Urban population in China is expected to increase by 42% over the next two decades, and roughly 70% of the total population in China is estimated to be living in urban areas by 2030 (Berkelmans and Wang, 2012). With a higher level of living standards required in urban areas, increasing energy use in the urban domestic sector has become an urgent issue to address. Based on a review of studies into domestic energy use in China, the following three research gaps are identified: 1) the deficiency of knowledge about the link between occupants’ energy saving attitudes and behaviour in the context of China; 2) the limited research based on the socio-technical approach or mixed methods that cross disciplines to address the complexity of energy use behaviour; 3) the lack of research in using data-mining techniques to extract energy use related occupancy patterns from smart meter data. To address these gaps, this thesis focuses on three research dimensions, namely the practical dimension, the policy dimension, and the theoretical dimension. From the practical perspective, this thesis proposes to understand the characteristics of domestic energy use behaviour, explore the factors that affect occupants’ attitudes and behaviour, and figure out the reasons behind these attitudes and behavioural patterns. For the policy dimension, taking young urban households in Shanghai as a case study, this thesis aims to provide further insight into behavioural factors in domestic energy use and to explore which energy saving policies could be feasible in communicating with consumers more efficiently. Regarding the theoretical dimension, this thesis intends to investigate how useful the Theory of Planned Behaviour (TPB) is in understanding occupants in the context of domestic energy saving. The research has adopted a mixed-method approach. Initially, a survey study was designed to investigate energy use behaviour and factors that affect electricity use in different energy categories (from level 1 to level 3 consumers according to the progressive electricity pricing system in China). The survey responses were analysed by three statistical methods, includes descriptive statistics, correlation analysis and regression analysis. Subsequently, an interview study was carried out to explore the embedded reasons and motivations behind energy use behaviour and energy saving attitudes, drawing a distinguishing between “comfort-driven” consumers and “conscious” consumers. Finally, smart meter data was used to test whether it is possible to extract occupancy patterns from data sets for more practical policy design, and the iii energy saving potential of two behavioural measures as identified in the survey study was then tested with the single-zone simulation. Three types of data were collected through a) a survey in Shanghai area with 341 effective responses; b) 5 in-depth interviews; and c) smart meter data collected in 126 households in Shanghai, with more than 70 recorded days. The variables based on TPB were applied in the design of both the survey and the interview questions. Socio-psychological variables, including attitude, subjective norm and perceived behavioural control, are discussed in this thesis to determine factors in relation to the intention to save energy under potential energy saving policy instruments. In the analysis of the survey study, TPB was used to investigate the relationship between attitude and behaviour, and to identify the factors that affect these two components. Regarding the results from survey, the key findings were: 1) high consumers (level 3) are more likely to have higher income levels, and longer heating and cooling hours, as well as more extended heating and cooling seasons; 2) occupants are likely to have moderate comfort requirements (to accept a higher temperature in summer and to have shorter cooling hours per day) if they start to use air conditioners for cooling later in the summer; 3) respondents are more likely to accept fiscal incentives and communication instruments than consumption or price control in energy use; 4) even though high (level 3) consumers have more critical attitudes toward energy saving policies, they are more likely to accept real-time electricity pricing; 5) domestic energy use is strongly correlated to factors like family income level and family size; 6) energy saving intentions appear to be related to attitudes and also the subjective norms of family members. The interviews revealed the diversity of occupants’ energy use patterns and attitudes, even within the same target group of young urban households in Shanghai. Different attitudes, norms, and perceived control, resulted in different occupancy patterns and final energy consumption. According to their responses of their attitudes, norms and controls, two types of consumers were identified, including “comfort-driven” consumers and “conscious” consumers. Based on the TPB and the interview results, it was summarised norms shaped by educational background and influence from family members were identified as primary factors that shape energy use behaviour and attitudes. The “conscious” consumers’ attitudes towards energy saving were shaped by their education background or previous experiences and their norms and energy use practices were affected by their parents and partners. Two “conscious” interviewees mentioned iv

they started to pay attention to energy use and bill costs after they got married and need to pay the bill themselves. From the cluster analysis of smart meter data, three groups of energy users were summarised based on temporal differences in their occupancy patterns, concluding that 1) high consumers use air conditioners for the highest number of recorded days in winter and summer, even when air conditioners are not necessary; 2) high consumers have longer occupational hours at home; 3) the base load of the high consumers is higher. Based on the single-zone simulation, it was found each behavioural measure (adjust heating and cooling temperature and reduce heating and cooling hours) could have a potential energy saving of 20% on average, but with a wide variation. These two behavioural measures were identified from the previous survey results as key parameters to bring level 2 consumers closer to level 1 patterns. In conclusion, based on the findings from each empirical chapter, four policy initiatives were identified to address two groups of consumers, includes smart meters with in-home displays; a dynamic real-time energy pricing system; a review, reward and restrain feedback system; and stricter standards for buildings and appliances.