Nine Thousand Europeans Reveal Eight Distinct Energy Citizen Personas

A survey of 9,414 people across 16 European countries has been distilled into eight distinct “energy citizen personas,” each representing a statistically separate group of people who differ in what they know about the energy transition, what they are willing to do about it, and what kind of information they need to act. The study, published open access in Energy Informatics, used a Research in the Wild approach from human-computer interaction research to turn raw survey data into design-ready profiles that researchers say can guide the development of energy interfaces, services and local policies.

Energy citizenship describes the participation of individuals and communities in energy ecosystems, whether by adopting green energy solutions, being energy efficient, or forming and joining energy communities. Policymakers view positive and active manifestations of this citizenship as a vital part of the green transition, giving everyone a role in achieving climate targets. But the authors note that not everyone has the same circumstances, willingness or capacity to act. Someone in an apartment block may have no control over installing solar panels yet could agree with other tenants to enroll the building in a demand response scheme, while two low-income households with identical solar potential differ sharply if only one has access to a municipal grant covering the initial outlay.

The research team, led by Ajesh Kumar and colleagues, began with proto-personas, hypothetical profiles created through brainstorming rather than user research. Six of these were positioned on a ladder of participation that conceptualizes energy citizens as progressing from unawareness of energy issues to awareness, activity or advocacy. The proto-personas were tested in an in-situ workshop with 17 participants drawn from six different energy communities across Europe, held at a community-owned renewable biofuel-based power plant. Working in six groups, participants searched through more than 50 types of energy data cards, covering categories such as electric vehicles, solar, heating, smart meters, building informatics and demographics, and matched them to personas to design energy interface scenarios. Most participants found they could conveniently identify information needs this way, validating the approach before the team moved to a broader, data-driven stage.

The survey itself contained 104 questions grouped into six themes, designed by a multidisciplinary team spanning energy and electrical engineering, computer science and social science. It was run online by a professional survey company selected through an open tender, remained available for one month, and was translated into local languages. Respondents were residents of Austria, Belgium, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, the Netherlands, Poland, Portugal, Romania and Spain, chosen to give a geographical spread across Northern, Southern, Eastern and Western Europe. The sample was checked against 2021 Eurostat data, with age and gender quotas set for each country so that it was representative of the adult population in terms of those two dimensions. Participation was voluntary, with incentives provided by the third-party vendor after completion.

To convert the responses into groups, the researchers used ClicoT, a clustering method for mixed-type data that follows the minimum description length principle. It starts with a single cluster and iteratively splits the costliest cluster into two until the total cost, defined as the sum of coding costs for numerical and categorical features plus the model encoding cost, cannot be improved. A key feature is its use of concept trees that preserve semantic relationships, so that, for example, solar PV and wind remain related under the category of renewables, which the authors say acts as a guardrail preventing arbitrary or noise-driven partitions. Before clustering, the data was pre-processed into 163 features, 59 numerical and 104 categorical, from an original set of 101 numerical and 474 categorical variables. Demographic and background information such as age, income, gender, family composition and occupation was deliberately excluded from the clustering parameters to prevent bias, then reintroduced later as explanatory factors.

The clustering produced eight interpretable clusters, which a six-member expert panel then contextualized over three iterations into personas. The panel analyzed 62 cluster snapshots, visualizations of responses to 62 selected questions covering aspects such as background, relationship with energy and environment, attitude, proficiency, policy context, information sources and trust, emotion, personal experience and satisfaction, using color-coded notes on a shared board to record the most prominent attributes of each cluster. Each final profile includes a label, a position on the ladder of participation, a summary description with potential motivators, persona-specific statements and aspirations, and information guidelines for designing intuitive interfaces.

The eight personas span the ladder from unaware to advocate and differ along five dimensions: motivation, whether environmental concern, social belonging or financial reasoning; resource constraints such as income, tenure and technological access; perception of agency, meaning belief about whether individual action matters; trust and information practices; and energy and digital literacy. Among them, the Energy Practical and Energy Advocate personas are the most involved in energy transition activities and both are likely to advocate for climate topics in their social circles, though Energy Practical personas think more frugally about investments given their economic situation. The Energy Indifferent persona is typically unaware of the harmful impacts of fossil fuel-based energy consumption and believes individual contribution is insignificant, whereas the Energy Skeptic is aware of environmental issues but appears deterred by a lack of trust in both official and unofficial information sources.

Other contrasts are telling. Energy Constrained and Energy Mindful both sit at the aware level, but Energy Constrained is the largest cluster with the greatest share of older people, constrained by circumstances from participating, while Energy Mindful is the smallest cluster with the most younger respondents, who may lack independence or resources now but whose circumstances have more potential to change over time. Energy Social and Energy Investor are both active without necessarily advocating to others: Energy Social personas participate because they enjoy the social aspects of green action and give more time to initiatives, while Energy Investor personas care about the environment but prefer to contribute through investments. Geographically, Energy Investor personas are most prominent in Southern European countries such as Portugal and Spain, which receive more sunlight and saw more energy transition investment, and less common in Finland, where northern latitudes limit the scope for solar energy.

The authors are careful about scope. The survey was conducted only in a European context and does not reflect energy citizenship beyond these geographical boundaries, so the personas should not be used to inform global policy; new research would be needed to extend their applicability. The survey was also written to identify positive actions people take or intend to take toward the green transition, so no cluster mapped to the active-negative part of the scale, meaning personas representing opposition, such as people framed as NIMBYists who resist wind farms for aesthetic, visual or ecological reasons, are not captured. The team argues such personas may be particularly important for revealing conflicts between the positive actions of one group and the negative actions of another. They also note the personas should remain valid even as individuals move between levels of participation, since the profiles capture information needs rather than fixed populations.

The personas are framed as design-led tools rather than marketing segments, intended for the pre-design phase to elicit energy information needs rather than to target customers after acquisition. The authors suggest applications ranging from co-creation workshops for Positive Energy Districts, where the personas have already been tested in two settings as gamified characters, to communication of energy information, gamified energy transition games, and policy design such as identifying where and to whom grants should be offered. The survey data and model may be made available on request, and the process, from questionnaire design through clustering to contextualization, is described as transferable to other contexts with similar datasets.

Subject of Research: Development of eight energy citizen personas from a 16-country European survey to identify people's energy information needs

Article Title: Identifying people’s energy information needs through Energy Citizen Personas

Article References: Kumar, A., Kuronen, T., Naqvi, B., Annala, S., Lensu, L., Massari, M., Boulanger, S. O. M., & Wolff, A. (2026). Identifying people’s energy information needs through Energy Citizen Personas. Energy Informatics, 9(1), Article 76. https://doi.org/10.1186/s42162-026-00691-0

Image Credits: AI Generated

DOI: 10.1186/s42162-026-00691-0

Keywords: energy citizenship, personas, energy transition, survey, clustering, ClicoT, Research in the Wild, energy information, human-computer interaction, Europe, energy communities, green transition