The purpose of this thesis is to develop a deeper understanding of the socioeconomic benefits of implementing smart metering infrastructure to monitor energy consumption in the domestic sector. Throughout a series of Randomised Control Trials (RCTs) in chapters 2 and 3, and a natural experiment in chapter 4, I bring empirical evidence of the opportunities granular energy smart meter data can offer when framing information back to consumers to foster pro-environmental behaviours.\par

In the second chapter, I compare various forms of energy consumption feedback facilitated by smart meters and smart phone apps through a large-scale RCT among 40,000 customers of a large energy retailer in the UK. Following the 2016 smart meter derogation in the UK, I investigate whether and how more advanced feedback involving peer group comparisons as well as disaggregation of total energy consumption can generate greater reduction in energy usage than basic feedback apps used in conjunction with in-home displays. \par

In the third chapter, I analyse residential energy demand flexibility through a series of field experiments on adoption and usage of WiFi-enabled smart plugs to overcome the limited attention or rebound effects associated with demand shifting. With 146 active participants, I study the adoption and user interaction of devices for automated dynamic energy demand-side management. This difference-in-difference design allows me to explore whether and how devices and incentive systems may be designed and deployed to improve load balancing in the centralised energy grid. \par

In the fourth chapter, I explore the impact of changes in teleworking and commuting habits during the 2020 spring lockdown on the domestic energy demand in the UK and the associated effect on households' carbon footprint. Alongside a customer survey, electricity and gas consumption data from around 1,164 participating households, including 452 survey respondents, is collected for 2019 and 2020. The last chapter concludes.