Modelling electricity demand using the STAR (Smooth Transition Auto-Regressive) model in Pakistan

doi:10.1016/j.energy.2014.10.040

Energy

Volume 78, 15 December 2014, Pages 535-542

https://doi.org/10.1016/j.energy.2014.10.040 Get rights and content

Highlights

•
This study estimates electricity demand function for Pakistan using the STAR model.
•
Electricity demand follows a nonlinear path if price uses as a transition variable.
•
The price of electricity is below the optimal level i.e. 4.32 in Pakistan.
•
The projected electricity demand would be three times those of today in 2020.
•
Continuous investment in power sector should be required to meet the future needs.

Abstract

This study attempts to estimate Pakistan's electricity demand by applying STAR (Smooth Transition Auto-Regressive) model. The covered study period is 41 years – from 1971 to 2012. The results show that the electricity demand follows a non-linear path if the real electricity price is used as a transition variable. We find that the average real price of electricity is below the optimal level. In addition, the electricity demand is primarily determined by the level of development. The forecast statistics reveal that for a presumed GDP (Gross Domestic Product) growth rate of 6 percent, the electricity demand would jump almost three folds in 2020 as compared to the demand in 2012. Owing to a weak relationship between electricity demand and its price, a strategy built on price escalation may not work towards curtailing demand. To meet the future electricity demand, the following measures are important: i) shifting energy mix from thermal to renewable ii) increasing power sector's efficacy iii) adopting an integrated institutional approach and iv) creating a culture of conservation and responsibility.

Introduction

Electricity – a key energy resource – is the backbone of development process. Industries, production, transportation, education, construction activities, households as well as large businesses and small entrepreneurs heavily rely on electricity power. The availability of electricity is central to economic growth resulting into Pakistan's prosperity and eventual sustainability. Severe electricity shortage turns out to be one of the major causes of Pakistan's current sluggish economic performance. In June 2013, the electricity shortfall reached 4250 MW per day with demand standing at 16,400 MW per day and generation at 12,150 MW per day. Through its negative impact on employment, trade, and poverty, the prolonged power crisis stands out as a key devastating factor in the dismal economic growth of the country [1]. Pakistan has suffered from low GDP (Gross Domestic Product) growth rates during the times of electricity shortage [2]. Sporadic power outages are impeding the industrial sector of Pakistan. According to the World Bank, approximately 75 percent of the firms in Pakistan have identified electricity shortage as a major constraint to their growth. These firms are facing 8.2 percent annual sales losses because of power outages.¹ Around 40 percent of factories and industry units have remained closed, and nearly 7.5 percent of the labour force is unemployed simply because of the electricity power crisis.²

Studies on the power sector have highlighted bad governance, poor transmission, distribution losses, circular debt, and a large increase in the electricity demand as leading factors contributing to the persistent power crisis in Pakistan [1], [3]. There is an ever growing electricity demand that stems from urbanisation, industrialisation as well as expansion in agriculture and service sectors. Electrification in rural areas of the country and establishment of new households because of population increase and marriages are seen as other factors behind the increase in power demand. The urbanisation has increased at a rate of 3 percent per annum: the highest percentage of urbanisation in South Asia. The trend in village electrification also exhibits an approximate annual escalation of 8 percent per annum through the village electrification program of the government [2]. All the above-mentioned factors – among others – have led to an unprecedented increase in electricity demand.

The precise assessment of electricity demand always remains a critical concern for policymakers in Pakistan. There are a number of studies that have estimated the electricity demand function in Pakistan [4], [5], [6], [7], [8], [9], [10]. These studies have mainly employed a linear functional form for analysing the energy-economic growth nexus. These studies have considered only linear co-integration framework for estimating the energy demand model while ignoring the possibility of non-linear co-integration model that may lead to the misleading conclusion [11]. Various studies have shown that when a non-linear effect is incorporated into the estimation process, the explanatory power of an energy consumption-economic growth model is improved [12], [13]. This study also aims at adhering to the new trend.

The objectives of this study are the following: i) to estimate the electricity demand function for Pakistan using a non-linear estimation procedure ii) to determine the optimal level of electricity price, and iii) to project the future electricity demand under different growth scenarios. This study employs the STAR (Smooth Transition Auto-Regressive) model, the most important regime-switching model, to examine the relationship between electricity consumption, real income, and electricity price in Pakistan using time-series data over the period 1971–2012. Various studies have shown that use of the STAR model is an efficient non-linear approach to estimating the energy demand function [11], [12], [14], [15]. The STAR model captures the non-linear relationship between variables using a transition function in a continuous manner [12], [15]. This study contributes to the existing literature in various manners: First, to the best of the authors' knowledge, no studies to date have estimated the electricity demand function in Pakistan using the STAR model. Second, this study calculates the optimal price level for electricity. Finally, this study uses different growth scenarios to project the future electricity demand.

The paper is structured as follows: Section 2 provides an overview of the electricity sector in Pakistan; Section 3 summarizes the existing literature concerned with the electricity demand function; Section 4 explains the data sources, and sketches the modelling framework and estimation methodology; Section 5 presents empirical results and discussion; and, Section 6 concludes the whole discussion and suggests the policy implications.

Section snippets

Pakistan's electricity sector

Pakistan has faced an electricity crisis since its inception. In 1947, Pakistan had the capacity to produce only 60 MW for its 31.5 million population. In order to address the electricity shortage through institutional intervention, the WAPDA (Water and Power Development Authority) was established in 1958. WAPDA constructed two dams with the capacity of 4478 MW in the late 1970s to overcome the energy crisis. Pakistan continued facing power shortages even in the 1980s despite some haphazard

Literature review

There is an extensive literature available on the electricity demand encompassing Pakistan, region as well as the entire world. Most of it, however, is focused on the causal relationship between electricity consumption and economic growth indicating mixed results. For example, a few studies predict unidirectional causality running from output to electricity consumption [16], [17], [18], [19], [20], yet others suggest causality running from the electricity consumption to output [21], [22], [23],

Modelling framework

The electricity demand is determined by various factors such as growing population, extensive urbanisation, rural electrification, industrialisation, rapid growth in domestic demand, and rising per capita income. Among these, income, population, and energy prices are considered critical determinants of electricity demand [44]. The impact of real income on electricity consumption occurs through various channels. First, economic growth causes expansion in the industrial and commercial sectors,

Empirical results and discussion

The descriptive statistics of the variables are presented in Table 1. The analysis provides information on the mean range and the scale of the relation between the variables. The descriptive statistics show that the per capita average electricity consumption is 5.5 kWh and the average GDP per capita is 10.04. The average real price of electricity is 1.29 and ranges from 0.78 to 1.68. The correlation coefficient matrix shows that GDP per capita has a positive and significant correlation with

Conclusions and policy implication

The present study has estimated the non-linear electricity demand function for Pakistan using time-series data over the period of 1971–2012. The study has employed a logistic STAR (Smooth Transition Auto-Regression) model for estimation. Time-series properties show that all variables are stationary at first difference with the possibility of structural break. The estimation results show that there is a long-run relationship between electricity consumption, GDP per capita, and electricity

References (61)

I.N. Kessides
Chaos in power: Pakistan's electricity crisis
Energy Policy
(2013)
A.M.M. Masih et al.
Energy consumption, real income and temporal causality: results from a multi-country study based on co-integration and error-correction modelling techniques
Energy Econ
(1996)
C.-C. Lee
Energy consumption and GDP in developing countries: a cointegrated panel analysis
Energy Econ
(2005)
F. Jamil et al.
The relationship between electricity consumption, electricity prices and GDP in Pakistan
Energy Policy
(2010)
K. Zaman et al.
Determinants of electricity consumption function in Pakistan: old wine in a new bottle
Energy Policy
(2012)
M. Javid et al.
Electricity consumption-GDP nexus in Pakistan: a structural time series analysis
Energy
(2014)
C.-C. Lee et al.
Modeling OECD energy demand: An international panel smooth transition error-correction model
Int Rev Econ Financ
(2013)
E. Gabreyohannes
A non-linear approach to modelling the residential electricity consumption in Ethiopia
Energy Econ
(2010)
L.J. Esso
Threshold co-integration and causality relationship between energy use and growth in seven African countries
Energy Econ
(2010)
J. Moral-Carcedo et al.
Modelling the non-linear response of Spanish electricity demand to temperature variations
Energy Econ
(2005)

A.H. Kani et al.

Estimation of demand function for natural gas in Iran: evidences based on smooth transition regression models

Econ Model

(2014)

S.-H. Yoo et al.

Electricity generation and economic growth in Indonesia

Energy

(2006)

J. Squalli

Electricity consumption and economic growth: bounds and causality analyses of OPEC members

Energy Econ

(2007)

P.K. Narayan et al.

Electricity consumption–real GDP causality nexus: evidence from a bootstrapped causality test for 30 OECD countries

Energy Policy

(2008)

H.-T. Pao

Forecast of electricity consumption and economic growth in Taiwan by state space modeling

Energy

(2009)

A. Shiu et al.

Electricity consumption and economic growth in China

Energy Policy

(2004)

Y. Wolde-Rufael

Electricity consumption and economic growth: a time series experience for 17 African countries

Energy Policy

(2006)

J. Yuan et al.

Electricity consumption and economic growth in China: co-integration and co-feature analysis

Energy Econ

(2007)

V.G.R. Chandran et al.

Electricity consumption–growth nexus: the case of Malaysia

Energy Policy

(2010)

A.E. Akinlo

Electricity consumption and economic growth in Nigeria: evidence from co-integration and co-feature analysis

J Policy Model

(2009)

A. Acaravci et al.

Electricity consumption-growth nexus: evidence from panel data for transition countries

Energy Econ

(2010)

H. Gurgul et al.

The electricity consumption versus economic growth of the Polish economy

Energy Econ

(2012)

S.-T. Chen et al.

The relationship between GDP and electricity consumption in 10 Asian countries

Energy Policy

(2007)

S.A. Solarin et al.

Trivariate causality between economic growth, urbanisation and electricity consumption in Angola: co-integration and causality analysis

Energy Policy

(2013)

M.L. Polemis et al.

The electricity consumption and economic growth nexus: evidence from Greece

Energy Policy

(2013)

C.F. Tang et al.

Exploring the nexus of electricity consumption, economic growth, energy prices and technology innovation in Malaysia

Appl Energy

(2013)

W.N. Cowan et al.

The nexus of electricity consumption, economic growth and CO₂ emissions in the BRICS countries

Energy Policy

(2014)

I. Arisoy et al.

Estimating industrial and residential electricity demand in Turkey: a time varying parameter approach

Energy

(2014)

C. Hamzacebi et al.

Forecasting the annual electricity consumption of Turkey using an optimized grey model

Energy

(2014)

A. Pielow et al.

Modeling short-run electricity demand with long-term growth rates and consumer price elasticity in commercial and industrial sectors

Energy

(2012)

Cited by (29)

A multi-period-sequential-index combination method for short-term prediction of small sample data
2024, Reliability Engineering and System Safety
Based on multi-period-sequential-index combination (MPSIC) technology, three forecasting methods (auto-MPSIC, IV- MPSIC, MSEI-MPSIC) were proposed for short-term prediction of small sample data. Natural gas datasets, coal datasets, electricity datasets and atmosphere datasets were separately tested by using MPSIC method, and then impact of weighting factors, forecasting accuracy analysis were carried out for MPSIC method as well as other comparative methods. The results showed that, auto-MPSIC method was partial to use statistical indicators, such as peak-to-peak, average, root mean square, to decrease prediction error, and meanwhile was also inclined to use sequential index at time of t_i-₁ next to t_i to improve prediction accuracy. It was also concluded that: the proposed MPSIC method could achieve higher prediction accuracy compared with other methods; the robustness of auto-MPSIC method was slightly better than that of IV-MPSIC and MSEI-MPSIC under condition of noisy data, which was attributed to an adaptive weight allocation technology considering statistical distribution of forecasting errors.
Energy poverty, climate shocks, and health deprivations
2021, Energy Economics
Citation Excerpt :
Inadequate and unaffordable energy leads to adverse consequences on livelihoods, health and the economy (Awaworyi Churchill et al., 2020; Kahouli, 2020; Kose, 2019; Zhang et al., 2019). Energy has been associated with income, household welfare and poverty (Bridge et al., 2016; Krauss, 2016; Nawaz et al., 2014, 2013). Living standards depend on various forms of energy through cooking, lighting, heating and cooling (Welsch and Biermann, 2017).
This study examines the impacts of multidimensional energy poverty and climate shocks on multidimensional health deprivations in Pakistan. Results achieved by the empirical analysis, addressing the endogenous nature of the relationship between energy poverty and health outcomes, show that energy poverty has a positive and significant impact on health poverty in Pakistan. Similarly climate shocks, including rainfalls, floods and temperature, have positive and significant impacts on health debilitation. The results further highlight adverse effects of shortage of energy and climate shocks on child health as well. In other checks, it has been noticed that the above-mentioned problems have led to higher per capita health expenditures. Findings suggest the need of government's efforts to increase the provision of energy services and relevant subsidies, where required, in order to promote a healthy society.
The impact of unconditional cash transfer on fuel choices among ultra-poor in Pakistan: Quasi-experimental evidence from the Benazir Income Support Program
2020, Energy Policy
We analyze the impact of the unconditional cash transfer program, namely the Benazir Income Support Program (BISP) on fuel choices among ultra-poor in Pakistan using two rounds of household-level data collected in 2011 and 2016. The analysis based on regression discontinuity design shows that BISP cash transfer has a significant impact on interfuel substitution. Cash transfer increases the use of modern fuels among BISP beneficiaries. Besides, cash transfer also encourages the use of intermediate fuels, even in some cases traditional fuels. Cash transfer also increases the share of fuel expenses in total household expenses. Therefore, the fuel stacking theory provides a better explanation of interfuel substitution than the energy ladder theory of the cash transfer program. The provincial analysis shows that interfuel substitution exists due to cash transfer but varies from province to province, signifying the role of regional heterogeneities. The net impact on fuel choices may depend on the availability of different fuel components. From policy perspective, the expansion of the cash transfer program, as planned by the government, would require a reasonable investment in the energy sector to ensure an uninterrupted supply of modern fuels.
Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption
2020, Energy
Electricity consumption forecasting is essential for intelligent power systems. In fact, accurate forecasting of monthly consumption to predict medium- and long-term demand substantially contributes to the appropriate dispatch and management of electric power systems. Most existing studies on monthly electricity consumption forecasting require large datasets for accurate prediction, which is severely undermined when scarce data are available. However, in practical scenarios, data is not always sufficient, thereby hindering the accurate forecasting of monthly electricity consumption. The Holt–Winters exponential smoothing allows to accurately forecast periodic series with relatively few training samples. Based on this method, we propose a hybrid forecasting model to predict electricity consumption. The fruit fly optimization algorithm is used to select the best smoothing parameters for the Holt–Winters exponential smoothing. We used electricity consumption data from a city in China to comprehensively evaluate the forecasting performance of the proposed model compared to similar methods. The results indicate that the proposed model can substantially improve the prediction accuracy of monthly electricity consumption even when few training samples are available. Moreover, the computation time of the proposed model is the shortest among the evaluated hybrid benchmark algorithms.
Forecasting annual natural gas consumption using socio-economic indicators for making future policies
2019, Energy
Natural gas is a foreign-dependent source of energy in many countries and a rapid increase of its consumption is mainly associated with the increase of living standards and needs. In this work, Turkey was taken as a case study with high degree of foreign dependence of energy, and the future natural gas consumption was predicted by several different multiple regression models using socio-economic indicators as the descriptor variables. Among these, gross domestic product and inflation rate were found to be the only significant ones for this prediction. Next, three different projections for the future values of the significant descriptor variables were tested, and the natural gas consumption was predicted to rise gradually in the range 1.3 ± 0.2 billion m³ per year reaching to a consumption of 64.0 ± 3.5 billion m³ in the year 2025. It was then discussed that this additional natural gas can be compensated by utilizing local lignite sources or by starting a nuclear energy program although these two methods to reduce the future natural gas consumption have some conflictions with the general European energy matrix and environmental politics. Thus, it was concluded that resuming the wind and solar-based electricity generation programs can be considered as a more reasonable option.
Impacts of climate change on electricity demand in China: An empirical estimation based on panel data
2019, Energy
Citation Excerpt :
The results showed that the annual electricity demand would rise by 6% compared to the “no climate change” scenario and the cost might exceed 730 million Euros, which means that the country may need to forego economic growth for one or two years to cope with the additional electricity demand caused by climate change. With the gradual progress in developing countries, scholars have begun to focus on the impact of climate change on electricity demand in developing countries [12,20–23]. For example, Mahmood et al. [22] used Pakistan's monthly temperature data to explore the relationship between electricity demand and temperature and forecasted the impact of climate change on the electricity demand in Pakistan; the results showed that the summer electricity demand would rise sharply because of the climate effect.
Electricity sector is sensitive to climate change. In this study, a fixed-effect regression feedback model is used to estimate the impacts of climatic factors on electricity demand in China by using panel data of 30 provinces from 1995 to 2016. We also forecast the potential impacts of climate change on future electricity demand under three climate change scenarios. The results show that (1) there is a positive effect of the heating degree day (HDD) and cooling degree day (CDD) on the per capita electricity demand. A 1% increase in the CDD will result in a 0.094% increase in per capita electricity demand, while the same rise of HDD will increase per capita electricity demand by 0.061%. In addition, the per capita electricity demand will decrease by 0.017% if the sunshine duration increases 1%, while the effect of rainfall is not significant. (2) The total changes in electricity demand caused by climatic factors by 2100 under the RCP2.6, RCP4.5, and RCP8.5 scenarios will be 69.52 billion kWh, 222.74 billion kWh, and 518.58 billion kWh, representing 1.0%, 3.53%, and 8.53% of the total electricity consumption in China in 2017, respectively. The effect of climate warming on China's electricity demand is apparent.

View all citing articles on Scopus

View full text

Modelling electricity demand using the STAR (Smooth Transition Auto-Regressive) model in Pakistan

Highlights

Abstract

Introduction

Section snippets

Pakistan's electricity sector

Literature review

Modelling framework

Empirical results and discussion

Conclusions and policy implication

Energy Policy

Energy Econ

Energy Econ

Energy Policy

Energy Policy

Energy

Int Rev Econ Financ

Energy Econ

Energy Econ

Energy Econ

Econ Model

Energy

Energy Econ

Energy Policy

Energy

Energy Policy

Energy Policy

Energy Econ

Energy Policy

J Policy Model

Energy Econ

Energy Econ

Energy Policy

Energy Policy

Energy Policy

Appl Energy

Energy Policy

Energy

Energy

Energy