Conference Agenda

Transitioning to clean energy is necessary to meet the climate targets of the Paris Agreement. Accelerating decarbonisation requires improving energy efficiency and making large-scale green energy investments, inter alia in residential homes. Household energy behaviours and investment decisions are mostly suboptimal as individuals often face significant psychological barriers and are subjected to various cognitive biases. Consequently, one-size-fits-all interventions, that are aimed at fostering green energy behaviours, lead to information overload and rebound effects, thereby being inefficient. A growing proposition in behavioural sciences is to personalise the delivery of behavioural interventions (BIs) to facilitate the uptake of energy-efficient behaviours. This is typically done, for example, by tailoring different BIs to individuals to overcome individual biases in the adoption of green appliances and renovations. Nonetheless, there is no clear know-how to use different statistical methods to tailor BIs. While researchers rely on various techniques to customise BIs for specific groups, this segmentation process lacks coherence overall. In this paper, we systematically review and sort the literature on statistical classification and clustering models, including machine learning methods, that have been used to optimize behavioural interventions for improving residential energy efficiency. Our review provides a holistic overview of these different methods, along with clear recommendations for practitioners to use them. It further highlights the role that machine learning algorithms can play in automating BIs, for example, by using sophisticated data analysis and pattern recognition to identify intricate relationships between decision-making factors, leading to highly optimised personalised strategies for increased energy efficiency.

People differ in their underlying economic preferences and needs for energy retrofits. Accelerating the energy transition, therefore, requires tailoring personalised solutions for distinct groups of individuals. In this paper, we create behavioural phenotypes of green energy investors in the residential sector of the Netherlands. Using a latent class analysis on a representative sample of 2,245 respondents, we identify four distinct classes of investors: Comfort-driven Rationalists, Financially Driven Rationalists, Policy-driven Environmentalists, and Erratic Choosers. We innovate upon the literature by linking class profiling to economic preferences and behavioural biases, alongside socio-demographic and household characteristics. Our findings can help practitioners design bottom-up tailored behavioural interventions to accelerate the uptake of green energy investments.

Household retrofit decisions are shaped by economic constraints and behavioural frictions. Using a pre-registered discrete choice experiment with a nationally representative sample of 2,200 Dutch households, we (1) estimate willingness to pay for six key attributes of energy retrofit programmes—upfront cost, municipal energy advisory services, CO₂ savings, payback time, comfort and disruptiveness—and (2) examine how four behavioural mechanisms—risk aversion, loss aversion, status quo bias and present bias—shape preferences over these attributes. We find that Dutch households are willing to pay a premium for improved comfort (ca. €4,800), enhanced municipal energy advisory services (ca. €2,100), and higher CO₂ savings (ca. €2,600). By contrast, they require substantial compensation for long payback times (ca. €7,500) and more disruptive retrofit works (ca. €1,100). These preferences are significantly moderated by individual behavioural biases: risk aversion and present bias are associated with lower willingness to pay for higher upfront costs, loss aversion is associated with stronger aversion to longer payback times, and status quo bias is associated with a stronger preference for municipal energy advisory services. Our findings underscore the importance of designing short-term, low-hassle retrofit offers that combine low-risk financial support with credible guarantees and low-disruption delivery models to accelerate the residential energy transition.

We study whether “persona”-targeted letters increase household engagement with energy renovation programmes. Using nationally representative discrete choice data, we train a random forest to predict three personas: financially driven, policy driven, and comfort driven, from open data. These predictions determine framings of municipal letters sent within the National Insulation Programme. In a field experiment ( N=2,460 ), we randomize households into a control and two treatment groups with untargeted vs targeted messages. We find no statistically significant effect of treatment. However, we observe heterogeneity across framings, with policy- and finance-oriented messages performing relatively better. Ongoing scale-up will enable address-level analysis and longer-run outcomes.