Abstract
This paper develops an agent-based model with linking variables (ABML) to investigate the influencing factors for the new energy vehicles (NEVs) market in China. The ABML is a framework with three-level variables including micro, linking, and macro variables, which can reduce the complexity of the simulation. The emergence from bottom to top occurs between linking and macro variables, while the best–worst scaling describes the mapping between micro and linking variables. In the case study, Rookie, Veteran, and New Generation consumers are assumed as the three types of consumers in China’s market. A specification of the three types of variables is presented, where the value of linking variables obeys uniform distribution. By introducing the population density and the interaction frequency, the number of agents is determined with an experiment. All parameters in the model are estimated by calibrating the realistic vehicle sales. We compare different scenarios and obtain some management insights for improving the market penetration of NEVs in China.
Highlights
New energy vehicles (NEVs), including battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), have broad development prospects in China, but their current market share is relatively low
Increase the upper bound of NEV purchase index (NEVP) to 500, which indicates that an agent has a
This paper aims to develop an agent-based model with linking variables (ABML) to analyze China’s NEVs market
Summary
New energy vehicles (NEVs), including battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), have broad development prospects in China, but their current market share is relatively low. The percentages of different class NEV sales fluctuate every year. It is reported in some consultations that the public is very interested in purchasing NEVs, but the sales indicate that many potential buyers are hesitant to purchase NEVs [2] The solution to this problem relies on a nexus of multiple influences: electric range, the availability of charging stations, and infrastructure environment, etc. The. ABM, with a three-level variable structure, which includes knowledge (micro variables), association (linking variables), and conception (macro variables), will help us simulate the purchase process of NEVs in China, explore the interaction among three types of consumers, and analyze the effects of different policies.
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