Underlying Energy Demand Trend Research Articles

Modeling and forecasting industrial electricity demand for Saudi Arabia: Uncovering regional characteristics

This study aims to investigate and forecast Saudi Arabia's industrial electricity consumption at the regional level. By applying structural time series modeling to annual data from 1990 to 2019, we find the electricity demand's long-run income and price elasticities to vary across regions. Also, the underlying energy demand trend analysis indicates efficiency improvements in industrial electricity consumption patterns in all regions. In the low-case scenario, we project the 2030 demand to be 11.6 terawatt-hours (TWh), 63.5 TWh, 0.7 TWh, and 6.8 TWh for the central, eastern, southern, and western operation areas, respectively, with the total demand forecast to be 82.5 TWh. The study's findings can help policymakers understand the effect of future energy policies and economic growth trajectories.

Income and price elasticities of natural gas demand in Pakistan: A disaggregated analysis

The study estimates income and price elasticities of natural gas demand for industrial, commercial, and residential sectors of Pakistan. The study also examines the relationship between natural gas consumption and underlying energy demand trend (UEDT) which follows a stochastic process that is captured by a structural time series modeling approach to the annual data for the period 1972–2019. Empirical estimates show that, the real price of gas, economic activity and the UEDT determine the shape of natural gas demand in Pakistan. The upward slopes of the UEDT in all three sectors indicate that exogenous factors have dominated the energy efficiency improvements in the country due to the technical progress. The more substantial effect of exogenous factors, represented by the estimated UEDT, than the real price of gas in all sectors suggests that policies to curtail natural gas demand through energy efficient appliances will be more useful than price-induced policies.

Carbon emissions and electricity generation modeling in Saudi Arabia.

Fossil fuel electricity generation in Saudi Arabia increased greatly from 1980 to 2017. This paper aims to quantify the electricity generation effect on the environmental quality of Saudi Arabia and explore the role of energy-efficient technological innovation. A structural time series model (STSM) to estimate long-run elasticities and logarithmic mean Divisia index (LMDI) is employed. The results showed that variables (GDP, electricity generation, and population) have a significant effect on carbon dioxide (CO2) emissions. Also, the underlying energy demand trend (UEDT) showed an upward slope for the entire period, which suggests that over the study time there is no improvement in energy efficiency. In decomposing the factors for carbon emissions growth in Saudi Arabia, the findings of applying additive LMDI analysis showed a 1377.56 million tonne (MT) increase in CO2 emissions from the three factors between 1980 and 2017 in the country. The results of additive decomposition showed that the primary factor that drives the carbon emissions growth in Saudi Arabia was the structure effect. Saudi Arabian policymakers could make more informed decisions regarding electricity generation by focusing on increasing energy efficiency and demanding strict environmental regulations to contribute to sustainable economic growth.

Modelling U.S. gasoline demand: A structural time series analysis with asymmetric price responses

This research aims to estimate a gasoline demand function for the U.S. using a stochastic exogenous trend model with asymmetric price responses. It is, as far as is known, the first attempt to model U.S. gasoline demand using this combined approach. The Structural Time Series Model is therefore employed for annual data over the period 1949–2019 allowing for both asymmetric price responses (for technical progress to affect demand endogenously) and an underlying energy demand trend for gasoline (for technical progress and other factors to affect demand exogenously in a linear or non-linear way). It is found that for U.S. per capita gasoline demand, the estimated long-run income elasticity is 0.41, the estimated long-run price-max elasticity is −0.31, the estimated long-run price-recovery elasticity is −0.15, and the estimated long-run price-cut elasticity is −0.14. In addition, the estimated underlying energy demand trend for U.S. per capita gasoline demand is non-linear with periods when it is increasing and periods when it is decreasing.

Impact of climate change on sectoral electricity demand in Turkey

ABSTRACT This paper analyzes the impact of climate change on Turkey’s residential, industrial and commercial electricity demand by applying the Structural Time Series Model. The model consists of explanatory variables and uses annual time series data for the period from 1980 to 2017. The variables included are electricity price, income per capita, urbanization rate, manufacturing added value, population-weighted cooling degree days (as a climate variable) and, finally, stochastic Underlying Energy Demand Trend. The effect of cooling degree days on the residential, industrial and commercial electricity demand is found to be significantly elastic. Moreover, residential, industrial and commercial electricity demands in 2031 are expected to increase considerably, and at different ratios, depending on the chosen climate change scenario. The findings of this study are significant for efforts to establish regulations to shape energy security policies in the presence of climate change.

Estimating the Price and Income Elasticities of Crude Oil Import Demand for Turkey

In this study, the price and income elasticities of Turkey’s imported crude oil demand are analysed. For this purpose, annual time series data covering the period 1970-2018 are preferred for imported crude oil, real price for crude oil and real GDP. As known, Turkey is an energy dependent country especially in fossil fuels. Therefore, estimating Turkey’s crude oil demand equations is very significant to analyse the consumption trend and the future expectations in terms of this energy resource. This study employs Harvey’s Structural Time Series Modelling Method (STSM) with the underlying energy demand trend (UEDT) concept for determining the relations among income, price and crude oil import demand. The empirical results show that the income and price elasticities of crude oil demand in Turkey are 0.66 and -0.11, respectively. The estimated elasticities suggest that income and price elasticities for the imported crude oil demand are inelastic.

Modelling industrial energy demand in Saudi Arabia

Between 1986 and 2016, industrial energy consumption in Saudi Arabia increased by tenfold, making it one of the largest end-use sectors in the Kingdom. Despite its importance, there appear to be no published econometric studies on aggregate industrial energy demand in Saudi Arabia. We model aggregate industrial energy demand in Saudi Arabia using Harvey’s (1989) Structural Time Series Model, showing that it is both price and income inelastic, with estimated long-run elasticities of −0.34 and 0.60, respectively. The estimated underlying energy demand trend suggests improvements in energy efficiency starting from 2010.Applying decomposition analysis to the estimated econometric equation highlights the prominent roles of the activity effect (the growth in industrial value added) and the structure effect (the shift towards energy-intensive production) in driving industrial energy demand growth. Moreover, the decomposition shows how exogenous factors such as energy efficiency helped mitigate some of that growth, delivering cumulative savings of 6.8 million tonnes of oil equivalent (Mtoe) between 2010 and 2016.Saudi Arabia implemented a broad energy price reform program in 2016, which raised electricity, fuel, and water prices for households and industry. The decomposition results reveal that, holding all else constant, higher industrial energy prices in 2016 reduced the sector’s energy consumption by 6.9 %, a decrease of around 3.0 Mtoe. Saudi policymakers could therefore build on the current policy of energy price reform and energy efficiency standards to mitigate the rate of growth of industrial energy consumption, increase economic efficiency, and maintain industrial sector competitiveness.

Modelling and forecasting world oil demand: a regional analysis accounting for asymmetric price responses and technical progress

AbstractThis study applies the structural time‐series modelling technique to investigate the relationship between aggregate oil consumption, income and prices across six regions of the world over the period 1970–2017. Following arguments in the energy economics literature on how to appropriately capture the impact of technical progress (TP) in modelling energy demand, this paper assumes a general model that incorporates asymmetric price responses (to capture endogenous TP) and an Underlying Energy Demand Trend (UEDT) (to capture exogenous TP and other factors) to estimate price and income elasticities for each region. These estimates are then used to produce future forecast scenarios of oil demand for each of the six world regions up to 2040 based on different assumptions about the future path of key variables that drive oil consumption. The results suggest that for the reference‐case scenario, global oil demand is projected to rise from 98 mb/d in 2017 to 118 mb/day in 2040 consisting of strong growth in the Middle East, Africa and Asia Pacific regions while oil consumption in North America, South/Central America and Europe/Eurasia regions is projected decline.

Demand of automotive fuels in Brazil: Underlying energy demand trend and asymmetric price response

This paper analyzes the role of Asymmetric Price Response (APR) and Underlying Energy Demand Trend (UEDT) in the Brazilian automotive fuel demand from June 2001 to December 2016. The demand functions of automotive gasoline, ethanol and compressed natural gas (CNG) were estimated by employing the autoregressive distributed lag (ARDL) model and Harvey's Structural Time Series Model (STSM). The importance of considering a more flexible approach incorporating both UEDT and APR was confirmed by the data. We identified that consumer response to changes in price is not linear. The model also inferred a high substitutability between gasoline and ethanol. Both in the short and the long term, demand for ethanol is more price elastic than demand for gasoline. Empirical analysis suggests that the decision to refuel a vehicle with CNG is not influenced by price variations in ethanol, which indicates that competition only occurs between CNG and gasoline. Therefore, the inclusion of UEDT and APR provides more precise information on the effect of price and income changes on automotive fuel demand. Such information is relevant for establishing public policies, as refinery expansion plannning and CO2 emission mitigation.

Imperatives for Modelling and Forecasting Petroleum Products Demand for Nigeria

Abstract Modelling and forecasting petroleum products demand is a key input for energy use planning and policy formulation. This paper employs the Structural Time Series Model to estimate and forecast demand for two key petroleum products – gasoline and diesel in Nigeria. The STSM can account for structural changes in an economy through the underlying energy demand trend. STSM incorporate stochastic rather than deterministic trend which is more general and therefore argued to be more appropriate in this study. The result suggests that the demand for petroleum products in Nigeria is both price and income inelastic and fall within the range reported in the literature while the underlying demand trends were generally stochastic in nature. The demand models were forecast under three scenarios; reference case, low and high demand. The reference or base scenario describes the future based on current economic and policy environment, that is, without any specific policy shock. Under the reference case scenario, gasoline and diesel demand were forecast to reach 45.3 and 8.03 million litre per day in 2030 representing demand increase of 42% and 66% for gasoline and diesel respectively in the next fifteen years. This presents a significant challenge towards attaining self-sufficiency in meeting Nigeria’s petroleum products demands through local refining. There is therefore urgent need for the government to revamp existing refineries and encourage private investors to build new ones in order to reduce dependence on product imports.

Modelling residential electricity demand in the GCC countries

This paper aims at understanding the drivers of residential electricity demand in the Gulf Cooperation Council countries by applying the structural time series model. In addition to the economic variables of GDP and real electricity prices, the model accounts for population, weather, and a stochastic underlying energy demand trend as a proxy for efficiency and human behaviour. The resulting income and price elasticities are informative for policy makers given the paucity of previous estimates for a region with particular political structures and economies subject to large shocks. In particular, the estimates allow for a sound assessment of the impact of energy-related policies suggesting that if policy makers in the region wish to curtail future residential electricity consumption they would need to improve the efficiency of appliances and increase energy using awareness of consumers, possibly by education and marketing campaigns. Moreover, even if prices were raised the impact on curbing residential electricity growth in the region is likely to be very small given the low estimated price elasticities—unless, that is, prices were raised so high that expenditure on electricity becomes such a large proportion of income that the price elasticities increase (in absolute terms).

Modeling Residential Electricity Demand in the GCC Countries: The Importance of Weather and Exogenous Stochastic Trends

In a world with increased international focus on energy use, comparing energy consumption behavior across countries can inform decision makers about their country’s relative performance and opportunities for future improvement. In particular, during recent decades, understanding the drivers of residential electricity consumption – and by association the intensity and productivity of residential electricity use – has become a considerable dimension for policy-related international cross-country comparisons. However, the contrasts are arguably more meaningful when comparisons are normalized for uncontrollable exogenous factors, weather being a prime example. Given the rapid development of space climate control technologies the interdependency between climate variation and residential electricity consumption has in all probability increased with space heating and cooling representing the largest share of building energy consumption in many countries (Perez-Lombard et al., 2008). Moreover, analyzing the effect of weather on residential electricity demand is of special relevance to the Gulf Cooperation Council (GCC) countries – Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates (UEA) – which, by virtue of being located near the tropics, are characterized by one of the hottest and most arid climates in the world.Furthermore, residential electricity consumption of the GCC countries has been exponentially increasing over recent decades coupled by a steep population growth and economic leap (Squalli, 2007; Reiche 2010). At a time when residential electricity prices have been administered by the state and therefore fixed in nominal terms for a number of years between adjustments. Within this context, this paper attempts to model residential electricity demand for the six GCC countries in order to estimate the GDP, price and population elasticities as well as controlling and quantifying the effect of climate conditions. The model utilized recognizes that electricity is a derived demand based on the demand for energy services such as heating, cooling, and cooking (Hunt and Ryan, 2015). Hence in addition to the key drivers of GDP, prices, population and weather an explicit allowance is made for energy efficiency and other exogenous effects by estimating a stochastic underlying energy demand trend (UEDT), as suggested by Hunt et al. (2003a, b). This paper is divided into five sections as follow: after the introduction, Section 2 discusses the background to the work and relevant previous literature, Section 3 details the methodology adopted, Section 4 discusses the data and estimation results, and Section 5 closes with a summary and conclusion.

Modelling Renewable Energy Economy in Ghana with Autometrics

Renewable energy consumption has been identified as a potential solution to the intermittent power supply in Ghana. Recently, a Renewable Energy Act has been passed which has a target of 10% of renewable energy component in Ghana’s energy mix by 2020. Whilst effort is been made to enhance supply through feed in tariffs, education and tax reduction on renewable energy related equipment, there is the need to understand the drivers of renewable energy demand. In this study, the general unrestricted model through Autometrics is used to estimate the determinants of renewable energy demand in Ghana. The results indicate that both economic factors and non-economic affect the demand for renewable energy. In addition, the underlying energy demand trend exhibits energy using behaviour. The study recommends that economic factors such as consumer subsidies should be considered when promoting renewable energy demand.

Gasoline Demand in Greece: The Importance of Shifts in the Underlying Energy Demand Trend

This paper explores the relative importance of factors other than price and income in explaining gasoline demand in Greece between 1978 and 2008. Using a structural time series model (STSM) the long-run elasticities of income and price are 0.45 and −0.32, respectively. Further, it is shown using the estimated underlying energy demand trend (UEDT) that other exogenous factors have been shifting the gasoline demand curve to the right, thus reflecting more energy-intensive lifestyles in Greece. Given the results, it is contended that the kinds of policies that governments can use to manage gasoline demand and move toward sustainable transportation go beyond the usual price mechanism.

Accounting for asymmetric price responses and underlying energy demand trends in OECD industrial energy demand

This paper explores the way technical progress and improvements in energy efficiency are captured when modelling OECD industrial energy demand. The industrial sectors of the developed world involve a number of different practices and processes utilising a range of different technologies. Consequently, given the derived demand nature of energy, it is vital when modelling industrial energy demand that the impact of technical progress is appropriately captured. However, the energy economics literature does not give a clear guide on how this can be achieved; one strand suggests that technical progress is ‘endogenous’ via asymmetric price responses whereas another strand suggests that it is ‘exogenous’. More recently, it has been suggested that potentially there is a role for both ‘endogenous’ technical progress and ‘exogenous’ technical progress and consequently the general model should be specified accordingly.This paper therefore attempts to model OECD industrial energy demand using annual time series data over the period 1962–2010 for 15 OECD countries. Using the Structural Time Series Model framework, the general specifications allow for both asymmetric price responses (for technical progress to impact endogenously) and an underlying energy demand trend (for technical progress and other factors to impact exogenously, but in a non-linear way). The results show that almost all of the preferred models for OECD industrial energy demand incorporate both a stochastic underlying energy demand trend and asymmetric price responses. This gives estimated long-run income elasticities in the range of 0.34 to 0.96; estimated long-run price-maximum elasticities in the range of −0.06 to −1.22; estimated long-run price-recovery elasticities in the range of 0.00 to −0.27; and estimated long-run price-cut elasticities in the range of 0.00 to −0.18. Furthermore, the analysis suggests that when modelling industrial energy demand there is a place for ‘endogenous’ technical progress and an ‘exogenous’ underlying energy demand trend; consequently, it is argued that, any modelling strategy should start by including both and only imposed restrictions if accepted by the data.

What drives natural gas consumption in Europe? Analysis and projections

Natural gas is an important fuel source for Europe and it is expected to remain so in the near future. The market power of suppliers is an important structural element of the European natural gas market and long-term investment and contracts necessitate reliable projections of natural gas demand. This research attempts to address this need. It investigates the impact of income, real natural gas prices and the underlying energy demand trend (UEDT) on OECD-Europe natural gas consumption by applying the structural time series technique to annual data over the period 1978 to 2011. The results suggest that, in order of importance, income, the UEDT and natural gas prices all play a role in driving OECD-Europe natural gas consumption with the estimated UEDT having both increasing (gas using) and decreasing (gas saving) periods over the estimation period and the estimated long run income and price elasticities being 1.19 and −0.16 respectively. Furthermore, based upon the estimated relationship, OECD-Europe natural gas consumption is predicted to be somewhere between 572 and 646 bcm (about 472 and 533 mtoe) by 2020.

The importance of engaging residential energy customers' hearts and minds

In an attempt to reduce the contribution of residential greenhouse gas emissions the EU has implemented a variety of policy measures. The focus has been to promote domestic energy efficiency and ultimately a reduction in residential energy demand. In this study we estimate residential energy demand using Underlying Energy Demand Trend (UEDT) and Asymmetric Price Responses for 14 European OECD countries between 1978 and 2008. Our results support the conclusion that policies to reduce residential energy consumption and the consequent emissions need to account for behavioural, lifestyle and cultural factors in order to be effective.

Electricity consumption-GDP nexus in Pakistan: A structural time series analysis

This study investigates the relationships among electricity consumption, real economic activity, real price of electricity and the UEDT (underlying energy demand trend) at the aggregate and sectoral levels, namely, for the residential, commercial, industrial, and agricultural sectors. To achieve this goal, an electricity demand function for Pakistan is estimated by applying the structural time series technique to annual data for the period from 1972 to 2012. In addition to identifying the size and significance of the price and income elasticities, this technique also uncovers UEDT for the whole economy as well as for sub-sectors. The results suggest that the nature of the trend is not linear and deterministic but stochastic in form. The UEDT for the electricity usage of the commercial, agricultural and residential sectors shows an upward slope. This upward slope of the UEDT suggests that either energy efficient equipment has not been introduced in these sectors or any energy efficiency improvements due to technical progress is outweighed by other exogenous factors.

Transportation oil demand, consumer preferences and asymmetric prices

PurposeThe aim of this paper is to establish the role of asymmetric price decompositions in UK road transportation fuel demand, make explicit the impact of the underlying energy demand trend, and disaggregate the estimation for gasoline and diesel demand as separate commodities.Design/methodology/approachDynamic UK transport oil demand functions are estimated using the Seemingly Unrelated Structural Time Series Model with decomposed prices to allow for asymmetric price responses.FindingsThe importance of starting with a flexible modelling approach that incorporates both an underlying demand trend and asymmetric price response function is highlighted. Furthermore, these features can lead to different insights and policy implications than might arise from a model without them. As an example, a zero elasticity for a price‐cut is found (for both gasoline and diesel), implying that price reductions do not induce demand for road transportation fuel in the UK.Originality/valueThe paper illustrates the importance of joint modelling of gasoline and diesel demand incorporating both asymmetric price responses and stochastic underlying energy demand trends.

Turkish aggregate electricity demand: An outlook to 2020

This paper investigates the relationship between Turkish aggregate electricity consumption, GDP and electricity prices in order to forecast future Turkish aggregate electricity demand. To achieve this, an aggregate electricity demand function for Turkey is estimated by applying the structural time series technique to annual data over the period 1960 to 2008. The results suggest that GDP, electricity prices and a UEDT (Underlying Energy Demand Trend) are all important drivers of Turkish electricity demand. The estimated income and price elasticities are found to be 0.17 and −0.11 respectively with the estimated UEDT found to be generally upward sloping (electricity using) but at a generally decreasing rate. Based on the estimated equation, and different forecast assumptions, it is predicted that Turkish aggregate electricity demand will be somewhere between 259 TWh and 368 TWh in 2020.