Introduction and Background
With the recent policy interest around the world in the promotion of AFVs such as Battery Electric, Plug-in hybrids and Hydrogen Fuel Cell vehicles, there have been a number of key papers which model their uptake using a system dynamics approach. Struben and Sterman (2008) developed a system dynamics framework for modelling the uptake of AFVs consisting of three main elements: a fleet turnover or stock model, a discrete choice model of the purchase decision and a social/technology diffusion process. The framework extends the Bass diffusion concept to include the impacts of word of mouth, marketing and social exposure to the new vehicles and powertrains. It allows for changes in the vehicle types available to and considered by consumers over time, including the potential success and failure of market solutions. The possibility of an initial uptake of new technology which potentially fails is an example of where system dynamics models can bring something different to the process of policy assessment. The removal of subsidies for CNG vehicles in Canada and New Zealand provides a case in point for such developments (Flynn, 2002).
Other studies extend the system dynamics framework by accounting for additional dimensions such as regulation, manufacturer responses, and subsequently provide indicators for the effect policy changes have on fuel duty revenues (Shepherd et al, (2012); Kwon (2012); Harrison and Shepherd, (2013)). In terms of impact of policies, most studies find that the uptake of AFVs is not affected greatly by subsidies but more by regulation and infrastructure. However, as both Struben and Sterman (2008) and Shepherd et al, (2012) point out, the results may be more sensitive to the assumed strength of the word of mouth or marketing effects rather than to changes in the technical attributes of the available vehicles. The ability of system dynamics to bring in soft issues, such social exposure, and quickly demonstrate the sensitivity of results to assumed policy parameters form a significant strength of the approach.
The choice models built-in most system dynamics models are, however, relatively simplistic and based on aggregate input data providing aggregate predictions of market shares. This is in sharp contrast with the advanced models used to explain individual demand for electric vehicles (Daziano and Bolduc, 2012) and leaves little room to account for:
- (changes in) consumer attitudes and social norms and heterogeneity in consumer preferences
- the contribution of vehicle in-service benefits (such as free parking at city centres, exemption from congestion or other road charges, reduction in taxi license fees, etc.),
- the impacts of spatial differences (both inter-regional and urban/rural) in information / awareness
- the contribution of car buying behaviour in the second-hand market and its impact on residual values
To incorporate such spatial and behavioural detail, the aggregate structure of the inclusive choice models should be adapted. Using an agent-based approach is one way to introduce such elements and this has been done previously by Huetink et al (2010), but using an agent-based approach alone loses the higher level policy or large actor responses reflected by the system dynamic model. This project aims to investigate a combined agent-based and system dynamics approach to better represent the issues surrounding the uptake of alternative fuel vehicles within the city/regional level context.
Aims and Approach
Given the background above, the project aims to:
- understand the key factors involved in the diffusion of new vehicle technologies both at the consumer level and in the aggregate market
- expand the modelling of the diffusion of new vehicle technologies under a system dynamics framework by combining an agent-based approach with a system dynamics approach.
- develop inclusive choice models in line with the state-of-art choice modelling literature.
- understand and quantify how cities can apply specific policy tools (e.g. fiscal exemptions) to enhance the uptake of AFVs
Applicants for a research degree should have or expect to obtain a first or upper second class honours degree or equivalent, preferably in a quantitative discipline. A Master's degree (not necessarily in transport) may be advantageous but is not essential. Candidates should have a clearly specified and achievable research goal and specific interest in numerical methods.
Please visit our LARS scholarship page for more information and further opportunities: https://www.environment.leeds.ac.uk/study/postgraduate-research-degrees/lars-scholarships/
Daziano, R. and Bolduc, D. (2012) Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian hybrid choice model. Transportmetrica A, 9(1), 74-106.
Flynn, P. (2002). "Commercializing an alternate vehicle fuel: lessons learned from natural gas for vehicles." Energy Policy 30(7): 613-619.
Harrison, G. and Shepherd, S.P. (2013) An interdisciplinary study to explore impacts from policies for the introduction of low carbon vehicles, Transportation Planning and Technology
Huétink, F.J., van der Vooren, A., and Alkemade, F. (2010) Initial infrastructure development strategies for the transition to sustainable mobility. Technological Forecasting & Social Change 77 (2010) 1270–1281
Kwon, T. (2012) Strategic niche management of alternative fuel vehicles: A system dynamics model of the policy effect. Technological Forecasting & Social Change 79 (2012) 1672–1680
Shepherd, S.P., Bonsall, P.W., and Harrison G. (2012) Factors affecting future demand for electric vehicles: a model based study. Transport Policy, (20) March 2012, pp 62-74.
Struben, J. and Sterman, J.D. (2008). "Transition challenges for alternative fuel vehicle and transportation systems." Environment and Planning B-Planning & Design 35(6): 1070-1097.
Walther, G., Wansart, J., Kieckhafer, K., Schnieder, E. & Spengler, T. S. 2010. Impact assessment in the automotive industry - mandatory market introduction of alternative powertrain technologies. System Dynamics Review, 26, 239-261.