How to Promote Traditional Automobile Companies’ Intelligent-Connected Transformation under the New Dual-Credit Policy? A Tripartite Evolutionary Game Analysis Combined with Funding Time Delay

Based on the interactive integration between smart cities and intelligent transportation, this paper discusses how traditional automobile companies achieve intelligent-connected transformation and how to promote the development of intelligent connected vehicles. First, we construct a tripartite evolutionary game model of traditional automobile companies, internet companies, and financial institutions under the dual-credit policy. Second, we define an ideal event and analyze the impacts of cost factors, market factors, and policy factors on system evolution. Finally, funding time delay is combined with the evolutionary game analysis. Results indicate that: (1) Compared with traditional automobile companies and internet companies, financial institutions are more sensitive to the profit-sharing coefficient and cost-sharing coefficient; (2) The probability of an ideal event is more sensitive to credit trading price than new energy vehicle (NEV) credit accounting coefficients and the NEV credit ratio requirement; (3) The government should fully consider the linkage between policy factors and market factors, and it is unreasonable for the government to consider the range of any factor alone; (4) Both the financing amount and regulatory cost have specific threshold ranges within which tripartite collaboration can be facilitated.

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Publication Details

Journal
Systems
Published
2024-08-23
DOI
https://doi.org/10.3390/systems12090319
Citations
2
Primary Topic
Digital Platforms and Economics
Type
article
Field-Weighted Citation Impact
1.11

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article

How to Promote Traditional Automobile Companies’ Intelligent-Connected Transformation under the New Dual-Credit Policy? A Tripartite Evolutionary Game Analysis Combined with Funding Time Delay

Dan Zhao, Jinhuan Tang, Y.P. Li, Jian Wang
2 citations
Systems
Digital Platforms and Economics
1.11
article

How to Promote Traditional Automobile Companies’ Intelligent-Connected Transformation under the New Dual-Credit Policy? A Tripartite Evolutionary Game Analysis Combined with Funding Time Delay

Dan Zhao, Jinhuan Tang, Y.P. Li, Jian Wang
article en
2 citations

Abstract

Based on the interactive integration between smart cities and intelligent transportation, this paper discusses how traditional automobile companies achieve intelligent-connected transformation and how to promote the development of intelligent connected vehicles. First, we construct a tripartite evolutionary game model of traditional automobile companies, internet companies, and financial institutions under the dual-credit policy. Second, we define an ideal event and analyze the impacts of cost factors, market factors, and policy factors on system evolution. Finally, funding time delay is combined with the evolutionary game analysis. Results indicate that: (1) Compared with traditional automobile companies and internet companies, financial institutions are more sensitive to the profit-sharing coefficient and cost-sharing coefficient; (2) The probability of an ideal event is more sensitive to credit trading price than new energy vehicle (NEV) credit accounting coefficients and the NEV credit ratio requirement; (3) The government should fully consider the linkage between policy factors and market factors, and it is unreasonable for the government to consider the range of any factor alone; (4) Both the financing amount and regulatory cost have specific threshold ranges within which tripartite collaboration can be facilitated.

SystemsVol. 12(9)
Shenyang Aerospace University (CN), Anhui University of Technology (CN)
National Natural Science Foundation of China, National Social Science Fund of China
Openalex Percentile: Top 15%
Digital Platforms and Economics
1.11
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How to Promote Traditional Automobile Companies’ Intelligent-Connected Transformation under the New Dual-Credit Policy? A Tripartite Evolutionary Game Analysis Combined with Funding Time Delay — Dan Zhao, Jinhuan Tang, et al. · Systems (2024) | TGRS Research Map | TGRS