Reducing greenhouse gas emissions is a challenge for how we use energy. Complex models of the global energy system are widely used to assess the potential for, and cost of climate change mitigation (Riahi et al. 2007, Johannson et al. 2012). Models have to make simplified assumptions about how change in the energy system occurs. With technology choices, for example, models typically assume that the lowest cost technologies are selected to meet a given demand for energy in residential, industrial, or transportation settings (Pizer & Popp 2008). These assumptions add up to a particular representation of how energy users and technology adopters make decisions and behave. They prefer lowest cost alternatives, they are price-responsive, and - across a population - they behave in relatively consistent ways.

Modelling assumptions on behavioural rationality are at odds with abundant evidence in real world settings of how people deviate from these prescriptive norms (Geroski 2000). The aims of this project are: (1) to explore the most important ways in which model assumptions and observed behaviours differ; and (2) to explore what might be done as a result to improve model representations of behaviour.

These are broad aims, and applicants are encouraged to define their own more specific area of research. This could focus on particular types of behaviour, particular settings or sectors, or particular behavioural principles or theories. With this in mind, the first stage of the project will to be systematically review the evidence for how energy is used or technologies adopted. What are the key drivers, influences and constraints on technological and behavioural change? How do different energy users or technology adopters vary? Is this variability systematic? The detail and complexities of this initial scoping study will then have to be distilled down into a series of priority challenges or recommendations for improving the representation of behaviour in models. This will inform the second stage of the project which is to implement one or more of the recommendations in a modelling framework. This is a major research frontier within the modelling community and significant efforts have already been made (e.g., Horne et al. 2005). Depending on the expertise and modelling experience of the successful applicant, this could involve collaboration with an existing IAM group, development of a simplified (e.g., agent-based) modelling framework, or work at a more conceptual level.

This project is associated with a major EU research consortium developing the next generation of IAMs. Many opportunities for collaboration exist within this consortium, particularly with the MESSAGE group at the International Institute for Applied Systems Analysis (IIASA) in Austria.
 

Riahi, K., A. Grubler and N. Nakicenovic (2007). "Scenarios of long-term socio-economic and environmental development under climate stabilization." Technological Forecasting and Social Change 74(7): 887-935.

Horne, M., M. Jaccard, et al. (2005). "Improving behavioral realism in hybrid energy-economy models using discrete choice studies of personal transportation decisions." Energy Economics 27(1): 59-77.

Johansson, T.B., N. Nakicenovic, A. Patwardhan and L. Gomez-Echeverri (2012). Global Energy Assessment: Towards a Sustainable Future. Cambridge, UK, Cambridge University Press.

Geroski, P. A. (2000). "Models of technology diffusion." Research Policy 29: 603-625.
Pizer WA, Popp D (2008) Endogenizing technological change: Matching empirical evidence to modeling needs. Energy Economics 30 (6):2754-2770