A system dynamics model for vehicle fleet transformation towards energy efficiency and low-carbon development: A case study of Sri Lanka and its strategies.

Improving the energy efficiency of a vehicle fleet is a complex task that can be identified as having multiple variables and nonlinear feedback loops influenced by different mobility, socio-economical, and environmental factors. Due to the prevailing concerns on the fuel economy and vehicular emissions, formulating a clear policy for electric and other alternative mobility forms requires a clear decision support tool to adequately consider the complexities involved. This paper presents a novel System Dynamics (SD) approach based on the cause-and-effect analysis and feedback loop structures as a suitable tool. The proposed SD model comprises four sub-models: socio-economic, new vehicle registrations, environmental influence, and fuel economy and e-mobility policy measures. The study presents the analysis of historical data and results from the impact analysis of fuel economy policies in Sri Lanka, including the introduction of electric mobility. The authors leveraged the Vensim SD software package for modelling, expert-in-the-loop sensitivity analysis and optimization. Three strategic fuel economy and e-mobility policy intervention scenarios have been developed to estimate the forecasted fuel economy for Four-Wheeled Passenger Vehicles (FWPVs) as per strategic interventions to change the fuel type of new vehicles registered. It is shown that hybrid and electric FWPVs can improve fuel economy by between 13 and 39% by 2030. • A System Dynamics approach is proposed to model the energy efficiency of Four-Wheeled Passenger Vehicles' registrations. • Four sub-models are developed to report the impacts of fuel economy and e-mobility policies. • Three policy scenarios have been formulated to test the application of a target of reaching 50% of the conventional fuel registrations. • The effective change of fuel economy improvement under three scenarios vary between 13 to 39%. [ABSTRACT FROM AUTHOR]