Fuel Demand across UK Industrial Subsectors.

• Author: Agnolucci, Paolo
• Position: Report

1. INTRODUCTION

   A considerable amount of energy is used by the industrial sector across the world, yet econometric studies of industrial energy demand are surprisingly scarce, as argued in Bernstein and Madlener (2015). Following Pesaran et al. (1998), who advocated estimation of energy demand functions on a set of consumers that is as homogeneous as possible, the aim of this paper is to explore the possible advantages of adopting a cointegration estimation approach at a disaggregated level by making use of a standard dataset, collected by most national offices for statistics across the world (Eurostat, 2018).

   We implement an approach introduced by Pesaran et al (1998) for a number of Asian countries using an annual dataset spanning 17 years, and, as far as we are aware, not implemented in the literature ever since, although a similar approach has been adopted in Moller (2017) discussed below. Our approach estimates fuel demand by taking advantage of the dynamic specifications typical of cointegration studies and the system-wide approaches, which are typical of studies based on the translog function. It implies modelling the demand for different fuels as shares in a cointegrating VAR system with as many cointegrating vectors as the number of fuels being modelled, each representing the long-run demand for a specific fuel. Our approach presents a number of important advantages and can be implemented by using standard software packages and readily available datasets. Firstly, a system approach enables us to model the simultaneous determination of demand for different fossil fuels within a consistent framework. Secondly, a VAR setting allows us to exploit the cross-equation restrictions implied by the VECM representation, which offer a useful means to reduce the number of parameters to estimate. Finally, additional gains in terms of degrees of freedom are ensured by the fact that we model shares rather than fuel intensities (like in Moller, 2017). This enables us to drop one demand equation from the system as fuel shares sum up to one.

   We implement this methodological innovation for a number of UK industrial subsectors using data for the time period between 1990 and 2014. Fuel consumption in the industrial sector as a whole has received considerable attention in the literature, as testified in the reviews mentioned below, but estimation at the subsectorial level is surprisingly scarce. Bernstein and Madlener (2015) is the first study that makes use of subsectorial industrial data to estimate electricity demand elasticities in a cointegrating framework. This study progresses that line of enquiry by estimating demand for multiple fuels simultaneously, rather than for electricity on its own. By delivering this methodological innovation at a level of disaggregation largely unexplored in the literature, we are aiming to cast light on the existing lack of agreement on the magnitude of the elasticities associated with demand for energy fuels.

   In addition, estimating the long-run equilibrium relationship between energy consumption and its main determinants enables us to investigate a number of key questions related to: 1) the dynamic impact of price changes and the presence of scale effects on the demand for energy fuels; and 2) the level of heterogeneity across industrial subsectors which are normally aggregated in typical empirical studies. (1) Our main result, the emergence of substantial differences in the systematic behaviour of firms across subsectors, provides a note of caution to authors imposing homogeneity in the fuel demands across subsectors, those estimating fuel share elasticities for the industrial sector as a whole or focusing on energy consumption rather than fuel consumption. In addition, we find that price elasticities in the UK industrial sector are larger than many previous estimates in the literature, and we confirm that gas consumption is more sensitive to price variations than electricity consumption.

   These conclusions are important not only from a modelling perspective, in a way which we would expect to be replicated for other countries, but also for policy-making purposes. As a matter of fact, the elasticities we present in this paper represent key information for policies that rely on price signals, e.g. the EU ETS and British policies such as the Climate Change Levy and the Carbon Reduction Commitment (2), to achieve fuel substitution in a way which helps steering the economy towards decarbonisation. Such considerations arise as a direct consequence of our work with policy makers, given that the initial motivation for the analysis reported in this paper was the development of the new industrial energy demand model currently adopted by the UK government Department of Business, Energy and Industrial Strategy (BEIS), as part of their wider Energy Demand Model.

   The structure of the paper is as follows. In Section 2 we discuss the existing literature and assess its main conclusions. After describing our methodological approach in Section 3, we discuss the data in Section 4, and in Section 5 we present our results in relation to unit root tests, cointegration analysis and estimation of fuel demand equations. Our findings are discussed in Section 6, while Section 7 offers concluding remarks.

2. LITERATURE REVIEW

   Demand for fossil fuels has been a topic of interest in energy economics for considerable time, with perhaps the policy motivations shifting from concerns about energy security and therefore substitution away from oil, to climate change and therefore substitution away from C[O.sub.2] intensive fuels (Bardazzi et al. a 2016; Stern 2012; [Angstrom]hman et al. 2016). Reflecting the policy relevance of this topic, the literature is large, with several published reviews which have surveyed the state of the work at different points in time - Bohi (1981), Bohi and Zimmerman (1984), Dahl (1993), Dahl (2011), Espey and Espey (2004), Stern (2012), Taylor (1975) and Taylor (1977). (3) There is, however, little consensus about the exact magnitude of the elasticities for the demand of energy fuels (Bhattacharya, 1996). As an example, the interquartile range of long-run price elasticities for electricity in the studies surveyed by in Dahl (2011) covers values from -0.82 to -0.10. Heterogeneity of consumers modelled in applied studies, consequent aggregation bias and adopted methodologies are all factors which are recognized to have an impact on estimated elasticities. These are discussed in turn in the remainder of this section.

   The topic of heterogeneity has gained importance in energy economics, as testified by several contributions taking into account the impact of this factor, for example when modelling energy efficiency (Burnett and Madariaga, 2018), and the rebound effect (Frondel et al., 2012). As for estimation of energy demand, researchers have either adopted an econometric approach allowing for heterogeneity, e.g. through heterogeneous panel (Agnolucci, 2009) and shrinkage estimators (Andersen et al., 2011), or assumed a specific multilevel structure (Sharimakin et al., 2018). (4) Using microdata is becoming a popular choice in the energy literature in order to account for heterogeneity, although the extent to which this is an effective strategy depends on the size of the sample in the survey. Traditional longitudinal data surveys might be helpful in dealing with heterogeneity, but only when a considerable number of annual observations are collected so that one is not confined to using traditional homogenous estimators. On the other hand, the growing availability of high-frequency datasets opens the possibility of adopting data-intensive approaches, such as machine-learning approaches (Barassi and Zhao, 2018), directly quantifying the heterogeneity across economic actors and econometric model structures.

   Aggregation of parameters over micro units, which is related to heterogeneity within the sample is a widely explored topic in econometrics (Lee et al., 1990). (5) The existence and the importance of the "aggregation bias", defined as the deviation of the macro parameters from the average of the corresponding micro parameters, depend on a number of factors such as the type of aggregation, the functional relationship, and the extent and type of heterogeneity (Blundell and Stoker, 2005). (6) Whether or not aggregation bias is a problem is ultimately an empirical question, as the impact of aggregation can vary across parameters of a specific model. (7) In the energy field, it is generally accepted that own price elasticities are smaller (in absolute value) and income elasticities larger when using aggregate data (Bohi, 1981 and Bohi and Zimmermann, 1984). Stern (2012) confirms that the magnitude of elasticities of substitution of energy as a production input tends to decrease with increasing levels of data aggregation.

   The impact of adopted methodologies on estimated elasticities is also widely acknowledged in the literature. In the case of elasticities of substitution, estimates from cross-sectional regressions are generally largest, those from time-series smallest and those from fixed effects panel models lie somewhere in the middle (Stern, 2012). Studies adopting a single equation approach when modelling the demand for a specific fuel normally estimate cross-price elasticities by inserting the price of at least one alternative fuel in the equation. Some arbitrariness is intrinsically part of this approach, especially with regard to the choice of the additional fuel price. Comparison is complicated by the fact that different additional fuels are chosen in different studies for different countries. In Andersen et al. (2011), the prices of all other fuels are taken into account when modelling demand for gas in the industrial sector, while in several other instances no price of any other fuel is used. This assumes no substitution or complementarity across fuels, an assumption in evident...