SSRScientific and Social Research2661-43322981-9946Bio-Byword Scientific Publishing Pty. Ltd.10.26689/ssr.v8i2.14008ArticleAdjustments to Automobile Policies in Europe, the US, and Japan, and Countermeasures for China’s Automotive Industryhttps://artdesignp.com/journal/SSR/8/2/10.26689/ssr.v8i2.14008YaoZhanhui,GePeng,ShiJian2026822026-03-18In 2025, major countries and regions worldwide signed and implemented a series of policy measures related to new energy vehicles. Overall, Europe, the United States, and Japan exhibited distinct differences in their support policies for new energy vehicles. The U.S. tended to favor traditional fuel-powered vehicles, curbing the development of new energy vehicles, shifting from policy-driven to market-led autonomy. Some European countries no longer maintain their original subsidies for new energy vehicles, instead focusing on advancing charging infrastructure and investing in technological R&D for these vehicles. Meanwhile, Japan continued to encourage new energy vehicle consumption while optimizing its domestic new energy vehicle supply chain to drive industrial upgrades and enhance competitiveness in the international market.

Yang W, Tao QZ, Chai SS, et al., 2024, Evolutionary Game Analysis of the Adoption of New Energy Logistics Vehicles under Dual Credit Policy. In Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR’24), ACM, 91–95. https://doi.org/10.1145/3718751.3718766

Chen YX, 2024, Analyzing the Impact of Various Factors and Policies on China’s New Energy Electric Vehicles. In Proceedings of the 2024 International Conference on Digital Society and Artificial Intelligence (DSAI’24), ACM, 379–384. https://doi.org/10.1145/3677892.3677951

Zhang YT, Zhang F, 2022, Construction of Marketing Competitiveness Model of New Energy Vehicles at Home and Abroad Based on Big Data Algorithm. In Proceedings of the 5th International Conference on Information Technologies and Electrical Engineering (ICITEE’22), ACM, 75–79. https://doi.org/10.1145/3582935.3582949

Mateos-Melero E, Moralejo-Piñas J, Martínez-Gil F, et al. 2025, Evolutionary Optimization of the Gas/Charging Stations Topology for the Electric Vehicle Market. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO’25 Companion), ACM, 867–870. https://doi.org/10.1145/3712255.3726622

Chen JSX, Zhang YT, Gao CZ, et al., 2024, Based On Authoritative Information Retrieval, The BSLSM Trend Model of the New Energy Market and Its Development in China. In Proceedings of the 5th International Conference on Computer Information and Big Data Applications (CIBDA’24), ACM, 264–267. https://doi.org/10.1145/3671151.3671199

Xiang LJ, Liu JY, Zhu YB, 2025, Forecast of China’s New Energy Passenger Vehicle Sales Based on System Dynamics and Other Methods. In Proceedings of the 2025 2nd International Conference on Innovation Management and Information System (ICIIS’25), ACM, 410–414. https://doi.org/10.1145/3745676.3745737

Liu Q, He JN, 2024, The Impact of ESG Investment on the Diffusion of Green Technology Innovation in New Energy Vehicle Enterprises under Complex Network Game. In Proceedings of the 2024 International Conference on Cloud Computing and Big Data (ICCBD’24), ACM, 515–520. https://doi.org/10.1145/3695080.3695169

Aydeger A, Zeydan E, Mangues-Bafalluy J, et al., 2025, Enhancing Electric Vehicle Security and Privacy through Decentralized Identity Management. Digital Threats: Research and Practice, 6(3): 1–20. https://doi.org/10.1145/3743151

Li Y, 2021, A Case Study on Profitability and Prospects of New Energy Vehicle Industry. In Proceedings of the 2021 3rd International Conference on E-Business and E-commerce Engineering (EBEE’21), ACM, 237–242. https://doi.org/10.1145/3510249.3510291

Liu R, Lei HZ, 2023, Evolutionary Strategies for Collaborative Innovation in Artificial Intelligence-Based New Energy Vehicle Enterprises. In Proceedings of the 2023 3rd International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR’23), ACM, 865–869. https://doi.org/10.1145/3656766.3656909