The impact of hydrogen refueling station subsidy strategy on China's hydrogen fuel cell vehicle market diffusion

Lack of hydrogen refueling stations (HRSs) has hindered the diffusion of hydrogen fuel cell vehicles (HFCVs) in the Chinese transport market. By combining the agent-based model (ABM) and the experience weighted attraction (EWA) learning algorithm, this paper explores the impact of government subsidy strategy for HRSs on the market diffusion of HFCVs. The actions of the parties (government, HRS planning department and consumers) and their interactions are taken into account. The new model suggests dynamic subsidy mode based on EWA algorithm yields better results than static subsidy mode: HFCV purchases, HRS construction effort, total number of HRSs and expected HRS planning department profits all outperform static data by around 27%. In addition, choosing an appropriate initial subsidy strategy can increase the sales of HFCVs by nearly 40%. Early investment from government to establish initial HRSs can also increase market diffusion efficiency by more than 76.7%. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study combines agent-based model and experience weighted attraction learning algorithm to explore the impact of government subsidy strategy for hydrogen refueling stations on the market diffusion of hydrogen fuel cell vehicles. The new model suggests that dynamic subsidy mode based on EWA algorithm yields better results than static subsidy mode, and choosing an appropriate initial subsidy strategy can significantly increase the sales of HFCVs. Early investment from government can also greatly improve market diffusion efficiency.