Multidimensional Analysis of Development Indicators for New Energy Vehicles: A Study Based on Principal Component Analysis and Grey Correlation Analysis Model

Jianya Zhao; Libing Chen; Xuhan Chen

doi:10.54097/1mrjgt02

Authors

Jianya Zhao
Libing Chen
Xuhan Chen

DOI:

https://doi.org/10.54097/1mrjgt02

Keywords:

New Energy Vehicles, Development Indicators, Principal Component Analysis, Grey Correlation Analysis.

Abstract

The recent rapid growth of the new energy vehicle(NEV)industry has established it as a pivotal force in sustainable development. Yet, challenges in technology, cost, market, and policy hinder its high-quality development. Understanding these factors is essential for policy-making, fostering innovation, and guiding investment. This paper study employed principal component analysis to examine primary indicators' impact on NEV development and a grey relational model to assess various secondary indicators' significance and correlation with industry progress. The study found that while policy measures in China have persistently hampered the industry, market factors have begun positively influencing it since 2019. Furthermore, NEV indicators increasingly shape their own development, with economic factors exerting relatively limited influence. Market and economic indicators like Per Capita GDP and Carbon Trading Price exhibit strong correlations, while indicators linked to NEV characteristics such as range, fuel prices, and vehicle prices closely drive industry advancement, particularly in China's NEV sector.

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References

Le, H., et al., Dynamic Interlinkages between Carbon Risk and Volatility of Green and Renewable Energy: A Wavelet Analysis. Available at SSRN 4359925, 2023.

Sun, L., et al., Reducing energy consumption and pollution in the urban transportation sector: A review of policies and regulations in Beijing. Journal of cleaner production, 2021. 285: p. 125339.

Hassouna, F.M.A. and K. Al-Sahili, Environmental impact assessment of the transportation sector and hybrid vehicle implications in Palestine. Sustainability, 2020. 12(19): p. 7878.

Kendall, M., Fuel cell development for New Energy Vehicles (NEVs) and clean air in China. Progress in Natural Science: Materials International, 2018. 28(2): p. 113-120.

IEA, J., Global EV Outlook 2022: Tech. Rep. 2021.

Solaymani, S., CO2 emissions patterns in 7 top carbon emitter economies: The case of transport sector. Energy, 2019. 168: p. 989-1001.

Dixon, J., et al., Scheduling electric vehicle charging to minimise carbon emissions and wind curtailment. Renewable Energy, 2020. 161: p. 1072-1091.

Liu, C., et al., A time-delayed evolutionary game analysis of new energy vehicles development considering subsidy and carbon tax. Heliyon, 2024.

Li, B., X. Lv, and J. Chen, Demand and supply gap analysis of Chinese new energy vehicle charging infrastructure: Based on CNN-LSTM prediction model. Renewable Energy, 2024. 220: p. 119618.

Ren, J., New energy vehicle in China for sustainable development: Analysis of success factors and strategic implications. Transportation Research Part D: Transport and Environment, 2018. 59: p. 268-288.

Meng, X., et al., Identification of marine microplastics based on laser-induced fluorescence and principal component analysis. Journal of Hazardous Materials, 2024. 465: p. 133352.

Zhu, X.-F., et al., Principal component analysis of mineral and element composition of ores from the Bayan Obo Nb-Fe-REE deposit: Implication for mineralization process and ore classification. Ore Geology Reviews, 2024: p. 105972.

Song, W., M. Zhang, and H. Wu, Gray correlation analysis between mechanical performance and pore characteristics of permeable concrete. Journal of Building Engineering, 2024: p. 108793.

Zhou, M. and W. Dong. Grey correlation analysis of macro-and micro-scale properties of aeolian sand concrete under the salt freezing effect. in Structures. 2023. Elsevier.

Zhang, B., et al., Assessment of the impact of pyrolysis conditions on char reactivity through orthogonal experimental-based grey relational analysis. Journal of Analytical and Applied Pyrolysis, 2024: p. 106426.

Yue, L., et al., grey correlation analysis on influencing factors of Yang deficiency constitution. Digital Chinese Medicine, 2023. 6(2): p. 151-159.