| ID | 117250 |
| Title Proper | Incorporating technology buying behaviour into UK-based long term domestic stock energy models to provide improved policy analysis |
| Language | ENG |
| Author | Lee, Timothy ; Yao, Runming |
| Publication | 2013. |
| Summary / Abstract (Note) | 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. |
| `In' analytical Note | Energy Policy Vol. , No.52; Jan 2013: p.363-372 |
| Journal Source | Energy Policy Vol. , No.52; Jan 2013: p.363-372 |
| Key Words | Domestic ; Energy Model ; Buying Behaviour |