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Understanding homeowners' renovation decisions is essential for policy and business activity to improve the efficiency of owner-occupied housing stock. This paper develops, validates and applies a novel modelling framework for explaining renovation decisions, with an emphasis on energy-efficiency measures. The framework is tested using quantitative data from a nationally-representative survey of owner-occupied households in the UK (n = 1028). The modelling advances formal representations of renovation decisions by including background conditions of domestic life to which renovating is an adaptive response. Path analysis confirms that three conditions of domestic life are particularly influential on renovation decisions: balancing competing commitments for how space at home is used; signalling identity through homemaking activities; and managing physical vulnerabilities of household members. These conditions of domestic life also capture the influence of property characteristics (age, type) and household characteristics (size, composition, length of tenure) on renovation decisions but with greater descriptive realism. Multivariate probit models are used to provide rigorous, transparent and analytically tractable representations of the full renovation decision process. Model fits to the representative national sample of UK homeowners are good. The modelling shows that renovation intentions emerge initially from certain conditions of domestic life at which point energy efficiency is not a distinctive type of renovation. The modelling also shows clearly that influences on renovation decisions change through the decision process. This has important implications for policy and service providers. Efficiency measures should be bundled into broader types of home improvements, and incentives should target the underlying reasons why homeowners decide to renovate in the first place. [ABSTRACT FROM AUTHOR]

Improved methods for quantifying energy savings in buildings need to be supported by empirical measures rather than modeled estimates of future annual energy demand. This paper uses power temperature gradient (PTG, W/K), or the slope of power demand in response to changes in external air temperature; first, to categorise dwelling energy performance from daily energy data (when 0–15 °C outside); second, to investigate variations in 24-h profiles of delivered power. Estimates of PTG were obtained from 567 UK dwellings with 118,000 days of gas and electricity data. From a multivariable regression model, PTG was predicted by dwelling characteristics (number of bedrooms, number of floors, dwelling type, and dwelling age category (all p < 0.001)) but not by number of occupants. When dwellings were grouped into quintiles of PTG, mean PTG had threefold increase from the first to fifth quintile (188 to 563 W/K, respectively). This was reflected in 24-h profiles of delivered power (30 min intervals): at 0 °C, each 100 W/K decline in PTG corresponded to ∼2.5 kW decline in mean morning and evening peak power. Using PTG to estimate reductions in peak power as equivalent ‘negawatts’ reframes potential benefits of energy efficiency retrofits and for grid resilience. [ABSTRACT FROM AUTHOR]