Energy efficiency improvement (EEI) benefits the climate and matters for energy security. The potential emission and energy savings due to EEI may however not fully materialize due to the rebound effect. In this study, we measure the size of rebound effect for the two energy types fuel and electricity within the four most energy intensive sectors in Sweden – pulp and paper, basic iron and steel, chemical, and mining. We use a detailed firm-level panel data set for the period 2000-2008 and apply Stochastic Frontier Analysis (SFA) for measuring the rebound effect. We find that both fuel and electricity rebound effects do not fully offset the potential for energy and emission savings. Furthermore, we find CO2 intensity and fuel and electricity share as the two main determinants of rebound effect in Swedish heavy industry. Our results seems to imply that it matters both to what extent and where to promote EEI, as the rebound effect varies between sectors as well as between firms within sectors.
This paper estimates firm level energy efficiency and its determinants in 14 sectors of Swedish manufacturing by using stochastic frontier analysis (SFA). We derive energy demand frontiers both from cost minimizing and profit maximizing perspectives. To account for firms’ heterogeneity, Greene’s true random effects model is adopted. Results show that, from both firm behavior perspectives, there is room to improve energy efficiency in all sectors of Swedish manufacturing. The EU ETS seem to have had a moderate or no effect on Swedish firms’ efficient use of energy. Moreover, we found that energy intensity or energy productivity (energy use over production value) is not an appropriate proxy for energy efficiency.
This paper assesses energy efficiency in Swedish industry. Using unique firm-level panel data covering the years 2001–2008, the efficiency estimates are obtained for firms in 14 industrial sectors by using data envelopment analysis (DEA). The analysis accounts for multi-output technologies where undesirable outputs are produced alongside with the desirable output. The results show that there was potential to improve energy efficiency in all the sectors and relatively large energy inefficiencies existed in small energy-use industries in the sample period. Also, we assess how the EU ETS, the carbon dioxide (CO2) tax and the energy tax affect energy efficiency by conducting a second-stage regression analysis. To obtain consistent estimates for the regression model, we apply a modified, input-oriented version of the double bootstrap procedure of Simar and Wilson (2007). The results of the regression analysis reveal that the EU ETS and the CO2 tax did not have significant influences on energy efficiency in the sample period. However, the energy tax had a positive relation with the energy efficiency.