To assess rebound effects, it is useful to first determine whether economic or socio-psychological preconditions are fulfilled. Only if the efficiency increases result in lower production costs or affect consumers’ perception of the final products are rebound effects likely to occur. While these conditions are usually met to some degree (and rebound effects therefore common), the actual cost reduction or change in consumers’ perception may be very minor and the size of the resulting rebound effects too small to be of relevance. What effect sizes are considered relevant must be determined individually within the context of a particular assessment.
Paul et al. (2019) present a framework for assessing rebound effects in agricultural land and soil management (Figure 1). Similar to a flow chart, users can answer “yes” or “no” questions to find out whether the occurrence of different rebound types is likely. Factors determining the size of the rebound effects are given in blue boxes.
There is scientific consensus regarding the existence of rebound effects, but little agreement on their size. Values provided in scientific literature for increases in energy efficiency vary considerably (for an overview of studies, see Huesemann & Huesemann, 2008 and Kolstad et al., 2014). However, while it is difficult to estimate effect sizes, the implicit assumption of zero rebound effects made by studies that choose to ignore them is not supported by scientific evidence (Maxwell et al., 2011). Because rebound effects result from changes in the behaviour of different actors and are based on multiple variables, it is challenging to assess their effect size (particularly in ex-ante assessment). In many cases, only a very rough estimate will be possible. For rebound effects resulting from economic feedbacks, general equilibrium models can help to estimate reactions of market participants. For rebounds due to social-psychological factors, a major research gap exists (Maxwell, 2011). The use of the rebound effect assessment framework (Paul et al., submitted) may facilitate an assessment of rebound effect sizes.
In general, the following factors are considered to be positively related with the size of rebound effects (de Haan et al., 2015):
• Relative size of the efficiency improvement (%)
• Relative share of savings of total production costs (%)
• Degree to which production is limited by availability of the more efficiently used resource (e.g. limited allocation of irrigation water, limited amount of allowable fertiliser application)
• Degree to which the more efficient process can be used to substitute other production factors
• Relative price reductions for the final product (%)
• Degree by which the consumption of the product is perceived as more positive (e.g. social or environmentally friendly) than before
• Demand elasticity, degree to which is demand is currently unsaturated
• Low degree of other costs associated with product consumption (e.g. in the field of mobility, travel-time is often more relevant than travel-cost)
• Degree to which the more efficiently used resource is also used in the production of alternative goods and services (the demand for which might increase with increasing wealth)
• Degree to which the more efficient resource use triggers technical innovation in other sectors
