<p style='text-indent:20px;'>This article constructs a two-stage supply chain consisting of a manufacturer (producing both fuel vehicles (FV) and new energy vehicles) and a retailer (selling both fuel vehicles and new energy vehicles) based on dual credit policy, considers three different power structure models, including the vertical Nash game model, the manufacturer Stackelberg game model, the retailer Stackelberg game model, and explores the operational strategy issues of new energy vehicle (NEV) enterprises under the dual credit policy. By comparing the optimal equilibrium solutions under different channel power structures, our findings indicate that (1) When the demand function is linear, the vertical Nash game model can achieve the highest system profit of the automotive supply chain, and in the manufacturer Stackelberg game model, the profit of the automaker is higher than that of the retailer, while in the retailer Stackelberg game model, the profit of the automaker is lower than that of the retailer. (2) The demand and pricing for FV and NEV in different models are determined by the range of FV and NEV production costs, credit trading prices, and the proportion of NEV credits. (3) Both the increase in the credit trading prices and the proportion of NEV credits will promote increased profits for the manufacturer and retailer. (4) The increase of the price sensitivity coefficient will reduce the demand as well as the profit of auto supply chain members.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.