Abstract/Summary

Measurable.energy's innovative sockets use LED indicators that reflect the carbon intensity of electricity sourced from the National Grid. The color-coded system provides real-time information about the energy source: green indicates predominantly renewable energy (e.g., wind, solar), amber represents a mix of renewable and non-renewable sources (including fossil fuels and nuclear) and red signifies energy primarily derived from non-renewable sources. This feature is designed to assist users in making informed decisions about when to use their devices, thereby reducing their carbon footprint. This study aims to provide a comprehensive understanding of how Measurable.energy smart sockets impact user’s energy consumption behaviour and to investigate their overall efficiency. The implementation of smart energy sockets in the Park Eat restaurant has led to a notable reduction in energy consumption. For example, bar sockets experienced significant reductions in energy use and emissions, with some achieving up to 56.2% decreases in energy consumption and up to 41.5% reductions in CO₂ emissions. In contrast, coffee machines showed varied results: while some sockets experienced higher energy consumption, they also achieved higher emissions reduction. This suggests that while automation can increase certain operational aspects, its effectiveness can differ depending on the specific application and context. The results indicate that high-energy appliances, such as those used in bar areas and for coffee machines, exhibit the most favorable payback periods. Conversely, kitchen appliances show varied payback periods while others (such as manually controlled sockets) have longer durations due to lower cost reductions. Incorporating Personal Moral Norms and Self-Determined Motivation with the Theory of Planned Behaviour strengthens the role of Attitudes in predicting Intention, but the impact of Personal Moral Norms remains ambiguous and the Intention-Behaviour link is still non-significant, while adding Past Behaviour highlights Attitude and Past Behaviour as significant predictors of Intention but does not result Behaviour prediction.