Industrial Electricity Demand for Turkey: A Structural Time Series Analysis

This research investigates the relationship between Turkish industrial electricity consumption, industrial value added and electricity prices in order to forecast future Turkish industrial electricity demand. To achieve this, an industrial electricity demand function for Turkey is estimated by applying the structural time series technique to annual data over the period 1960 to 2008. In addition to identifying the size and significance of the price and industrial value added (output) elasticities, this technique also uncovers the electricity Underlying Energy Demand Trend (UEDT) for the Turkish industrial sector and is, as far as is known, the first attempt to do this. The results suggest that output and real electricity prices and a UEDT all have an important role to play in driving Turkish industrial electricity demand. Consequently, they should all be incorporated when modelling Turkish industrial electricity demand and the estimated UEDT should arguably be considered in future energy policy decisions concerning the Turkish electricity industry. The output and price elasticities are estimated to be 0.15 and -0.16 respectively, with an increasing (but at a decreasing rate) UEDT and based on the estimated equation, and different forecast assumptions, it is predicted that Turkish industrial electricity demand will be somewhere between 97 and 148 TWh by 2020.Turkish Industrial Electricity Demand; Energy Demand Modelling and Forecasting; Structural Time Series Model (STSM); Future Scenarios.

Modelling and Forecasting Turkish Residential Electricity Demand

This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period 1960 to 2008. Household total final consumption expenditure, real energy prices and an underlying energy demand trend are found to be important drivers of residential electricity demand with the estimated short run and the long run total final consumption expenditure elasticities being 0.38 and 1.57 respectively and the estimated short run and long run price elasticities being -0.09 and -0.38 respectively. Moreover, the estimated underlying energy demand trend, (which, as far as is known, has not been investigated before for the Turkish residential sector) should be of some benefit to Turkish decision makers in terms of energy planning. It provides information about the impact of the implementation of past policies, the influence of technical progress, the changes in consumer behaviour and the effects of energy market structure. Furthermore, based on the estimated equation, and different forecast assumptions, it is predicted that Turkish residential electricity consumption will be somewhere between 48 and 80 TWh by 2020 compared to 40 TWh in 2008.Turkish Residential Electricity Demand, Structural Time Series Model (STSM), Future Scenarios, Energy Demand Modelling and Forecasting.

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 an underlying energy demand trend (UEDT) 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.Turkish Turkish Aggregate Electricity Demand; Structural Time Series Model (STSM); Energy Demand Modelling and Future Scenarios.

US Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier Approach

This paper estimates a US ‘frontier’ residential aggregate energy demand function using panel data for 48 ‘states’ over the period 1995 to 2006 using stochastic frontier analysis (SFA). Utilizing an econometric energy demand model, the (in) efficiency of each state is modelled and it is argued that this represents a measure of the inefficient use of residential energy in each state (i.e. ‘waste energy’). This underlying efficiency for the US is therefore observed for each state as well as the relative efficiency across the states. Moreover, the analysis suggests that energy intensity is not necessarily a good indicator of energy efficiency, whereas by controlling for a range of economic and other factors, the measure of energy efficiency obtained via this approach is. This is a novel approach to model residential energy demand and efficiency and it is arguably particularly relevant given current US energy policy discussions related to energy efficiency.Energy demand; US residential energy demand; efficiency and frontier analysis; state energy efficiency.

Modelling Underlying Energy Demand Trends and Stochastic Seasonality: An Econometric Analysis of Transport Oil Demand in the UK and Japan

This paper demonstrates the importance of adequately modelling the Underlying Energy Demand Trend (UEDT) and seasonality when estimating transportation oil demand for the UK and Japan. The structural time series model is therefore employed to allow for a stochastic underlying trend and stochastic seasonals using quarterly data from the early 1970s, for both the UK and Japan. It is found that the stochastic seasonals are preferred to the conventional deterministic dummies and, more importantly, the UEDT is found to be highly nonlinear for both countries, with periods where it is both upward and downward sloping.energy demand, stochastic trend model, unobservable underling trend, seasonality.

What drives the change in UK household energy expenditure and associated CO2 emissions? Implication and forecast to 2020

Given the amount of direct and indirect CO2 emissions attributable to UK households, policy makers need a good understanding of the structure of household energy expenditure and the impact of both economic and non-economic factors when considering policies to reduce future emissions. To help achieve this, the Structural Time Series Model is used here to estimate UK ‘transport’ and ‘housing’ energy expenditure equations for 1964-2009. This allows for the estimation of a stochastic trend to measure the underlying energy expenditure trend and hence capture the non-trivial impact of ‘non-economic factors’ on household ‘transport’ and ‘housing’ energy expenditure; as well as the impact of the traditional ‘economic factors’ of income and price. The estimated equations are used to show that given current expectations, CO2 attributable to ‘transport’ and ‘housing’ expenditures will not fall by 29% (or 40%) in 2020 compared to 1990, and is therefore not consistent with the latest UK total CO2 reduction target. Hence, the message for policy makers is that in addition to economic incentives such as taxes, which might be needed to help restrain future energy expenditure, other policies that attempt to influence lifestyles and behaviours also need to be considered.Household energy expenditure; CO2 emissions; Structural Time Series Model

Quantifying the Impact of Exogenous Non-Economic Factors on UK Transport Oil Demand

This paper attempts to quantify the impact of exogenous non-economic factors on UK transport oil demand (in addition to income, price, and fuel efficiency) by estimating the demand relationship for oil transport for 1960-2007 using the Structural Time Series Model. From this, the relative impact on UK transport oil demand from income, price, and efficiency are quantified. Moreover, the impact of the non-economic factors is also quantified, based on the premise that the estimated stochastic trend represents behavioural responses to changes in socio-economic factors and changes in lifestyles and attitudes. The estimated elasticities for income, price and efficiency are 0.6, -0.1, and -0.3 respectively and it is shown that for efficiency and price the overall contribution is relatively small, whereas the contribution from income and non-economic factors is relatively large. This has important implications for policy makers keen to reduce transport oil consumption and associated emissions, but not willing to reduce the trend rate of economic growth. Taxes and improved efficiency only have a limited impact; hence, a major thrust of policy should perhaps be on educating and informing consumers to persuade them to change their lifestyle and attitudes and thus reduce their consumption through the non-economic instruments route.Transport oil demand; Structural Time Series Model, STSM; Underlying Energy Demand Trend, UEDT; Exogenous Non-Economic Factors, ExNEF.

OECD Energy Demand: Modelling Underlying Energy Demand Trends using the Structural Time Series Model

Aggregate energy demand functions for 17 OECD countries are estimated with data for 1960-2003 using the Structural Time Series Model (STSM) thus allowing for a stochastic Underlying Energy Demand Trend (UEDT). It is found that the estimated long-run income and price elasticities range from 0.5 to 1.5 and -0.1 to -0.4 respectively. Furthermore the stochastic form for the UEDT is preferred for all countries suggesting a wide variation in the exogenous effects of energy saving technical progress in addition to other pertinent exogenous factors such as economic structure, consumer preferences, and socio-economic influences.OECD Energy Demand, Modelling, Underlying Stochastic Trends

Modelling OECD Industrial Energy Demand: Asymmetric Price Responses and Energy – Saving Technical Change

The industrial sector embodies a multifaceted production process consequently modelling the ‘derived demand’ for energy is a complex issue; made all the more difficult by the need to capture the effect of technical progress of the capital stock. This paper is an exercise in econometric modelling of industrial energy demand using panel data for 15 OECD countries over the period 1962 – 2003 exploring the issue of energy-saving technical change and asymmetric price responses. Although difficult to determine precisely, it is tentatively concluded that the preferred specification for OECD industrial energy demand incorporates asymmetric price responses but not exogenous energysaving technical change.OECD Industrial energy demand; Asymmetry; Energy-saving technical change; Modelling

Electricity Demand for Sri Lanka: A Time Series Analysis

This study estimates electricity demand functions for Sri Lanka using six econometric techniques. It shows that the preferred specifications differ somewhat and there is a wide range in the long-run price and income elasticities with the estimated long-run income elasticity ranging from 1.0 to 2.0 and the long run price elasticity from 0 to –0.06. There is also a wide range of estimates of the speed with which consumers would adjust to any disequilibrium, although the estimated impact income elasticities tended to be more in agreement ranging from 1.8 to 2.0. Furthermore, the estimated effect of the underlying energy demand trend varies between the different techniques; ranging from being positive to zero to predominantly negative. Despite these differences the forecasts generated from the six models up until 2025 do not differ significantly. Thus on one hand it is encouraging that the Sri Lanka electricity authorities can have some faith in econometrically estimated models used for forecasting. However, by the end of the forecast period in 2025 there is a variation of around 452MW in the base forecast peak demand; which, in relative terms for a small electricity generation system like Sri Lanka’s, represents a considerable difference.Developing Countries, Electricity Demand Estimation, Sri Lanka