 (0.017) 1.107 (0.005) 1.158 (0.000) 0.775 (0.000)
0.011 (0.476) 0.213 (0.000) 0.216 (0.002) 0.126 (0.000)
0.001 (0.982) 0.193 (0.000) 0.192 (0.000) 0.134 (0.000)
0.508 (0.725) 21.178 (0.320) 14.717 (0.000) 10.647 (0.000) Industry Reg 1 0.752 (0.004)
1.197 (0.033) 15.826 (0.251) 14.342 (0.000) 10.893 (0.000)
0.041 (0.291) 0.539 (0.000) 0.478 (0.000) 0.043 (0.764)
0.172 (0.185) 0.495 (0.072) 0.471 (0.078) 0.109 (0.388)
0.010 (0.899) 0.416 (0.008) 0.439 (0.000) 0.196 (0.000)
0.060 (0.027) 0.449 (0.000) 0.454 (0.000) 0.385 (0.000)
Reg 2 0.613 (0.000)
Reg 2 0.649 (0.017)
Table 3 Transition probabilities. Total energy GDP
Manufacturing
Industry
Emission
Reg-1 Reg-2 Duration Reg-1 Reg-2 Duration Reg-1 Reg-2 Duration Reg-1 Reg-2 Duration
Emission Reg-1 Reg-2 Duration
Electricity Reg-1 Reg-2 0.162 0.837 0.139 0.861 1.194 7.176 0.978 0.021 0.127 0.873 46.608 7.898 0.881 0.119 0.058 0.941 8.408 17.089 0.946 0.053 0.148 0.851 18.721 0.851 Manufacturing Reg-1 0.936 0.054 15.672
Reg-1 0.163 0.139 1.194 0.842 0.072 6.349 0.943 0.149 17.690 0.839 0.126 6.222
Coal Reg-2 0.837 0.861 7.176 0.157 0.927 13.866 0.056 0.851 6.709 0.161 0.874 7.906
Reg-1 0.861 0.837 7.176 0.977 0.0263 42.848 0.973 0.023 37.447 0.976 0.025 42.393
Reg-2 0.064 0.946 18.624
recession in order to recover economic growth, because the cost of energy inputs falls during this period. This finding is consistent with earlier studies that show electricity consumption is a significant energy component in the Malaysian economy that positivity affects GDP growth [25,32]. This finding is further supported by Shahbaz and Lean [38] and Tang and Shahbaz [43] who found a positive linkage between electricity consumption and economic growth in Pakistan. Soytas and Sari [40] found electricity consumption to positively contribute to the economy in Turkey. Natural gas and fossil fuel are found to have significantly improved GDP growth in Malaysia. This finding is justified by the huge supply of fuel and gas facilitated by PETRONAS which subsidises the gas and fuel resources to promote Malaysian economic growth [32]. The findings that show the linkage between economic growth and energy consumption are presented in Table 4 in which most studies indicate that energy consumption contribute to increased economic growth. Moreover, the mean regimes of manufacturing productivity are found to be interdependent with energy consumption. Interestingly, all of the disaggregate energy inputs except fossil fuel are significant in both regimes in improving manufacturing productivity. These findings are consistent with that of Rahman et al. [32] who showed that the majority of energy sectors in both aggregate and disaggregate levels are used in manufacturing productivity to develop local income levels to meet the Malaysian 2020-vision (high income nation). The findings imply that Malaysia's emerging economy is moving from the agricultural sector
Fossil Reg-2 0.139 0.163 1.194 0.023 0.974 38.059 0.027 0.977 43.117 0.024 0.975 39.363
Reg-1 0.163 0.139 1.134 0.041 0.001 1.042 0.944 0.118 17.786 0.922 0.112 12.850 Industry Reg-1 0.912 0.035 11.401
Mineral Reg-2 0.837 0.861 7.176 0.959 0.999 10574.09 0.056 0.882 8.457 0.077 0.887 8.901
Reg-1 0.861 0.837 7.176 0.980 0.043 49.054 0.979 0.043 49.129 0.976 0.025 41.227
Natural gas Reg-2 0.139 0.163 1.194 0.020 0.957 23.273 0.020 0.957 23.331 0.024 0.975 40.717
Reg-1 0.163 0.139 1.194 0.976 0.024 41.023 0.976 0.024 41.246 0.973 0.025 37.492
Reg-2 0.837 0.861 7.176 0.024 0.976 40.964 0.024 0.975 40.613 0.027 0.975 40.416
Reg-2 0.088 0.965 28.765
to manufacturing and industrial sectors in order to achieve its 2020-vision to be a high-income nation. Furthermore, all of the energy inputs are significant in both regimes indicating that disaggregate energy units play a noteworthy role in improving the industrial productivity of the Malaysian economy during booming and recession periods. This finding complies with transition probabilities that indicate that both manufacturing and industrial productivity maintain the transmission of regime movement. The productivity of these two sectors in both booming and recession periods are contributed to by disaggregate energy consumption in both periods, except manufacturing productivity by fossil fuel. The finding highlights that both manufacturing and industrial productivity depend on the available energy inputs and exhaust them in the same regime. This finding is consistent with previous studies (Table 4) that indicate that disaggregate energy inputs improve sectoral productivity. Furthermore, the fossil fuel that contains radioactive materials is the least consumed energy input in the manufacturing sectors. It implies that the manufacturing sector avoids consuming this fossil fuel due to the serious environmental concern. Fossil fuels such as kerosene and propane causes heavy carbon emission. The burning of this energy inputs produces around 21.3 billion tonnes of carbon dioxide (CO2) every year [16]. The awareness of fossil fuel usage has increased globally, where the United States Environmental Protection Agency (USEPA) imposes regulations to reduce the airborne emission and enforce to reduce the emission by 70% by 2018. Inversely, it is motivated to use the coal that generates fly
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Table 4 Existing studies and their findings on the relationship between growth, energy and emission. Author
Period
Model
Sample
Nexus
Kraft and Kraft [20] Akarca and Long [1] Yu and Hwang [52] [53]
1947–1974 1950–1970 1947–1979 1950–1979
Granger causality Sims' technique Sims' technique Sims' and Granger causality
USA USA USA 5 countries
Erol and Yu [12]
1950–1982
Sims' and Granger causality
6 countries
Nachane et al., [26]
1950–1985
16 countries
Tang and Shahbaz [43] Masih and Masih [24]
1972–2010
Engle-Granger cointegration Sims' and Granger Causality Johansen Cointegration and Toda-Yamamoto Test Johansen Cointegration and Granger causality
GNP-EC GNP—EC GNP—EC GDP—EC (UK, Poland, USA) GDP-EC(Korea) EC-GDP (Philippines) EC2GNP (Japan) GDP-EC (Germany, Italy) EC-GDP (Canada) EC—GDP (France, UK) GDP2EC (except Colombia and Venezuela) Electricity-GDP Electricity2Manufacturing Growth Energy-GDP (India) GDP-Energy (Indonesia) GDP2Energy (Pakistan)
Chandran, Sharma (2010) [41]
1955–1990 (India, Pakistan and Malaysia); 1960–1990 (Indonesia and Singapore); 1955–1991 (Philippines) 1971–2003
Pakistan 6 countries
ARDL and Granger causality
Malaysia
Electricity-GDP
1950–1992
Johansen cointegration and Granger causality
G-7 countries
Yoo and Jung [50]
1977–2002
Korea
Masih and Masih [22]
1955–1991
Johansen cointegration and Granger causality Johansen cointegration and Granger causality
EC2GDP (Argentina) GDP-EC (Italy, Korea) EC-GDP (Turkey, France, Japan, Germany) Energy-GDP
Masih and Masih [23]
Johansen cointegration and Granger causality Johansen Cointegration test and VECM
Korea and Taiwan
Rafiq
1955–1991(Korea); 1952–1992(Taiwan) 1965–2006
Wang et al., [46] Tang and Tan [45]
1972–2006 1972–2009
China Malaysia
Ghali and El-Sakka [13] Soytas and Sari [40] Chang [9]
1961–1997
ARDL and Granger causality Johansen cointegration, ARDL and Granger causality Johansen cointegration and VEC
Energy-GDP(China) GDP-Energy(India) GDP2Energy (Thailand) Energy—GDP (Malaysia, Indonesia, Philippines) Energy-GDP EC2GDP
Canada
EC2GDP
1968–2002 1981–2006
Johansen cointegration and VEC Johansen cointegration and VEC
Turkey China
1968–2005
ARDL and Granger causality
Turkey
1971–2011 1971–2002
ARDL and Granger causality Engle–Granger cointegration and Hsiao's Granger causality test
China Indonesia, Malaysia, Singapore, and Thailand
Tang [42] 1970–2005 Ang [4] 1971–1999 Shaari, Hussain (2014) 1980–2010
ARDL and Granger causality Johansen cointegration and ECM Johansen cointegration and Granger causality
Malaysia Malaysia Malaysia
Park and Yoo [30] Gross [14]
1965–2011 1970–2007
Johansen cointegration and ECM ARDL and Granger causality
Malaysia USA
Liu a, Wang [49]
1992–2014
DEA model
China
Electricity2Value added GDP2Emission GDP2Energy (coal, crude oil and electricity) Energy—GDP Emission—GDP Energy-GDP GDP2 Electricity (Malaysia, Singapore); Electricity-GDP (Indonesia, Thailand) GDP2 Electricity Energy-GDP Energy—GDP (oil, coal) GDP- Electricity Gas-GDP GDP2 Oil Commercial Growth -energy Transport Growth2 energy Industrial output2Energy consumption
Ozturk and Acaravci [29] Shahbaz et al., [37] Yoo [51]
Thailand and Sri Lanka
6 countries
GDP—Energy (Thailand) Energy-GDP (Sri Lanka) EC2GDP
Note: “-” stands for “unidirectional Granger cause”, “—“ stands for “does not Granger cause” and “2” stands for “bidirectional Granger cause”, “ARDL” stands for Autoregressive Distributed Lags, “ECM” stands for error-correction model.
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ash and bottom ash. These materials are environmental friendly and are used in different productive applications. Therefore, the Malaysian manufacturing sector perhaps reduces the fossil fuel consumption in the domestic production, while focuses more on other energy inputs such as coal, electricity, mineral and natural gas in responding to environmental issues. Finally, carbon emission is used to show whether it depends on energy consumption. The finding shows that all of the disaggregate energy inputs except minerals cause the carbon emission, environmental pollution, and greenhouse effect [17]. Although energy consumption improves the sectoral productivity, it causes negative externalities to the society. The manufacturing and industrial productivity are found in both switching coefficients and transition probability to positively relate with that carbon emissions. This implies that the higher the productivity of these two sectors, the greater the environmental pollution. This means that carbon emissions increase with increases in manufacturing and industrial production, implying that the Malaysian manufacturing and industrial sectors use high amounts of energy that ultimately harm the natural environmental. This finding is consistent with Rahman et al. [32] who indicated that manufacturing growth causes carbon emissions in Malaysia. Saboori and Sulaiman [33] concluded the positive relationship between manufacturing growth and emissions in Malaysia, Indonesia and Philippines. The same finding is also observed in the study of Bastola and Sapkota [6] for Nepal, Kivyiro and Arminen [18] for Sub-Saharan Africa, and Tang and Tan [44] for Vietnam. Table 4 presents a summary of the causal relationships between sectoral productivity and carbon emission found for comparative purposes. Both carbon emission and sectoral productivity increase in the same regime when high-energy inputs are consumed. Therefore, carbon emission directly results in excessive energy consumption producing negative externalities to the society. The details of the interdependent relationship between sectoral productivity and energy consumption, and between carbon emission and sectoral productivity (manufacturing and industrial sectors) are summarised in Table 5.
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Productivity growth is maintained to meet the vision 2020 and achieve the status of being a developed nation. This has led policy makers to concentrate on energy inputs that directly help increase sectoral productivity while minimising emissions. Unlike other studies, this study presents the interdependency of energy consumption with sectoral productivity in both booming and recession periods. We found that electricity, natural gas, and fossil fuel help GDP growth during recessional periods. The production sectors become more competitive during this period in consuming electricity, natural gas, and fossil fuel energy inputs so that they can enjoy subsidies from the government and PETRONAS in order to recover from the economic recession. The GDP growth during economic expansion is mostly contributed to by other elements such as FDI, non-production sectors (agro-based firms), and so forth. For this reason, most of the disaggregate energy units do not maintain an interdependent relationship with GDP growth during a booming economy. Moreover, the manufacturing and industrial productivity in both expansion and contraction periods depend on the disaggregate energy consumption to maintain continuous productivity. Finally, the interdependent relationship between sectoral productivity and carbon emission causes negative externalities that harm the society. Rather than threatening the health of the society due to carbon emissions, there is a need to maintain a balanced productivity system in which sectoral productivity would increase while ensuring public safety. To this end, the production sectors are required to prioritise the energy inputs that do not cause carbon emissions. Minerals are found in this study not to cause the carbon emission as it is more environmental friendly. Therefore, energy inputs should be consumed more from mineral resources. Furthermore, other sources of energy inputs such as green technology could be used to mitigate the emission problems along with renewal and non-renewal energy inputs so that the Malaysian economy can continue maintaining high productivity, while achieving its 2020-vision.
5. Future study 4. Discussion Malaysia is an emerging and energy-dependent economy. Numerous energy inputs are consumed in different sectoral levels to improve economic, manufacturing, and industrial growth.
This study employs the Markov Switching approach to examine the interdependent relationship between disaggregate energy consumption and sectoral productivity on the basis of regimemovement. Since this estimation technique is still new in this area of research, future studies could accommodate this technique but
Table 5 direction of interdependent relationship between sectoral productivity and disaggregate energy inputs. Disaggregate energy vs GDP growth
Disaggregate energy vs Manufacturing productivity
Disaggregate energy vs Industrial productivity
Disaggregate energy vs Carbon emission
Total energy (Regime 1)–GDP growth Total energy (Regime 2)-GDP growth
Total energy (Regime 1)-Manufacturing Total energy (Regime 2)-Manufacturing
Total energy (Regime 1)-Industry Total energy (Regime 2)-Industry
Electricity (Regime 1)–GDP growth Electricity (Regime 2)-GDP growth Coal (Regime 1)–GDP growth coal (Regime 2)–GDP growth Fossil (Regime 1)–GDP growth Fossil (Regime 2)-GDP growth Mineral (Regime 1)–GDP growth Mineral (Regime 2)–GDP growth Natural gas (Regime 1)–GDP growth Natural gas (Regime 2)-GDP growth Sectoral productivity vs carbon emission Manufacturing (Regime 1)-Emission Manufacturing (Regime 2)-Emission
Electricity (Regime 1)-Manufacturing Electricity (Regime 2)-Manufacturing Coal (Regime 1)-Manufacturing coal (Regime 2)-Manufacturing Fossil (Regime 1)–Manufacturing Fossil (Regime 2)–Manufacturing Mineral (Regime 1)-Manufacturing Mineral (Regime 2)-Manufacturing Natural gas (Regime 1)-Manufacturing Natural gas (Regime 2)-Manufacturing
Electricity (Regime 1)-Industry Electricity (Regime 2)-Industry Coal (Regime 1)-Industry coal (Regime 2)-Industry Fossil (Regime 1)-Industry Fossil (Regime 2)-Industry Mineral (Regime 1)-Industry Mineral (Regime 2)-Industry Natural gas (Regime 1)-Industry Natural gas (Regime 2)-Industry
Total energy (Regime 1)-Emission Total energy (Regime 2)Emission Electricity (Regime 1)-Emission Electricity (Regime 2)-Emission Coal (Regime 1)-Emission coal (Regime 2)-Emission Fossil (Regime 1)-Emission Fossil (Regime 2)-Emission Mineral (Regime 1)–Emission Mineral (Regime 2)–Emission Natural gas (Regime 1)-Emission Natural gas (Regime 2)-Emission
Note: - is used to show the interdependent relationship, – indicates non-relationship
Industry (Regime 1)-Emission Industry (Regime 2)-Emission
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use disaggregate energy components other than the one used in this study. Furthermore, this technique can be applied in other emerging economies to determine whether use of fossil fuel is reduced to protect the negative externality and environmental pollution in both booming and recession economic periods. 5.1. Concluding remarks and policy implications This study explores the regime-based effect between disaggregated energy consumption and sectoral productivity by employing the Markov Switching technique. The productivity in all sectors (GDP growth, manufacturing and industrial sectors) has shown significant improvement demonstrated by both statistical and empirical findings. The objective of this study is satisfied by statistical and empirical findings presented through an in-depth analysis. The mean values and correlation of coefficients imply that the sectoral productivity has increased over the period. The empirical findings of this study present several outcomes. Firstly, the contribution of majority disaggregated energy inputs is very low during a booming economy due to higher cost price. During a booming economy, the local government and PETRONAS cut the subsidy. This increases the production cost which reduces production and affects the GDP growth. Secondly, the economic policy during the recession policy is changed in order to recover GDP growth. The implementation of an expansionary policy reduces the cost of disaggregated energy inputs especially for electricity, fossil fuel, and natural gas. Hence, consumption of these energy inputs significantly influences the GDP growth in Malaysia during regime-2. On the other hand, manufacturing and industrial productivity depends on the majority of the energy inputs in both regimes in order to satisfy both local and foreign-export demand. The fossil fuel energy input is not used in the manufacturing production as a response to global awareness to reduce the carbon emission and greenhouse effect. Finally, carbon emission is seriously affected by manufacturing and industrial production. The higher the manufacturing and industrial production, the greater the carbon emission aggravating environmental pollution. Furthermore, the findings present significant insights in order to implement the further policy implications that would continuously improve the GDP growth and productivity in both manufacturing and industrial sectors, while reducing the carbon emission and environmental pollution. The manufacturing and industrial sectors can better contribute to the economy and society if the energy consumption is reduced to an efficient level. The excessive amount of energy consumption provides inefficient production and negative externalities. The excessive consumption of energy units leads to both carbon emission and shortage of aggregated energy inputs causing a decline in overall productivity. Proper initiatives in the form of green technology have to be taken to minimise the energy consumption to an efficient level to increase the marginal productivity and save the social environment, while also maintaining high sectoral productivity.
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