Abstract
Transportation is the key carbon emission source after energy supply and industrial production. Under the vision of carbon peak and carbon neutralization, the pressure of reducing carbon emissions in transportation will be greater in the future. This paper constructs a model that takes transportation carbon emission as the main target and freight transportation utility value as the auxiliary target. The constructed model satisfies the constraints of freight turnover in the whole society, freight economic and social benefits, and the ecological constraints of the freight system. With MATLAB, the freight turnover of roadways, railways, and waterways (excluding ocean transportation) in 2030 is solved by using the adaptive genetic algorithm. The results indicate that (I) compared with the current freight structure of China, the roadway freight sharing rate in 2030 will decrease by 8.07%, and the railway freight sharing rate and the waterway freight sharing rate (excluding ocean transportation) will increase by 0.93% and 7.13%, respectively. (II) After optimization, the energy consumption and carbon emission are reduced by 42,471,500 tons (10.3%) and 91,379,400 tons (10.2%) of standard coal, respectively. (III) The adaptive genetic algorithm outperforms the traditional genetic algorithm in terms of convergence speed and accuracy. (IV) As the weight coefficient of carbon emission increases, the utility value of freight transportation consistently decreases, and the sensitivity increases. Meanwhile, as the carbon emission weight coefficient increases, carbon emission keeps decreasing, and the sensitivity decreases.
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Hang Ke almost was responsible for the whole of the paper; Guangying Xu guided the thesis writing; Chuntang Li, Jing Gao, Xinrui Xiao, Xin Wu, and Quanwei Yan were responsible for modifying the format and searching data.
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Ke, H., Xu, G., Li, C. et al. Optimization of China’s freight transportation structure based on adaptive genetic algorithm under the background of carbon peak. Environ Sci Pollut Res 30, 85087–85101 (2023). https://doi.org/10.1007/s11356-023-28407-x
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DOI: https://doi.org/10.1007/s11356-023-28407-x