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Open AccessArticle

Investigating the Linkage between Economic Growth, Electricity Access, Energy Use, and Population Growth in Pakistan

Research Center of Agricultural-Rural-Peasants, Anhui University Hefei, Hefei 230039, China
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Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2442; https://doi.org/10.3390/app8122442
Received: 1 November 2018 / Revised: 21 November 2018 / Accepted: 23 November 2018 / Published: 1 December 2018
(This article belongs to the Section Energy)

Abstract

Electricity is a versatile form of energy that plays a vital role in fulfilling the daily requirements of human life. The primary aim of this study was to investigate and explore the link between economic growth, electricity access, energy use, and population growth in Pakistan for the period 1990–2016. An autoregressive distributed lag (ARDL) bounds testing approach to cointegration was applied to investigate the causality link between the study variables. These tests shed light on the long-run connection among the variables; further, the results revealed that the electricity access to the total population, electricity access to the urban population, energy usage, population growth, and urban population growth had a significant impact on economic growth, while the electricity access to the rural population and rural population growth had a negative impact on the economic growth in Pakistan. According to these findings, this study recommends that the government of Pakistan pay further attention to increasing its electricity production from different sources, including hydroelectric, solar, oil, and gas, and nuclear in order to fulfill the country’s demands.
Keywords: economic growth; electricity access; energy production; population growth; ARDL economic growth; electricity access; energy production; population growth; ARDL

1. Introduction

Energy plays a dominant role in the economic development and is also a fundamental part of any nation’s economy. It relates to energy security, economic development, and social stability. Electricity possesses vital value, and is considered the source of energy that supports every aspect of the economy [1,2,3,4]. Over the past few decades, policy failures in the energy sector of Pakistan plunged the country into a severe power crisis, leading to the poor economic performance of the country. The demand of electricity is determined by the population growth as well as other factors, including electricity prices, people’s migration to cities, and the weather. However, Pakistan’s unique problems and the transformation of the electricity shortage and crisis are caused by theft, abuse, and the excessive usage of electricity in the industrial sectors and homes, unreasonably causing huge line losses, corruption, mismanagement, institutional weakness, and political controversy [5]. In 2011, the population growth in Pakistan was 176.17 million as compared to 79.98 million in 1980, and the growing population’s demands are increasing, which directly affects the electricity escalation [6].
The South Asian Region (SAR) faces several deficiencies that affect the national system of electricity for a particular time. The electricity supply has not kept stride with the relevant growth and demand, resulting in a long-term downtime and frequent unplanned outages. These conditions have created difficulties for families and industries, and have hampered new investment in the business of any economy [7,8,9,10]. Pakistan has a population of about 184 million people, and the rural population that is associated with agriculture is high. The agricultural sector has contributed about 25% of the gross domestic product (GDP), and provides employment for more than 40% of the labor force [11]. The installed power generation in 2011 was 21,036 megawatts. The demand of electricity is increasing by about 9% a year, while the supply is only about 7%, and the gap in the summer is even greater [12,13]. Electricity generation sources in Pakistan include thermal energy (natural gas and oil), nuclear power, and hydropower. Renewable energy and coal are currently playing a secondary role, but are hoped to be boosted significantly in the coming decades [14]. The identified energy renewable resources are primarily solar energy, wind energy, and biomass. Hydropower, thermal, and nuclear power plants are a part of the hybrid industry in Pakistan. About 31% of electricity is produced by hydropower systems, 66.8% are produced by thermal systems, and the remaining 2.2% are produced by nuclear power. Further, the country imports 29.4% of its natural gas, 37.8% of its oil, 29.4% of its hydropower, and 0.26% of its natural gas in order to meet its energy needs. Coal and nuclear power have limited contributions to their energy supply, at 0.1% and 3.02%, respectively [15].
Currently, in Pakistan, the energy demand is of an average of 17,000 megawatts, while the shortage is of about 4000–5000 megawatts. In the coming years, the energy demand will increase further by approximately 500 megawatts in the next 10 years [16]. The electricity shortage reached 5500 megawatts in 2015, and the supply was 15,500 megawatts, with 23,000 megawatts of the installed capacity. The demand will rise in different sectors, including construction, agriculture, education, manufacturing, and most importantly, in sustainable development in order to boost the economic sector [17]. During the period of 2014–2015, the total electricity generation was 109,059 GWh, of which nearly two-thirds came from the thermal sources [18]. The electricity demand in Pakistan is driven by several issues, such as the rapidly growing population, electricity prices, economic expansion, urban resident flows, and weather. However, the major, specific problems of the country are the crisis that led to electricity shortages, which were caused by theft and the excessive use of electricity in domestic and industrial sectors, resulting in a huge loss of power lines and mismanagement and political controversy in mega-power projects [19]. Pakistan suffers from energy shortage due to production and supply. The major objective of this study was to explore and investigate the relationships between economic growth, electricity access to the total population, electricity access to the rural population, electricity access to the urban population, rural and urban population growth, total population growth, and energy usage in Pakistan. Time-span data was used in this study, which was collected from the World Development Indicators (WDI). We employed the Augmented Dickey–Fuller unit root test and Phillips–Perron unit root test to check the stationarity of the variables. The autoregressive distributed lag (ARDL) bounds testing approach to cointegration and the analysis of the long-run and short-run was used to gauge the dynamics causality among the study variables. Apart from the introduction section, the remaining paper has been organized as follows. Section 2 presents the existing literature regarding electricity production. Section 3 is the materials and methods section, which presents the data sources and model specification. Section 4 represents the empirical estimation strategy, and Section 5 is the results and discussion section that illustrates the results of the unit root tests, the results of the cointegration test, long-run and short-run results. Section 6 is conclusion and policy recommendation.

2. Existing Literature

The energy sector of any country plays a vital role in economic growth and development. Energy shortage in Pakistan has hampered the country, and caused severe crisis in the last several decades. The electricity sector received a great deal of attention due to rapid growth in the demand. Similarly, other factors, including inadequate water supply, water pollution, air pollution, and pasture degradation, are chief challenges that the country is facing [20]. Electricity, as a form of energy, plays an important role in boosting the economic growth of a country, and includes all of the sectors. Life quality and social well-being can improve with the severe production of sustainable electricity [21]. The total installed electricity capacity was 24,823 megawatts in 2015, with a maximum demand of 26,437 megawatts [22]. In response to this severe power shortage, long-term debates regarding energy production occurred, and participation in energy summits discovered a panacea in order to compensate for the shortage of electricity. Numerous conceivable regenerative and renewable electricity production sources are presently being deliberated upon, with suggestions for a short-term, medium-term, and long-term solutions to this trouble [23].
The supply of electricity in the rural communities contributes to economic growth, leading to improvements in agriculture, education, health, gender equality, and sustainable development [24,25]. Outdated equipment, improperly installed capacity, an inability to operate transmission systems, and deprived monetary administration are the main reasons behind the failure of the electricity sector in Pakistan [26,27]. The shortage is due to the lack of political instability and large investment, which has hindered the hydropower or coal projects, thereby increasing the dependence on imported expensive fuels and plummeting the local natural gas [28]. The country’s growing population, industrialization, and average household income have contributed to the growth in the demand for electricity [29]. Social and economic progress depends on energy flow. Currently, the country is producing insufficient energy and thus is facing a crisis. Despite renewable energy sources, traditional energy generation methods are still being used in Pakistan. In the present period, energy efficiency has amplified, but the energy generation systems have not been updated to fulfill the energy requirements [30,31]. The electricity deficit in 2013 was of 6000 megawatts (MW), which is more than the usual 4000–5000 megawatts per year, and the gross domestic product (GDP) declined by 3–4% due to the energy crisis. The crisis has seriously affected the economy of Pakistan as a result of industry closures [32,33].
Electricity is an important component of the infrastructure for a country’s socioeconomic development, and it represents a robust correlation between the consumption of electricity and economic growth [34]; however, growth in electricity production is extremely sensitive to local differences and domestic income levels [35]. The traditional electricity generation systems typically rely on a large quantity of power generation equipment. Considering the great size, it should be placed in a suitable geographic location. The generated electricity will be delivered to the grid station through heavy-duty transmission lines, and then be transmitted from the grid station to the users. These sources belong to renewable sources, including solar, hydro, and wind [36].
Pakistan ranks high in the world in terms of agricultural and industrial products, but energy problems still exist in the country due to a lack of sufficient measures by the government. However, the primary cause is associated with the government management measures, and Pakistan is also facing a severe energy crisis due to geopolitical uproar and a lack of interest [37,38,39]. Electricity is a key source of energy in the agricultural and industrial sector; it contributes almost 50% of Pakistan’s economy. The industrial, commercial, and agricultural sectors consume about 27.7%, 7.5%, and 12.5% of the nation’s consumption of electricity, respectively [40,41]. In order to produce adequate, inexpensive, and environmentally-friendly energy and establish alternative combinations and existing renewables sources of energy, the relevant necessary steps require implementation. Numerous authors have suggested that developing and developed countries should use renewable energy sources such as alternative and sustainable energy over conventional energy sources [42,43,44,45,46,47,48,49,50,51].
Furthermore, various studies have been conducted in order to highlight the relationship between energy consumption, electricity consumption, CO2 emissions, employment, real income, residential demand for electricity, exports, GDP, and economic growth by employing cointegration approaches and Granger causality tests [52,53,54,55,56,57,58,59,60,61,62]. However, the key motive of this study is to analyze the link between economic growth, electricity access to rural population, electricity access to urban population, electricity access to total population, rural and urban population growth, total population growth, and energy usage in Pakistan. Pakistan is located in South Asia, and most of the population living in rural areas is not linked to the power grid. The key component of rural grid electrification simply does not exist. The reason behind this is that certain rural areas have complex geography, moderately low electricity demand, and a huge cost of long delivery systems. Furthermore, there is a daily shortage of electricity in rural areas connected to the grid, mainly during the summertime.

3. Materials and Methods

3.1. Data Source

Time span data from 1990–2016 was used in this study, which was collected from the WDI (World Development Indicators). Below, Table 1 represents the variables used in this study:
The electricity access of the total population, electricity access of the rural population, electricity access of the urban population, energy usage, population growth, and rural and urban population growth from 1990–2016 is illustrated in Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7 and Figure 8, and data was taken from the WDI (World Development Indicators).
Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7 and Figure 8 represents the electricity access to the total population, electricity access to the rural population, electricity access to the urban population, energy usage, population growth, and rural and urban population growth, respectively.

3.2. Model Specification

To check the association among dependent and independent variables, the model follows the Fatai (2014) [63] specification to adopt the regression procedure. The multivariate regression model specification is as follows in its implicit forms as:
GDPPC t = f ( EAP t , EARP t , EAUP t , EN t , PG t , RPG t , UPG t )
In Equation (1), GDPPC t indicates the gross domestic product per capita, EAP t represents the electricity access to the total population, EARP t indicates the electricity access to the rural population, EAUP t represents the electricity access to the urban population, EN t indicates the energy use, PG t show the population growth in Pakistan, RPG t represent the rural population growth, and UPG t indicates the urban population growth.
GDPPC t = Ψ 0 + Ψ 1 EAP t + Ψ 2 EARP t + Ψ 3 EAUP t + Ψ 4 EN t + Ψ 5 PG t + Ψ 6 RPG t + Ψ 7 UPG t + μ t
By using natural logarithm to Equation (2), a log-linear model is as follows:
lnGDPPC t = Ψ 0 + Ψ 1 lnEAP t + Ψ 2 lnEARP t + Ψ 3 lnEAUP t + Ψ 4 lnEN t + Ψ 5 lnPG t + Ψ 6 lnRPG t + Ψ 7 lnUPG t + μ t
Equation (3) presents the log-linear form of the variables. lnGDPPC t represents the natural logarithm of the gross domestic product per capita; lnEAP t represents the natural logarithm of electricity access to the total population; lnEARP t represents the natural logarithm of the electricity access to the rural population; lnEAUP t represents the natural logarithm of the electricity access to the urban population; lnEN t represents the natural logarithm of energy use; lnPG t represents the natural logarithm of population growth in Pakistan; lnRPG t represents the natural logarithm of rural population growth; lnUPG t represents the natural logarithm of urban population growth; t is the time dimension; μ t is the error term; Ψ 0 indicates the constant intercept; and the coefficients of the model Ψ 1 to Ψ 7 represent the elasticity for the longrun.

4. Empirical Estimation Strategy

4.1. Unit Root Test for Stationarity

Despite the fact that the autoregressive distributed lag (ARDL) model requires no pre-testing for inspection of variables stationarity through the unit root test. The Augmented Dickey–Fuller (1979) [64] unit root test and Phillips–Perron (1988) [65] unit root test with trend and intercept was used to determine that none of the variables considered were integrated to order two. This is because the ARDL bounds testing approach is invalidated in cases where I(2) variables are used. Therefore, the unit root test was performed using Equation (3):
Δ Z t = α 0 + β 0 T + β 1 Z t 1 + i = 1 m α 1 Δ Z t 1 + μ t
where Z indicates the variables being tested for the unit root, T represents a linear trend, Δ indicates the first difference, t shows the time, μt is the error term, and m represents achieving white noise residuals.

4.2. Cointegration with ARDL Model

Pesaran and Shin (1998) [66] developed the ARDL bounds testing approach to check the analysis of long-run and short-run relationships, which was further protracted by Pesaran et al. (2001) [67], and Narayan et al. (2004) [68]. The cointegration testing approach is applicable regardless of the integration order with concerned variables, I(0) and or I(1), except for the occurrence of I(2). The long-run and short-run relations examined the ARDL representation of the unrestricted error correction model (UECM) of Equation (2), as depicted in Equation (5):
Δ lnGDPPC t = γ 0 + i = 1 P γ 1 i Δ lnGDPPC t i + i = 1 q 1 γ 2 i Δ lnEAP t i + i = 1 q 2 γ 3 i Δ lnEARP t i + i = 1 q 3 γ 4 i Δ lnEAUP t i + i = 1 q 4 γ 5 i Δ lnEN t i + i = 1 q 5 γ 6 i Δ lnPG t i + i = 1 q 6 γ 7 i Δ lnRPG t i + i = 1 q 7 γ 8 i Δ lnUPG t i + Ψ 1 lnGDPPC t 1 + Ψ 2 lnEAP t 1 + Ψ 3 lnEARP t 1 + Ψ 4 lnEAUP t 1 + Ψ 5 lnEN t 1 + Ψ 6 lnPG t 1 + Ψ 7 lnRPG t 1 + Ψ 8 lnUPG t 1 + ε t
where Δ indicates the difference operator, γ 0 represents the constant intercept, Ψ indicates the coefficients of long-run, while γ indicatesthe coefficients of short-run. The long-run co-movement among the variables of interest is ascertained on the basis of the estimated F-statistic. Pesaran et al. (2001) constituted two values available for the test of cointegration: first, the critical values of lower bound, where the variables are integrated of order zero I(0), and secondly, the critical values of the upper bound; where the variables are integrated of order one I(1). The hypothesis of no presence of long-run association is excluded if the F-statistic estimation exceeds the critical values on the upper bound. Hence, we use the small sample critical values provided by Narayan (2005) [69]. Eventually, this empirical study investigates the long-run elasticity and short-run adjustment parameters in Equation (5).

5. Results and Discussions

5.1. Unit Root Tests Results

Table 2 reports the results of the Augmented Dickey–Fuller unit root test and Phillips–Perron unit root test with intercept, and then both intercept and trend.
Augmented Dickey–Fuller unit root test results and Phillips–Perron unit root test results indicated that the significance of variables at 1%, 5%, and 10%, and none of the variables was integrated with the order of I(2); then, the ARDL model employed. Numerous cointegration approaches are available in empirical literature to test cointegration between the series, but the ARDL bounds testing is considered to be superior and preferable due to its various advantages such as: (i) no need for all of the variables in the system to be of an equal order of integration; (ii) it is an efficient estimator, even if samples are small and some of the regressors are endogenous; (iii) it allows the variables to maybe have different optimal lags; and (iv) it employs a single reduced-form equation. Thus, we opted for the ARDL approach to cointegration because of its simplicity and its suitability to models where the involved variables are of mixed order of integration [70].

5.2. Cointegration Test

A cointegration test was used when the F or W-statistic applies an upper bound of the selected significant level. It is worth observing that the F test assumes that there is no cointegration null hypothesis between variables. Cointegration results are illustrated in the Table 3.
The bounds tests shown in the table summarizes the existence of a cointegration connection among dependent and independent variables at the 1%, 5%, and 10% significance levels. Furthermore, we also employed the Johansen and Juselius, (1990) [71] cointegration test, and the results are interpreted in Table 4 with trace statistics and maximum eigenvalues.

5.3. Long-Run Analysis Results

Long-run analysis results are interpreted in Table 5.
Focusing on the elasticity of the variables in the long-run analysis, the results revealed that the electricity access to the total population of Pakistan has a positive and significant impact, as the economic growth has a coefficient of 1.310100 with a p-value of 0.6983. Similarly, the coefficients of electricity access to the urban population, energy usage, population growth, and urban population growth had a positive and significant impact along with economic growth. The coefficients of the electricity access to the urban population, energy usage, population growth, and urban population growth are 3.079896, 2.288282, 6.617094, and 0.308340, with their p-values of 0.3127, 0.0016, 0.0399, and 0.8886, respectively. Whereas, the results of the electricity access of the rural population and rural population growth had a negative impact on the economic growth, having coefficients −0.891821 and −3.988076 with p-values of 0.6426 and 0.0089, respectively. The negative impact regarding the electricity access of the rural population was caused due to insufficient electricity production in the country and its supply to the rural population of the country. There is a huge gap between the supply and demand of energy, which has flared with the passage of time, and the country has limited sources for producing electricity from reliable sources, including solar, natural gas, wind energy, hydropower, and nuclear. The urban regions in the country are facing abundant load-shedding, while the rural regions face even greater load-shedding compared to their urban counterparts [72,73].

5.4. Short-Run Analysis Results

Table 6 depicted the short-run analysis results. Among the connection of variables, the cointegration presence requires an error correction model (ECM) to imprison the dynamics of the short-run relation with its coefficient, which measures the adjustment speed.
The estimated value of R-squared is 0.996705 in the dynamics of the short-run relation, which demonstrates that about 99% variation in the economic growth was described in the model by the independent variables. The joint significance regarding the independent variables confirmed the F-statistic at a 1% level of significance. The value of the Durbin-Watson (DW) statistic was 2.575, which was not equal to the standard DW value for the nonappearance of resistance of any autocorrelation. However, this is good enough to expose whether any autocorrelation exists in the model.
Diagnostic and stability tests results are presented in Table 7.
Table 7 shows the Breusch–Godfrey Serial Correlation Test, and heteroskedasticity test with p-values of 0.1346 and 0.5095 respectively.

5.5. Structural Stability Test

The stability tests using the CUSUM and CUSUM square point to stabilize the long-run and short-run constraints. The graph of both CUSUM test and CUSUM square test are mentioned in Figure 9 and Figure 10, which specify that all of the values lie within critical boundaries at a significance level of 5%. It confirms the stability of the long-run and short-run parameters.

6. Conclusions and Recommendation

Pakistan has suffered from an energy crisis for the last few decades due to the insufficient production and supply of energy, causing an electricity shortage in the country. The key objective of this study was to explore and investigate the link between electricity access, energy usage, population growth, and economic growth in Pakistan. The Augmented Dickey–Fuller unit root test and Phillips–Perron unit root test were employed to gauge the stationarity of the variables, and an ARDL bounds testing approach to cointegration was applied to check the causality relationship between the study variables. The results of the study revealed that electricity access to the total population, electricity access to the urban population, energy use, population growth, and urban population growth had a significant correlation with economic growth, while the electricity access to the rural population and rural population growth present a negative correlation with economic growth. The population of Pakistan is growing with the passage of time; more electricity is required to fulfill the country’s demands. For the production of electricity, new policies require implementation in order to boost the energy sector of the country. The government should also pay attention to producing energy from alternative sources. These alternative sources include natural gas, coal, solar power, and wind. Natural gas and oil are the dominant sources that are used to produce energy in the country. Possible initiatives need to be undertaken to produce energy using solar power in order to supply cheap electricity to the population of the country. Regarding production from hydropower, necessary steps should be taken to build new dams in the country for storing water, which is also crucial for agricultural growth to boost the country’s economic growth. This will also present other benefits, as Pakistan will also face a water crisis in the coming years, which will be a serious threat to the country. The government should also formulate short-term, medium-term, and long-term energy production plans in order to produce cheap energy for fulfilling the country’s demand for electricity.

Author Contributions

A.R. conceived the study, collected the data, estimated the econometric model and drafted the manuscript. Z.D. read and made suggestions of the manuscript. All authors read and approved the final manuscript.

Funding

This research received no external funding.

Acknowledgments

The authors are grateful to the Research Center of Agricultural-Rural-Peasants, Anhui University Hefei, China for its financial and moral support. Moreover, the authors are also indebted to the reviewers for their positive suggestions that helped to improve the content of this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Arrillaga, J.; Bradley, D.; Bodger, P.S. Power System Harmonics; John Wiley & Sons: Hoboken, NJ, USA, 1985. [Google Scholar]
  2. Dugan, R.C.; McGranaghan, M.F.; Beaty, H.W.; Santoso, S. Electrical Power Systems Quality; Mcgraw-Hill: New York, NY, USA, 1996; Volume 2. [Google Scholar]
  3. Arrillaga, J.; Smith, B.C.; Watson, N.R.; Wood, A.R. Power System Harmonic Analysis; John Wiley & Sons: New York, NY, USA, 1997. [Google Scholar]
  4. Caciotta, M.; Leccese, F.; Trifirò, T. From power quality to perceived power quality. In Proceedings of the IASTED International Conference on Energy and Power Systems, Chiang Mai, Thailand, 29–31 March 2006; pp. 94–102. [Google Scholar]
  5. Government of Pakistan. Pakistan Economic Survey 2013–2014; Ministry of Finance, Finance Division, Economic Adviser’s Wing: Islamabad, Pakistan, 2014.
  6. SESRIC (The Statistical, Economic and Social Research and Training Centre for Islamic Countries). 2014. Available online: http://www.sesric.org/publications-books.php?year=2014 (accessed on 27 September 2018).
  7. World Bank. Doing Business 2014: Understanding Regulations for Small and Medium-Size Enterprises; World Bank: Washington, DC, USA, 2014; Available online: http://www.doingbusiness.org/reports/global-reports/doing-business-2014 (accessed on 13 October 2018).
  8. De Abreu, J.P.G.; Emanuel, A.E. The need to limit subharmonics injection. In Proceedings of the Ninth International Conference on Harmonics and Quality of Power, Orlando, FL, USA, 1–4 October 2000; Volume 1, pp. 251–253. [Google Scholar]
  9. Fuchs, E.F. Power Qualityin Power Systems and Electric Machines; ECEN 5787, Course Notes; Department of Electrical and Computer Engineering, University of Colorado: Boulder, CO, USA, 2005. [Google Scholar]
  10. Leccese, F. Rome, a first example of Perceived Power Quality of electrical energy: The telecommunication point of view. In Proceedings of the 29th International Telecommunications Energy Conference, Rome, Italy, 30 September–4 October 2007; pp. 369–372. [Google Scholar]
  11. Rehman, A.; Jingdong, L.; Shahzad, B.; Chandio, A.A.; Hussain, I.; Nabi, G.; Iqbal, M.S. Economic perspectives of major field crops of Pakistan: An empirical study. Pac. Sci. Rev. B Humanit. Soc. Sci. 2015, 1, 145–158. [Google Scholar] [CrossRef]
  12. Government of Pakistan. Pakistan Economic Survey 2010–2011; Ministry of Finance, Finance Division, Economic Adviser’s Wing: Islamabad, Pakistan, 2011.
  13. Pakistan Energy Yea book 2012; Hydrocarbon Development Institute of Pakistan, Ministry of Petroleum and Natural Resources, Government of Pakistan: Islamabad, Pakistan, 2012.
  14. Sahir, M.H.; Qureshi, A.H. Assessment of new and renewable energy resources potential and identification of barriers to their significant utilization in Pakistan. Renew. Sustain. Energy Rev. 2008, 12, 290–298. [Google Scholar] [CrossRef]
  15. Government of Pakistan. Pakistan Economic Survey 2012–2013; Ministry of Finance, Finance Division, Economic Adviser’s Wing: Islamabad, Pakistan, 2013.
  16. KazmiZ. Pakistan’s Energy Security. 2014. Available online: http://tribune.com.pk/tory/655573/pakistans-energy-security (accessed on 13 September 2018).
  17. Santoyo-Castelazo, E.; Azapagic, A. Sustainability assessment of energy systems: Integrating environmental, economic and social aspects. J. Clean. Prod. 2014, 80, 119–138. [Google Scholar] [CrossRef]
  18. NEPRA (National Electric Power Regulatory Authority). State of Industry Report 2015. Available online: http://nepra.org.pk/industryreports.htm (accessed on 10 September 2018).
  19. Government of Pakistan. National Power Policy Pakistan; Islamabad, Pakistan; 2015. Available online: http://www.ppib.gov.pk/Power%20Policy%202015.pdf (accessed on 29 September 2018).
  20. Government of Pakistan. National Power Policy Pakistan; Islamabad, Pakistan; 2013. Available online: www.ppib.gov.pk/National%20Power%20Policy%202013.pdf (accessed on 29 September 2018).
  21. Maxim, A. Sustainability assessment of electricity generation technologies using weighted multi-criteria decision analysis. Energy Policy 2014, 65, 284–297. [Google Scholar] [CrossRef]
  22. Government of Pakistan. Pakistan Economic Survey 2014–2015; Ministry of Finance, Finance Division, Economic Adviser’s Wing: Islamabad, Pakistan, 2015.
  23. Valasai, G.D.; Uqaili, M.A.; Memon, H.R.; Samoo, S.R.; Mirjat, N.H.; Harijan, K. Overcoming electricity crisis in Pakistan: A review of sustainable electricity options. Renew. Sustain. Energy Rev. 2017, 72, 734–745. [Google Scholar] [CrossRef]
  24. Danish, M.; Naqvi, M.; Farooq, U.; Naqvi, S. Characterization of South Asian agricultural residues for potential utilization in future ‘energy mix’. Energy Procedia 2015, 75, 2974–2980. [Google Scholar] [CrossRef]
  25. Palit, D.; Bandyopadhyay, K.R. Rural electricity access in South Asia: Is grid extension the remedy? A critical review. Renew. Sustain. Energy Rev. 2016, 60, 1505–1515. [Google Scholar] [CrossRef]
  26. Rauf, O.; Wang, S.; Yuan, P.; Tan, J. An overview of energy status and development in Pakistan. Renew. Sustain. Energy Rev. 2015, 48, 892–931. [Google Scholar] [CrossRef]
  27. Naseem, I.; Khan, J. Impact of energy crisis on economic growth of Pakistan. Int. J. Afr. Asian Stud. 2015, 7, 33–43. [Google Scholar]
  28. Aziz, R.; Ahmad, M.B. Pakistan’s Power Crisis: Special Report. 2015. Available online: http://www.usip.org/sites/default/files/SR375-Pakistans-Power-Crisis-The-Way-Forward.pdf (accessed on 28 September 2018).
  29. Kessides, I.N. Chaos in power: Pakistan’s electricity crisis. Energy Policy 2013, 55, 271–285. [Google Scholar] [CrossRef]
  30. Raza, W.; Hammad, S.; Shams, U.; Maryam, A.; Mahmood, S.; Nadeem, R. Renewable energy resources current status and barriers in their adaptation for Pakistan. J. Bioprocess Chem. Eng. 2015, 3, 1–9. [Google Scholar]
  31. Nisar, J.; Razaq, R.; Farooq, M.; Iqbal, M.; Khan, R.A.; Sayed, M.; Shah, A.; ur Rahman, I. Enhanced biodiesel production from Jatropha oil using calcined waste animal bones as catalyst. Renew. Energy 2017, 101, 111–119. [Google Scholar] [CrossRef]
  32. Government of Pakistan, Ministry of Finance. 2016. Available online: http://www.finance.gov.pk/survey/chapters_13/14-Energy.pdf (accessed on 5 October 2018).
  33. Uddin, W.; Khan, B.; Shaukat, N.; Majid, M.; Mujtaba, G.; Mehmood, A.; Ali, S.M.; Younas, U.; Anwar, M.; Almeshal, A.M. Biogas potential for electric power generation in Pakistan: A survey. Renew. Sustain. Energy Rev. 2016, 54, 25–33. [Google Scholar] [CrossRef]
  34. Kantar, E.; Keskin, M. The relationships between electricity consumption and GDP in Asian countries, using hierarchical structure methods. Phys. A Stat. Mech. Appl. 2013, 392, 5678–5684. [Google Scholar] [CrossRef]
  35. Karanfil, F.; Li, Y. Electricity consumption and economic growth: Exploring panel-specific differences. Energy Policy 2015, 82, 264–277. [Google Scholar] [CrossRef]
  36. Guerrero, J.M.; Blaabjerg, F.; Zhelev, T.; Hemmes, K.; Monmasson, E.; Jemei, S.; Comech, M.P.; Granadino, R.; Frau, J.I. Distributed generation: Toward a new energy paradigm. IEEE Ind. Electron. Mag. 2010, 4, 52–64. [Google Scholar] [CrossRef]
  37. Ali, G.; Bashir, M.K.; Ali, H.; Bashir, M.H. Utilization of rice husk and poultry wastes for renewable energy potential in Pakistan: An economic perspective. Renew. Sustain. Energy Rev. 2016, 61, 25–29. [Google Scholar] [CrossRef]
  38. Khan, S.A.R.; Zaman, K.; Zhang, Y. The relationship between energy-resource depletion, climate change, health resources and the environmental Kuznets curve: Evidence from the panel of selected developed countries. Renew. Sustain. Energy Rev. 2016, 62, 468–477. [Google Scholar] [CrossRef]
  39. Arshad, M.; Bano, I.; Khan, N.; Shahzad, M.I.; Younus, M.; Abbas, M.; Iqbal, M. Electricity generation from biogas of poultry waste: An assessment of potential and feasibility in Pakistan. Renew. Sustain. Energy Rev. 2018, 81, 1241–1246. [Google Scholar] [CrossRef]
  40. Shahbaz, M.; Lean, H.H. The dynamics of electricity consumption and economic growth: A revisit study of their causality in Pakistan. Energy 2012, 39, 146–153. [Google Scholar] [CrossRef][Green Version]
  41. Fuller, J.F.; Fuchs, E.F.; Roesler, D.J. Influence of harmonics on power distribution system protection. IEEE Trans. Power Deliv. 1988, 3, 549–557. [Google Scholar] [CrossRef]
  42. Kumar, A.; Holuszko, M.; Espinosa, D.C.R. E-waste: An overview on generation, collection, legislation and recycling practices. Resour. Conserv. Recycl. 2017, 122, 32–42. [Google Scholar] [CrossRef]
  43. Amri, F. The relationship amongst energy consumption (renewable and non-renewable), and GDP in Algeria. Renew. Sustain. Energy Rev. 2017, 76, 62–71. [Google Scholar] [CrossRef]
  44. Chang, Y.C.; Wang, N. Legal system for the development of marine renewable energy in China. Renew. Sustain. Energy Rev. 2017, 75, 192–196. [Google Scholar] [CrossRef]
  45. Kenfack, J.; Bossou, O.V.; Tchaptchet, E. How can we promote renewable energy and energy efficiency in Central Africa? A Cameroon case study. Renew. Sustain. Energy Rev. 2017, 75, 1217–1224. [Google Scholar] [CrossRef]
  46. Marques, A.C.; Fuinhas, J.A.; Neves, S.A. Ordinary and Special Regimes of electricity generation in Spain: How they interact with economic activity. Renew. Sustain. Energy Rev. 2018, 81, 1226–1240. [Google Scholar] [CrossRef]
  47. Kamran, M. Current status and future success of renewable energy in Pakistan. Renew. Sustain. Energy Rev. 2018, 82, 609–617. [Google Scholar] [CrossRef]
  48. Zameer, H.; Wang, Y. Energy production system optimization: Evidence from Pakistan. Renew. Sustain. Energy Rev. 2018, 82, 886–893. [Google Scholar] [CrossRef]
  49. Qazi, U.; Jahanzaib, M. An integrated sectoral framework for the development of sustainable power sector in Pakistan. Energy Rep. 2018, 4, 376–392. [Google Scholar] [CrossRef]
  50. Rehman, A.; Deyuan, Z. Pakistan’s energy scenario: A forecast of commercial energy consumption and supply from different sources through 2030. Energy Sustain. Soc. 2018, 8, 26. [Google Scholar] [CrossRef]
  51. Gondal, I.A.; Masood, S.A.; Khan, R. Green hydrogen production potential for developing a hydrogen economy in Pakistan. Int. J. Hydrogen Energy 2018, 43, 6011–6039. [Google Scholar] [CrossRef]
  52. Narayan, P.K.; Smyth, R. Electricity consumption, employment and real income in Australia evidence from multivariate Granger causality tests. Energy Policy 2005, 33, 1109–1116. [Google Scholar] [CrossRef]
  53. Narayan, P.K.; Smyth, R. The residential demand for electricity in Australia: An application of the bounds testing approach to cointegration. Energy Policy 2005, 33, 467–474. [Google Scholar] [CrossRef]
  54. Narayan, P.K.; Smyth, R.; Prasad, A. Electricity consumption in G7 countries: A panel cointegration analysis of residential demand elasticities. Energy Policy 2007, 35, 4485–4494. [Google Scholar] [CrossRef]
  55. Narayan, P.K.; Smyth, R. Multivariate Granger causality between electricity consumption, exports and GDP: Evidence from a panel of Middle Eastern countries. Energy Policy 2009, 37, 229–236. [Google Scholar] [CrossRef]
  56. Ozturk, I. A literature survey on energy–growth nexus. Energy Policy 2010, 38, 340–349. [Google Scholar] [CrossRef]
  57. Ozturk, I.; Acaravci, A. CO2 emissions, energy consumption and economic growth in Turkey. Renew. Sustain. Energy Rev. 2010, 14, 3220–3225. [Google Scholar] [CrossRef]
  58. Ozturk, I.; Aslan, A.; Kalyoncu, H. Energy consumption and economic growth relationship: Evidence from panel data for low and middle income countries. Energy Policy 2010, 38, 4422–4428. [Google Scholar] [CrossRef]
  59. Shahbaz, M.; Tang, C.F.; Shabbir, M.S. Electricity consumption and economic growth nexus in Portugal using cointegration and causality approaches. Energy Policy 2011, 39, 3529–3536. [Google Scholar] [CrossRef][Green Version]
  60. Dergiades, T.; Tsoulfidis, L. Revisiting residential demand for electricity in Greece: New evidence from the ARDL approach to cointegration analysis. Empir. Econ. 2011, 41, 511–531. [Google Scholar] [CrossRef]
  61. Arrillaga, J.; Watson, N.R.; Chen, S. PowerSystemQualityAssessment; Wiley: New York, NY, USA, 2000. [Google Scholar]
  62. Leccese, F.; Cagnetti, M.; Di Pasquale, S.; Giarnetti, S.; Caciotta, M. A new power quality instrument based on raspberry-pi. Electronics 2016, 5, 64. [Google Scholar] [CrossRef]
  63. Fatai, B.O. Energy consumption and economic growth nexus: Panel co-integration and causality tests for Sub-Saharan Africa. J. Energy S. Afr. 2014, 25, 93–100. [Google Scholar]
  64. Dickey, D.A.; Fuller, W.A. Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 1979, 74, 427–431. [Google Scholar] [CrossRef]
  65. Phillips, P.C.; Perron, P. Testing for a unit root in time series regression. Biometrika 1988, 75, 335–346. [Google Scholar] [CrossRef]
  66. Pesaran, M.H.; Shin, Y. An autoregressive distributed-lag modelling approach to cointegration analysis. Econ. Soc. Monogr. 1998, 31, 371–413. [Google Scholar]
  67. Pesaran, M.H.; Shin, Y.; Smith, R.J. Bounds testing approaches to the analysis of level relationships. J. Appl. Econ. 2001, 16, 289–326. [Google Scholar] [CrossRef][Green Version]
  68. Narayan, P.K. Reformulating Critical Values for the Bounds F-Statistics Approach to Cointegration: An Application to the Tourism Demand Model for Fiji; Monash University: Clayton, Australia, 2004; pp. 1–40. [Google Scholar]
  69. Narayan, P.K. The Saving and Investment Nexus for China: Evidence from Cointegration Tests. Appl. Econ. 2005, 37, 1979–1990. [Google Scholar] [CrossRef]
  70. Dergiades, T.; Tsoulfidis, L. Estimating residential demand for electricity in the United States, 1965–2006. Energy Econ. 2008, 30, 2722–2730. [Google Scholar] [CrossRef]
  71. Johansen, S.; Juselius, K. Maximum likelihood estimation and inference on cointegration—With applications to the demand for money. Oxf. Bull. Econ. Stat. 1990, 52, 169–210. [Google Scholar] [CrossRef]
  72. WAPDA. Water and Power Development Authority. 2016. Available online: http://www.wapda.gov.pk (accessed on 17 August 2018).
  73. Rafique, M.M.; Rehman, S. National energy scenario of Pakistan—Current status, future alternatives, and institutional infrastructure: An overview. Renew. Sustain. Energy Rev. 2017, 69, 156–167. [Google Scholar] [CrossRef]
Figure 1. Electricity Access to the Total Population.
Figure 1. Electricity Access to the Total Population.
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Figure 2. Electricity Access to the Rural Population.
Figure 2. Electricity Access to the Rural Population.
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Figure 3. Electricity Access to the Urban Population.
Figure 3. Electricity Access to the Urban Population.
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Figure 4. Energy Use in Pakistan.
Figure 4. Energy Use in Pakistan.
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Figure 5. Rural Population Growth in Pakistan.
Figure 5. Rural Population Growth in Pakistan.
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Figure 6. Urban Population Growth in Pakistan.
Figure 6. Urban Population Growth in Pakistan.
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Figure 7. Population Growth in Pakistan.
Figure 7. Population Growth in Pakistan.
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Figure 8. Gross Domestic Product (GDP) Per Capita in Pakistan.
Figure 8. Gross Domestic Product (GDP) Per Capita in Pakistan.
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Figure 9. Plot of CUSUM.
Figure 9. Plot of CUSUM.
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Figure 10. Plot of CUSUM of Square.
Figure 10. Plot of CUSUM of Square.
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Table 1. Description of Variables and Data Sources. WDI: World Development Indicators.
Table 1. Description of Variables and Data Sources. WDI: World Development Indicators.
VariablesExplanationData Sources
GDPPCGross Domestic Product Per CapitaWDI
EAPElectricity Access to PopulationWDI
EARPElectricity Access to Rural PopulationWDI
EAUPElectricity Access to Urban Population WDI
ENEnergy UseWDI
PGPopulation GrowthWDI
RPGRural Population GrowthWDI
UPGUrban Population GrowthWDI
Note: the units of the variables are in USD and %.
Table 2. Augmented Dickey–Fuller and Phillips–Perron Unit Root Test Results.
Table 2. Augmented Dickey–Fuller and Phillips–Perron Unit Root Test Results.
Augmented Dickey–Fuller Unit Root TestPhillips–Perron Unit Root Test
VariablesAt LevelFirst DifferenceAt LevelFirst Difference
LnEAP−1.272365−13.17405 *−2.945287−16.37533 *
LnEARP−1.664904−11.97595 *−2.602248−13.37829 *
LnEAUP−4.350711−5.926737 *−4.972231−21.59359 *
LnEN−0.673060−4.635827 *−0.744445−4.634863 *
LnGDPPC−1.575934−4.340708 *−1.575934−4.318162 *
LnPG−1.607271−3.544057 ***−1.703555−3.745637 **
LnRPG−1.232135−4.406125 *−1.851566−3.908624 **
LnUPG−2.216098−4.502952 *−2.422004−4.281174 **
*, **, *** show the rejection of null hypothesis at 1%, 5% and 10% level of significance.
Table 3. Autoregressive Distributed Lag (ARDL) Bounds Test for Cointegration Results.
Table 3. Autoregressive Distributed Lag (ARDL) Bounds Test for Cointegration Results.
ARDL Bounds Test for Cointegration Results
F-StatisticSignificance LevelLower BoundUpper BoundDecision
5.35510810%2.033.13Cointegrated
5%2.323.5
1%2.964.26
Table 4. Results of the Johansen Cointegration test Using Trace Statistic and Maximum Eigenvalues.
Table 4. Results of the Johansen Cointegration test Using Trace Statistic and Maximum Eigenvalues.
Trace Statistic
Hypothesized No. of CE(s)EigenvalueTrace Statistic0.05 Critical ValueProb.**
None *0.993717396.7264143.66910.0000
At most 1 *0.976870269.9806111.78050.0000
At most 2 *0.944234175.815183.937120.0000
At most 3 *0.816141103.650560.061410.0000
At most 4 *0.73833061.3108740.174930.0001
At most 5 *0.56283627.7940724.275960.0173
At most 60.2470147.10788112.320900.3145
At most 70.0006070.0151704.1299060.9198
Maximum Eigenvalue Statistic
Hypothesized No. of CE(s)EigenvalueMax-Eigen Statistic0.05 Critical ValueProb.**
None *0.993717126.745848.877200.0000
At most 1 *0.97687094.1654642.772190.0000
At most 2 *0.94423472.1646136.630190.0000
At most 3 *0.81614142.3396430.439610.0011
At most 4 *0.73833033.5168024.159210.0020
At most 5 *0.56283620.6861917.797300.0179
At most 60.2470147.09271011.224800.2415
At most 70.0006070.0151704.1299060.9198
* Denotes rejection of the hypothesis at the 0.05 level, ** MacKinnon–Haug–Michelis (1999) p-values.
Table 5. Long-Run Analysis.
Table 5. Long-Run Analysis.
ARDL Cointegrating and Long-Run Form
Cointegrating Form
VariablesCoefficientStd. Errort-StatisticProb.
D(LNEAP)−1.8287901.992169−0.9179890.3783
D(LNEARP)1.1799091.1598031.0173360.3308
D(LNEAUP)0.1841811.9071630.0965730.9248
D(LNEN)1.5496700.7273772.1304900.0565
D(LNPG)6.8255613.0676692.2249990.0479
D(LNRPG)−6.9646343.127366−2.2269970.0478
D(LNUPG)10.1144016.1578311.6425270.1287
Coint. Equation(-1)−1.0315040.233261−4.4221120.0010
Long-Run Coefficients
VariablesCoefficientStd. Errort-StatisticProb.
LNEAP1.3101003.2917780.3979920.6983
LNEARP−0.8918211.868824−0.4772100.6426
LNEAUP3.0798962.9106611.0581430.3127
LNEN2.2882820.5482494.1738040.0016
LNPG6.6170942.8407782.3293240.0399
LNRPG−3.9880761.257097−3.1724500.0089
LNUPG0.3083402.1511630.1433360.8886
C−27.08299111.476944−2.3597740.0378
Table 6. Short-Run Analysis.
Table 6. Short-Run Analysis.
Short-Run Analysis
VariablesCoefficientStd. Errort-StatisticProb.*
LNGDPPC(-1)−0.0315040.233261−0.1350610.8950
LNEAP−1.8287901.992169−0.9179890.3783
LNEAP(-1)3.1801642.0764491.5315400.1539
LNEARP1.1799091.1598031.0173360.3308
LNEARP(-1)−2.0998261.266322−1.6582090.1255
LNEAUP0.1841811.9071630.0965730.9248
LNEAUP(-1)2.9927451.8320341.6335640.1306
LNEN1.5496700.7273772.1304900.0565
LNEN(-1)0.8107030.7321271.1073250.2918
LNPG6.8255613.0676692.2249990.0479
LNRPG−6.9646343.127366−2.2269970.0478
LNRPG(-1)2.8509172.8737680.9920480.3425
LNUPG10.114406.1578311.6425270.1287
LNUPG(-1)−9.7963486.585777−1.4875010.1650
C−27.9362211.64342−2.3993140.0353
R-squared0.996705Mean dependent var.6.569321
Adjusted R-squared0.992510S.D. dependent var.0.448658
S.E. of regression0.038828Akaike info criterion−3.365683
Sum squared resid.0.016584Schwarz criterion−2.639858
Log likelihood58.75388Hannan-Quinn criter.−3.156672
F-statistic237.6347Durbin-Watson stat2.575936
Prob(F-statistic)0.000000
Table 7. Diagnostic and Stability Tests.
Table 7. Diagnostic and Stability Tests.
Diagnostic and Stability Tests
Test Statistics (LM Version)F-StatisticProb.
Breusch–Godfrey Serial Correlation2.8578810.1346
Heteroscedasticity0.6964660.5095
CUSUM (Cumulative Sum) Stable
CUSUMSQ (Cumulative Sum of Square) Stable
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