RESUMEN
The development of new energy vehicles (NEVs) cannot be separated from the support of subsidy policies. However, the effectiveness of different subsidy policies remains to be verified. To investigate a more effective way of NEV subsidy and maximize the effect of subsidy policies, this study proposes two subsidy strategies, namely, consistent subsidy and adaptive subsidy, and constructs a network-based evolutionary game model for NEV diffusion. The effects of different subsidy policies are then comprehensively evaluated from the supply and demand sides, and their internal influence mechanisms are further investigated. Results show that: 1) from the supply side, subsidy for both policy achieves the highest NEV diffusion, but subsidy for enterprises is more efficient; 2) from the demand side, NEV diffusion increases NEV sales in the same proportion. Surprisingly, the increase in NEV diffusion rate benefits traditional vehicle manufacturers by expanding their average market demand; 3) from the cost-benefit analysis, the adaptive subsidy is more efficient than consistent subsidy; 4) The higher the initial benefits of NEV enterprises, the higher the level of NEV diffusion. The government should implement the adaptive subsidy and focus on providing subsidies to NEV enterprises to increase the NEV diffusion rate and achieve efficient resource allocation.
Asunto(s)
Comercio , Políticas , Gobierno , ChinaRESUMEN
New energy vehicle (NEV), an Eco-friendly innovation to alleviate the problems of energy scarcity and environmental pollution, is increasingly popular in many countries. Various new energy vehicles are provided with quantity of basic information (e.g., performance, quality, and price), which hinders potential users from effectively finding the most desired or interested new energy vehicles to satisfy their personalized requirements. This paper proposes a three-stage recommendation method for facilitating users to find the proper NEV considering users' preferences and social trust relationship. In the first stage, the users' preferences on evaluation criteria are determined by best-worst method (BWM) through hesitant fuzzy preference comparison vectors. In the second stage, the users' demographic similarity is obtained considering different formats of information, and then users' trust degrees are generated from the entire propagation paths using n dimensional path-ordering-induced order-weighted averaging (NP-IOWA) operator, thereby obtaining the trust-based similarity. In the third stage, the comprehensive user-rating matrix is constructed with the obtained weights, and then, it is combined with the trust-based similarity to recommend NEV based on collaborative filtering technique. A case study is given to illustrate the feasibility of the proposed method and the comparative analysis is conducted to show the advantages of the proposed method.