Energy Policy

Volume 52, January 2013, Pages 363-372
Energy Policy

Incorporating technology buying behaviour into UK-based long term domestic stock energy models to provide improved policy analysis


The UK has a target for an 80% reduction in CO2 emissions by 2050 from a 1990 base. Domestic energy use accounts for around 30% of total emissions. This paper presents a comprehensive review of existing models and modelling techniques and indicates how they might be improved by considering individual buying behaviour. Macro (top-down) and micro (bottom-up) models have been reviewed and analysed. It is found that bottom-up models can project technology diffusion due to their higher resolution. The weakness of existing bottom-up models at capturing individual green technology buying behaviour has been identified. Consequently, Markov chains, neural networks and agent-based modelling are proposed as possible methods to incorporate buying behaviour within a domestic energy forecast model. Among the three methods, agent-based models are found to be the most promising, although a successful agent approach requires large amounts of input data. A prototype agent-based model has been developed and tested, which demonstrates the feasibility of an agent approach. This model shows that an agent-based approach is promising as a means to predict the effectiveness of various policy measures.


► Long term energy models are reviewed with a focus on UK domestic stock models. ► Existing models are found weak in modelling green technology buying behaviour. ► Agent models, Markov chains and neural networks are considered as solutions. ► Agent-based modelling (ABM) is found to be the most promising approach. ► A prototype ABM is developed and testing indicates a lot of potential.


Energy model
Buying behaviour

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