Game-Theoretic Analysis for Green R&D Investment Strategies in the Vehicle Market

With the implementation of new environmental policies such as “carbon peak” and “carbon neutrality”, reducing carbon emissions through the development of clean technology in the automobile industry has become a key priority. However, the high cost of researching and developing green technology has led to high vehicle prices, which poses a major barrier to expanding the market share of such vehicles. The decision of whether to invest in research and development (R&D) has become a challenging one for automobile manufacturers. In this paper, we propose a game theory analysis scheme to study the R&D investment decisions of two original equipment manufacturers (OEMs) — an electric vehicle manufacturer (EM) and a fuel vehicle manufacturer (FM) — who, respectively, produce electric vehicles (EVs) and fuel vehicles (FVs). Since the manufacturers exhibit bounded rationality and their R&D investment decision-making involves a long-term, continuously learning and adjusting process, we model this dynamic R&D investment decision-making process as an evolutionary game to study manufacturers’ stable evolutionary behaviors in optimal R&D investment strategies. Different from previous literatures, where the prices for vehicles with high or low R&D investment were predetermined, we optimize the price of each vehicle, market shares, and optimal utilities of OEMs using a two-stage Stackelberg game for each investment strategy profile. Additionally, we use the Personal Carbon Trading (PCT) mechanism to help reduce carbon emissions. The main contribution of this paper is exploring the conditions for the evolutionary stable strategies (ESSs) of the evolutionary game based on the optimal utilities of the OEMs under different strategy profiles. The impact of preference parameters and green R&D coefficients on the OEMs’ decisions, as well as consumers’ purchase choices are also discussed. Finally, numerical simulations using real-world data are conducted to verify the theoretical results on ESSs.