A sensitive orange fluorescent calcium ion indicator for imaging neural activity.

In an ideal situation, we could recognize climate change mitigation measures, or their combinations, that are effective, ethically good and widely supported. However, effective measures that significantly decrease emissions often lack wide public support (Drews and van den Bergh ). Therefore, it is important to better understand citizens’ attitudes toward different climate mitigation measures and the factors associated with these attitudes. This study focuses on energy poverty as one such factor related to public support for climate policies. Energy poverty has received growing attention in both research and public debate. It can be approached from objective and subjective perspectives, both capturing different forms of deprivation. Objective energy poverty typically refers to measurable relationships between energy costs and income, such as the widely used threshold of spending more than 10% of disposable income on energy (Boardman ). More recent approaches estimate the energy required to reach basic comfort levels while accounting for household characteristics and geographic conditions (Faiella and Lavecchia ) and may also incorporate cooling needs (Gouveia et al. ). Energy poverty has been shown to affect health, well-being, social inclusion, and carbon reduction efforts (Hills ). Recent empirical work further highlights its multidimensional nature. Some households deliberately underconsume energy due to financial pressure, masking hardship in expenditure-based metrics (Eisfeld and Seebauer ). Community-level studies link energy poverty to poorer physical and mental health and increased financial stress (Riva et al. ). Comparative analyses across EU countries show that alleviation is most effective when policies simultaneously address income, housing energy performance, and energy prices (Kyprianou et al. ). Energy poverty is therefore a structural issue that cannot be resolved solely through individual choices (Middlemiss and Gillard ). Reflecting this complexity, Castaño-Rosa et al. () classify indicators into income/expenditure measures, self-reported housing conditions, and risk-based assessments, while Charlier and Legendre () propose a non-binary index capturing monetary constraints, poor energy efficiency, and heating deprivation.