Models built for strategic European energy policy assessments often make compromises in terms of how they represent human behaviour and decision making, which is a design choice that is usually forced on model designers by a lack of data availability on consumer preferences. As a result, it is often difficult for energy models to depict uptake levels and technology diffusion rates for new consumer technologies in households that are in line with real world observations. As part of the REEEM project, a multinational research team from the United Kingdom (UK), Finland and Croatia took steps to address this critical shortcoming in energy models by carrying out detailed surveys on 3000 European households in their respective countries, and using these to build databases of attitudes, preferences, and lifestyles. Discrete choice modelling, a technique used to understand which factors drive decision making, was used to identify the critical determinants of consumer purchases in domestic heating and privately-owned vehicles. It was found that costs are usually an influential factor when it comes to technology choice, but also that there are a range of other considerations that exert a powerful influence on decision-making.
Message 10: While consumers’ choices for energy technologies are affected by costs, they are not the only, or even the main driver of decisions
The survey data and modelling showed that a range of different factors affect the stated technology choice of individuals (see Table 2 and Table 3). Perhaps most importantly, while costs are often found to be a significant factor, this is not always the case. This demonstrates that policies only providing economic incentives may not always be effective and further underlines the dangers in considering end users as economically optimising agents in models describing an energy system transition.
Message 11: Consumers have a strong preference for the technologies they are familiar with
One factor that was found to be especially influential in determining the technology choices of the respondents was the current ownership of a specific technology. This was often found to be more important than costs of the different alternatives. This suggests that there is significant inertia and “stickiness” in people’s preferences, which makes it more difficult for policy makers to influence their technology choices, especially with economic incentives.
Message 12: While many factors affecting choices are shared across the three countries, there are also clear differences
As Table 2 demonstrates, decisions of the respondents in the three countries are often influenced by similar factors. With that said, there are also a number of decision drivers that are more important in specific countries. For example, decisions are less often based on costs in the UK than in the other two countries. Similarly, respondents in Finland are less concerned about their own control of the heating system than those in the UK and Croatia. Differences like these underline the importance of designing policies for the local circumstances – as the determinants of decisions differ, the required incentives are likely to do so as well.
Message 13. Many factors that may affect decisions in one sector, may not do so in another
The survey focused on assessing stated preferences for two investments: Heating technologies and automobiles. Some factors affecting decisions were linked to the specific use case (e.g. driving frequency), but many were more general socio-demographic elements. These, however, did not necessarily have a similar impact when decisions about automobiles on the one hand and heating technologies on the other were considered. For example, gender affected decisions for heating technology, but not for cars in Finland. Age of the respondent played a role for heating choice in all countries, but in the UK it didn’t do so for the car choice. This finding further emphasises the previous point about the importance of tailored, rather than blanket, policies.