Understanding the factors that shape household water and energy use is essential for designing targeted conservation interventions that promote both sustainability and well-being. While studies in this area often rely on traditional “frequentist” statistical methods, which can struggle to capture the complex interdependencies among demographic, behavioural, psychological, and material influences. This paper introduces Bayesian network (BN) analysis as a novel and adaptable method with useful applications in water and energy studies and a wide variety of other social sciences. The paper offers a primer on how to conduct BN analysis, including underlying logic and range of choice of software platforms, before presenting a brief worked example based on the authors’ current research into household water and energy consumption in a UK city. The paper shows how Bayesian networks can generate valuable insights from relatively small and complex datasets, capture non-linear relationships, and support scenario-based reasoning, making them well-suited for exploratory studies, “what if?” scenario-testing and policy effectiveness review. The findings contribute to a more nuanced understanding of domestic water and energy consumption and offer a practical framework that can inform the design of targeted, evidence-based interventions to encourage sustainable water and energy use in households. We argue that there is much to be gained by proliferation of this analytical approach throughout the social sciences.

Conditional probability, household behaviour, sustainability, survey data, wa-ter demand management