Frontier Analysis of UK Distribution Networks and the Question of Mergers: A Critique of Ofgem

Since privatization, the 14 UK electricity distribution network operators (DNOs), being natural monopolies, have been subject to RPI-X regulation by the UK regulator (Ofgem). Mergers between the 14 DNOs have formed 7 identifiable ownerships (management teams). It is argued in this research that Ofgem has not used a sufficiently robust approach to benchmarking, and has therefore failed to accurately assess network efficiency gains. Furthermore, Ofgem has used invalid arguments against further mergers. By using more informative panel datasets, as well as a more robust estimation technique (Stochastic Frontier Analysis), this research reveals two crucial facts. Firstly, there is almost no more room for the DNOs in question to become more cost efficient, as the industry is operating close to minimum efficient scale. This suggests that Ofgem needs to widen its scope of benchmarking and regulation (e.g. quality-incorporated benchmarking). Secondly, there seems to be no increasing returns to scale in the industry, a more appropriate reason why further mergers should not take place.Ofgem, Frontier Analysis, Mergers.

Gasoline Demand, Pricing Policy and Social Welfare in Iran

This study estimates a gasoline demand function for Iran using the structural time series model over the period 1968-2002 and uses it to estimate the change in social welfare for 2003 and 2004 of a higher gasoline price policy. It is found that short and long run demand price elasticities are inelastic, although the response is greater in the long run. Hence, social welfare is estimated to fall because of the higher gasoline price (ceteris paribus). However, allowing all variables in the model to change, social welfare is estimated to increase since the changes in the other variables more than compensate for the negative effects of the policy.

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

Asymmetric Price Responses and the Underlying Energy Demand Trend: Are they Substitutes or Complements? Evidence from Modelling OECD Aggregate Energy Demand

A number of energy demand studies have considered the importance of modelling Asymmetric Price Responses (APR), for example, the often-cited work of Gately and Huntington (2002). Griffin and Schulman (2005) questioned the asymmetric approach arguing that this is only capturing energy saving technical progress. Huntington (2006), however, showed that for whole economy aggregate energy and oil demand there is a role statistically for both APR and exogenous energy saving technical change. In a separate strand of the literature the idea of the Underlying Energy Demand Trend (UEDT) has been developed, see for example Hunt et al. (2003a and 2003b) and Dimitropoulos et al. (2005). They argue that it is important, in time series energy demand models, to allow for stochastic trends (or UEDTs) based upon the structural time series/dynamic regression methodology recommended by Harvey (1989, 1997). This paper attempts to bring these strands of the literature together by conducting tests for the UEDT and APR in energy demand models within both a panel context (consistent with the Huntington, 2006 approach) and the structural time series modelling framework. A set of tests across a range of specifications using time-series and panel data are therefore undertaken in order to ascertain whether energy saving technical change (or the more general UEDT) and APR are substitutes for each other when modelling energy demand or whether they are actually picking up different influences and are therefore complements. Using annual whole economy data for 17 OECD countries over the period 1960 – 2004 the results suggest that in general the UEDT and ARP are complementary estimation methodologies when modelling aggregate energy demand. It is argued therefore that energy demand modellers should not assume at the outset that one method is superior to the other. Moreover, wherever possible, a general model (be it in a time series or panel context) that includes a ‘non linear UEDT’ and APR should be initially estimated, and only if accepted by the data should symmetry and/or a more restrictive UEDT be imposed.Energy Demand, OECD, Asymmetric Price Responses, Underlying Energy Demand Trend.

Food Superstores, Food Deserts and Traffic Generation in the UK: A Semi-Parametric Regression Approach

This study contributes another route towards explaining and tackling ‘food desert’ effects. It features the estimation of a (semi-parametric) trip attraction model for food superstores in the UK using a composite dataset. The data comprises information from the UK Census of Population, the NOMIS (National Online Manpower Information System) archive and traffic and site-specific data from the TRICS (Trip Rate Information Computer System) databases. The results indicate that traffic to a given food superstore, ceteris paribus, increases with household car ownership, store parking provision, site size (floor space), and distance to the nearest competitor. Furthermore, increases in public transport provision are shown to be associated with increasing car trips. This latter effect is discussed in the light of planning policy for development control purposes and a role linked to the reinforcement of ‘food deserts’. The results also reveal activity-specific household economies of scope and scale. It is suggested how these may also further perpetuate unsustainable development and ‘food desert’ characteristics.Traffic Generation, Food Superstores, Food Deserts, Activity Based Travel, Sustainable Development, Modelling

Optimal sliding scale regulation: An application to regional electricity distribution in England and Wales

This paper examines optimal price (i.e. ‘sliding scale’) regulation of a monopoly when productivity and managerial effort are not observed. We show how to operationalise this model of incentive regulation and use actual data from electricity distribution in England and Wales to make welfare comparisons of sliding scale regulation with a stylised price cap regime and the First-Best (the full information case). Our method enables us to quantify technical uncertainty as faced by the electricity regulator in the 1990s and shows that there are significant welfare gains from a sliding scale relative to the stylised price cap regime.Sliding scale, regulation, electricity distribution

Estimating Underlying Energy Demand Trends using UK Annual Data

Employing the Structural Time Series Model (STSM) approach suggested by Harvey (1989, 1997), and based on annual data for the UK from 1967-2002, this paper reiterates the importance of using a stochastic rather than a linear deterministic trend formulation when estimating energy demand models, a practice originally established by Hunt et al. (2003a,b) using quarterly UK data. The findings confirm that important non-linear and stochastic trends are present as a result of technical change and other exogenous factors driving demand, and that a failure to account for these trends will lead to biased estimates of the long-run price and income elasticities. The study also establishes that, provided these effects are allowed for, the estimated long-run elasticities are robust to the different data frequencies used in the modelling.Energy Demand, Underlying Trends.

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.

To what extent can non-price/income instruments influence the demand for energy?

The demand for energy is not simply a function of price and income, but can be shown also to be a function also of the underlying energy demand trend (UEDT). The UEDT captures behavioural responses to non-fiscal instruments, including technological change, but also encapsulating attitudinal responses/changes in demand that might result for instance from increased public awareness of how environmentally damaging energy use can be, hence reflecting underlying consumer preferences. This study estimates a longitudinal econometric model for the aggregate demand functions of a sample of 17 OECD countries for the period 1960-2005. This approach to modelling will enable UEDT’s to be observed for each of the countries, as well as the normal price and income elasticities. The model results will provide an indication of the extent to which price/income based instruments can be used to reduce the demand for energy, as well as indicating the extent to which consumers have responded to non-price/income instruments.OECD Aggregate energy demand; Asymmetry; Exogenous non-economic factors.

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