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Frontiers in Energy

Front. Energy    2020, Vol. 14 Issue (1) : 27-41     https://doi.org/10.1007/s11708-019-0656-5
RESEARCH ARTICLE
Impact of inter-fuel substitution on energy intensity in Ghana
Boqiang LIN1(), Hermas ABUDU2
1. School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Xiamen 361005, China
2. School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Xiamen 361005, China; Belt and Road Research Institute, Xiamen University, Xiamen 361005, China
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Abstract

Energy intensity and elasticity, together with inter-fuel substitution are key issues in the current development stage of Ghana. Translog production and ridge regression are applied for studying these issues with a data range of 2000–2015. The current energy dynamics reveal the expected inverse relationship: higher energy intensity and lower elasticity with economic growth. There are evidences of energy-economic challenges: high energy cost, inefficiency and backfire rebound effect. The implications are higher energy losses in the system, more consumption of lower-quality energy together with low energy technology innovation. Energy is wasted and directly not productive with economic activities. It is observed further that the higher energy intensity invariably increases CO2 emission because approximately 95% of total energy is derived from hydrocarbons and biomass. An inter-fuel substitution future scenario design was further conducted and the results were positive with growth, lower energy intensity, and improved energy efficiency. Therefore, government and energy policymakers should improve energy efficiency, cost, and productiveness. That is, they should change energy compositions and augment energy technology innovation, thus, increasing renewable share to 15% by 2026, reducing wood and charcoal by about 69%, and increasing natural gas to about 776%. Energy policymakers should enhance the installation of smart energy, cloud energy solution, tokenization of energy system and storage.

Keywords energy intensity      energy elasticity      inter-fuel substitution prospects      energy contribution      Translog production approach      ridge regression     
Corresponding Authors: Boqiang LIN   
Online First Date: 25 December 2019    Issue Date: 16 March 2020
 Cite this article:   
Boqiang LIN,Hermas ABUDU. Impact of inter-fuel substitution on energy intensity in Ghana[J]. Front. Energy, 2020, 14(1): 27-41.
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http://journal.hep.com.cn/fie/EN/10.1007/s11708-019-0656-5
http://journal.hep.com.cn/fie/EN/Y2020/V14/I1/27
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Boqiang LIN
Hermas ABUDU
Type of energy Primary energy/ktoe %ktoe Final energy/ktoe %ktoe
Petroleum 5577 58 3320.2 47
Biomass 3602 37 2783.4 39
Hydro 479 0.5
Solar 2 0.0
Electricity 982.0 14
Total 9660 100 7085.6 100
Tab.1  Primary and final energy mix in Ghanain 2016
Variable GDP Labor Capital Energy VIF
GDP 199.8552
Labor 0.952217(0.0010) 475.9238
Capital 0.976650(0.0001) 0.937144(0.0001) 26.32741
Energy 0.741805(0.0043) 0.862312(0.0010) 0.790315(0.0043) 216.9465
Tab.2  Pearson correlation and variance inflation factor (VIF) for multicollinearity test
Fig.1  Ridge trace plot (Y-axis represents the standard coefficient/VIF and X-axis represents k values (Source: EViews software generated, 2018).
Variable Standard ridge VIF
lnK 0.206749 0.0218
lnL 0.760515 0.1008
lnE -0.542156 0.01842
lnK*lnL 0.009607 0.0276
lnK*lnE 0.011541 0.01640
lnL*lnE 0.001363 0.0380
12(lnK)2 0.009203 0.0151
12(lnL)2 0.046585 0.0122
12(lnE)2 -0.062491 0.1067
Constant 23.63265
Ridge K 0.20
Coefficient of determination(R2) 0.978647
F-statistic 0.8972
Tab.3  Ridge regression results
Year sKt sLt sEt
2000 0.651426 1.803036 -0.816053
2001 0.652767 1.805038 -0.81256
2002 0.651205 1.804012 -0.812626
2003 0.654005 1.80754 -0.804305
2004 0.657658 1.811896 -0.800906
2005 0.65952 1.814371 -0.797315
2006 0.66329 1.820018 -0.793651
2007 0.664799 1.823286 -0.792029
2008 0.667222 1.827485 -0.790056
2009 0.667684 1.829564 -0.79817
2010 0.672147 1.83578 -0.792899
2011 0.675592 1.840866 -0.795327
2012 0.679285 1.846167 -0.798632
2013 0.679966 1.84783 -0.800903
2014 0.678174 1.846959 -0.804239
2015 0.677601 1.847295 -0.807526
Average 0.665771 1.825696 -0.80107
Tab.4  Total output elasticity of factor inputs
Year δKt/% δLt/% δEt/% Residual/% Percent/%
2000 174.02 63.07 -79.23 -7.86 100
2001 -71.43 27.59 -81.26 225.10 100
2002 121.43 18.69 -81.26 41.15 100
2003 168.25 25.49 -80.43 -13.31 100
2004 77.36 19.57 -80.09 83.16 100
2005 38.11 19.10 -79.73 122.52 100
2006 43.78 64.41 -79.37 71.18 100
2007 103.68 87.53 -79.20 -12.01 100
2008 99.56 -131.38 -79.73 211.55 100
2009 147.86 53.89 -79.82 -21.94 100
2010 78.45 55.38 -79.53 45.70 100
2011 289.90 193.69 -80.75 -302.84 100
2012 0.01% 56.09 -79.86 123.77 100
2013 79.44 -35.81 -80.09 136.47 100
2014 322.21 -253.33 -79.86 110.99 100
2015 130.99 -80.12 -77.65 126.79 100
Average 113 11 -80 56 100
Tab.5  Contribution of labor, capital, energy, and residual factor
Year sKL
2000 1.115601
2001 1.115395
2002 1.11559
2003 1.115184
2004 1.114667
2005 1.114398
2006 1.113835
2007 1.113577
2008 1.113201
2009 1.113086
2010 1.112453
2011 1.111961
2012 1.111443
2013 1.111326
2014 1.111518
2015 1.11156
Tab.6  Elasticity substitution possibilities for capital and labor
Energy mix 2016 Consumption Total ktoe Variation in energy/% Total ktoe
Natural gas 921.10 Increase to 776% 7147.73
Crude oil 2,399.10 Maintain consumption 2399.10
Biomass 2,783.40 Reduce by 69.45% 850.32
Electricity 982 Increase RE & redu.elec.loss

Increasing renewable energy (RE) consumption from the current 5% to 15% and reduce electricity losses to 18%. Electricity is made of both hydro and other renewables.

1296.24
Total 7085.6 11693.39
Year GDP Capital Labor Energy
2016 $ 42690000000 $ 10170781235 12134966 11693.39
2026 2% projected 1.5% projected 2% projected 14% projected
Tab.7  Scenario data: inter-fuel substitution possibility and enhancement electricity losses
Variable Ridge coefficient VIF
lnK 0.162154 0.0000
lnL 0.160244 0.0000
lnE 0.161517 0.0022
lnK·lnL 0.161206 0.0000
lnK·lnE 0.161570 0.0080
lnL·lnE 0.161497 0.0114
12(lnK)2 0.010145 0.0011
12(lnL)2 0.090123 0.0015
12(lnE)2 0.070418 0.0003
R-Square= 0.998911, F-statistic= 0.85067
Tab.8  New results for energy intensity after inter-fuel substitution scenario design
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