MEASURING THE EFFICIENCY OF ENERGY-INTENSIVE INDUSTRIES ACROSS 23 EU COUNTRIES
- LanguageENG
- URLhttp://db.koreascholar.com/Article/Detail/271941
pp.1225-1226
Energy demand is growing significantly in most countries and is expected to continue to expand—perhaps by 45% between now and 2030, and by more than 300% by the end of the century (Brown & Sovacool, 2012). Industry is generally the largest consumer of energy, currently consuming about 37% of the world’s total delivered energy, and the highest in energy-related CO2 emissions among the major sectors of energy use in an economy. Sadly enough, large amounts of energy consumed by industry are used inefficiently because of lack of awareness about proper energy management and weak energy policies and measures, among others. As a result, the industrial development across the world results in more energy use and leads to more concentration of greenhouse gases emissions. Hence, finding ways to increase energy efficiency in the industrial sector is highly important because the global climate and the region’s energy security depend on it. In this paper the efficiency trends of seven energy-intensive industries namely manufacturing, chemicals, electricity-gas and water supply, construction, mining and quarrying, machinery, and transport in 23 EU countries over the period 2000–2009 is analysed. The performance of the sectors is evaluated in terms of an input/output production framework described by capital stock, employment, total energy consumption, value added, and GHG emissions. On the methodological side, we use the Data Envelopment Analysis (DEA) to measure the relative efficiency of each industrial sector. DEA is a popular nonparametric efficiency analysis technique with many applications energy efficiency assessment (Sarica & Ilhan, 2007; Mukherjee, 2008; Azadeh, Amalnic, Ghaderi, & Asadzadeh, 2007). Given the panel nature of the considered data set, the Malmquist Productivity Index (MPI) is used to assess the trends in energy efficiency over time and to distinguish between the effect of efficiency change and technical change. At the second stage of our analysis, we focus on the analysis of the relationship between the energy efficiency estimates and a set of explanatory factors related to the structural characteristics of considered sectors and the countries. For most sectors MPI has been higher than 1 in most years, thus indicating an improving trend. This trend appears to be stronger in chemicals, electricity, machinery, and mining. In fact, electricity and mining have improved steadily since 2003-04. On the other hand, construction and transport exhibit fluctuations, but in most cases their MPI has been lower than 1. The observed efficiency changes reflected in the MPI could be the result of changes in technical efficiency (efficiency change) and/or in the underlying production technology (technology change). It is evident that most sectors have been driven by technology change. Overall it is apparent that improvements due to efficiency change have been modest at best (e.g., no more than 5-10%), whereas improvements due to changes in the best practices (technology factor) have been significant in most of the sectors. This study’s results not only provide a general evaluation of the investigated industries, but also facilitate various interesting efficiency comparisons, with respect to factors that have the highest explanatory power. Taking into account the results of this study, policy makers could identify the main steps that should be followed to improve each industry’s energy efficiency. Furthermore, the significance of each step can be measured, leading to more informed decisions in terms of priorities given.