Paper
21 July 2024 Research on relationship between development of new energy vehicles and dual carbon in Yangtze river delta based on index system prediction
Jiayao Long
Author Affiliations +
Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132193X (2024) https://doi.org/10.1117/12.3035249
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China
Abstract
The rise of the two major industries of new energy and new energy vehicles has created prerequisites for achieving carbon emission reduction. The Yangtze River Delta is the largest carbon emission economic zone in China, so it is urgent to reduce carbon emissions. By analyzing the development of new energy vehicles in the Yangtze River Delta, this paper constructs the index system affecting carbon emission, establishes the carbon emission prediction model based on genetic algorithm parameter optimization of extreme learning machine, predicts the carbon emission trend, and provides an effective plan for the development and emission reduction of new energy vehicles in the Yangtze River Delta. After analysis and validation, the model in this paper can comprehensively analyze the development of new energy vehicles in the Yangtze River Delta (YRD) and the country as a whole, explore the relationship between the development of new energy vehicles and the goal of carbon neutrality, predict the time of carbon peak and carbon neutrality in the YRD region, and provide a theoretical basis for the implementation of relevant policies.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Jiayao Long "Research on relationship between development of new energy vehicles and dual carbon in Yangtze river delta based on index system prediction", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132193X (21 July 2024); https://doi.org/10.1117/12.3035249
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KEYWORDS
Carbon

Solar energy

Data modeling

Genetic algorithms

Mathematical optimization

Industry

Extreme learning machines

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