Assessing the Rebound Effect in Energy Intensive Industries: A Factor Demand Model Approach with Asymmetric Price Response

Abstract

The purpose of this paper is to analyze the direct rebound effect potentially prevailing in energy intense industries. The rebound effect represents economic mechanisms that will offset energy savings from energy efficiency improvements. For this purpose, a factor demand model is applied incorporating an asymmetric energy price response. Asymmetric prices imply that firms respond more strongly to energy price increases than to energy price decreases. In the empirical model we use a firm level, unbalanced panel covering the years 2001 to 2012 and four major Swedish energy-intensive industries; pulp and paper, iron and steel, chemical, and mining. The result indicates that the rebound effect is considerable in these industries. To mitigate this effect, the results suggest that policies stimulating an increase in energy efficiency should be combined with a raise in energy taxes.

A Dynamic Analysis of Industrial Energy Efficiency and the Rebound Effect

“…Amjadi et al (2018) used a stochastic energy demand frontier model to estimate fuel and electricity rebound effects using a firm-level panel dataset for the period 2000-2008, finding that the average fuel rebound effect was 58-65%, while the average electricity rebound effect was 76-86%. Dahlqvist et al (2020) also estimated electricity and fuel rebound effects using a factor demand model approach and a firm-level dataset. Their estimates of electricity rebound effects showed a backfire response, while the fuel rebound effect were 24-80% across the four energy intensive sectors.…”

“…Their estimates of electricity rebound effects showed a backfire response, while the fuel rebound effect were 24-80% across the four energy intensive sectors. Methodological differences between these two studies mean that their results are complementary -Amjadi et al 2018focused on movement towards the energy efficiency frontier, while Dahlqvist et al (2020) were looking at energy-related technological changes that were moving the frontier itself.…”

“…Each of these methods have limitations and drawbacks. For instance, one group of studies estimated the rebound effect indirectly through estimating price elasticities of energy demand (e.g., Bentzen, 2004;Saunders, 2013, Dahlqvist et al, 2020. These estimates provided proxies for the rebound effect but were potentially biased for two reasons (Sorrell et al, 2009).…”

“…Using such elasticities potentially overestimates the size of the energy rebound effect, because energy demand responds more strongly to price increases than price decreases (see e.g. Bentzen, 2004;Dahlqvist et al, 2020). Second, as opposed to energy price changes (which in most cases are exogenous), behavioral responses to EEI are driven endogenously by investments to replace 2 For instance, there is a target to reduce global energy intensity, i.e.…”