Aug 2017 – Briskee, Deliverables & reports
[D 3.3 Working paper on household appliances]
Behavioural response to invesment risks in energy efficiency - working paper on energy demand modelling for household appliances
Authors: Benjamin Fries, Sibylle Braungardt, Martin Kreuzer (Fraunhofer ISI)
Energy efficiency is one of the main pillars of the European Union's strategy for climate change mitigation. The “energy efficiency first” principle is one of the three main goals of the so-called “winter package” released by the European Commission in November 2016 (European Parliament, 2016). The International Energy Agency has highlighted energy efficiency as “the first fuel” (International Energy Agency, 2014), meaning that energy savings can contribute to climate change more than any other energy technology.
With the residential sector accounting for 27 % of the EU's energy consumption, a key challenge is how policy measures can guide consumers’ investment decisions towards more efficient choices (European Environment Agency, 2015). Residential energy consumption depends largely on consumer decisions and available technologies. In this context, the large cost-effective potentials reflect the so-called energy efficiency gap between the actual uptake of energy efficiency innovations and the economically optimal level (Allcott & Greenstone, 2012; Jaffe, Newell, & Stavins, 2004). The energy efficiency gap describes the observed situation that consumers do not adopt energy efficiency measures even if they are economically favourable for them.
Energy demand modelling is gaining increasing importance in EU energy policy making and policy evaluation. For example, the 2020 and 2030 EU energy efficiency targets are defined with respect to the projections of the EU Reference Scenario (Vita et al., 2016). Furthermore, modelling played a key role when setting the level of ambition in the energy efficiency target. In general, modelling results for future energy and climate policies affect whether more ambitious decarbonisation targets are supported or opposed (Riley, 2015).
Key parameters in energy-economic models are the so called discount rates, used by policy makers in designing and evaluating energy efficiency policies. Discount rates are employed in energy models to capture household investment decisions and include behavioural parameters (Steinbach & Staniaszek, 2015). The role of discount rates in energy models has been discussed controversially, and a need to increase empirical evidence for implementing purchase decisions in energy models has been identified (BRISKEE, 2015).
Independent of the implications for energy modelling, several studies have investigated the factors that influence energy efficiency decision-making, with partly contradictory results across countries and methodological approaches. Besides economic parameters like purchase price or energy costs, there are additional factors influencing the purchase behaviour, such as for example their attitudes towards energy savings, social context and habits (Gaspar & Rui, 2013). A survey by Yamamoto et el. revealed, that consumers may even have little awareness of prices concerning electrical appliances and electricity, but rather made consumer decisions based on particular appliance characteristics (Yamamoto, Suzuki, Fuwa, & Sato, 2008).
Several studies have observed a positive correlation between income and the probability of investing in energy efficient household appliances. Empirical results from a OECD survey suggest, that households’ propensity to invest in EE depends among other factors on its income (Ameli & Brandt, 2015). High-income households are more likely to invest in EE than low-income households (Ameli & Brandt, 2015). A national representative survey of Spanish households concluded, that households belonging to higher income groups are more likely to invest in EE (Ramos et al., 2016).
Some studies across countries have indicated significant gender differences in environmental attitudes, showing that women have higher pro-environmental attitudes than men. Torgler et al. collected data from 33 Western and Eastern European countries and reported of indications that women have a stronger preference towards the environment and a stronger willingness to contribute (Torgler et al., 2008). Eisler et al. found that the attitude towards nature and environment to be perceived less important by males than by females in Germany, Sweden, USA and Japan (Eisler et al., 2003). On the other side, studies have revealed conflicting results about the effect of gender on environmental behaviour. Sardianou estimated the energy conservation patterns of Greek households and did not find that gender affects the choice of energy-conserving actions undertaken (Sardianou, 2007).
Several studies find that people with strong environmental preferences (e.g. environmental identification) are more likely to invest in energy conservation technologies (Ameli & Brandt, 2015; Olli et al., 2001).
The impact of age on households' purchase criteria is widely discussed in the literature. Older household heads may be less likely to adopt energy efficient technologies because the expected rate of return is lower than for households with younger heads. For example, households in Spain with older members are less likely to invest in EE and show fewer eco-friendly habits (Ramos et al., 2016; Torgler & Garcia-Valinas, 2007). A negative correlation between age and environmental preferences was also observed in a study covering 33 Western European countries (Torgler et al., 2008). In contrast, a study about Swedish energy consumers showed, that older generations tend to consume less energy by energy saving behaviours (Carlsson-Kanyama et al., 2005). Younger households may be more likely to move and hence be also less inclined to invest in energy efficiency improvements. On the other hand, younger households tend to prefer up-to-date technology, which is usually also more energy efficient (Carlsson-Kanyama et al., 2005). Some studies suggest that middle-aged people are probably more willing to adopt to energy efficient technologies (Kostakis & Sardianou, 2012; Mills & Schleich, 2012).
Some studies suggest that individuals with children do adopt more likely to energy efficient technologies (Ameli & Brandt, 2015; Michelsen & Madlener, 2012; Nair et al., 2010; Sardianou et al., 2010).
This article presents a cross-country comparison of empirical data of purchase criteria for residential appliances and shows how the findings can be implemented in energy demand modelling. Section 2 presents the methodological approach and results of the survey data analysis. Section 3 outlines how the results are implemented in energy demand modelling. Section 4 presents the results of the energy demand projections. Conclusions are drawn in Section 5.